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      Transcriptome Analysis and Differential Gene Expression on the Testis of Orange Mud Crab, Scylla olivacea, during Sexual Maturation

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          Abstract

          Adequate genetic information is essential for sustainable crustacean fisheries and aquaculture management. The commercially important orange mud crab, Scylla olivacea, is prevalent in Southeast Asia region and is highly sought after. Although it is a suitable aquaculture candidate, full domestication of this species is hampered by the lack of knowledge about the sexual maturation process and the molecular mechanisms behind it, especially in males. To date, data on its whole genome is yet to be reported for S. olivacea. The available transcriptome data published previously on this species focus primarily on females and the role of central nervous system in reproductive development. De novo transcriptome sequencing for the testes of S. olivacea from immature, maturing and mature stages were performed. A total of approximately 144 million high-quality reads were generated and de novo assembled into 160,569 transcripts with a total length of 142.2 Mb. Approximately 15–23% of the total assembled transcripts were annotated when compared to public protein sequence databases (i.e. UniProt database, Interpro database, Pfam database and Drosophila melanogaster protein database), and GO-categorised with GO Ontology terms. A total of 156,181 high-quality Single-Nucleotide Polymorphisms (SNPs) were mined from the transcriptome data of present study. Transcriptome comparison among the testes of different maturation stages revealed one gene (beta crystallin like gene) with the most significant differential expression—up-regulated in immature stage and down-regulated in maturing and mature stages. This was further validated by qRT-PCR. In conclusion, a comprehensive transcriptome of the testis of orange mud crabs from different maturation stages were obtained. This report provides an invaluable resource for enhancing our understanding of this species’ genome structure and biology, as expressed and controlled by their gonads.

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          Most cited references64

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          Comparing Bioinformatic Gene Expression Profiling Methods: Microarray and RNA-Seq

          Understanding the control of gene expression is critical for our understanding of the relationship between genotype and phenotype. The need for reliable assessment of transcript abundance in biological samples has driven scientists to develop novel technologies such as DNA microarray and RNA-Seq to meet this demand. This review focuses on comparing the two most useful methods for whole transcriptome gene expression profiling. Microarrays are reliable and more cost effective than RNA-Seq for gene expression profiling in model organisms. RNA-Seq will eventually be used more routinely than microarray, but right now the techniques can be complementary to each other. Microarrays will not become obsolete but might be relegated to only a few uses. RNA-Seq clearly has a bright future in bioinformatic data collection.
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            Mining transcriptome sequences towards identifying adaptive single nucleotide polymorphisms in lake whitefish species pairs (Coregonus spp. Salmonidae).

            Next-generation sequencing allows the discovery of large numbers of single nucleotide polymorphisms (SNPs) in species where little genomic information was previously available. Here, we assembled, de novo, over 130 mb of non-normalized cDNA using 454 pyrosequencing data from dwarf and normal lake whitefish and backcross hybrids. Our main goals were to gather a large data set of SNP markers, document their distribution within coding regions, evaluate the effect of species divergence on allele frequencies and combine results with previous genomic studies to identify candidate genes underlying the adaptive divergence of lake whitefish. We identified 6094 putative SNPs in 2674 contigs (mean size: 576 bp, range: 101-6116) and 1540 synonymous and 1734 non-synonymous mutations for a genome-wide non-synonymous to synonymous substitution rate ratio (p(N)/p(S)) of 0.37. As expected based on the young age (<15 000 years) of whitefish species pair, the overall level of divergence between them was relatively weak. Yet, 89 SNPs showed pronounced allele frequency differences between sympatric normal and dwarf whitefish. Among these, SNPs in genes annotated to energy metabolic functions were the most abundant and this, in addition to previous experimental data at the gene expression and phenotypic level, brings compelling evidence that genes involved in energy metabolism are prime candidates explaining the adaptive divergence of lake whitefish species pairs. Finally, we unexpectedly identified 44 contigs annotated to transposable elements and these were predominantly composed of backcross hybrids sequences. This indicates an elevated activity of transposable elements, which could potentially contribute to the reduced fitness of hybrids previously documented.
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              Reproductive regulators in decapod crustaceans: an overview.

              Control of reproductive development in crustaceans requires neuropeptides, ecdysone and methyl farnesoate (MF). A major source of neuropeptides is the X-organ-sinus gland (XO-SG) complex located in the eyestalk ganglia of crustaceans. The other regulatory factors (either peptides or neuromodulators) are produced in the brain and thoracic ganglia (TG). Two other regulatory non-peptide compounds, the steroid ecdysone and the sesquiterpene MF, are produced by the Y-organs and the mandibular organs, respectively. In the current review, I have tried to recapitulate recent studies on the role of gonadal regulatory factors in regulating crustacean reproduction.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 January 2017
                2017
                : 12
                : 1
                : e0171095
                Affiliations
                [1 ]Institute of Tropical Aquaculture, Universiti Malaysia Terengganu, Kuala Terengganu, Terengganu, Malaysia
                [2 ]School of Fisheries and Aquaculture Sciences, Universiti Malaysia Terengganu, Kuala Terengganu, Terengganu, Malaysia
                [3 ]Institute of Marine Biotechnology, Universiti Malaysia Terengganu, Kuala Terengganu, Terengganu, Malaysia
                Xiamen University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: KW HF MSS MI.

                • Data curation: KW MSS.

                • Formal analysis: KW HF JHZM SN SS.

                • Funding acquisition: LLW MI.

                • Investigation: KW HF.

                • Methodology: KW HF JHZM SN SS.

                • Project administration: KW HF MI.

                • Resources: KW HF SN MI.

                • Software: KW HF SS.

                • Supervision: MSS MI.

                • Validation: KW HF MSS MI.

                • Visualization: KW HF MSS MI.

                • Writing – original draft: KW HF.

                • Writing – review & editing: KW HF MSS LLW MI.

                Author information
                http://orcid.org/0000-0002-2632-3612
                Article
                PONE-D-16-24329
                10.1371/journal.pone.0171095
                5279790
                28135340
                144cf7be-0019-4a34-ac84-c2ba5f678118
                © 2017 Waiho et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 June 2016
                : 15 January 2017
                Page count
                Figures: 9, Tables: 8, Pages: 29
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004565, Kementerian Pendidikan;
                Award ID: 53131
                Support for this project was provided by the Ministry of Education Malaysia ( http://www.moe.gov.my/en/home) under the Niche Research Grant Scheme (NRGS) (grant number: 53131). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Animals
                Invertebrates
                Arthropoda
                Crustaceans
                Crabs
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Organisms
                Animals
                Invertebrates
                Arthropoda
                Crustaceans
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Gene Ontologies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Gene Ontologies
                Biology and Life Sciences
                Organisms
                Animals
                Invertebrates
                Arthropoda
                Insects
                Termites
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Research and analysis methods
                Extraction techniques
                RNA extraction
                Custom metadata
                All transcriptome sequence data are available from the GenBank database (accession number GDRN00000000, BioProject accession number PRJNA289610).

                Uncategorized
                Uncategorized

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