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Citation: Li, Z.; Bao, X.; Liu, X.;
Wang, W.; Yang, J.; Zhu, X.; Wang, S.
Transcriptome Profiling Based at
Different Time Points after Hatching
Deepened Our Understanding on
Larval Growth and Development of
Amphioctopus fangsiao.Metabolites
2023,13, 927. https://doi.org/
10.3390/metabo13080927
Academic Editor: David J. Beale
Received: 9 July 2023
Revised: 22 July 2023
Accepted: 4 August 2023
Published: 8 August 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
metabolites
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Article
Transcriptome Profiling Based at Different Time Points after
Hatching Deepened Our Understanding on Larval Growth and
Development of Amphioctopus fangsiao
Zan Li 1, Xiaokai Bao 1, Xiumei Liu 2, Weijun Wang 1, Jianmin Yang 1,*, Xibo Zhu 3 ,* and Shuhai Wang 4
1School of Agriculture, Ludong University, Yantai 264025, China
2College of Life Sciences, Yantai University, Yantai 264005, China
3Fishery Technology Service Center of Lanshan District, Rizhao 276800, China
4Ocean and Aquatic Research Center of Hekou District, Dongying 257200, China
*Correspondence: yangjianmin@ldu.edu.cn (J.Y.); hyghjjk@rz.shandong.cn (X.Z.)
Abstract:
As the quality of life improves, there is an increasing demand for nutrition-rich marine
organisms like fish, shellfish, and cephalopods. To address this, artificial cultivation of these or-
ganisms is being explored along with ongoing research on their growth and development. A case
in point is Amphioctopus fangsiao, a highly valued cephalopod known for its tasty meat, nutrient
richness, and rapid growth rate. Despite its significance, there is a dearth of studies on the A. fangsiao
growth mechanism, particularly of its larvae. In this study, we collected A. fangsiao larvae at 0, 4,
12, and 24 h post-hatching and conducted transcriptome profiling. Our analysis identified 4467,
5099, and 4181 differentially expressed genes (DEGs) at respective intervals, compared to the 0 h
sample. We further analyzed the expression trends of these DEGs, noting a predominant trend of
continuous upregulation. Functional exploration of this trend entailed GO and KEGG functional
enrichment along with protein–protein interaction network analyses. We identified GLDC, DUSP14,
DPF2, GNAI1, and ZNF271 as core genes, based on their high upregulation rate, implicated in larval
growth and development. Similarly, CLTC, MEF2A, PPP1CB, PPP1R12A, and TJP1, marked by high
protein interaction numbers, were identified as hub genes and the gene expression levels identified
via RNA-seq analysis were validated through qRT-PCR. By analyzing the functions of key and core
genes, we found that the ability of A. fangsiao larvae to metabolize carbohydrates, lipids, and other
energy substances during early growth may significantly improve with the growth of the larvae.
At the same time, muscle related cells in A. fangsiao larvae may develop rapidly, promoting the
growth and development of larvae. Our findings provide preliminary insights into the growth and
developmental mechanism of A. fangsiao, setting the stage for more comprehensive understanding
and broader research into cephalopod growth and development mechanisms.
Keywords:
Amphioctopus fangsiao; larval growth and development; protein–protein interaction
network; transcriptome
1. Introduction
Cephalopods, globally distributed marine mollusks, predominantly inhabit the deep
sea. Characterized by their sophisticated nervous systems and adept swimming abilities,
they prove challenging to be captured in large amounts [
1
–
3
]. As consumer demand
surpasses the quantity caught, research into the artificial breeding of cephalopods and
their growth and development becomes increasingly critical. Amphioctopus fangsiao, an
economically significant cephalopod, is recognized for its rapid growth, abbreviated life
cycle, and high nutritional value [
4
–
6
]. In spite of this, research into A. fangsiao has chiefly
focused on its survival circumstances, dietary practices, and antibacterial activity, with
limited studies concentrating on its growth and development [7,8].
Metabolites 2023,13, 927. https://doi.org/10.3390/metabo13080927 https://www.mdpi.com/journal/metabolites
Metabolites 2023,13, 927 2 of 14
The larval stage is generally characterized by its relative vulnerability [
9
,
10
]. The
quality of growth among larvae directly contributes to the quality of life in the adult
stage, affecting factors such as vitality and physique. Existing research illustrates that
the transcriptome can be effectively used to identify crucial genes and pathways during
larval growth and development. For example, Bassim et al. used transcriptomic methods
to identify 16 genetic regulators linked to the early growth of Mytilus edulis larvae [
11
].
Similarly, eight distinct genes associated with the early development and metamorphosis
of Sinonovacula constricta larvae were discovered by Niu et al. [
12
]. Huan et al. utilized
transcriptomics to investigate the mechanisms of growth and development in Meretrix
meretrix larvae at various stages [
13
]. In the same vein, transcriptome analysis can aid in
identifying the key genes involved in the growth and development of A. fangsiao larvae.
This study involves the collection of A. fangsiao larvae at intervals of 0 h, 4 h, 12 h, and
24 h. At each juncture, nine larvae have been randomly partitioned into three groups for the
purpose of serving as triplicate biological replicates. Following RNA extraction, an analysis
of the larvae’s transcriptome was conducted, which encompassed library construction,
gene functional annotation, a differential expression analysis, and trend analysis, functional
enrichment analysis, as well as protein–protein interaction (PPI) network analysis. Subse-
quently, the expression levels of 13 genes, which regulate larval growth and development,
were validated using quantitative RT-PCR (qRT-PCR). These results have deepened our
knowledge of the growth model of A. fangsiao and have offered insight into molecular
mechanisms of cephalopods.
2. Materials and Methods
2.1. Sample Collection and RNA Preparation
Mature A. fangsiao specimens carrying eggs were identified and collected from the
Rizhao Sea region. Following a short period of nurturing, these specimens produced eggs,
which were safeguarded meticulously until they hatched. The eggs were incubated in
flowing seawater maintained at a temperature range of 19–20.8
◦
C, leading to hatching
after 29 days. Subsequently, the larvae were held transiently for a duration of 24 h in
flowing seawater. A sample of larvae was then gathered at intervals of 0 h, 4 h, 12 h, and
24 h post-hatching, promptly frozen in liquid nitrogen, and secured for RNA extraction
employing the TRIzol technique. Nine lively and robust A. fangsiao larvae were arbitrarily
picked from each group for RNA extraction. From these, three larvae were randomly
chosen at each time interval, and an equivalent molar mass of RNA was condensed in
each of the replicates for constructing the transcriptome library. The same procedure was
adopted to amalgamate equivalent molar masses of RNA from the trio of larvae into the
second replicate. The equilibrium larval molar mass RNA was focused in the third replicate.
Ultimately, the residual RNA was conserved for qRT-PCR validation.
2.2. Library Construction and Illumina Sequencing
The RNA-seq process was executed at Novogene Co., Ltd. (Beijing, China) as per the
protocol described by Li et al. [
14
]. Sample libraries were constructed using the NEBNext
®
Ultra
™
RNA Library Prep Kit for Illumina
®
(San Diego, CA, USA). The process involved
several discrete steps. First, mRNA was extracted from total RNA using poly-T oligo-
attached magnetic beads. The purified mRNA was then fragmented within the fragmenta-
tion buffer. In the subsequent stage, the first-strand cDNA was synthesized using random
hexamers. Additionally, second-strand cDNA was generated in a buffer containing dNTPs,
DNA polymerase I, and RNase H. After this, the cDNA underwent several processes such
as purification, end repair, poly-A linking, and adaptor ligation. The cDNA was then
amplified via PCR, and AMPure XP beads were deployed for product purification. Finally,
samples across four larval group periods (0 h, 4 h, 12 h, and 24 h) were sequenced using an
Illumina HiSeq 4000 platform, with the detailed data presented in Table S1.
Metabolites 2023,13, 927 3 of 14
2.3. De Novo Assembly
The NCBI Short Read Archive (SRA) database received the raw data submission,
identified by accession numbers ranging from SRR15204591 to SRR15204602. To enhance
the precision of the de novo assembly outcomes, we removed any reads that included
adapters, exceeded 10% in unknown nucleotides, or had more than 50% Q-value at or
below 20 bases. Clean reads, following these eliminations, underwent assembly with
Trinity. Initially, Inchworm facilitated contig assembly from the clean reads. Minimal
overlap contigs were then grouped into components through Chrysalis’ aid. Using these
contigs, Butterfly established the transcripts, which were subsequently organized into
clusters. Within each cluster, we identified the lengthy transcript as a unigene. The Trinity
operation utilized a k-mer length of 25 and opted to use default parameters.
2.4. Gene Expression Level Analysis and Function Annotation
The functions of unigenes were determined by annotating them using NR, NT, Swis-
sProt, Pfam, GO, and KOG public databases. They were identified through BLASTX with
an E_value cut-off of
≤
1
×
10
−5
for NR, NT, SwissProt, and KOG annotations; the Hmmer
3.0 package with an E_value of
≤
0.01 for Pfam annotation and GO functional annotation
were conducted both by the Blast2GO program and WEGO software. Clean reads were
then mapped to the assembled transcriptome, utilizing FPKM to quantify RNA-seq gene
expression levels. Differentially expressed genes (DEGs) were pinpointed through the
usage of DESeq2 software. Results were refined for multiple testing, employing an FDR
parameter of <0.01. DEGs, denoted as genes with an absolute log
2
fold change of
≥
1 and a
p-value of
≤
0.05, were taken into account. The common points of these DEGs were used
for further analysis.
2.5. Trend Analysis and Identification of Core Gene
The gene expression trend distribution was discerned via trend analysis. Our research
entailed the utilization of the STEM method for analyzing and clustering DEGs’ trends.
Within this method, the Maximum Unit Change in model profiles between time points was
defined as 1, and the Maximum Output Profiles Number was set at 10, wherein identical
profiles were consolidated. Further, the DEG fold changes were set as no less than 2.0, and
significant trends with a threshold p-value of equal to or less than 0.05 were filtered [
15
].
Subsequently, the trend of utmost significance was filtered. This trend was used to construct
fitting curves for gene expression where the five genes with the steepest slope of the fitting
curve were identified as core genes.
2.6. Functional Enrichment Analyses
The DAVID v6.8 software was employed to enrich DEGs into the GO terms and
KEGG signaling pathways. All annotated genes were considered the background gene set.
Meanwhile, DEGs that showed the most significant trend were utilized as a validation set
to investigate the functions of DEGs in regulating growth and development. Subsequently,
the DEGs were enriched into KEGG signaling pathways and GO terms linked to the
biological processes, molecular function, and cellular components. Eventually, significantly
enriched GO terms and KEGG signaling pathways were identified to further understand
the mechanism of A. fangsiao larval growth and development.
2.7. Functional Protein Association Networks Construction
DEGs that were significantly enriched were utilized to construct a PPI network via
STRING v11.0, using default parameters. Ten DEGs characterized by high protein inter-
action numbers were chosen and considered core genes in controlling the growth and
development of A. fangsiao larvae. Among them, the five DEGs with the top protein interac-
tion numbers were identified as hub genes likely to govern larval growth and development.
This methodology was outlined by Szklarczyk et al. [
16
]. In brief, protein sequences were
initially provided to STRING and then mapped to its database. Following this, proteins
Metabolites 2023,13, 927 4 of 14
were identified based on their functions, which were used in the following network con-
struction. The final step involved adjusting parameters and removing proteins that did not
interact with any other proteins.
2.8. Quantitative RT-PCR Validation
The expression levels of 13 genes which regulate larval growth and development
were verified utilizing qRT-PCR. Gene-specific primers were created using the software
Primer Premier 5.0. The DEGs and corresponding primer sequences can be observed in
Table S2. The stabilities of
β
-actin, 18S, and GAPDH genes throughout various stages
of
A. fangsiao
embryonic development and across different tissue types were assessed.
Notably,
β
-actin served as the endogenous control because of its stable expression. The
qRT-PCR methodology employed was based on the protocol described by Li et al. [17].
3. Results
3.1. Distribution and Expression Analysis of DEGs
The results of the differential expression analysis revealed that there were 4467, 5099,
and 4181 differentially expressed genes (DEGs) identified at 4 h, 12 h, and 24 h post-
hatching, respectively, in comparison with the baseline at 0 h. Out of these DEGs, we
observed 2270 upregulated and 2197 down-regulated DEGs at 4 h; 2729 upregulated and
2370 down-regulated DEGs at 12 h; and 1637 upregulated and 2544 down-regulated DEGs at
24 h
(Figure 1A). The Venn diagram displayed 332 DEGs that were differentially expressed
at all three time points, which were used for subsequent analysis (Figure 1B). The heatmap
helped visualize the clustering distribution of DEGs (Figure 1C). The expression levels of
DEGs at different time points had significant variations, and a considerable quantity of
DEGs were upregulated compared to the larvae at 0 h.
Metabolites 2023, 13, x FOR PEER REVIEW 5 of 15
Figure 1. The screening, hierarchical clustering, and expression trends analyses of DEGs. (A), The
volcano plot shows the screening and expression of DEGs. (a) DEG distributions between Oo-C and
Oo-4h. Each dot represents a gene. The downregulated DEGs are shown as yellow dots; the upreg-
ulated DEGs are shown as red dots; and the indifferent genes are shown as blue dots. (b) DEG
distributions between Oo-C and Oo-12h. (c) DEG distributions between Oo-C and Oo-24h. (B), The
Venn diagram shows DEG distributions between groups. Blue represents DEGs identified only at 4
hours (2903); red indicates DEGs identified only at 12 hours (3508); yellow stands for DEGs identi-
fied only at 24 hours (2814). DEGs identified at 4 and 12 hours are displayed in purple (728); DEGs
identified at 4 and 24 hours are displayed in green (504); DEGs identified at 12 and 24 hours are
displayed in orange (531). Dark green represents that DEGs are differentially expressed at all three
time points (332). (C), The DEG hierarchical clustering heatmap at each time point. Each row repre-
sents a gene, and each column represents a group. Colors represent DEG expressions. Red repre-
sents upregulation, and green stands for down-regulation. (D), Analysis of DEGs expression trends.
(a) Four of the 10 trends are significantly enriched (p-value ≤ 0.05) and represented by different col-
ors. A trend without color means it is not significantly enriched. (b) DEG numbers enriched in each
trend. (c) Expression trend of DEGs in Profile 9. 202 DEGs are enriched in this trend, and the p-value
is 2.3 × 10−61. The x-axis stands for larval growth time after hatching and the y-axis represents log2
(fold change).
3.2. Trend Analysis of DEGs
We analyzed the expression trends of DEGs in Figure 1B using the STEM method.
Out of the ten trends, four showed significant expression trends (p-value < 0.05) as repre-
sented in Figure 1D. The most noteworthy trend was Profile 9 (presented in Figure 1(Dc))
having the smallest p-value (2.3 × 10−61), suggesting its high significance. This profile con-
tained the maximum number of DEGs (202) and was deemed the most pertinent to the
growth and development of A. fangsiao larvae. Of these DEGs, 13 genes showed a con-
sistent upregulation alongside the growth of A. fangsiao larvae. The expression levels of
these genes were utilized to construct the fiing curves (refer to Table S3). Out of the five
genes—GLDC, DUSP14, DPF2, GNAI1, and ZNF271—with the steepest curve slope, they
were identified as the key genes responsible for larval growth and development.
Figure 1.
The screening, hierarchical clustering, and expression trends analyses of DEGs. (
A
), The
volcano plot shows the screening and expression of DEGs. (
a
) DEG distributions between Oo-C
and Oo-4h. Each dot represents a gene. The downregulated DEGs are shown as yellow dots; the
upregulated DEGs are shown as red dots; and the indifferent genes are shown as blue dots. (
b
) DEG
Metabolites 2023,13, 927 5 of 14
distributions between Oo-C and Oo-12h. (c) DEG distributions between Oo-C and Oo-24h. (B), The
Venn diagram shows DEG distributions between groups. Blue represents DEGs identified only at 4 h
(2903); red indicates DEGs identified only at 12 h (3508); yellow stands for DEGs identified only at
24 h (2814). DEGs identified at 4 and 12 h are displayed in purple (728); DEGs identified at 4 and
24 h
are displayed in green (504); DEGs identified at 12 and
24 h
are displayed in orange (531). Dark
green represents that DEGs are differentially expressed at all three time points (332). (
C
), The DEG
hierarchical clustering heatmap at each time point. Each row represents a gene, and each column
represents a group. Colors represent DEG expressions. Red represents upregulation, and green
stands for down-regulation.
(D), Analysis
of DEGs expression trends. (
a
) Four of the
10 trends
are
significantly enriched (
p-value ≤0.05
) and represented by different colors. A trend without color
means it is not significantly enriched. (
b
) DEG numbers enriched in each trend.
(c) Expression
trend
of DEGs in Profile 9. 202 DEGs are enriched in this trend, and the p-value is 2.3
×
10
−61
. The x-axis
stands for larval growth time after hatching and the y-axis represents log2(fold change).
3.2. Trend Analysis of DEGs
We analyzed the expression trends of DEGs in Figure 1B using the STEM method. Out
of the ten trends, four showed significant expression trends (p-value < 0.05) as represented
in Figure 1D. The most noteworthy trend was Profile 9 (presented in Figure 1(Dc)) having
the smallest p-value (2.3
×
10
−61
), suggesting its high significance. This profile contained
the maximum number of DEGs (202) and was deemed the most pertinent to the growth
and development of A. fangsiao larvae. Of these DEGs, 13 genes showed a consistent
upregulation alongside the growth of A. fangsiao larvae. The expression levels of these
genes were utilized to construct the fitting curves (refer to Table S3). Out of the five genes—
GLDC, DUSP14, DPF2, GNAI1, and ZNF271—with the steepest curve slope, they were
identified as the key genes responsible for larval growth and development.
3.3. GO and KEGG Functional Enrichment Analyses
The top 10 level-3 terms within the three designated categories namely, Biological
Process (BP), Molecular Function (MF), and Cellular Component (CC), were identified
through GO enrichment analysis as depicted in Figure 2A. These terms comprise cell–cell
adhesion, cell–cell junction organization, positive activation of Hippo signaling, and mitotic
cell cycle, among others, all of which have significant relevance to growth and development.
Concurrently, two notable KEGG signaling pathways, the endocytosis signaling pathway
and metabolic pathways, were also identified.
3.4. Construction of the PPI Network
We utilized 202 DEGs’ protein sequences to construct PPI networks. Our intent was to
identify key genes closely associated with larval growth and development, as illustrated
in Figure 2B. Through our method, we found ten key genes that exhibited a high number
of protein interactions, as shown in Table 1. Out of these, five genes—CLTC, MEF2A,
PPP1CB, PPP1R12A, and TJP1—displayed the highest number of protein interactions and
were hence identified as hub genes. Specific parameters of the network are provided in
Supplementary Table S4.
Metabolites 2023,13, 927 6 of 14
Metabolites 2023, 13, x FOR PEER REVIEW 6 of 15
3.3. GO and KEGG Functional Enrichment Analyses
The top 10 level-3 terms within the three designated categories namely, Biological
Process (BP), Molecular Function (MF), and Cellular Component (CC), were identified
through GO enrichment analysis as depicted in Figure 2A. These terms comprise cell–cell
adhesion, cell–cell junction organization, positive activation of Hippo signaling, and mi-
totic cell cycle, among others, all of which have significant relevance to growth and devel-
opment. Concurrently, two notable KEGG signaling pathways, the endocytosis signaling
pathway and metabolic pathways, were also identified.
Figure 2. Results of GO functional enrichment and PPI network analyses. (A), GO classifications of
DEGs in A. fangsiao larvae within 24 h of growth and development. The y-axis represents DEG num-
bers involved in 10 significantly enriched GO terms of each category; the x-axis indicates specific
GO terms. (B), PPI network based on 202 DEGs enriched in Profile 9. Each node represents a protein.
Different edges represent different relationships between proteins, which are described in the leg-
ends below the network.
Figure 2.
Results of GO functional enrichment and PPI network analyses. (
A
), GO classifications
of DEGs in A. fangsiao larvae within 24 h of growth and development. The y-axis represents DEG
numbers involved in 10 significantly enriched GO terms of each category; the x-axis indicates specific
GO terms. (
B
), PPI network based on 202 DEGs enriched in Profile 9. Each node represents a protein.
Different edges represent different relationships between proteins, which are described in the legends
below the network.
Metabolites 2023,13, 927 7 of 14
Table 1. Summary of key and hub DEGs.
Gene Name
(Abbreviation)
Gene Name
(Official Full Name)
Number of Protein–Protein
Interactions
CLTC clathrin heavy chain 12
MEF2A myocyte enhancer factor 2A 10
PPP1CB protein phosphatase 1 catalytic subunit beta 10
PPP1R12A protein phosphatase 1 regulatory subunit
12A 9
TJP1 tight junction protein 1 9
ROS1 ROS proto-oncogene 1, receptor tyrosine
kinase 8
WWC2 WW and C2 domain containing 2 8
CDC14A cell division cycle 14A 7
CDH23 cadherin related 23 7
DDB1 damage specific DNA binding protein 1 6
Number of protein–protein interactions: the interaction numbers between a protein corresponding to the gene
and other proteins in the network.
3.5. qRT-PCR Verification
We executed a quantitative evaluation of the relative expression levels for 13 genes,
which ostensibly regulate larval growth and development, using qRT-PCR. Our data
revealed that all DEGs measures yielded single products. Remarkably, the results from
qRT-PCR and RNA-Seq displayed significant correlation. Moreover, the gene expression
trends acquired via these two methodologies were found to be parallel, as depicted in
Figure 3.
Metabolites 2023, 13, x FOR PEER REVIEW 8 of 15
Figure 3. The relative expression level verification of hub and core genes regulating larval growth
and development using qRT-PCR. We normalized Ct values of these 13 genes to the β-actin Ct value
to determine their relative fold differences. Gene expression levels of Oo-C are normalized to 1, and
relative expression levels are calculated relative to a calibrator using a formula 2−ΔΔCt. The x-axis
indicates times of larval growth after hatching; the y-axis represents fold changes of gene expres-
sions at four time points.
4. Discussion
4.1. DEG Expression Trend Analysis
Compared to the entire collection of differentially expressed genes (DEGs), the indi-
vidual genes with differential expression at distinct time points are more likely to play
pivotal roles in the growth and development of A. fangsiao larvae. Trend analysis indicates
a continuous upregulation in the expression of most genes; the interplay between these
genes possibly facilitates the insects’ development. Five critical genes—GLDC, DUSP14,
DPF2, GNAI1, and ZNF271—showed continuous upregulation effectively regulate larval
growth and development.
GLDC, a key enzyme in the glycine cleavage system, is involved in glycine metabo-
lism and the conversion of glycine into a one-carbon unit, thereby enhancing glucose me-
tabolism, energy metabolism, and other metabolic processes [18–20]. Prior research
demonstrates that elevated expression of GLDC boosts cell proliferation, reinforces cell
antioxidant capacity, improves cell survival, and significantly expedites the development
of an organism’s tissues and organs [21–23]. Consequently, the uninterrupted upregula-
tion of GLDC likely enhances the growth and development of A. fangsiao larvae by boost-
ing cell proliferation and metabolic processes, like glycine metabolism, glucose metabo-
lism, and energy metabolism.
Dual-specificity phosphatases (DUSPs) are enzymes that dictate metabolism and cell-
based processes such as growth and differentiation [24]. DUSP14, a crucial DUSP predom-
inantly found in the liver, invokes glucose and lipid metabolism by regulating the MAPK
signaling pathway, thereby maintaining liver metabolic equilibrium [25–27]. It also facili-
tates the proliferation and differentiation of liver cells, diminishes cell mortality, and stim-
ulates liver development [28]. In this study, DUSP14 was continuously upregulated with
the growth of larvae, indicating that DUSP14 may be a core gene that regulates liver en-
ergy metabolism and promotes liver cell growth and development.
Figure 3.
The relative expression level verification of hub and core genes regulating larval growth
and development using qRT-PCR. We normalized Ct values of these 13 genes to the
β
-actin Ct value
to determine their relative fold differences. Gene expression levels of Oo-C are normalized to 1, and
relative expression levels are calculated relative to a calibrator using a formula 2
−∆∆Ct
. The x-axis
indicates times of larval growth after hatching; the y-axis represents fold changes of gene expressions
at four time points.
Metabolites 2023,13, 927 8 of 14
4. Discussion
4.1. DEG Expression Trend Analysis
Compared to the entire collection of differentially expressed genes (DEGs), the indi-
vidual genes with differential expression at distinct time points are more likely to play
pivotal roles in the growth and development of A. fangsiao larvae. Trend analysis indicates
a continuous upregulation in the expression of most genes; the interplay between these
genes possibly facilitates the insects’ development. Five critical genes—GLDC, DUSP14,
DPF2, GNAI1, and ZNF271—showed continuous upregulation effectively regulate larval
growth and development.
GLDC, a key enzyme in the glycine cleavage system, is involved in glycine metabolism
and the conversion of glycine into a one-carbon unit, thereby enhancing glucose metabolism,
energy metabolism, and other metabolic processes [
18
–
20
]. Prior research demonstrates
that elevated expression of GLDC boosts cell proliferation, reinforces cell antioxidant capac-
ity, improves cell survival, and significantly expedites the development of an organism’s
tissues and organs [
21
–
23
]. Consequently, the uninterrupted upregulation of GLDC likely
enhances the growth and development of A. fangsiao larvae by boosting cell prolifera-
tion and metabolic processes, like glycine metabolism, glucose metabolism, and energy
metabolism.
Dual-specificity phosphatases (DUSPs) are enzymes that dictate metabolism and
cell-based processes such as growth and differentiation [
24
]. DUSP14, a crucial DUSP
predominantly found in the liver, invokes glucose and lipid metabolism by regulating the
MAPK signaling pathway, thereby maintaining liver metabolic equilibrium [
25
–
27
]. It also
facilitates the proliferation and differentiation of liver cells, diminishes cell mortality, and
stimulates liver development [
28
]. In this study, DUSP14 was continuously upregulated
with the growth of larvae, indicating that DUSP14 may be a core gene that regulates liver
energy metabolism and promotes liver cell growth and development.
The role of gene expression regulation in organismal growth is fundamental. Both
DPF2 and ZNF271 are zinc-finger domain carrying proteins. Existing research indicates
that DPF2 functions as a transcription factor regulating cell apoptosis and DNA-protein
binding [
29
]. ZNF271, however, lacks elucidated functionality. These two genes are
significantly involved in the positive regulation of transcription from the RNA polymerase
II promoter term and the RNA polymerase II promoter term regulation. Unsystematic gene
expression can trigger a cascade of pathological reactions, so regularity in expression is
vital [30]. The transcription of RNA polymerase II promoter is crucial for gene expression
and regulation in organisms. It mediates cell growth, development, and differentiation
by controlling the timing and level of gene expression [
31
–
33
]. In addition, it governs the
cell cycle and growth factor expression, thereby promoting growth and development in
organisms [
34
,
35
]. As DPF2 and ZNF271 exhibit continuous upregulation, we hypothesize
that they likely influence the cellular process and gene expression to foster A. fangsiao larvae
growth and development.
GNAI1, a chief protein regulating cell signal transduction, significantly impacts the
regulation of cellular processes like proliferation, migration, and differentiation [
36
,
37
]. It
is ubiquitously expressed in organisms, sustains the stability of the nervous and motor
systems, and facilitates growth [
38
,
39
]. The continuous upregulation of GNAI1 may favor
proliferation, differentiation, signal transduction, and other cellular processes, as well as
enhance the nervous system, and boost the physical agility of the A. fangsiao larvae.
Conclusively, the continuously upregulated genes potentially promote A. fangsiao lar-
vae development through the regulation of cell growth, differentiation, survival, activation
of growth factors, and the augmentation of glucose and lipid metabolism. The precise roles
that these genes play in A. fangsiao larvae, however, remain ill-defined and warrant further
investigation in subsequent experiments.
Metabolites 2023,13, 927 9 of 14
4.2. Enrichment Analysis of GO Terms KEGG Signaling Pathways
Our functional enrichment analysis, based on GO and KEGG, has identified several
terms and signaling pathways relevant to growth and development. A case in point is the
upregulated terms of cell–cell adhesion and cell–cell junction organization. These findings
suggest an enhanced cell–cell and cell-matrix adhesion, promoting cellular proliferation,
differentiation, migration, signal transduction, and transmembrane transport, thus fostering
tissue development in larval stages of A. fangsiao [
40
–
44
]. Similarly, the observed enrichment
of the positive regulation of Hippo signaling strengthens the notion of promoted cellular
proliferation and differentiation, gradually stabilizing tissue function and accelerating
growth and development in A. fangsiao larvae [
45
–
48
]. Further evidence of rapid growth
is provided by the identification of the mitotic cell cycle term, indicating enhanced cell
proliferation and organ development in A. fangsiao larvae [
49
,
50
]. The above results indicate
that the growth of A. fangsiao larvae during early development may be regulated by a
complex network, and cellular processes such as proliferation and differentiation may be
the core processes of network regulation.
Endocytosis, a universal cellular process evident across varying tissues, governs
transmembrane transport and cellular signal transduction. This regulation, impacting the
binding and internalization of extracellular macromolecules and cell surface receptors,
influences cell proliferation, polarization, and migration [
51
]. Endocytosis also mediates
the synthesis of proteins, DNA, and lipids, thereby facilitating cell growth [
52
]. Recent
scholarship suggests a strong connection between endocytosis and the activation and
regulation of growth factors. These growth factors heighten early larval development
and preserve tissue stability by manipulating cell proliferation, differentiation, survival,
and migration [
53
,
54
]. Endocytosis, in its capacity as a signal regulator, modulates the
transference of growth factor receptor signals to control the expression of growth factors,
thus fostering organismal growth and development [
55
]. In this study, the upregulation of
the endocytosis signaling pathway suggests heightened endocytosis activity in A. fangsiao
larvae. This may result in large-scale expression of growth factors, potentially significantly
enhancing larval growth and development whilst retaining tissue stability.
Metabolic stability plays a crucial role in growth and development. The notable enrich-
ment of metabolic pathways in A. fangsiao larvae suggests an accelerated metabolic process,
indicating rapid growth. Specifically, three genes—ACSM3, CYP2B4, and FOLH1—are
enriched within this pathway. ACSM3 is primarily involved in two metabolic processes:
regulating fatty acid metabolism as an early stage key enzyme [
56
,
57
], and participating
in butyric acid metabolism. This gene converts butyric acid into butyryl-CoA for reg-
ulating polyketone biosynthesis and expression of growth factors, thus contributing to
the growth of organisms [
58
]. CYP2B4, an endoplasmic reticulum resident protein, has a
significant role in drug metabolism [
59
] and also ensures metabolic stability by catalyz-
ing the metabolism of various substrates and optimizing some protein functions [
60
,
61
].
FOLH1 participates in the metabolism of folic acid compounds, maintaining the stability of
glucose and lipid metabolism [
62
,
63
]. Thus, it can be inferred that the metabolic processes
involving fatty acids, butyric acids, folic acids, and other processes in early stage A. fangsiao
larvae are enhanced, likely promoting developmental growth through the upregulation of
growth factors.
4.3. Speculation of Hub Genes
Proteins synergistically regulate the growth and development of organisms. Central
proteins in the interaction network often play crucial roles in biological processes. This
study identifies five genes—CLTC, MEF2A, PPP1CB, PPP1R12A, and TJP1—each one
exhibiting a high number of protein interactions, potentially functioning as hub genes for
larval growth and development regulation.
CLTC, a clathrin coated scaffold protein, has a pivotal role in endocytosis, promoting
intercellular material transport and signal transduction by internalizing extracellular sub-
stances on the plasma membrane [
60
,
64
,
65
]. CLTC also regulates muscle tissue growth and
Metabolites 2023,13, 927 10 of 14
development. An elevated expression of CLTC augments the proliferation and differenti-
ation of muscle cells, increases muscle tension, and improves swimming power [
65
–
67
].
MEF2A, akin to CLTC, participates in musculature development regulation. It directs
the expression of growth factors and triggers both WNT and MAPK signaling pathways,
governing numerous cellular processes in muscle cells such as proliferation, differentiation,
growth, migration, and apoptosis. This, in turn, bolsters the muscle tissue growth and
development [
68
–
71
]. The substantial upregulation of CLTC and MEF2A demonstrated in
this study insinuates that these processes may enhance the motility of A. fangsiao larvae.
Protein phosphatase type 1 (PPP1), a pivotal phosphatase ubiquitously present across
many tissues, regulates cell growth, differentiation, and muscle development along with
intercellular signal transduction [
72
–
74
]. It consists of catalytic subunit PPP1C, and reg-
ulatory subunit PPP1R [
75
]. Specifically, PPP1CB, predominantly expressed in muscle,
promotes muscle tissue growth through its involvement in muscle cell proliferation and
differentiation [
76
,
77
]. PPP1R12A regulates muscle cell characteristics such as growth, cycle,
adhesion, and migration, and has a pivotal role in the control of muscle contraction and
relaxation [
78
,
79
]. These results propose that PPP1CB and PPP1R12A have instrumental
roles during early locomotor system development in A. fangsiao larvae, regulating muscle
growth and development from multiple perspectives.
TJP1, a membrane-associated cell barrier protein, influences growth factor expression
and signal transduction between cells by controlling cell connection and adhesion [
80
].
Further, it participates in managing the protein network’s structure between actin as
well as the tight junction protein overall, thus maintaining cell structure and function
stability [
81
]. For instance, in epithelial cells TJP1 joins transmembrane proteins and the
cytoskeleton to form scaffold proteins, facilitating epithelial cell growth and modulating
cell proliferation and differentiation [
82
–
84
]. A notable upregulation of TJP1 suggests
the cell–cell interaction significantly tightens during the early growth stage in A. fangsiao
larvae, facilitating rapid proliferation and differentiation while upholding cell integrity,
thus fostering tissue development.
In conclusion, we discovered that the central genes in the PPI network are significantly
correlated with muscle tissue growth and development. The primary reason might be the
initially poor mobility of hatched A. fangsiao larvae. As the muscle development progresses,
this effectively increases the swimming and predatory capabilities of the larvae, thereby
enhancing environmental adaptability.
5. Conclusions
We analyzed the growth mechanism of A. fangsiao larvae within 24 h post hatching
using transcriptome analysis, pinpointing ten principle genes: GLDC, DUSP14, DPF2,
GNAI1, ZNF271, CLTC, MEF2A, PPP1CB, PPP1R12A, and TJP1. Our findings suggest
that fundamental cellular processes in A. fangsiao larvae, such as growth, proliferation,
differentiation, as well as metabolic processes involving glucose, folic acid, glycine, are
enhanced. Of noteworthy mention is the rapid development of larval muscle tissue,
potentially augmenting their swimming and predation capabilities. These results furnish
insight into the growth mechanisms of octopus larvae, underscoring a solid foundation for
future exploration of cephalopod physiological processes. The muscle development and
metabolic rate of A. fangsiao larvae are very fast, which may require vast swimming space
and suitable bait. We suggest feeding bait that is beneficial for muscle growth and contains
rich metabolic materials during the artificial breeding process of A. fangsiao larvae, as well
as providing reasonable breeding space. These measures will be conducive to the rapid
growth of octopus larvae.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/metabo13080927/s1, Table S1: Summary of sequencing results;
Table S2: Primer list for quantitative RT-PCR verification; Table S3: Continuous upregulation of DEG
expression; Table S4: Network statistics of PPI network.
Metabolites 2023,13, 927 11 of 14
Author Contributions:
Conceptualization, Z.L. and J.Y.; methodology, Z.L., X.B., X.L., W.W., X.Z.
and S.W.; writing—original draft preparation, Z.L.; writing—review and editing, J.Y., X.Z. and S.W.;
supervision, J.Y.; project administration, Z.L. and J.Y.; funding acquisition, J.Y. All authors have read
and agreed to the published version of the manuscript.
Funding:
This research was funded by the Ministry of Agriculture of the People’s Republic of China
with grant number CARS-49.
Institutional Review Board Statement:
Amphioctopus fangsiao samples caught in Rizhao Sea area
were temporarily cultured and experimented in commercial hatchery. This research was conducted
in accordance with the protocols of the Institutional Animal Care and Use Committee of the Ludong
University (protocol number LDU-IRB20210308NXY) and the China Government Principles for
the Utilization and Care of Invertebrate Animals Used in Testing, Research, and Training (State
Science and Technology Commission of the People’s Republic of China for No. 2, 31 October 1988.
http://www.gov.cn/gongbao/content/2011/content_1860757.htm (accessed on 2 May 2021)).
Informed Consent Statement: Not applicable.
Data Availability Statement:
The original contributions presented in the study are publicly available
in NCBI using accession numbers SRR15204591–SRR15204602 at the following link: https://www.
ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA748210&o=acc_s%3Ad (accessed on 30 May 2023).
Conflicts of Interest: The authors declare no conflict of interest.
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