Content uploaded by Chang-Wen Huang
Author content
All content in this area was uploaded by Chang-Wen Huang on Jun 06, 2021
Content may be subject to copyright.
ORIGINAL ARTICLE
Identification of Functional SSR Markers in Freshwater Ornamental
Shrimps Neocaridina denticulata Using Transcriptome Sequencing
Chang-Wen Huang
1,2
&Pei-Yun Chu
1
&Yu-Fang Wu
1
&Wei-Ren Chan
1
&Yeh-Hao Wang
3
Received: 3 January 2020 /Accepted: 18 May 2020
#Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
The amazing colors and patterns are fascinating characteristics in all of the aquarium species. However, genetic and breeding
molecular investigations of ornamental shrimps are rather limited. Here, we present the first transcriptomic analysis and appli-
cation of microsatellites based on the chromatophore-encoded genes of Neocaridina denticulata to assist freshwater ornamental
shrimp germplasm enhancement and its extensive applications. A total of 65,402 unigenes were annotated, and 4706 differen-
tially expressed genes were screened and identified between super red shrimp and chocolate shrimp strains. Several gene ratios
were examined to put in perspective possible genetic markers for the different strains of normal pigmentation development,
including flotillin-2-like, keratin, the G protein–coupled receptor Mth2-like, annexin A7, and unconventional myosin-IXb-like.
Five simple sequence repeat markers were effective for colored shrimps and were used to develop a marker-assisted selection
platform for systematic breeding management program to maintain genetic diversity of the species. These markers could also be
used to assist the identification of pure strains and increase the genetic stability of ornamental shrimp color phenotypes.
Consequently, our results of microsatellite marker development are valuable for assisting shrimp genetic and selection breeding
studies on freshwater ornamental shrimp and related crystal shrimp species.
Keywords Freshwater ornamental shrimp .Identification .Color traits .DNA molecular markers
Introduction
Ornamental aquatic species have ever-changing and attractive
external appearances, shapes, and vivid colors (Ertl et al.
2013). The number of artificially cultivated varieties continues
to expand, and innovations in breeding techniques and equip-
ment and popularity of feed nutrition science as well as con-
venience of transportation have been the primary factors pro-
moting the rapid worldwide development of the ornamental
aquatic animal industry (Nguyen et al. 2014; Negisho et al.
2019; Pinnegar and Murray 2019).
Freshwater shrimp Neocaridina denticulata is a decapod
crustacean ornamental species originated in Taiwan.
Initially, traditional selective breeding methods were used by
the industry to produce shrimp with high ornamental and eco-
nomic value through long-term genetic improvements from
black shell shrimp, which was used as fish fodder and sold
by kilogram (Hung et al. 1993; Ariyanathan and Serebiah
2016). Owing to their small size, ease of raising, short life
cycle, convenient to transport, and high productivity, these
shrimp have been welcomed by many aquatic animal lovers
in developed countries (Nur and Christianus 2013; Mykles
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s10126-020-09979-y) contains supplementary
material, which is available to authorized users.
*Chang-Wen Huang
cwhuang@mail.ntou.edu.tw
Pei-Yun Chu
kimi0396@gmail.com
Yu-Fang Wu
fang.w.may1991@gmail.com
Wei-Ren Chan
eric010164@gmail.com
Yeh-Hao Wang
contrailwhite@gmail.com
1
Department of Aquaculture, National Taiwan Ocean University, 2
Beining Road, Jhongjheng District, Keelung City 20224, Taiwan
2
Center of Excellence for the Oceans, National Taiwan Ocean
University, Keelung, Taiwan
3
Larmax International Co., Ltd. No.9, Yuanxi 2nd Rd.,
Changzhi, Pingtung, Taiwan
https://doi.org/10.1007/s10126-020-09979-y
/ Published online: 11 June 2020
Marine Biotechnology (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
and Hui 2015). In addition to adding esthetic value to aquar-
ium landscapes, shrimp can consume residual food and clear
algae from water, thereby promoting ecological stability and
becoming a popular item in small aquaria worldwide (Patoka
et al. 2015;Levitt-Barmatsetal.2019).
From breeding experience and long-term observation of
commercially available colored shrimp, subtle genetic varia-
tions in multiple ornamental traits have been developed by
breeding different strains (Levitt-Barmats et al. 2019).
Currently, colored shrimp can be divided into red, orange,
yellow, green, blue, indigo, black, and white strains, including
super red, red rili, blue velvet, chocolate, sunkist, snowball,
snow white, and over 20 colored commercial strains, with
continuous addition of more strains.
Understanding the mechanisms responsible for the devel-
opment of color and patterning in ornamental aquatic animals
can effectively enable breeders to increase the heritability of
color traits for genetic breeding, achieve a more stable pro-
duction, or develop more varied novel commercial strains, and
has significance for their physiology and behavior
(McNamara and Milograna 2015). In recent years, color poly-
morphism has also received attention for its impact on speci-
ation and adaptation (Yue et al. 2015). Color changes are
primarily regulated by substances secreted from radially
branching chromatophores located in epidermal connective
tissue and differentiated from neural crest cells (Wade et al.
2008). A variety of chromatophores contain granules of white,
red, yellow, blue, brown, and black pigments, which originate
from carotenoids in food. These pigments bind to proteins,
and chromatophores exhibit different degrees of dispersion
and accumulation, and thus produce different colors.
However, no scientific research has yet been reported on the
molecular regulation mechanisms or genetic markers of body
surface color formation of freshwater ornamental shrimp.
In a study of cold-blooded animals, five types of chromato-
phores, i.e., melanophores, erythrophores, xanthophores,
iridophores, and leucophores, were determined to contain dif-
ferent pigments and have different functions (Ben et al. 2003).
Among them, melanophores contain melanin, which primari-
ly manifests as black or brown body color; xanthophores and
erythrophores primarily contain pteridine and carotenoid and
manifest as red, orange, or yellow body color, while
iridophores and leucophores contain guanine and other pu-
rines and produce lustrous metallic colors such as blue, green,
and white through light reflection (Lynn Lamoreux et al.
2005).
Body pigment development is mainly divided into two
stages (Woolley et al. 2014): during embryonic development,
neural crest cells migrate and differentiate into different types
of melanophores, xanthophores, and iridophores under specif-
ic regulation of the genes Kit (kit type III receptor tyrosine
kinase) (Parichy et al. 1999; Kottler et al. 2013), Ednrb
(endothelin receptor B) (Parichy et al. 2000), Mitf
(microphthalmia-associated transcription factor), and pnp4a
(purine nucleoside phosphorylase) (Curran et al. 2010)(1),
and chromatophores produce specific pigments through met-
abolic processes (2).
With the rapid development of molecular biology and ge-
nomics research methods, next-generation sequencing (NGS)
platforms can generate genomic DNA sequence libraries of
various species of ornamental aquatic organisms (Ghaffari
et al. 2014; Kenny et al. 2014;Xuetal.2014;Yuetal.
2014; Sin et al. 2015). Moreover, DNA molecular markers
have been used to identify new strains of ornamental aquatic
organisms and to select color-related properties (Yue and
Chang 2010). From the analysis of genetic variation between
the transcriptomes of the Fenneropenaeus merguiensis and
the Macrobrachium olfersi by NGS technology and microar-
ray analysis, the Ca
2+
/cGMP signaling pathways were found
to participate in pigment aggregation, with the carotenoid
astaxanthin, crustacyanin, red pigment concentrating hormone
(RPCH), and G protein–coupled receptor (GPCR) factors be-
ing differentially expressed (Ertl et al. 2013; Milograna et al.
2014,2016).
The objective of this study was to reveal differential ex-
pression genes from the transcriptome database between two
pure strains of the freshwater ornamental shrimps
N. denticulata, i.e., light-colored super red shrimp (SRS) and
dark-colored chocolate shrimp (CS), by using the (NGS) plat-
form, and to explore biological pathways and develop molec-
ular markers involving the putative chromatophore-encoded
functional genes. Furthermore, these analyses may not only
provide new insight into the causes of coloration in colored
ornamental shrimp but also enable breeders to pursue higher
quality, more stable heritability of chromogenic phenotypic
traits, and even create novel attractive varieties for trade in
the future.
Materials and Methods
Experimental Shrimp, Genomic DNA Extraction
Experimental colored shrimp samples were obtained from
Larmax International Co., Ltd. in southern Taiwan, including
SRS and CS. The native strain black shell shrimp (BSS) was
used as the experimental control.
Each shrimp strain was separately reared in a smart breed-
ing cycle system containing a 3- or 10-L feeding cylinder with
independent drainage, aeration, and a water flow of approxi-
mately 0.12 L/h. Water quality was maintained by a filter
circulation system with temperature, pH, and general hardness
(GH) controlled at 22–25 °C, 6.5–7.0, and 3.0–4.0,
respectively.
The MasterPure™DNA Purification Kit (Epicenter,
Madison, USA) was used to extract genomic DNA from
773Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
shrimp. Genomic DNA was diluted to 25–40 ng/μL and sam-
ples were labeled stating the tissue number to be tested, sam-
ple name, extraction date, and other information related to the
origin of the sample. Samples were stored at −20 °C for use in
subsequent PCR analyses.
Total RNA Extraction
The experimental animals were 1–4-week-old SRS and CS.
Total RNA was extracted using EasyPure Total RNA Spin Kit
(Bioman, Taipei, Taiwan).
Fresh shrimp tissue samples were collected and placed in a
1.5-mL microcentrifuge tube containing 500 μLRNATriPure
Isolation Reagent (Roche Applied Science, Germany). Three
stainless steel beads (3 mm) and a 1–5-mm steel bead
(LabTurbo
®
) were added to the tube and the mixture was
placed in a SpeedMill PLUS high-speed tissue homogenizer
(Analytik Jena AG). The sample was disrupted three times for
1 min each and placed at room temperature for 5 min. The
tissue homogenate was placed in a 2-mL filter column and
centrifuged at 10,000×gfor 2 min at 4 °C. The filtrate was
collected and placed in a new 1.5-mL microcentrifuge tube,
and 400 μL 70% ethanol was added and mixed. After
discarding the liquid in the collection tube, the RB column
was placed in a 1.5-mL microcentrifuge tube. RNase-free wa-
ter (50 μL) was placed in the RB column, which was allowed
to stand for 5 min until the RNAse-free water was completely
absorbed, and centrifuged at 10,000×gfor 2 min at 4 °C.
Purified total RNA was collected and the MaestroNano spec-
trophotometer (Maestrogen, Las Vegas, NV, USA) was used
to measure optical density (OD
260
and OD
280
) and calculate
the concentration.
High-Throughput Next-Generation Transcriptome
Sequencing
SamplesoftotalRNA(10μg) from SRS and CS strains were
used to determine RNA quality and to construct a tran-
scriptome gene library. mRNA was enriched by using the
oligo (dT) magnetic beads. Mixed with the fragmentation
buffer, mRNA was divided into short fragments. Then, the
first strand of cDNA was synthesized by using random
hexamer-primer, and buffer, dNTPs, RNase H, and DNA po-
lymerase I were added to synthesize the second strand. The
double strand cDNA was purified with magnetic beads. Then,
3′-end single nucleotide A (adenine) addition was performed.
Finally, sequencing adaptors were ligated to the fragments and
these were enriched by PCR amplification. During the quality
control step, Agilent 2100 Bioanaylzer (Agilent
Technologies, USA) and ABI StepOnePlus Real-Time PCR
System (Applied Biosystems, Foster City, USA) were used to
qualify and quantify the sample library.
The Illumina HiSeq 2000 NGS platform was used to ana-
lyze sequences and data on differential gene expression (Lee
2017). The premise of NGS was the fragmentation of total
RNA followed byreverse transcription into cDNA, along with
the addition of adaptors and primers before sequencing. The
transcriptome de novo assembly of high-quality sequence read
data was achieved with Trinity (Fu et al. 2012) short read
assembly software (http://trinityrnaseq.sourceforge.net) and
with the Japanese swamp shrimp native species (N.
denticulata) provided by the BioProject database on NCBI
(https://www.ncbi.nlm.nih.gov/bioproject/PRJNA240382),
screening of unigenes with NCBI non-redundant protein (Nr),
Gene Ontology (GO) (E-value < 10
−5
) (Conesa et al. 2005),
Clusters of Orthologous Groups (COG), and Kyoto
Encyclopedia of Genes and Genomes (KEGG) (Kanehisa
et al. 2008) pathway enrichment analysis. Blast2GO (v2.5.0)
was used to obtain GO annotations (E-value < 10
−5
)onthe
basis of the Nr notes. In addition, unigenes were functionally
aligned and annotated using GO/KEGG/COG annotation, and
coding proteins were predicted and classified according to the
COG database. Molecular marker gene locus detection online
software (www.genome.jp/kegg/kegg4.html) was used to
search for appropriate simple sequence repeat (SSR) markers
to screen and search against the KOG database through
BLASTX and KGGG pathways for color properties in orna-
mental shrimp.
An NGS platform was used to compare with unigene
databases and annotations, including unigene protein and
COG functional annotations. First, unigene sequences were
compared using BLASTX; then, the NR gene (Fig. S1)and
Swiss-Prot protein databases were used. The Swiss-Prot da-
tabase is a database of screened proteins, KEGG is a data-
base used to analyze the metabolic pathways and functions
of gene products, and COG is a database used to directly
compare phylogeny (E-value < 10
−5
)(Fig.S2). The two
databases were compared; unigenes were compared with
the Nt nucleotide database using BLASTN (E-value <
10
−5
), and the proteins with the highest similarity to
unigenes were obtained, thereby providing protein func-
tional annotation information on those unigenes. Next,
these unigenes were compared with the NR, NT, Swiss-
Prot, KEGG, COG, and GO databases (Fig. S3), and the
unigenes were subjected to further annotation.
Differential Analysis of Gene Expression
To establish the maximum and minimum values of the log
2
ratio (CS/SRS) between the expression of transcriptome li-
brary genes in the SRS and CS strains of N. denticulata,dif-
ferentially expressed genes (DEGs) were investigated be-
tween different samples, as shown by fragments per kilobase
of exon per million fragments mapped (FPKM).
774 Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
FPKM is calculated from the following equation:
FPKM ¼106C
NL=103
where Cis the number of uniquely matched gene fragments, N
is the total number of uniquely matched gene fragments, and L
is the number of bases in the gene (Mortazavi et al. 2008).
Analysis of Microsatellite Markers
MicroSAtellite (MISA) (Thiel et al. 2003) molecular marker
gene locus prediction software was used to detect DEGs and
identify microsatellite markers. Microsatellite markers of at
least 150 bp were screened; ClustVis online software was used
for pattern clustering analysis of large amounts of data obtain-
ed from different strains. The data were presented as a heat
map. Twenty-nine microsatellite markers were identified in
this manner and used for subsequent analyses.
Twenty-six microsatellite markers were used to screen col-
or properties in four offspring groups of P1 [SRS (female) ×
SRS (male)], P2 [CS (female) × CS (male)], H1 [SRS (fe-
male) × CS (male)], and H2 [CS (female) × SRS (male)] on
50 shrimps from each group (Fig. S4). Multiple fluorescence
labelling was used during the first PCR amplification. A for-
ward primer containing an adaptor was annealed to gDNA
fragments. PCR was performed using a 96-well Veriti® ther-
mal cycler (Applied Biosystems Life Technologiess,
Carlsbad, CA, USA). The eight microsatellite markers were
amplified a second time using fluorescent forward primers.
PCR products of all samples were separated using 2% agarose
gel electrophoresis. To reduce the probability of dual-allele
gene recognition errors, PCR was performed in duplicate for
each sample, and the microsatellite genotype of the sample
was confirmed if both duplicates obtained the same results.
The four PCR products with different fluorescent labels were
mixed thoroughly. The capillary electrophoresis instrument
ABI PRISM® 3730xl automated DNA Analyzer (Applied
Biosystems, USA) was used to separate the SSR fragments
of each sample. The output was analyzed using GeneMapper
software (versions 4.0, Applied Biosystems).
Statistical Analysis
Geneious software (v6.1.6) package (http://www.geneious.
com/) was used to interpret and analyze multiple fluorescent
PCR polymorphic marker genotypes of all microsatellite
marker data. Then, multi-allelic data scorings (A, B, C, etc.)
were imported into POPGENE32 software version 1.32 (Yeh
et al. 2000) for the statistical analysis of parameters, including
the number of alleles (Na), allele frequency (Ne), and popula-
tion diversity measures, i.e., observed heterozygosity (H
o
),
expected heterozygosity (H
e
), polymorphism information
content (PIC), and fixation index (F
IS
), of each microsatellite
gene locus (Pan and Yang 2010). Population diversity mea-
sures were calculated as:
Ho¼Nhet Nhom þNhetðÞ;
where N
het
is the number of heterozygous individuals and
N
hom
is the number of homozygous individuals.
He¼1−∑
n
i¼1
Pi2
where nis the number of allele at each locus, and P
i
is the
frequency of the ith gene (Nei 1978).
PIC ¼1−∑
n
i¼1
Pi2−∑
k−1
i¼1
∑
n
j¼iþ1
2Pi2Pj2
where nis the number of alleles and P
i
and P
j
are the frequen-
cies of the ith and jth alleles, respectively (Botstein et al.
1980).
FIS ¼1−HoHe
IBM SPSS Statistics v22.0.0 software was used to deter-
mine whether the genotypes were significantly correlated with
the pvalues obtained from strain analysis, and M-ANOVA
was used to test multiple variables.
Results
High-Throughput Next-Generation Transcriptome
Sequencing
For high-throughput NGS of ornamental shrimp tran-
scriptome, three 1–4-week-old SRS and three 1–4-week-old
CS were obtained from Larmax International Co., Ltd. for
transcriptomic analysis. Using the Illumina HiSeq 2000 se-
quencing platform, a total of 9,838,273,000 nt of data were
obtained from the SRS and CS strains (Table 1). After assem-
bly of transcriptome contigs, 105,892 unigenes with a total
length of 125,528,321 nt were obtained, with an average
length and N50 of 1185 and 3254 nt, respectively (Table 1).
Overall, 36,665, 18,150, 30,864, 27,751, 15,407, and 12,642
unigenes were annotated in NR, NT, Swiss-Prot, KEGG,
COG, and GO, respectively, and a total of 65,402 unigenes
were annotated common to all strains (Table 2)(TableS1-5).
Analysis of Different Gene Expression in Colored
Shrimp
A Venn diagram of genes commonly expressed found in col-
ored shrimp and of specific genes (Fig. 1)showedthatSRS,
CS, and JSS have 65,402 genes in common, with 2317 and
775Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
2228 genes specific to SRS and CS, respectively. All unigenes
were classified as high-expressed, low-expressed, or similar
(Fig. 2a). Using the log
2
ratio (CS/SRS) between expression
levels in CS and SRS, 2230 and 2476 genes were classed as
high- and low-expressed, respectively, in SRS relative to CS
(Fig. 2b). KEGG pathway enrichment analysis of the 20
Fig. 1 Venn diagram showing relationships between the three
transcriptome datasets, i.e., Japanese swamp shrimp (JSS), super red
shrimp (SRS), and chocolate shrimp (CS). Numbers in parentheses
represent the total number of expressed genes in each strain. A total of
65,402 unigenes were annotated common to all strains
Table 1 Summary statistics of
sequencing reads from the
Illumina HiSeq sequencing in
super red shrimp (SRS) and
chocolate shrimp (CS) strains of
N. denticulata transcriptomes
Output characteristics Super red shrimp (SRS) Chocolate shrimp (CS)
Reads and nucleotides quality
Number of raw reads generated 51,428,132 50,134,976
Total clean reads
1
49,946,561 48,436,169
Total clean nucleotides (nt)
2
4,994,656,100 4,843,616,900
Q20 (%)
3
97.66 97.54
N(%)
4
0.00 0.00
GC (%)
5
42.82 42.13
Contigs quality
Number of assembled contigs 145,592 151,994
Total length (nt) 60,842,467 59,117,368
Mean length (nt) 403 400
N50 1003 996
Unigene quality
Number of assembled unigenes 85,629 86,816
Total length (nt) 83,446,672 82,459,701
Mean length (nt) 975 950
N50 2639 2566
Total consensus sequences
6
85,629 86,816
Distinct clusters
7
15,965 15,906
Distinct singletons
8
69,664 70,911
1, 2
Total clean reads and total clean nucleotides are actually clean reads and clean nucleotides. Total clean
nucleotides (nt) = total clean reads 1 × read 1 size + total clean reads 2 ×read 2 size
3
Q20 percentage is proportion of nucleotides with quality value greater than 20
4
Npercentage is proportion of unknown nucleotides in clean reads
5
GC percentage is proportion of guanidine and cytosine nucleotides among total nucleotides
6
Total consensus sequences represents the all assembled unigenes
7
Distinct clusters represents the cluster unigenes, the same cluster contains some high similar (more than 70%)
unigenes, and these unigenes may come from same gene or homologous gene
8
Distinct singletons represents this unigene come from a single gene
Table 2 Functional annotation of the N. denticulata transcriptome
Annotated database Annotated number 300–1000 bp ≧1000 bp
Nr_annotation 36,665 8354 21,136
NT_annotation 18,150 3535 12,173
Swiss-Prot_annotation 30,864 6274 21,757
KEGG_annotation 27,751 5372 19,869
COG_annotation 15,407 2522 11,949
GO_annotation 12,642 2393 8779
Total 65,402 10,166 24,812
776 Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
distinct metabolic pathways identified based on DEGs be-
tween SRS and CS is listed in Fig. 3. Among them, the top
five pathways were amoebiasis (ko05146), vibrio cholera in-
fection (ko05110), salivary secretion (ko04970), protein di-
gestion and absorption (ko04974), and dorso-ventral axis for-
mation (ko04320) (Fig. 3). Furthermore, a significant propor-
tion of sequences in our transcriptome were involved in the
function classification of “general function prediction only”
based on both COG and KEGG databases, and the “replica-
tion, recombination, and repair,”“translation, ribosomal struc-
ture and biogenesis,”“transcription,”“carbohydrate transport
and metabolism,”and “signal transduction mechanisms”path-
ways were detected (Fig. S2). Among DEGs (Fig. 4),
polymorphic gene–based SSR markers were screened and
used for subsequent genetic diversity analyses (Table S6).
Microsatellite Software Analysis
Using MISA analysis software, 25,355 microsatellite markers
containing one, two, three, four, five, or six base repeats
(Table 3,Fig.5)werefound.Overall,7051(27.81%),7589
(29.93%), 9076 (35.8%), 1352 (5.33%), 167 (0.66%), and 120
(0.47%) microsatellite markers had repeats comprising one,
two, three, four, five, and six bases, respectively. The most
common microsatellites with repeats of three bases were
AAT/ATT, AAG/CTT, AAG/CCT, and ATC/GAT with
2394 (26.38%), 1299 (14.31%), 1243 (13.70%), and 1174
(12.94%), respectively. The most common microsatellites
with repeats of two bases were AC/GT, AG/CT, and AT/
TA, with 2283 (30.08%), 3160 (41.64%), and 2131
(28.08%), respectively.
By screening DEGs and using bioinformatics analyses, in-
cluding GO annotations, pathway enrichment analysis, gene
locus prediction software, functional clustering, differential
expression, and microsatellite (SSR) marker mutant sequenc-
ing, 26 possible molecular markers of color-related functional
genes were found in colored shrimp, i.e., CL1904_4,
CL2407_4, CL2408_7, CL2911_2, CL32_6, CL3384_1,
CL3484_2, CL3550_2, CL3563_2, CL3771_4, CL4393_2,
CL5162_2, CL5500_1, CL5558_3, CL621_12, CL6971_3,
CL85_6, Unigene10624, Unigene14740, Unigene19134,
Unigene20167, Unigene28786, Unigene31684,
Unigene32861, Unigene36045, and Unigene64263. ClustVis
was used for pattern clustering analysis of DEGs, and heat
map analysis of these genes in both shrimp strains (Fig. 4)
revealed that the 26 markers were differentially expressed
between the two strains.
SRS and CS Microsatellite Marker Analysis
Transcriptome sequencing platform, MISA software analysis,
and heat map analysis, identified 26 microsatellite markers
that were correlated with DEGs (Table S7). Among these,
16 feasible markers were identified, from which (Table 4),
the five functional genes correlated with color properties of
colored shrimp, i.e., Unigene32861, Unigene28786,
CL5162_2, Unigene14740, and Unigene10624, were
screened, corresponding to flotillin-2-like, keratin (type I cy-
toskeletal 19), GPCR Mth2-like, annexin A7, and unconven-
tional myosin-IXb-like, respectively. Short tandem repeat
fragments of multiple fluorescent markers of alleles were ar-
ranged by capillary electrophoresis into A, B, and C ordered
by increasing size. The frequency of each allele was calculated
and POPGENE32 software was used to analyze population
genetic diversity. Because all markers were type I functional
markers and the number of alleles was small, the
Fig. 2 Comparison of unigeneexpression level between super red shrimp
(SRS) and chocolate shrimp (CS) of N. denticulata.aScatter plot analysis
of significant differential expression level between SRS and CS. X-axis,
gene expression of SRS; Y-axis, gene expression of CS. FDR < 0.001 and
the absolute value of log2 fold change ≥1 were used as the threshold to
judge the significance of gene expression differences. Blue and yellow
dots indicate the differentially expressed unigenes, and brown dots indi-
cate unigenes that were not differentially expressed between SRS and CS.
bThe numbers of differentially expressed genes high- (yellow) and low-
regulated (blue) in SRS versus CS
777Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Unigene14740 genotype was identified as the most common
among all markers, whereas the Unigene10624 genotype was
the least common. In SRS shrimp, the average observed and
expected heterozygosity among these five markers was 0.40
± 0.45 and 0.35 ± 0.32, respectively, while the average PIC
and F
IS
was 0.34 ± 0.32 and 0.14 ± 0.27, respectively
(Table 5). In CS shrimp, the average observed and expected
heterozygosity among these five markers was 0.47 ± 0.3 and
0.45 ± 0.24, respectively, while the average PIC and F
IS
was
0.44 ± 0.24 and −0.09 ± 0.33, respectively (Table 5).
Analysis of Correlations Between Marker Genes and
Strain
Regarding the flotillin-2-like gene (Unigene32861 locus), the
AA genotype of the 3′-UTR in the CS group was significantly
higher than that in the SRS group (p< 0.05). In the SRS strain,
the alleles were C > B > A, whereas in the CS strain, the alleles
were A > C > B. The genotype frequencies of AC and BC in the
SRS strain were 0.34 and 0.53, respectively, and the genotype
frequency of AA in the CS strain was 0.81. Moreover, the
genotypes were significantly correlated with strain (p<0.01).
Regarding the keratin (type I cytoskeletal 19)
(Unigene28786 locus), the frequency of CC genotype in the
SRS group was significantly higher than that in the CS group
(p<0.05).IntheSRSstrain,thealleles areC>B=A,where-
as in the CS strain, the allele frequency were C > B > A, mak-
ing the CC genotype frequency in the SRS strain 0.9, and
those of AC, BC, and CC in the CS strain 0.25, 0.28, and
0.41, respectively; the genotypes were significantly correlated
with strain (p<0.01).
Regarding the GPCR Mth2-like (CL5162 locus), the AA
genotype frequency in the SRS group was significantly higher
than that in the CS group (p< 0.05). Although the allele fre-
quencies in SRS and CS strains were the same (A > B > C),
the genotype frequencies of AA in the SRS strain was 0.78,
and those of AA and AC in the CS strain were 0.34 and 0.22,
respectively; the genotypes were significantly correlated with
strain (p< 0.01). Regarding the annexin A7 (Unigene14740
locus), the BC genotype frequency was significantly in the CS
group higher than that in the SRS group (p<0.05).IntheSRS
strain, the alleles were D > A > C > B > E, whereas in the CS
strain, the alleles were C > B = D > A = E; CS does not carry
A or E alleles. Thus, the genotype frequencies of CD and AD
in the SRS strain were 0.38 and 0.31, respectively, and those
Fig. 3 Scatterplot for Kyoto Encyclopedia of Genes and Genomes
(KEGG) Pathway enrichment analysis of identified differentially
expressed genes (DEGs) between SRS and CS strains. Rich factor repre-
sents the ratio of differentially expressed gene numbers annotated in this
pathway term to all gene numbers annotated in thispathway term. Greater
rich factor means greater intensiveness. Q-value is the corrected pvalue
ranging from 0 to 1, with lower values representing greater intensiveness.
The top 20 pathway entries in the degree of enrichment were shown. The
differentially expressed genes between SRS and CS strains were identi-
fied to be involved in 20 distinct metabolic pathways. Among them, the
top five pathways were amoebiasis (ko05146), vibrio cholera infection
(ko05110), salivary secretion (ko04970), protein digestion and absorption
(ko04974), and dorso-ventral axis formation (ko04320)
778 Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
of BC and CD in the CS strain 0.5 and 0.31, respectively. The
AA, AD, AE, and BC genotypes were significantly correlated
with strain (p< 0.01), and the BB and DE genotypes were
significantly correlated with strain (p<0.05).
Regarding the myosin-IXb-like (Unigene10624 locus), the
AA genotype frequency in the SRS group was significantly
higher than that in the CS group (p<0.05).IntheSRSandCS
strain, the alleles were A > B, with genotype frequencies of
AA in the SRS strain of 1.00, while those of AA and AB in the
CS strain were 0.78 and 0.22, respectively. AA and AB ge-
notypes were significantly correlated with strain (p<0.01).
Marker analysis of the SRS and CS strains revealed that
SRS-specific genetic markers, including the BC genotype of
flotillin-2-like and the AA, AD, AE, BB, and DE genotypes of
annexin A7, were significantly correlated with the SRS strain
(p< 0.01). CS-specific genetic markers, including the BC ge-
notype of keratin, the BC, CC, and AC genotypes of GPCR
Mth2-like, the BC genotype of annexin A7, and the AB ge-
notype of myosin-IXb-like, were significantly correlated with
the CS strain (p< 0.01) (Table 6).
Discussion
N. denticulata is an ornamental shrimp with a high added-
value product (Patoka et al. 2015; Levitt-Barmats et al.
2019). Studies on N. denticulata have mainly focused on mo-
lecular phylogeny (Shih and Cai 2007; von Rintelen et al.
2012), environmental adaptations (Suzuki and Kanou 2014),
microbiota diversity (Cheung et al. 2015;Cornejo-Granados
et al. 2018), and environmental toxicological and pharmaco-
logical applications (Sung et al. 2014;WuandLi2015;Hu
et al. 2019). However, the studies focusing on molecular
marker development and genetic diversity are limited, and
there is still no transcriptome analysis report about this spe-
cies, which blocks the in-depth study on N. denticulata
(Mykles and Hui 2015;Nongetal.2020).
RNA-Seq is an appropriate tool for obtaining transcriptome
data widely applied in various organisms (Mortazavi et al.
2008; Leu et al. 2011; Ghaffari et al. 2014;Chenetal.2015;
Yue et al. 2015;Wangetal.2018). This study presents the
first genetic and breeding research on whole organ tissues of
N. denticulata through transcriptome sequencing. Notably, a
large number of N. denticulata non-redundant unigenes
Fig. 4 Heat map representing functional gene expression of
N. denticulata transcripts containing microsatellite locus in super red
shrimp (SRS) and chocolate shrimp (CS) based on their relative frag-
ments per kilobase of exon per million fragments mapped (FPKM)
values. Transcripts were hierarchically cluster based on correlation dis-
tance and average linkage method. Red and green indicate high and low
level of expression, respectively. FPKM, fragments per kilobase of tran-
script per million mapped reads
Table 3 Repeat numbers and motif length distribution of putative SSR
markers
1
in the N. denticulata transcriptome
Number of repeats Nucleotide repeats
Mono-Di- Tri- Quad-Penta-Hexa-
4 0 0 0 0 158 120
5004296128280
6 0 2724 2446 68 1 0
7 0 1551 2191 2 0 0
8 0 902 139 0 0 0
9 0 831 2 0 0 0
10 0 1000 1 0 0 0
11 0 564 1 0 0 0
12 1925 17 0 0 0 0
13 1228 0 0 0 0 0
14 833 0 0 0 0 0
15 542 0 0 0 0 0
16 409 0 0 0 0 0
17 333 0 0 0 0 0
18 427 0 0 0 0 0
19 435 0 0 0 0 0
20 462 0 0 0 0 0
21 296 0 0 0 0 0
22 123 0 0 0 0 0
23 38 0 0 0 0 0
Subtotal 7051 7589 9076 1352 167 120
1
SSR detection is done with software MicroSAtellite (MISA) using
unigenes as reference
779Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
(105,892) were generated with the Illumina HiSeq 2000 plat-
form, and numerous expressed sequence tags (ESTs) were
available. Among the identified unigenes, 65,402 (61.76%)
were successfully annotated through BLAST searching
against the public Nr, GO, COG, KOG, and KEGG databases.
GO and COG analyses revealed the distribution of functional
genes in N. denticulata, while KEGG database searching suc-
cessfully revealed the functions of cellular-process genes and
the gene products of metabolic processes. With a high-quality
N. denticulata transcriptome assembly, the unigenes devel-
oped in this study would enable genetic studies of these fresh-
water ornamental shrimps, which would enrich our under-
standing of their regulating mechanism for color-related genes
and genetic diversity. Additionally, this is crucial for the man-
agement planning of genetic improvements in this ornamental
species.
The functional classification of these transcripts according
to the GO database showed that “binding”and “catalytic
Table 4 Characteristics of
Neocaridina denticulata
microsatellite loci
SSR locus Motif Number
of allele
Allele size
range (bp)
Genotype
CL2407_4 (T)
14
1 145 145/145
CL2911_2 (ACC)
5
1 130 130/130
CL32_6 (TGG)
5
1 154 154/154
CL3484_2 (T)
12
1 120 120/120
CL3550_2 (TCA)
5
1 130 130/130
CL4393_2 (ACC)
5
1 113 113/113
CL5162_2 (A)
14
3165–169 165/165, 165/167, 165/169, 167/167, 167/169,
169/169
CL5500_1 (CA)
6
1 128 128/128
CL85_6 (G)
12
1 160 160/160
Unigene10624 (CAA)
5
2152–155 152/152, 152/155
Unigene14740 (T)
12
5116–125 116/116, 116/122, 116/125, 118/118, 118/120,
118/122, 120/120, 120/122, 122/125
Unigene19134 (ATT)
5
1 173 173/173
Unigene28786 (GCG)
6
3129–153 129/153, 135/135, 135/153, 153/153
Unigene31684 (ACT)
6
1 180 180/180
Unigene32861 (AT)
6
3162–166 162/162, 162/164, 162/166
Unigene36045 (CTT)
5
1 132 132/132
Fig. 5 The percentage
distribution of the total number of
18,304 perfect microsatellite
SSRs among different nucleotide
classes of dimers, trimers,
quadmers, pentamers, and
hexamers motif sequences,
repeated nucleotide types, and
repeat number found in the
transcriptome of N. denticulata
780 Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
activity”were the dominant molecular functions (Fig. S3),
which was consistent with previous studies in other crusta-
ceans (Leu et al. 2011; Ertl et al. 2013; Wang et al. 2019;
Tang et al. 2020). This reflected that actin and tubulin secreted
by epithelial cells to the extracellular matrix and a signaling
molecule activated a specific receptor located on the cell sur-
face or inside the cell, and triggered a biochemical chain of
events in the accumulation and dispersion of pigment granules
among the chromatophores (Ertl et al. 2013). Therefore, it is
hypothesized that there are many complex interactions be-
tween different pigment cells of chromatophores, which is
potentially involved in the formation of variant body colora-
tion of N. denticulata.
Previous studies have identified various gene products in
crustaceans that regulate coloration, including crustacyanin,
sarcoplasmic calcium-binding protein, forms of actin,
RPCH, cyclic AMP (cAMP), pigment dispersing hormone
(α-PDH), and cAMP-dependent protein kinase (Ertl et al.
2013; Milograna et al. 2016).Our transcript analysis of these
gene products is consistent with the unigene-enrichment in
“secondary metabolite biosynthesis, transport and
catabolism.”
Molecular marker technology can be used to detect and
reflect genetic differences at the genomic DNA level with
individual specificity. It has the advantage of environmental
stability (Chow et al. 2018) and is commonly used as an im-
portant molecular tool for assessing genetic diversity (Sajeela
et al. 2019;Zhaoetal.2019). Among several kinds of molec-
ular markers, SSR techniques have the advantages of high
polymorphism, superior repeatability, and widespread appli-
cation. Moreover, it has been widely applied in the field of
aquatic breeding and genetic management in recent years
(Andriantahina et al. 2013). For the genetic improvement of
aquaculture species, research on shrimp germplasm resources
has developed and progressed from analysis of phenotype to
genotype (Nong et al. 2020).
RNA-sequencing is considered an effective way to acquire
EST sequences for identifying novel genes and developing
SSR markers (Chen et al. 2015;Wangetal.2018). In this
study, the overall analysis strategy was mainly to obtain infor-
mation about the regulation of biological functions of genes
and color-related SSR markers from the whole organ tissues
of different and monotonously colored-shell ornamental
shrimp strains, i.e., SRS (light-colored) and CS (dark-col-
ored), by using RNA-Seq technology. A comparative
transcriptomic analysis was performed to reveal the transcrip-
tional alterations in SRS and CS strains.
All 65,402 unigenes were used to detect SSRs, and a total
of 25,355 (38.77%) SSRs were identified, which is a lower
percentage than those in banana shrimp (Fenneropenaeus
merguiensis) (43.5%) (Wang et al. 2017), and giant freshwater
prawns (Macrobrachium rosenbergii) (48.76%) (Jiang et al.
2019), but higher than that in ridgetail white prawn
(Exopalaemon carinicauda) (10.97%) (Wang et al. 2018).
On average, the SSR loci were found at a distance of 4.95
kbp, which is higher than those in F. merguiensis (1.02
kbp), and M. rosenbergii (0.93 kbp), but lower than that in
E. carinicauda (6.6 kbp). Among the mined SSRs, the pro-
portion of mono- (7051, 27.81%), di- (7589, 29.93%) and tri-
(9076, 35.8%) nucleotide accounted for more than 93%. This
result was consistent with those for other crustacean species
(Wang et al. 2017;Wangetal.2018; Jiang et al. 2019).
According to multiple metabolic pathways with biological
functions, such as amoebiasis (ko05146), vibrio cholera infec-
tion (ko05110), salivary secretion (ko04970), protein diges-
tion and absorption (ko04974), dorso-ventral axis formation
Table 5 Analysis of genetic diversity of microsatellite markers related to strain in N. denticulata
Locus Strain N
G
N
A
H
o
H
e
PIC F
IS
Unigene32861 SRS 4 3 0.938 0.660 0.650 −0.443
CS 3 3 0.188 0.175 0.172 −0.088
Unigene28786 SRS 3 3 0.068 0.129 0.127 0.474
CS 4 3 0.656 0.551 0.543 −0.210
CL5162_2 SRS 3 2 0.156 0.246 0.242 0.354
CS 6 3 0.406 0.633 0.623 0.348
Unigene14740 SRS 8 5 0.844 0.702 0.691 −0.221
CS 4 3 0.906 0.669 0.659 −0.376
Unigene10624 SRS 1 1 0 0 0 –
CS 2 2 0.219 0.198 0.195 −0.123
Mean ± SD SRS 3.80 ± 2.59 2.80 ± 1.48 0.40 ± 0.45 0.35 ± 0.32 0.34 ± 0.32 0.14 ± 0.27
CS 3.80 ± 1.48 2.80 ± 0.45 0.47 ± 0.3 0.45± 0.24 0.44 ± 0.24 −0.09 ± 0.33
N
G
observed number of genotype, N
A
observed number of alleles, H
o
observed heterozygosity, H
e
expected heterozygosity, PIC polymorphic infor-
mation content, F
IS
individual fixation index
781Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
(ko04320), insulin signaling pathway (ko04910), and regula-
tion of actin cytoskeleton (ko04810), from the transcriptome
databank, we identified EST-SSR markers in candidate genes,
such as flotilin-2-like (Neumann-Giesen et al. 2007), keratin
or type I cytoskeletal 19 (Ertl et al. 2013), GPCR Mth2-like
(Ha et al. 2003), annexin A7 (Castle et al. 2003), and uncon-
ventional myosin (myosin-IXb-like) (Wirth et al. 1996;
Chieregatti et al. 1998). These genes have been shown to be
involved in phosphorylation of multiple tyrosines and endo-
cytosis (Riento et al. 2009), pigment particle transport (Ha
et al. 2003), melanin content and type in melanocytes (Tuma
and Gelfand 1999;VanderSalmetal.2005;Ertletal.2013),
cAMP signal transduction pathway activated by GPCR
(García-Borrón et al. 2005), calcium-dependent phospholipids
binding proteins (Brownawell and Creutz 1997), membrane-
associated protein distributions that are involved in a variety
of cell functions (McMichael et al. 2014), production of spe-
cific pigments, or chromatophore formation and transforma-
tion through metabolic processes. Based on a mixed linear
model, an association analysis of hybridization experiment
was performed on 200 F1 progeny individuals from two
groups of pure strain (SRS × SRS, CS × CS) and two recipro-
cal hybrid strains (SRS × CS, CS × SRS) of SRS and CS. The
genetic diversity level of SRS and CS N. denticulata pure and
hybrid germplasm resources was validated with fluorescently
labeled capillary electrophoresis.
Previous studies have explored EST-SSR polymorphism
results in Epinephelus lanceolatus (Zeng et al. 2008),
Megalobrama pellegrini (Wang et al. 2012), and
Pelteobagrus fulvidraco (Zhang et al. 2014), finding that the
polymorphism level is low, medium, and high when PIC is 0–
0.25, 0.25–0.5, and > 0.5, respectively (Zeng et al. 2008).
However, the SSR markers of five functional genes in
N. denticulata produced 16 alleles (numbered between 2 and
5) and 25 genotypes, with an average of 3.8 alleles per locus
(Na) and PIC of 0.34–0.44, showing a medium polymorphism
level. These results may be attributed to the expected number
of SSRs and polymorphisms in DNA protein-coding se-
quences being lower than that of non-coding sequences, and
the mutation rate within these regions being lower than that in
other DNA sequences. According to the theory, one polymor-
phic locus in this study was a high-level polymorphic locus,
and four of them were medium-level polymorphic loci. These
results indicated that 5 out of 26 primer pairs in this study
should be used for genetic diversity analysis of
N. denticulata. Genetic diversity analysis showed that most
N. denticulata resources clustered according to origin area,
indicating that geographic variation is a significant cause of
germplasm variation and genetic diversity. The false positive
rate of color phenotype identification (misrecognition) of the
single and combined markers in the two strains was lower than
5% (4.31%) and 0.01% (0.0018%), respectively.
The genetic polymorphismcoefficient and correlation anal-
ysis were used to distinguish the four breeding resources of
SRS and CS (Chen et al. 2017). The light-colored SRS pre-
sented a higher percentage of homozygous genotypes in genes
such as keratin, GPCR Mth2-like, and myosin-IXb-like than
that in CS. Meanwhile, the dark-colored CS presented a
higher percentage of homozygous genotypes in flotillin-2-
like genes than that in light-colored SRS, which were domi-
nated by homozygotes. This phenomenon can explain why
CS has more potential for selection and purifying efficiency
than SRS in the genetic improvement of color (Chen et al.
2017).
Coloration is affected and regulated by multiple genes and
environmental factors. Ornamental shrimp transcriptome li-
braries are important for studying the regulatory basis of
color-related properties in colored or crystal shrimp. These
findings will represent a breakthrough for the confirmation
Table 6 Five color-related microsatellite markers in four strains of
N. denticulata
Locus Genotype Freq. F p value
P1 P2 H1 H2
Unigene32861 AA 0.06 0.81 0.00 0.13 5.86 0.001
**
AB 0.06 0.03 0.50 0.63 21.68 0.000
**
AC 0.34 0.16 0.25 0.00 12.32 0.000
**
BB 0.00 0.00 0.13 0.00 17.73 0.000
**
BC 0.53 0.00 0.13 0.25 115.51 0.000
**
Unigene28786 AC 0.07 0.25 0.75 0.25 7.83 0.000
**
BB 0.03 0.06 0.00 0.00 1.62 0.191
CC 0.90 0.28 0.25 0.75 399.24 0.000
**
BC 0.00 0.41 0.00 0.00 5.70 0.001
**
CL5162_2 AA 0.78 0.34 0.38 0.13 3.97 0.011
*
AB 0.16 0.09 0.25 0.38 4.23 0.008
**
BB 0.06 0.19 0.38 0.38 10.02 0.000
**
BC 0.00 0.09 0.00 0.13 7.32 0.000
**
CC 0.00 0.06 0.00 0.00 4.65 0.005
**
AC 0.00 0.22 0.00 0.00 32.84 0.000
**
Unigene14740 AA 0.06 0.00 0.00 0.00 4.65 0.005
**
AD 0.31 0.00 0.50 0.00 104.03 0.000
**
AE 0.06 0.00 0.00 0.00 4.65 0.005
**
BB 0.03 0.00 0.00 0.00 2.09 0.108
BC 0.00 0.50 0.00 0.00 –0.000
**
BD 0.06 0.09 0.00 0.00 2.42 0.073
CC 0.00 0.00 0.00 0.25 68.40 0.000
**
CD 0.38 0.31 0.13 0.75 4.61 0.005
**
DD 0.06 0.09 0.38 0.00 7.79 0.000
**
DE 0.03 0.00 0.00 0.00 2.09 0.108
Unigene10624 AA 1.00 0.78 0.87 1.00 32.04 0.000
**
AB 0.00 0.22 0.13 0.00 32.04 0.000
**
P1, SRS(♀)xSRS(♂); P2, CS(♀)xCS(♂); H1, SRS(♀)xCS(♂); H2,
CS(♀)xSRS(♂);
*
p<0.05;
**
p<0.01
782 Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
of freshwater ornamental shrimp strains, and will benefit re-
search on color-related gene expression and on the basic
mechanisms regulating pigment deposition (Yamada et al.
1990; Wade et al. 2012). In addition, they provide genetic
comparison and reference value data for other related inverte-
brates. Therefore, in addition to testing different heterozygous
strains and offspring to identify multiple microsatellite
markers related to color properties, future identification of
genetic traits can be performed using a set of appropriate
DNA barcodes by comparing whole genome sequences.
A combination of specific genotypes can serve as indicators
for the improvement of molecular marker-assisted screening,
allowing the establishment of standard operation models for
molecular selective breeding techniques. Results generated
through research and development have potential application
and economic value in the development and modernization of
the ornamental shrimp industry, and provide industries with
more complete and scientific means to produce, market, and
certify brands, thus introducing a new element to the ornamen-
tal shrimp aquaculture industry. In the future, this is expected to
continue to enhance the ornamental aquarium and biotechnol-
ogy industry’s international market to create new opportunities.
Acknowledgments We are very grateful to Prof. CH Liou and YS Huang
for their helpful comments on this work. We thank the members of the
aquaculture team of our department for taking care of our ornamental
shrimp breeding stocks.
Author Contributions C.W.H. was involved in planning and supervised
the project. P.Y.C. and Y.F.W. contributed to the design and implemen-
tation of the research, to the analysis of the results, and to the writing of
the manuscript. P.Y.C., Y.F.W., and W.R.C. processed the experimental
data and performed the analysis. W.R.C. and Y.H.W. manufactured the
samples. C.W.H., P.Y.C., Y.F.W., W.R.C., and Y.H.W. discussed the
results and commented on the manuscript. C.W.H. finalized the paper.
Funding Information This research is supportedby the Fisheries Agency,
Council of Agriculture, Taiwan, with grant number 104AS-16.2.6-FA-
F1.
Compliance with Ethical Standards
Competing Interests The authors declare that they have no competing
interests.
References
Andriantahina F, Liu X, Huang H (2013) Genetic map construction and
quantitative trait locus (QTL) detection of growth-related traits in
Litopenaeus vannamei for selective breeding applications. PLoS
One 8:e75206
Ariyanathan G, Serebiah JS (2016) Selective and interbreeding of fresh-
water ornamental shrimps with endemic aquarium plants. Int J Curr
Res Biosci Plant Biol 3:144–150
Ben J, Lim TM, Phang VP, Chan WK (2003) Cloning and tissue expres-
sion of 6-pyruvoyl tetrahydropterin synthase and xanthine dehydro-
genase from Poecilia reticulata. Mar Biotechnol 5:568–578
Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of
genetic linkage map in man using restriction fragment length poly-
morphisms. Am J Hum Genet 32:314–331
Brownawell AM, Creutz CE (1997) Calcium-dependent binding of
sorcin to the N-terminal domain of synexin (annexin VII). J Biol
Chem 272:22182–22190
Castle J, Garrett-EngeleP, Armour CD, Duenwald SJ, Loerch PM, Meyer
MR, Schadt EE, Stoughton R, Parrish ML, Shoemaker DD, Johnson
JM (2003) Optimization of oligonucleotide arrays and RNA ampli-
fication protocols for analysis of transcript structure and alternative
splicing. Genome Biol 4:1
Chen X, Mei J, Wu J, Jing J, Ma W, Zhang J, Dan C, Wang W, Gui JF
(2015) A comprehensive transcriptome provides candidate genes for
sex determination/differentiation and SSR/SNP markers in yellow
catfish. Mar Biotechnol 17:190–198
Chen J, Glémin S, Lascoux M (2017) Genetic diversity and the efficacy
of purifying selection across plant and animal species. Mol Biol
Evol 34:1417–1428
Cheung MK, Yip HY, Nong W, Law PT, Chu KH, Kwan HS, Hui JH
(2015) Rapid change of microbiota diversity in the gut but not the
hepatopancreas during gonadal development of the new shrimp
model Neocaridina denticulata. Mar Biotechnol 17:811–819
Chieregatti E, Gärtner A, Stöffler HE, Bähler M (1998) Myr 7 is a novel
myosin IX-RhoGAP expressed in rat brain. J Cell Sci 111:3597–
3608
Chow LH, Ma KY, Hui JHL, Chu KH (2018) Isolation and characteriza-
tion of polymorphic microsatellite loci for Caridina cantonensis and
transferability across eight confamilial species (Atyidae, Decapoda).
Zool Stud 57:e19
Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M (2005)
Blast2GO: a universal tool for annotation, visualization and analysis
in functional genomics research. Bioinformatics 21:3674–3676
Cornejo-Granados F, Gallardo-Becerra L, Leonardo-Reza M, Ochoa-
Romo JP, Ochoa-Leyva A (2018) A meta-analysis reveals the envi-
ronmental and host factors shaping the structure and function of the
shrimp microbiota. PeerJ 6:e5382
Curran K, Lister JA, Kunkel GR, Prendergast A, Parichy DM, Raible DW
(2010) Interplay between Foxd3 and Mitf regulates cell fate plastic-
ity in the zebrafish neural crest. Dev Biol 344:107–118
Ertl NG, Elizur A, Brooks P, Kuballa AV, Anderson TA, Knibb WR
(2013) Molecular characterisation of colour formation in the prawn
Fenneropenaeus merguiensis. PLoS One 8:e56920
Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: accelerated for clus-
tering the next-generation sequencing data. Bioinformatics 28:3150-
3152
García-Borrón JC, Sánchez-Laorden BL, Jiménez-Cervantes C (2005)
Melanocortin-1 receptor structure and functional regulation.
Pigment Cell Res 18:393–410
Ghaffari N, Sanchez-Flores A, Doan R, Garcia-Orozco KD, Chen PL,
Ochoa-Leyva A, Lopez-Zavala AA, Carrasco JS, Hong C, Brieba
LG, Rudiño-Piñera E, Blood PD, Sawyer JE, Johnson CD, Dindot
SV, Sotelo-Mundo RR, Criscitiello MF (2014) Novel transcriptome
assembly and improved annotation of the whiteleg shrimp
(Litopenaeus vannamei), a dominant crustacean in global seafood
mariculture. Sci Rep 4:7081
Ha T, Naysmith L, Waterston K, Oh C, Weller R, Rees JL (2003)
Defining the quantitative contribution of the melanocortin 1 receptor
(MC1R) to variation in pigmentary phenotype. Ann N Y Acad Sci
994:339–347
Hu XL, Niu JJ, Meng Q, Chai YH, Chu KH, Chan KM (2019) Effects of
two juvenile hormone analogue insecticides, fenoxycarb and
methoprene, on Neocaridina davidi. Environ Pollut 253:89–99
Hung MS, Chan TY, Yu HP (1993) Atyid shrimps (Decapoda: Caridea)
of Taiwan, with descriptions of three new species. J Crustac Biol 13:
481–503
783Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Jiang J, Yuan X, Qiu Q, Huang G, Jiang Q, Fu P, Zhang Y, Jia Y, Yang
X, Jiang H (2019) Comparative transcriptome analysis of gonads for
the identification of sex-related genes in giant freshwater prawns
(Macrobrachium rosenbergii) using RNA sequencing. Genes 10:
E1035
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M,
Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y
(2008) KEGG for linking genomes to life and the environment.
Nucleic Acids Res 36:D480–D484
Kenny NJ, Sin YW, Shen X, Zhe Q, Wang W, Chan TF, Tobe SS,
Shimeld SM, Chu KH, Hui JH (2014) Genomic sequence and ex-
perimental tractability of a new decapod shrimp model,Neocaridina
denticulata. Mar Drugs 12:1419–1437
Kottler VA, Fadeev A, Weigel D, Dreyer C (2013) Pigment pattern for-
mation in the guppy, Poecilia reticulata, involves the Kita and
Csf1ra receptor tyrosine kinases. Genetics 194:631–646
Lee JH (2017) Quantitative approaches for investigating the spatial con-
text of gene expression. Wiley Interdiscip Rev Syst Biol Med 9:
e1369
Leu JH, Chen SH, Wang YB, Chen YC, Su SY, Lin CY, Ho JM, Lo CF
(2011) A review of the major penaeid shrimp EST studies and the
construction of a shrimp transcriptome database based on the ESTs
from four penaeid shrimp. Mar Biotechnol 13:608–621
Levitt-Barmats Y, Yanai Z, Cohen TM, Shenkar N (2019) Life-history
traits and ecological characteristics of the ornamental shrimp
Neocaridina denticulata (De Haan, 1844), recently introduced into
the freshwater systems of Israel. Aquat Invasions 14:684–702
Lynn Lamoreux M, Kelsh RN, Wakamatsu Y, Ozato K (2005) Pigment
pattern formation in the medaka embryo. Pigment Cell Res 18:64–
73
McMichael BK, Scherer KF, Franklin NC, Lee BS (2014) The RhoGAP
activity of myosin IXB is critical for osteoclast podosome pattern-
ing, motility, and resorptive capacity. PLoS One 9:e87402
McNamara JC, Milograna SR (2015) Adaptive color change and the
molecular endocrinology of pigment translocation in crustacean
chromatophores. In: The Natural History of the Crustacea. 4:68–102
Milograna SR, Ribeiro MR, Baqui MM, McNamara JC (2014) Pigment
granule translocation in red ovarian chromatophores from the
palaemonid shrimp Macrobrachium olfersi (Weigmann, 1836):
functional roles for the cytoskeleton and its molecular motors.
Comp Biochem Physiol A Mol Integr Physiol 178:90–101
Milograna SR, Ribeiro MR, Bell FT, McNamara JC (2016) Pigment
translocation in caridean shrimp chromatophores: receptor type, sig-
nal transduction, second messengers, and cross talk among multiple
signaling cascades. J Exp Zool A Ecol Genet Physiol 325:565–580
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008)
Mapping and quantifying mammalian transcriptomes by RNA-
Seq. Nat Methods 5:621–628
Mykles DL, Hui JH (2015) Neocaridina denticulata: a decapod crusta-
cean model for functional genomics. Integr Comp Biol 55:891–897
Negisho T, Gemeda G, Du Laing G, Wakjira M, Janssens GPJ (2019)
Diversity in micromineral distribution within the body of ornamen-
tal fish species. Biol Trace Elem Res. https://doi.org/10.1007/
s12011-019-01983-1
Nei M (1978) Estimation of average heterozygosity and genetic distance
from a small number of individuals. Genetics 89:583–590
Neumann-Giesen C, Fernow I, Amaddii M, Tikkanen R (2007) Role of
EGF-induced tyrosine phosphorylation of reggie-1/flotillin-2 in cell
spreading and signaling to the actin cytoskeleton. J Cell Sci 120:
395–406
Nguyen NH, Quinn J, Powell D, Elizur A, Thoa NP, Nocillado J, Lamont
R, Remilton C, Knibb W (2014) Heritability for body colour and its
genetic association with morphometric traits in Banana shrimp
(Fenneropenaeus merguiensis). BMC Genet 15:132
Nong W, Chai ZYH, Jiang X, Qin J,Ma KY, Chan KM, Chan TF, Chow
BKC, Kwan HS, Wong CKC, Qiu JW, Hui JHL, Chu KH (2020) A
crustacean annotated transcriptome (CAT) database. BMC
Genomics 21:32
Nur FAH, Christianus A (2013) Breeding and life cycle of Neocaridina
denticulata sinensis (Kemp, 1918). Asian J Anim Vet Adv 8:108–
115
Pan G, Yang J (2010) Analysis of microsatellite DNA markers reveals no
genetic differentiation between wild and hatchery populations of
Pacific threadfin in Hawaii. Int J Biol Sci 6:827–833
Parichy DM, Rawls JF, Pratt SJ, Whitfield TT, Johnson SL (1999)
Zebrafish sparse corresponds to an orthologue of c-kit and is re-
quired for the morphogenesis of a subpopulation of melanocytes,
but is not essential for hematopoiesis or primordial germ cell devel-
opment. Development 126:3425–3436
Parichy DM, Mellgren EM, Rawls JF, Lopes SS, Kelsh RN, Johnson SL
(2000) Mutational analysis of endothelin receptor b1 (rose) during
neural crest and pigmentpattern development in the zebrafish Danio
rerio. Dev Biol 227:294–306
Patoka J, Bláha M, Devetter M, Rylková K, Čadková Z, Kalous L (2015)
Aquarium hitchhikers: attached commensals imported with fresh-
water shrimps via the pet trade. Biol Invasions 18:457–461
Pinnegar JK, Murray JM (2019) Understanding the United Kingdom
marine aquarium trade - a mystery shopper study of species on sale.
JFishBiol94:917–924
Riento K, Frick M, Schafer I, Nichols BJ(2009) Endocytosis offlotillin-1
and flotillin-2 is regulated by Fyn kinase. J Cell Sci 122:912–918
Sajeela KA, Gopalakrishnan A, Basheer VS, Mandal A, Bineesh KK,
Grinson G, Gopakumar SD (2019) New insights from nuclear and
mitochondrial markers on the genetic diversity and structure of the
Indian white shrimp Fenneropenaeus indicus among the marginal
seas in the Indian Ocean. Mol Phylogenet Evol 136:53–64
Shih HT, Cai Y (2007) Two new species of the land-locked freshwater
shrimp genus, Neocaridina Kubo, 1938 (Decapoda: Caridea:
Atyidae), from Taiwan, with notes on speciation on the island.
Zool Stud 46:680–694
Sin YW, Kenny NJ, Qu Z, Chan KW, Chan KW, Cheong SP, Leung RW,
Chan TF, Bendena WG, Chu KH, Tobe SS, Hui JH (2015)
Identification of putative ecdysteroid and juvenile hormone pathway
genes in the shrimp Neocaridina denticulata. Gen Comp Endocrinol
214:167–176
Sung HH, Chiu YW, Wang SY, Chen CM, Huang DJ (2014) Acute
toxicity of mixture of acetaminophen and ibuprofen to green neon
shrimp, Neocaridina denticulate. Environ Toxicol Pharmacol 38:8–
13
Suzuki T, Kanou Y (2014) Two distinct arginine kinases in Neocaridina
denticulate: psychrophilic and mesophilic enzymes. Int J Biol
Macromol 67:433–438
Tang D, Shi X, Guo H, Bai Y, Shen C, Zhang Y, Wang Z (2020)
Comparative transcriptome analysis of the gills of Procambarus
clarkii provides novel insights into the immune-related mechanism
of copper stress tolerance. Fish Shellfish Immunol 96:32–40
Thiel T, Michalek W, Varshney RK, Graner A (2003) Exploiting EST
databases for the development and characterization of gene-derived
SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet
106:411–422
Tuma MC, Gelfand VI (1999) Molecular mechanisms of pigment trans-
port in melanophores. Pigment Cell Res 12:283–294
van der Salm AL, Metz JR, Wendelaar Bonga SE, Flik G (2005) Alpha-
MSH, the melanocortin-1 Receptor and Background Adaptation in
the Mozambique Tilapia, Oreochromis Mossambicus. Gen Comp
Endocrinol 144:140–149
von Rintelen K, Page TJ, Cai Y, Roe K, Stelbrink B, Kuhajda BR, Iliffe
TM, Hughes J, von Rintelen T (2012) Drawn to the dark side: a
molecular phylogeny of freshwater shrimps (Crustacea: Decapoda:
Caridea: Atyidae) reveals frequent cave invasions and challenges
current taxonomic hypotheses. Mol Phylogenet Evol 63:82–96
784 Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Wade NM, Melville-Smith R, Degnan BM, Hall MR (2008) Control of
shell colour changes in the lobster, Panulirus cygnus.JExpBiol
211:1512–1519
Wade NM, Anderson M, Sellars MJ, Tume RK, Preston NP, Glencross
BD (2012) Mechanisms of colour adaptation in the prawn Penaeus
monodon. J Exp Biol 215:343–350
Wang J, Yu X, Zhao K, Zhang Y, Tong J, Peng Z (2012) Microsatellite
development for an endangered bream Megalobrama pellegrini
(Teleostei, Cyprinidae) using 454 sequencing. Int J Mol Sci 13:
3009–3021
Wang W, Yang S, Wang C, Shi L, Guo H, Chan S (2017) Gill
transcriptomes reveal involvement of cytoskeleton remodeling and
immune defense in ammonia stress response in the banana shrimp
Fenneropenaeus merguiensis. Fish Shellfish Immunol 71:319–328
Wang J, Ge Q, Li J, Gao H, Li J, Zhao F (2018) Identification of novel
EST-SSR markers by transcriptome sequencing in ridgetail white
prawn Exopalaemon carinicauda. Genes Genom 40:207–215
Wang F, Li S, Xiang J, Li F (2019) Transcriptome analysis reveals the
activation of neuroendocrine-immune system in shrimp hemocytes
at the early stage of WSSV infection. BMC Genomics 20:247
Wirth JA, Jensen KA, Post PL, Bement WM, Mooseker MS (1996)
Human myosin-IXb, an unconventional myosin with a chimerin-
like rho/rac GTPase-activating protein domain in its tail. J Cell Sci
109:653–661
Woolley TE, Maini PK, Gaffney EA (2014) Is pigment cell pattern for-
mation in zebrafish a game of cops and robbers? Pigment Cell
Melanoma Res 27:686–687
Wu JP, Li MH (2015) Inhibitory effects of pain relief drugs on neurolog-
ical enzymes: implications on their potential neurotoxicity to aquatic
animals. Environ Toxicol Pharmacol 39:898–905
Xu G, Du F, Nie Z, Xu P, Gu R (2014) Complete mitochondrial genome
of Caridina nilotica gracilipes. Mitochondrial DNA 4:1–2
Yamada S, Tanaka Y, Sameshima M, Ito Y (1990) Pigmentation of
prawn (Penaeus japonicus) with carotenoids: I. effect of dietary
astaxanthin, β-carotene and canthaxanthin on pigmentation.
Aquaculture 87:323–330
Yeh FC, Yang R, Boyle TJ, Ye Z, Xiyan JM (2000) POPGENE32,
version 1.32. Microsoft Window-based freeware for population ge-
netic analysis. University of Alberta, Edmonton, Canada
Yu YQ, Yang WJ, Yang JS (2014) The complete mitogenome of the
Chinese swamp shrimp Neocaridina denticulata sinensis Kemp
1918 (Crustacea: Decapoda: Atyidae). Mitochondrial DNA 25:
204–205
Yue GH, Chang A (2010) Molecular evidence for high frequency of
multiple paternity in a freshwater shrimp species Caridina ensifera.
PLoS One 5:e12721
Yue X, Nie Q, Xiao G, Liu B (2015) Transcriptome analysis of shell
color-related genes in the clam Meretrix meretrix. Mar Biotechnol
17:364–374
Zeng HS, Ding SX, Wang J, Su YQ (2008) Characterization of eight
polymorphic microsatellite loci for the giant grouper (Epinephelus
lanceolatus Bloch). Mol Ecol Resour 8:805–807
Zhang J, Ma W, Song X, Lin Q, Gui JF, Mei J (2014) Characterization
and development of EST-SSR markers derived from transcriptome
of yellow catfish. Molecules 19:16402–16415
Zhao Y, Zhu X, Li Z, Xu W, Dong J, Wei H, Li Y, Li X (2019) Genetic
diversity and structure of Chinese grass shrimp, Palaemonetes
sinensis, inferred from transcriptome-derived microsatellite
markers. BMC Genet 20:75
Publisher’snoteSpringer Nature remains neutral with regard to jurisdic-
tional claims in published maps and institutional affiliations.
785Mar Biotechnol (2020) 22:772–785
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
A preview of this full-text is provided by Springer Nature.
Content available from Marine Biotechnology
This content is subject to copyright. Terms and conditions apply.
