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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

by
Zan Li
1,
Xiaokai Bao
1,
Xiumei Liu
2,
Weijun Wang
1,
Jianmin Yang
1,*,
Xibo Zhu
3,* and
Shuhai Wang
4
1
School of Agriculture, Ludong University, Yantai 264025, China
2
College of Life Sciences, Yantai University, Yantai 264005, China
3
Fishery Technology Service Center of Lanshan District, Rizhao 276800, China
4
Ocean and Aquatic Research Center of Hekou District, Dongying 257200, China
*
Authors to whom correspondence should be addressed.
Metabolites 2023, 13(8), 927; https://doi.org/10.3390/metabo13080927
Submission received: 9 July 2023 / Revised: 22 July 2023 / Accepted: 4 August 2023 / Published: 8 August 2023
(This article belongs to the Section Animal Metabolism)
Browse Figures
Versions Notes

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 organisms 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.

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 [1,2,3]. 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 [4,5,6]. 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].
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. Subsequently, 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 fragmentation 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.

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, SwissProt, 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 log2 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 interaction 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 interaction 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 were identified based on their functions, which were used in the following network construction. 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.

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.

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.

4. Discussion

4.1. DEG Expression Trend Analysis

Compared to the entire collection of differentially expressed genes (DEGs), the individual 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 [18,19,20]. 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 [21,22,23]. Consequently, the uninterrupted upregulation of GLDC likely enhances the growth and development of A. fangsiao larvae by boosting cell proliferation 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 [25,26,27]. 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 [31,32,33]. 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 larvae 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.

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 [40,41,42,43,44]. 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 [45,46,47,48]. 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 enrichment 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 regulating 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 catalyzing 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 substances on the plasma membrane [60,64,65]. CLTC also regulates muscle tissue growth and development. An elevated expression of CLTC augments the proliferation and differentiation of muscle cells, increases muscle tension, and improves swimming power [65,66,67]. 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 [68,69,70,71]. 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 [72,73,74]. It consists of catalytic subunit PPP1C, and regulatory 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 [82,83,84]. 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.

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.

References

  1. Cosmo, A.D.; Cristo, C.D. Neuropeptidergic control of the optic gland of Octopus vulgaris: FMRF-amide and GnRH immunoreactivity. J. Comp. Neurol. 2015, 398, 1–12. [Google Scholar] [CrossRef]
  2. Judkins, H.; Vecchione, M.; Sweeney, M. Cephalopod research across scales: From molecules to ecosystems. Bull. Mar. Sci. 2020, 96, 231–234. [Google Scholar] [CrossRef]
  3. Schnell, A.K.; Amodio, P.; Boeckle, M.; Clayton, N.S. How intelligent is a cephalopod? Lessons from comparative cognition. Biol. Rev. Camb. Philos. Soc. 2020, 96, 162–178. [Google Scholar] [CrossRef]
  4. Boletzky, S.V. A contribution to the study of yolk absorption in the cephalopoda. Z. Morph. Tiere. 1975, 80, 229–246. [Google Scholar] [CrossRef]
  5. Navarro, J.C.; Villanueva, R. The fatty acid composition of Octopus vulgaris paralarvae reared with live and inert food: Deviation from their natural fatty acid profile. Aquaculture 2003, 219, 613–631. [Google Scholar] [CrossRef]
  6. Wei, X.M.; Xu, J.; Yang, J.M.; Liu, X.Q.; Zhang, R.R.; Wang, W.J.; Yang, J.L. Involvement of a Serpin serine protease inhibitor (OoSerpin) from mollusc Octopus ocellatus in antibacterial response. Fish Shellfish Immun. 2014, 42, 79–87. [Google Scholar] [CrossRef]
  7. Bao, X.K.; Li, Y.; Zhang, J.B.; Chen, X.P.; Xu, X.H.; Feng, Y.W.; Sun, G.H.; Liu, X.M.; Li, B.; Wang, W.J.; et al. Transcriptome Profiling Based on Different Time Points After Hatching Provides a Core Set of Gene Resource for Understanding Larval Immune Response Mechanisms Against Vibrio anguillarum Infection in Amphioctopus fangsiao. Front. Mar. Sci. 2021, 8, 731517. [Google Scholar] [CrossRef]
  8. Boletzky, S.V. Recent studies on spawning, embryonic development, and hatching in the cephalopoda. Adv. Mar. Biol. 1989, 25, 85–115. [Google Scholar]
  9. Ginger, K.W.K.; Vera, C.B.S.; Dineshram, R.; Dennis, C.K.S.; Adela, L.J.; Yu, Z.N.; Thiyagarajan, V. Larval and post-larval stages of Pacific oyster (Crassostrea gigas) are resistant to elevated CO2. PLoS ONE 2013, 8, e64147. [Google Scholar] [CrossRef] [Green Version]
  10. Kaplan, M.B.; Mooney, T.A.; McCorkle, D.C.; Cohen, A.L. Adverse effects of ocean acidification on early development of squid (Doryteuthis pealeii). PLoS ONE 2013, 8, e63714. [Google Scholar] [CrossRef] [Green Version]
  11. Bassim, S.; Tanguy, A.; Genard, B.; Moraga, D.; Tremblay, R. Identification of Mytilus edulis genetic regulators during early development. Gene 2014, 551, 65–78. [Google Scholar] [CrossRef] [PubMed]
  12. Niu, D.H.; Wang, F.; Xie, S.M.; Sun, F.Y.; Wang, Z.; Peng, M.X.; Li, J.L. Developmental Transcriptome Analysis and Identification of Genes Involved in Larval Metamorphosis of the Razor Clam, Sinonovacula constricta. Mar. Biotechnol. 2016, 18, 168–175. [Google Scholar] [CrossRef]
  13. Huan, P.; Wang, H.X.; Liu, B.Z. Transcriptomic analysis of the clam Meretrix meretrix on different larval stages. Mar. Biotechnol. 2012, 14, 69–78. [Google Scholar] [CrossRef]
  14. Li, Z.; Liu, X.M.; Liu, J.X.; Zhang, K.; Yu, H.Y.; He, Y.; Wang, X.B.; Qi, J.; Wang, Z.G.; Zhang, Q.Q. Transcriptome profiling based on protein–protein interaction networks provides a core set of genes for understanding blood immune response mechanisms against Edwardsiella tarda infection in Japanese flounder (Paralichthys olivaceus). Dev. Comp. Immunol. 2017, 78, 100–113. [Google Scholar] [CrossRef]
  15. Ernst, J.; Bar-Joseph, Z. STEM: A tool for the analysis of short time series gene expression data. BMC Bioinform. 2006, 7, 191. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2018, 47, D607–D613. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Li, Z.; Fan, T.T.; Liu, X.T.; Liu, X.M.; Wang, W.J.; Wang, Q.Q.; You, L.H.; Wang, L.; Wei, X.M.; Yang, J.M. Characterization and functional study on Octopus ocellatus interleukin-17. J. Ocean Univ. China 2019, 18, 1443–1450. [Google Scholar] [CrossRef]
  18. Xie, H.Y.; Yan, T.T.; Lu, X.X.; Du, Y.Y.; Xu, S.G.; Kong, Y.; Yu, L.J.; Sun, J.; Zhou, L.H.; Ma, J. GLDC mitigated by miR-30e regulates cell proliferation and tumor immune infiltration in TNBC. Front. Immunol. 2022, 13, 1033367. [Google Scholar] [CrossRef]
  19. Yuan, Y.L.; Sun, L.G.; Wang, X.; Chen, J.X.; Jia, M.N.; Zou, Y.L.; Sa, H.L.; Cai, Y.Y.; Xu, Y.H.; Sun, C.; et al. Identification of a new GLDC gene alternative splicing variant and its protumorigenic roles in lung cancer. Future Oncol. 2019, 15, 4127–4139. [Google Scholar] [CrossRef]
  20. Zhang, W.C.; Shyh-Chang, N.; Yang, H.; Rai, A.; Umashankar, S.; Ma, S.M.; Soh, B.S.; Sun, L.L.; Tai, B.C.; Nga, M.E.; et al. Glycine decarboxylase activity drives non-small cell lung cancer tumor-initiating cells and tumorigenesis. Cell 2012, 148, 259–272. [Google Scholar] [CrossRef] [Green Version]
  21. Lin, J.; Lee, J.H.J.; Paramasivam, K.; Pathak, E.; Wang, Z.X.; Pramono, Z.A.D.; Lim, B.; Wee, K.B.; Surana, U. Induced-Decay of Glycine Decarboxylase Transcripts as an Anticancer Therapeutic Strategy for Non-Small-Cell Lung Carcinoma. Mol. Ther. Nucleic Acids 2017, 9, 263–273. [Google Scholar] [CrossRef] [PubMed]
  22. Mukha, D.; Fokra, M.; Feldman, A.; Sarvin, B.; Sarvin, N.; Nevo-Dinur, K.; Besser, E.; Hallo, E.; Aizenshtein, E.; Schug, Z.T.; et al. Glycine decarboxylase maintains mitochondrial protein lipoylation to support tumor growth. Cell Metab. 2022, 34, 775–782. [Google Scholar] [CrossRef]
  23. Wang, D.W.; Wu, L.W.; Cao, Y.; Yang, L.; Liu, W.; Xiao-Qiang, E.; Ji, G.R.; Bi, Z.G. A novel mechanism of mTORC1-mediated serine/glycine metabolism in osteosarcoma development. Cell. Signal. 2016, 29, 107–114. [Google Scholar] [CrossRef] [PubMed]
  24. Lountos, G.T.; Tropea, J.E.; Cherry, S.; Waugh, D.S. Overproduction, purification and structure determination of human dual-specificity phosphatase 14. Acta Crystallogr. D Biol. Crystallogr. 2009, 65, 1013–1020. [Google Scholar] [CrossRef]
  25. Owens, D.M.; Keyse, S.M. Differential regulation of MAP kinase signalling by dual-specificity protein phosphatases. Oncogene 2007, 26, 3203–3213. [Google Scholar] [CrossRef] [Green Version]
  26. Wang, S.Y.; Yan, Z.Z.; Yang, X.; An, S.M.; Zhang, K.; Qi, Y.; Zheng, J.L.; Ji, Y.X.; Wang, P.X.; Fang, C.; et al. Hepatocyte DUSP14 maintains metabolic homeostasis and suppresses inflammation in the liver. Hepatology 2018, 67, 1320–1338. [Google Scholar] [CrossRef]
  27. Xie, L.F.; Wang, P.X.; Zhang, P.; Zhang, X.J.; Zhao, G.N.; Wang, A.B.; Guo, J.H.; Zhu, X.Y.; Zhang, Q.; Li, H.L. DKK3 expression in hepatocytes defines susceptibility to liver steatosis and obesity. J. Hepatol. 2016, 65, 113–124. [Google Scholar] [CrossRef] [PubMed]
  28. Wang, X.Z.; Mao, W.Z.; Fang, C.; Tian, S.; Zhu, X.Y.; Yang, L.; Huang, Z.; Li, H.L. Dusp14 protects against hepatic ischaemia-reperfusion injury via Tak1 suppression. J. Hepatol. 2017, 8278, 32275–32284. [Google Scholar] [CrossRef]
  29. Zhang, W.; Xu, C.; Bian, C.B.; Tempel, W.; Crombet, L.; MacKenzie, F.; Min, J.R.; Liu, Z.L.; Qi, C. Crystal structure of the Cys2His2-type zinc finger domain of human DPF2. Biochem. Biophys. Res. Commun. 2011, 413, 58–61. [Google Scholar] [CrossRef]
  30. Gaertner, B.; Zeitlinger, J. RNA polymerase II pausing during development. Development 2014, 141, 1179–1183. [Google Scholar] [CrossRef] [Green Version]
  31. Core, L.; Adelman, K. Promoter-proximal pausing of RNA polymerase II: A nexus of gene regulation. Genes Dev. 2019, 33, 960–982. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Grünberg, S.; Hahn, S. Structural insights into transcription initiation by RNA polymerase II. Trends Biochem. Sci. 2013, 38, 603–611. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Jin, H.Y.; Kaplan, C.D. Relationships of RNA polymerase II genetic interactors to transcription start site usage defects and growth in Saccharomyces cerevisiae. G3 Genes Genomes Genet. 2014, 5, 21–33. [Google Scholar] [CrossRef] [Green Version]
  34. Hegre, S.A.; Samdal, H.; Klima, A.; Stovner, E.B.; Nørsett, K.G.; Liabakk, N.B.; Olsen, L.C.; Chawla, K.; Aas, P.A.; Sætrom, P. Joint changes in RNA, RNA polymerase II, and promoter activity through the cell cycle identify non-coding RNAs involved in proliferation. Sci. Rep. 2021, 11, 18952. [Google Scholar] [CrossRef]
  35. Schröder, S.; Herker, E.; Itzen, F.; He, D.; Thomas, S.; Gilchrist, D.A.; Kaehlcke, K.; Cho, S.; Pollard, K.S.; Capra, J.A.; et al. Acetylation of RNA polymerase II regulates growth-factor-induced gene transcription in mammalian cells. Mol. Cell 2013, 52, 314–324. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Gilman, A.G. G proteins: Transducers of receptor-generated signals. Annu. Rev. Biochem. 1987, 56, 615–649. [Google Scholar] [CrossRef]
  37. Yao, J.; Liang, L.H.; Zhang, Y.; Ding, J.; Tian, Q.; Li, J.J.; He, X.H. GNAI1 Suppresses Tumor Cell Migration and Invasion and is Post-Transcriptionally Regulated by Mir-320a/c/d in Hepatocellular Carcinoma. Cancer Biol. Med. 2012, 9, 234–241. [Google Scholar]
  38. Muir, A.M.; Gardner, J.F.; van Jaarsveld, R.H.; de Lange, I.M.; van der Smagt, J.J.; Wilson, G.N.; Dubbs, H.; Goldberg, E.M.; Zitano, L.; Bupp, C.; et al. Variants in GNAI1 cause a syndrome associated with variable features including developmental delay, seizures, and hypotonia. Genet. Med. 2021, 23, 881–887. [Google Scholar] [CrossRef]
  39. Solis, G.P.; Kozhanova, T.V.; Koval, A.; Zhilina, S.S.; Mescheryakova, T.I.; Abramov, A.A.; Ishmuratov, E.V.; Bolshakova, E.S.; Osipova, K.V.; Ayvazyan, S.O.; et al. Pediatric Encephalopathy: Clinical, Biochemical and Cellular Insights into the Role of Gln52 of GNAO1 and GNAI1 for the Dominant Disease. Cells 2021, 10, 2749. [Google Scholar] [CrossRef]
  40. Kong, F.; Li, Z.; Parks, W.M.; Dumbauld, D.W.; García, A.J.; Mould, A.P.; Humphries, M.J.; Zhu, C. Cyclic mechanical reinforcement of integrin-ligand interactions. Mol. Cell 2013, 49, 1060–1068. [Google Scholar] [CrossRef] [Green Version]
  41. Pinheiro, D.; Bellaïche, Y. Mechanical Force-Driven Adherens Junction Remodeling and Epithelial Dynamics. Dev. Cell 2018, 47, 3–19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Reglero-Real, N.; Colom, B.; Bodkin, J.V.; Nourshargh, S. Endothelial Cell Junctional Adhesion Molecules: Role and Regulation of Expression in Inflammation. Arter. Thromb. Vasc. Biol. 2016, 36, 2048–2057. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Schepis, A.; Sepich, D.; Nelson, W.T. αE-catenin regulates cell-cell adhesion and membrane blebbing during zebrafish epiboly. Development 2012, 139, 537–546. [Google Scholar] [CrossRef] [Green Version]
  44. Terahara, K.; Takahashi, K.G.; Mori, K. Pacific oyster hemocytes undergo apoptosis following cell-adhesion mediated by integrin-like molecules. Comp. Biochem. Phys. A 2005, 141, 215–222. [Google Scholar] [CrossRef] [PubMed]
  45. Ma, S.H.; Meng, Z.P.; Chen, R.; Guan, K.L. The Hippo Pathway: Biology and Pathophysiology. Annu. Rev. Biochem. 2019, 88, 577–604. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Masliantsev, K.; Karayan-Tapon, L.; Guichet, P.O. Hippo Signaling Pathway in Gliomas. Cells 2021, 10, 184. [Google Scholar] [CrossRef]
  47. Sahu, M.R.; Mondal, A.C. Neuronal Hippo signaling: From development to diseases. Dev. Neurobiol. 2021, 81, 92–109. [Google Scholar] [CrossRef]
  48. Wu, Z.M.; Guan, K.L. Hippo Signaling in Embryogenesis and Development. Trends Biochem. Sci. 2021, 46, 51–63. [Google Scholar] [CrossRef]
  49. Sun, Y.; Liu, Y.; Ma, X.L.; Hu, H. The Influence of Cell Cycle Regulation on Chemotherapy. Int. J. Mol. Sci. 2021, 22, 6923. [Google Scholar] [CrossRef]
  50. Swanhart, L.; Kupsco, J.; Duronio, R.J. Developmental control of growth and cell cycle progression in Drosophila. Methods Mol. Biol. 2005, 296, 69–94. [Google Scholar]
  51. Barbieri, E.; Fiore, P.P.D.; Sigismund, S. Endocytic control of signaling at the plasma membrane. Curr. Opin. Cell Biol. 2016, 39, 21–27. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Hinze, C.; Boucrot, E. Endocytosis in proliferating, quiescent and terminally differentiated cells. J. Cell Sci. 2018, 131, 216804. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Blume-Jensen, P.; Hunter, T. Oncogenic kinase signalling. Nature 2001, 411, 355–365. [Google Scholar] [CrossRef] [PubMed]
  54. Lemmon, M.A.; Schlessinger, J. Cell signaling by receptor tyrosine kinases. Cell 2010, 141, 1117–1134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Moore, R.; Pujol, M.G.; Zhu, Z.; Smythe, E. Interplay of Endocytosis and Growth Factor Receptor Signalling. Prog. Mol. Subcell. Biol. 2018, 57, 181–202. [Google Scholar]
  56. Gopal, R.; Selvarasu, K.; Pandian, P.P.; Ganesan, K. Integrative transcriptome analysis of liver cancer profiles identifies upstream regulators and clinical significance of ACSM3 gene expression. Cell. Oncol. 2017, 40, 219–233. [Google Scholar] [CrossRef]
  57. Ruan, H.Y.; Yang, C.; Tao, X.M.; He, J.; Wang, T.; Wang, H.; Wang, C.; Jin, G.Z.; Jin, H.J.; Qin, W.X. Downregulation of ACSM3 promotes metastasis and predicts poor prognosis in hepatocellular carcinoma. Am. J. Cancer Res. 2017, 7, 543–553. [Google Scholar]
  58. Rengaraj, D.; Lee, B.R.; Jang, H.J.; Kim, Y.M.; Han, J.Y. Comparative metabolic pathway analysis with special reference to nucleotide metabolism-related genes in chicken primordial germ cells. Theriogenology 2012, 79, 28–39. [Google Scholar] [CrossRef]
  59. Perera, R.; Sono, M.; Kinloch, R.; Zhang, H.; Tarasev, M.; Im, S.C.; Waskell, L.; Dawson, J.H. Stabilization and spectroscopic characterization of the dioxygen complex of wild-type cytochrome P4502B4 (CYP2B4) and its distal side E301Q, T302A and proximal side F429H mutants at subzero temperatures. Biochim. Biophys. Acta 2011, 1814, 69–75. [Google Scholar] [CrossRef] [Green Version]
  60. Barnaba, C.; Sahoo, B.R.; Ravula, T.; Medina-Meza, I.G.; Im, S.C.; Anantharamaiah, G.M.; Waskell, L.; Ramamoorthy, A. Cytochrome-P450-Induced Ordering of Microsomal Membranes Modulates Affinity for Drugs. Angew. Chem. Int. Ed. Engl. 2018, 57, 3391–3395. [Google Scholar] [CrossRef]
  61. Reed, J.R.; dela Cruz, A.L.; Lomnicki, S.M.; Backes, W.L. Inhibition of cytochrome P450 2B4 by environmentally persistent free radical-containing particulate matter. Biochem. Pharmacol. 2015, 95, 126–132. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Shafizadeh, T.B.; Halsted, C.H. gamma-Glutamyl hydrolase, not glutamate carboxypeptidase II, hydrolyzes dietary folate in rat small intestine. J. Nutr. 2007, 137, 1149–1153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Šilhavý, J.; Krijt, J.; Sokolová, J.; Zídek, V.; Mlejnek, P.; Šimáková, M.; Škop, V.; Trnovská, J.; Oliyarnyk, O.; Marková, I.; et al. Dissecting the role of Folr1 and Folh1 genes in the pathogenesis of metabolic syndrome in spontaneously hypertensive rats. Physiol. Res. 2018, 67, 657–662. [Google Scholar] [CrossRef] [PubMed]
  64. Latomanski, E.A.; Newton, H.J. Interaction between autophagic vesicles and the Coxiella-containing vacuole requires CLTC (clathrin heavy chain). Autophagy 2018, 14, 1710–1725. [Google Scholar] [CrossRef] [Green Version]
  65. Lu, A.; Zhou, C.J.; Wang, D.H.; Han, Z.; Kong, X.W.; Ma, Y.Z.; Yun, Z.Z.; Liang, C.G. Cytoskeleton-associated protein 5 and clathrin heavy chain binding regulates spindle assembly in mouse oocytes. Oncotarget 2017, 8, 17491–17503. [Google Scholar] [CrossRef] [Green Version]
  66. Vassilopoulos, S.; Gentil, C.; Laine, J.; Buclez, P.O.; Franck, A.; Ferry, A.; Précigout, G.; Roth, R.; Heuser, J.E.; Brodsky, F.M.; et al. Actin scaffolding by clathrin heavy chain is required for skeletal muscle sarcomere organization. J. Cell Biol. 2014, 205, 377–393. [Google Scholar] [CrossRef]
  67. Zhao, J.; Wang, L.; Zhou, H.X.; Liu, L.; Lu, A.; Li, G.P.; Schatten, H.; Liang, C.G. Clathrin heavy chain 1 is required for spindle assembly and chromosome congression in mouse oocytes. Microsc. Microanal. 2013, 19, 1364–1373. [Google Scholar] [CrossRef]
  68. Chen, X.Y.; Liu, G.L.; Zhang, W.; Zhang, J.N.; Yan, Y.G.; Dong, W.Q.; Liang, E.S.; Zhang, Y.; Zhang, M.X. Inhibition of MEF2A prevents hyperglycemia-induced extracellular matrix accumulation by blocking Akt and TGF-β1/Smad activation in cardiac fibroblasts. Int. J. Biochem. Cell Biol. 2015, 69, 52–61. [Google Scholar] [CrossRef]
  69. Wang, Y.N.; Mei, C.G.; Su, X.T.; Wang, H.B.; Yang, W.C.; Zan, L.S. MEF2A Regulates the MEG3-DIO3 miRNA Mega Cluster-Targeted PP2A Signaling in Bovine Skeletal Myoblast Differentiation. Int. J. Mol. Sci. 2019, 20, 2748. [Google Scholar] [CrossRef] [Green Version]
  70. Xiong, Y.J.; Wang, L.; Jiang, W.Y.; Pang, L.H.; Liu, W.H.; Li, A.Q.; Zhong, Y.; Ou, W.C.; Liu, B.R.; Liu, S.M. MEF2A alters the proliferation, inflammation-related gene expression profiles and its silencing induces cellular senescence in human coronary endothelial cells. BMC Mol. Biol. 2019, 20, 8. [Google Scholar] [CrossRef] [Green Version]
  71. Xiao, Q.; Gan, Y.Q.; Li, Y.M.; Fan, L.L.; Liu, J.Q.; Lu, P.Y.; Liu, J.X.; Chen, A.A.; Shu, G.; Yin, G. MEF2A transcriptionally upregulates the expression of ZEB2 and CTNNB1 in colorectal cancer to promote tumor progression. Oncogene 2021, 40, 3364–3377. [Google Scholar] [CrossRef]
  72. Mai, H.M.; Xie, H.S.; Hou, J.; Chen, H.T.; Zhou, B.; Hou, J.L.; Jiang, D.K. A Genetic Variant of PPP1CB Influences Risk of Hepatitis B Virus-Related Hepatocellular Carcinoma in Han Chinese: A Pathway Based Analysis. J. Hepatocell. Carcinoma 2021, 8, 1055–1064. [Google Scholar] [CrossRef]
  73. Kim, J.; Ahn, D.; Chung, S.J. Chebulinic Acid Suppresses Adipogenesis in 3T3-L1 Preadipocytes by Inhibiting PPP1CB Activity. Int. J. Mol. Sci. 2022, 23, 865. [Google Scholar] [CrossRef] [PubMed]
  74. Korrodi-Gregório, L.; Esteves, S.L.; Fardilha, M. Protein phosphatase 1 catalytic isoforms: Specificity toward interacting proteins. Transl. Res. 2014, 164, 366–391. [Google Scholar] [CrossRef] [PubMed]
  75. Cho, Y.L.; Min, J.K.; Roh, K.M.; Kim, W.K.; Han, B.S.; Bae, K.H.; Lee, S.C.; Chung, S.J.; Kang, H.J. Phosphoprotein phosphatase 1CB (PPP1CB), a novel adipogenic activator, promotes 3T3-L1 adipogenesis. Biochem. Biophys. Res. Commun. 2015, 467, 211–217. [Google Scholar] [CrossRef] [PubMed]
  76. Alessi, D.; MacDougall, L.K.; Sola, M.M.; Ikebe, M.; Cohen, P. The control of protein phosphatase-1 by targetting subunits. The major myosin phosphatase in avian smooth muscle is a novel form of protein phosphatase-1. Eur. J. Biochem. 1992, 210, 1023–1035. [Google Scholar] [CrossRef]
  77. Printen, J.A.; Brady, M.J.; Saltiel, A.R. PTG, a protein phosphatase 1-binding protein with a role in glycogen metabolism. Science 1997, 275, 1475–1478. [Google Scholar] [CrossRef]
  78. Hughes, J.J.; Alkhunaizi, E.; Kruszka, P.; Pyle, L.C.; Grange, D.K.; Berger, S.I.; Payne, K.K.; Masser-Frye, D.; Hu, T.; Christie, M.R.; et al. Loss-of-Function Variants in PPP1R12A: From Isolated Sex Reversal to Holoprosencephaly Spectrum and Urogenital Malformations. Am. J. Hum. Genet. 2020, 106, 121–128. [Google Scholar] [CrossRef] [PubMed]
  79. Zou, Z.H.; Liu, R.; Liang, Y.K.; Zhou, R.; Dai, Q.S.; Han, Z.D.; Jiang, M.Y.; Zhuo, Y.J.; Zhang, Y.X.; Feng, Y.F.; et al. Identification and Validation of a PPP1R12A-Related Five-Gene Signature Associated with Metabolism to Predict the Prognosis of Patients with Prostate Cancer. Front. Genet. 2021, 12, 703210. [Google Scholar] [CrossRef]
  80. Lee, E.Y.; Yu, J.Y.; Paek, A.R.; Lee, S.H.; Jang, H.; Cho, S.Y.; Kim, J.H.; Kang, H.G.; Yun, T.; Oh, S.E.; et al. Targeting TJP1 attenuates cell–cell aggregation and modulates chemosensitivity against doxorubicin in leiomyosarcoma. J. Mol. Med. 2020, 98, 761–773. [Google Scholar] [CrossRef]
  81. Lee, S.H.; Paek, A.R.; Yoon, K.; Kim, S.H.; Lee, S.Y.; You, H.J. Tight junction protein 1 is regulated by transforming growth factor-β and contributes to cell motility in NSCLC cells. BMB Rep. 2015, 48, 115–120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Balda, M.S.; Matter, K. Tight junctions and the regulation of gene expression. Biochim. Biophys. Acta 2009, 1788, 761–767. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Bauer, H.; Zweimueller-Mayer, J.; Steinbacher, P.; Lametschwandtner, A.; Bauer, H.C. The dual role of zonula occludens (ZO) proteins. J. Biomed. Biotechnol. 2010, 2010, 402593. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Zhang, L.N.; Feng, T.; Spicer, L.J. The role of tight junction proteins in ovarian follicular development and ovarian cancer. Reproduction 2018, 155, 183–198. [Google Scholar] [CrossRef] [Green Version]
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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 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).
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 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).
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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.
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.
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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 expressions at four time points.
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 expressions at four time points.
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Table 1. Summary of key and hub DEGs.
Table 1. Summary of key and hub DEGs.
Gene Name
(Abbreviation)		Gene Name
(Official Full Name)		Number of Protein–Protein Interactions
CLTCclathrin heavy chain12
MEF2Amyocyte enhancer factor 2A10
PPP1CBprotein phosphatase 1 catalytic subunit beta10
PPP1R12Aprotein phosphatase 1 regulatory subunit 12A9
TJP1tight junction protein 19
ROS1ROS proto-oncogene 1, receptor tyrosine kinase8
WWC2WW and C2 domain containing 28
CDC14Acell division cycle 14A7
CDH23cadherin related 237
DDB1damage specific DNA binding protein 16
Number of protein–protein interactions: the interaction numbers between a protein corresponding to the gene and other proteins in the network.
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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

AMA Style

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(8):927. https://doi.org/10.3390/metabo13080927

Chicago/Turabian Style

Li, Zan, Xiaokai Bao, Xiumei Liu, Weijun Wang, Jianmin Yang, Xibo Zhu, and Shuhai Wang. 2023. "Transcriptome Profiling Based at Different Time Points after Hatching Deepened Our Understanding on Larval Growth and Development of Amphioctopus fangsiao" Metabolites 13, no. 8: 927. https://doi.org/10.3390/metabo13080927

APA Style

Li, Z., Bao, X., Liu, X., Wang, W., Yang, J., Zhu, X., & Wang, S. (2023). Transcriptome Profiling Based at Different Time Points after Hatching Deepened Our Understanding on Larval Growth and Development of Amphioctopus fangsiao. Metabolites, 13(8), 927. https://doi.org/10.3390/metabo13080927

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