scholarly journals Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ying Jiang ◽  
Yi Shen ◽  
Liyan Ding ◽  
Shengli Xia ◽  
Liying Jiang
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Therapeutic Targets ◽  
Microarray Dataset ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Hub Genes ◽  
Serum Samples ◽  
Regulatory Pathways ◽  
Novel Mirna

Abstract Backgrounds As osteoarthritis (OA) disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized. Here, we aim to identify miRNA signatures in patients to fully elucidate regulatory mechanism of OA pathogenesis and advance in basic understanding of the genetic etiology of OA. Methods Six participants (3 OA and 3 controls) were recruited and serum samples were assayed through RNA sequencing (RNA-seq). And, RNA-seq dataset was analysed to identify genes, pathways and regulatory networks dysregulated in OA. The overlapped differentially expressed microRNAs (DEMs) were further screened in combination with the microarray dataset GSE143514. The expression levels of candidate miRNAs were further validated by quantitative real-time PCR (qRT-PCR) based on the GEO dataset (GSE114007). Results Serum samples were sequenced interrogating 382 miRNAs. After screening of independent samples and GEO database, the two comparison datasets shared 19 overlapped candidate micRNAs. Of these, 9 up-regulated DEMs and 10 down-regulated DEMs were detected, respectively. There were 236 target genes for up-regulated DEMs and 400 target genes for those down-regulated DEMs. For up-regulated DEMs, the top 10 hub genes were KRAS, NRAS, CDC42, GDNF, SOS1, PIK3R3, GSK3B, IRS2, GNG12, and PRKCA; for down-regulated DEMs, the top 10 hub genes were NR3C1, PPARGC1A, SUMO1, MEF2C, FOXO3, PPP1CB, MAP2K1, RARA, RHOC, CDC23, and CREB3L2. Mir-584-5p-KRAS, mir-183-5p-NRAS, mir-4435-PIK3R3, and mir-4435-SOS1 were identified as four potential regulatory pathways by integrated analysis. Conclusions We have integrated differential expression data to reveal putative genes and detected four potential miRNA-target gene pathways through bioinformatics analysis that represent new mediators of abnormal gene expression and promising therapeutic targets in OA.

Identification of MicroRNA-mRNA Regulatory Networks in Periodontitis by Bioinformatics Analysis

2021 ◽  
Author(s):  
Xiaoli Gao ◽  
Dong Zhao ◽  
Zuomin Wang ◽  
Zheng Zhang ◽  
Jing Han
Keyword(s):  
Gene Expression ◽  
Periodontal Disease ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Kegg Pathway ◽  
Microarray Dataset ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Key Genes

Abstract Background: Periodontitis is a complex infectious disease with various causes and contributing factors. In recent years, microRNAs (miRNAs) have been commonly accepted as having key regulatory functions in periodontal disease. The aim of this study was to identify miRNAs and hub genes involved in periodontal disease pathogenesis using a miRNA-mRNA interaction network.Methods: The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using P <0.05 and |log FC| ≥1. Potential upstream transcription factors and downstream target genes of candidate DEMis were predicted using the FunRich and miRNet programs, respectively. Subsequently, DEMs were uploaded to the STRING database, a protein-protein interaction (PPI) network was established, and the cytoHubba plugin was used to screen out key hub mRNAs. The key genes in the miRNA-mRNA regulatory network were extracted by intersecting the target genes of candidate DEMis and DEMs. Cytoscape software was used to visualise the interaction between miRNAs and mRNAs and to predict the hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyse the key genes in the regulatory network.Results: Ten DEMis and 161 DEMs were filtered out, from which we constructed a miRNA-mRNA network consisting of six miRNAs and 32 mRNAs. KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. Hsa-miR-203/CXCL8, hsa-miR-203/BTG2, and hsa-miR-203/DNAJB9 were identified as four potential regulatory pathways for periodontitis. Conclusion: In this study, a potential miRNA–mRNA regulatory network was first constructed and four regulatory pathways were identified for periodontitis to help clarify the aetiology of the disease and provide potential therapeutic targets.

scholarly journals Effective Biomarkers and Therapeutic Targets of Nerve-Immunity Interaction in The Treatment of Depression: A Integrated Analysis of miRNA-mRNA Regulatory Networks

2021 ◽  
Author(s):  
Zi-xuan Wu ◽  
Xuyan Huang ◽  
Min-jie Cai ◽  
Peidong Huang ◽  
Zunhui Guan
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Integrated Analysis ◽  
Daily Lives ◽  
Gene Expression Matrix ◽  
Treatment Of Depression ◽  
Negative Effect ◽  
Core Genes

Abstract Background: Major depressive disorder (MDD) is an emotional disorder that has a negative effect on patients' studies and daily lives. A great number of studies have found that miRNAs play an important role in the development of MDD and that they can be used as a biomarker for the diagnosis and treatment of MDD. However, there have been few investigations on nerve-immunity interaction therapy for MMD patients' brains.Methods: We attempted to evaluate MDD in the gene expression matrix database and miRNAs in plasma samples from healthy controls using bioinformatics methods. Four plasma miRNAs (DE-miRNAs) samples were found from MDD patients. Funrich planned the transcription factors and target genes of miRNAs, and the enrichment of TF and GO was examined. The intersecting mRNAs were discovered by comparing the various expressions of the projected target genes and 5 mRNAs (DE-mRNAs) samples. In the end, 34 DE-miRNAs, 386 DE-mRNAs, and 17 intersecting mRNAs were detected. Intersecting core genes were then investigated using GO and KEGG enrichment analysis to find the intersecting mRNA. Identify particular candidate genes and pathways in neurology and immunology that may be associated with MDD for further investigation.Results: We discovered 17 important HUB genes by the advance of a miRNA-mRNA network, and 5 HUB DE-MRNAs were derived following CytoNCA topology.Conclusion: Our findings from a comprehensive bioinformatics analysis of miRNAs and mRNAs in MDD show that DE-miRNAs like miR-338-3P and miR-206 may be excellent biomarkers and potential therapeutic targets for the treatment of MDD via nerve-immunity interaction.

scholarly journals Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Maryum Nisar ◽  
Rehan Zafar Paracha ◽  
Iqra Arshad ◽  
Sidra Adil ◽  
Sabaoon Zeb ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Early Stage ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Hub Genes

Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM–receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

scholarly journals Identification of differentially expressed genes in asthma by bioinformatics analysis

2021 ◽  
Vol 3 (2) ◽  
pp. 28-36
Author(s):  
Zhaojun Wang ◽  
Zhifeng Mo ◽  
Hongsen Liang ◽  
Qiwei Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Microarray Dataset ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
The Novel ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Healthy Donors

Objective Asthma is a common inflammatory disease of the airway, and its underlying mechanism is complex. The role of microRNAs (miRNAs) in asthma is unclear. The present study aimed to investigate miRNA-mRNA regulatory networks underlying asthma. Methods One microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differential expression of miRNAs (DEMs) was identified in bronchial epithelial cells (BECs) isolated from healthy donors and patients with asthma. MiRTarBase, mirDIP, and miRDB were used to predict target genes, followed by protein-protein interaction (PPI) network analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Ontology (GO) analysis; cytoHubba was used to predict the important nodes in the network. The miRNA-hub genes sub-network of interest was determined. Results This study constructed an asthma-associated miRNA-mRNA network, in which seven key miRNAs and 10 hub genes were identified. Conclusions The novel miRNAs and hub genes identified in the present study could be potential diagnostic and treatment biomarkers for asthma.

scholarly journals EPEN-21. IMPAIRED NEURONAL-GLIAL FATE SPECIFICATION IN PEDIATRIC EPENDYMOMA REVEALED BY SINGLE-CELL RNA-SEQ

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii311-iii312
Author(s):  
Bernhard Englinger ◽  
Johannes Gojo ◽  
Li Jiang ◽  
Jens M Hübner ◽  
McKenzie L Shaw ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Regulatory Networks ◽  
Target Genes ◽  
Target Identification ◽  
Rna Seq ◽  
Cell Models ◽  
Pediatric Ependymoma ◽  
Glial Fate ◽  
Anatomic Locations

Abstract Ependymoma represents a heterogeneous disease affecting the entire neuraxis. Extensive molecular profiling efforts have identified molecular ependymoma subgroups based on DNA methylation. However, the intratumoral heterogeneity and developmental origins of these groups are only partially understood, and effective treatments are still lacking for about 50% of patients with high-risk tumors. We interrogated the cellular architecture of ependymoma using single cell/nucleus RNA-sequencing to analyze 24 tumor specimens across major molecular subgroups and anatomic locations. We additionally analyzed ten patient-derived ependymoma cell models and two patient-derived xenografts (PDXs). Interestingly, we identified an analogous cellular hierarchy across all ependymoma groups, originating from undifferentiated neural stem cell-like populations towards different degrees of impaired differentiation states comprising neuronal precursor-like, astro-glial-like, and ependymal-like tumor cells. While prognostically favorable ependymoma groups predominantly harbored differentiated cell populations, aggressive groups were enriched for undifferentiated subpopulations. Projection of transcriptomic signatures onto an independent bulk RNA-seq cohort stratified patient survival even within known molecular groups, thus refining the prognostic power of DNA methylation-based profiling. Furthermore, we identified novel potentially druggable targets including IGF- and FGF-signaling within poorly prognostic transcriptional programs. Ependymoma-derived cell models/PDXs widely recapitulated the transcriptional programs identified within fresh tumors and are leveraged to validate identified target genes in functional follow-up analyses. Taken together, our analyses reveal a developmental hierarchy and transcriptomic context underlying the biologically and clinically distinct behavior of ependymoma groups. The newly characterized cellular states and underlying regulatory networks could serve as basis for future therapeutic target identification and reveal biomarkers for clinical trials.

scholarly journals Integrated Analysis of lncRNAs, mRNAs, and TFs to Identify Regulatory Networks Underlying MAP Infection in Cattle

2021 ◽  
Vol 12 ◽  
Author(s):  
Maryam Heidari ◽  
Abbas Pakdel ◽  
Mohammad Reza Bakhtiarizadeh ◽  
Fariba Dehghanian
Keyword(s):  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Johne's Disease ◽  
Johne’S Disease ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Complex Nature ◽  
Hub Genes ◽  
Cis And Trans

Johne’s disease is a chronic infection of ruminants that burdens dairy herds with a significant economic loss. The pathogenesis of the disease has not been revealed clearly due to its complex nature. In order to achieve deeper biological insights into molecular mechanisms involved in MAP infection resulting in Johne’s disease, a system biology approach was used. As far as is known, this is the first study that considers lncRNAs, TFs, and mRNAs, simultaneously, to construct an integrated gene regulatory network involved in MAP infection. Weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis were conducted to explore coexpression modules from which nonpreserved modules had altered connectivity patterns. After identification of hub and hub-hub genes as well as TFs and lncRNAs in the nonpreserved modules, integrated networks of lncRNA-mRNA-TF were constructed, and cis and trans targets of lncRNAs were identified. Both cis and trans targets of lncRNAs were found in eight nonpreserved modules. Twenty-one of 47 nonpreserved modules showed significant biological processes related to the immune system and MAP infection. Some of the MAP infection’s related pathways in the most important nonpreserved modules comprise “positive regulation of cytokine-mediated signaling pathway,” “negative regulation of leukocyte migration,” “T-cell differentiation,” “neutrophil activation,” and “defense response.” Furthermore, several genes were identified in these modules, including SLC11A1, MAPK8IP1, HMGCR, IFNGR1, CMPK2, CORO1A, IRF1, LDLR, BOLA-DMB, and BOLA-DMA, which are potentially associated with MAP pathogenesis. This study not only enhanced our knowledge of molecular mechanisms behind MAP infection but also highlighted several promising hub and hub-hub genes involved in macrophage-pathogen interaction.

scholarly journals ISMARA: Completely automated inference of gene regulatory networks from high-throughput data

2017 ◽  
Author(s):  
Mikhail Pachkov ◽  
Piotr J Balwierz ◽  
Phil Arnold ◽  
Andreas J Gruber ◽  
Mihaela Zavolan ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Regulatory Networks ◽  
Target Genes ◽  
Chromatin State ◽  
Response Analysis ◽  
Rna Seq ◽  
High Throughput Data ◽  
Micro Rnas ◽  
Gene Regulatory

As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.

scholarly journals Identification of critical pathways and potential therapeutic targets in Poorly differentiated duodenal papilla adenocarcinoma

2020 ◽  
Author(s):  
Yuanxiang Lu ◽  
Wensen Li ◽  
Ge Liu ◽  
Erwei Xiao ◽  
Senmao Mu ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Therapeutic Targets ◽  
Malignant Neoplasm ◽  
Differentially Expressed ◽  
High Recurrence Rate ◽  
Rna Seq ◽  
Duodenal Papilla ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Duodenal papilla carcinoma (DPC) is a rare malignancy of the gastrointestinal tract with high recurrence rate, and the pathogenesis of this highly malignant neoplasm is yet to be fully elucidated. This study aims to identify key genes to further understand the biology and pathogenesis underlying the molecular alterations driving DPC, which could be potential diagnostic or therapeutic targets.Methods: Tumor samples of three DPC patients were collected and integrating RNA-seq analysis of tumor tissues and matched normal tissues were performed to discover differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to understand the potential bio-functions of the DPC differentially expressed genes (DEGs) and protein–protein interaction (PPI) network was constructed for functional modules analysis and dentification of hub genes. Results: A total of 110 DEGs were identified from our RNA-Seq data, GO and KEGG analyses showed that the DEGs were mainly enriched in multiple cancer-related functions and pathways, such as cell proliferation, IL-17signaling pathway, Jak-STAT signaling pathway, PPAR signaling pathway. The PPI network screened out six hub genes including IL-6, LEP, LCN2, CCND1, FABP4 and MMP1, which were identified as core genes in the network and potential therapeutic targets of DPC. Discussion: The current study provides new insight into the exploration of DPC pathogenesis and the screened hub genes may serve as potential diagnostic indicator and novel therapeutic target.

scholarly journals Construction of a circRNA-miRNA-mRNA network based on differentially co-expressed circular RNA in gastric cancer tissue and plasma by bioinformatics analysis

2020 ◽  
Author(s):  
Yu Gong ◽  
Xiaoyang Qi ◽  
Jinjin Fu ◽  
Jun Qian ◽  
Yuwen Jiao ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Signaling Pathway ◽  
Cancer Progression ◽  
Regulatory Network ◽  
Target Genes ◽  
Human Cancer ◽  
Microarray Dataset ◽  
Circular Rnas ◽  
Cancer Tissue ◽  
Hub Genes

Abstract Background: Increasing evidence implicates circular RNAs (circRNAs) have been involved in human cancer progression. However, the mechanism remains unclear. In this study, we identified novel circRNAs related to gastric cancer and constructed a circRNA-miRNA-mRNA network.Methods: Microarray dataset GSE83521 and GSE93541 were obtained from Gene Expression Omnibus (GEO). Then, we used computational biology to select differentially co-expressed circRNAs in GC tissue and plasma and detected the expression of selected circRNAs in gastric cell lines by quantitative real‑time polymerase chain reaction (qRT‑PCR). We also chose the candidate miRNAs and their target genes for circRNAs through online tools. Combining the predictions of miRNAs and target mRNAs, a competing endogenous RNA regulatory network was established. Functional and pathway enrichment analyses were performed, and interactions between proteins were predicted by using String and Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the possible functions of these differentially expressed circRNAs.Results: The regulatory network constructed using the microarray datasets (GSE83521 and GSE93541) contained three differentially co-expressed circRNAs (DECs). A circRNA-miRNA-mRNA network was constructed based on 3 circRNAs, 43 miRNAs and 119 mRNAs. GO and KEGG analysis showed that regulation of apoptotic signaling pathway and PI3K−Akt signaling pathway were highest degrees of enrichment respectively. We established a protein-protein interaction (PPI) network consisting of 165 nodes and 170 edges and identified hub genes by MCODE plugin in Cytoscape. Furthermore, a core circRNA-miRNA-mRNA network was constructed base on hub genes. Hsa_circ_0001013 was finally determined to play an important role in the pathogenesis of GC according to the core circRNA-miRNA-mRNA network.Conclusions: We propose a new circRNA-miRNA-mRNA network associated with the pathogenesis of GC. The network may become a new molecular biomarker and be used to develop potential therapeutic strategies for gastric cancer.

scholarly journals Genome-wide identification and comparison of differentially expressed profiles of miRNAs and lncRNAs with associated ceRNA networks in the gonads of Chinese soft-shelled turtle, Pelodiscus sinensis

2019 ◽  
Author(s):  
Xiao Ma ◽  
Shuangshuang Cen ◽  
Luming Wang ◽  
Chao Zhang ◽  
Limin Wu ◽  
...  
Keyword(s):  
Messenger Rna ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Profiles ◽  
Integrated Analysis ◽  
Regulation Mechanism ◽  
Pelodiscus Sinensis ◽  
Specific Expression ◽  
Protein Coding ◽  
Protein Coding Genes

Abstract Abstract Background: Gonad is the major factor affecting the animal reproduction. The regulation mechanism of protein coding genes expression involved reproduction is still remains to be elucidated. Increasing evidence has shown that ncRNAs play key regulatory roles in gene expression in many life processes. The roles of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in reproduction had been investigated in some species. However, the regulation patterns of miRNA and lncRNA in sex biased expression of protein coding genes remains to be elucidated. In this study, we performed an integrated analysis of miRNA, messenger RNA (mRNA), and lncRNA expression profiles to explore their regulatory patterns in the female ovary and male testis of the soft-shelled turtle, Pelodiscus sinensis. Results: We identified 10 796 mature miRNAs, 44 678 mRNAs, and 58 923 lncRNAs in the testis and ovary. A total of 16 817 target genes were identified for miRNAs. Of these, 11 319 mRNAs, 10 495 lncRNAs, and 633 miRNAs were expressed differently. The predicted target genes of these differential expression (DE) miRNAs and lncRNAs included genes related to reproduction regulation. Furthermore, we found that 5 408 DElncRNAs and 186 DE miRNAs showed sex-specific expression. Of these, 3 miRNAs and 917 lncRNAs were testis specific and 186 DEmiRNAs and 4 491 DElncRNAs were ovary specific. We constructed compete endogenous lncRNA-miRNA-mRNA networks using bioinformatics, including 273 DEmRNAs, 5 730 DEmiRNAs, and 2 945 DElncRNAs. The target genes for the different expressed of miRNAs and lncRNAs included Wt1, Creb3l2, Gata4, Wnt2, Nr5a1, Hsd17, Igf2r, H2afz, Lin52, Trim71, Zar1, and Jazf1, etc. Conclusions: In animals, miRNA and lncRNA regulate the reproduction process, including the regulation of oocyte maturation and spermatogenesis. Considering their importance, the identified miRNAs, lncRNAs, and their targets in P. sinensis might be useful for genome editing to produce higher quality aquaculture animals. A thorough understanding of ncRNA-based cellular regulatory networks will aid in the improvement of P. sinensis reproduction traits for aquaculture.

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