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      Transcriptomic analysis of Crassostrea sikamea × Crassostrea angulata hybrids in response to low salinity stress

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          Abstract

          Hybrid oysters often show heterosis in growth rate, weight, survival and adaptability to extremes of salinity. Oysters have also been used as model organisms to study the evolution of host-defense system. To gain comprehensive knowledge about various physiological processes in hybrid oysters under low salinity stress, we performed transcriptomic analysis of gill tissue of Crassostrea sikamea ♀ × Crassostrea angulata♂ hybrid using the deep-sequencing platform Illumina HiSeq. We exploited the high-throughput technique to delineate differentially expressed genes (DEGs) in oysters maintained in hypotonic conditions. A total of 199,391 high quality unigenes, with average length of 644 bp, were generated. Of these 35 and 31 genes showed up- and down-regulation, respectively. Functional categorization and pathway analysis of these DEGs revealed enrichment for immune mechanism, apoptosis, energy metabolism and osmoregulation under low salinity stress. The expression patterns of 41 DEGs in hybrids and their parental species were further analyzed by quantitative real-time PCR (qRT-PCR). This study will serve as a platform for subsequent gene expression analysis regarding environmental stress. Our findings will also provide valuable information about gene expression to better understand the immune mechanism, apoptosis, energy metabolism and osmoregulation in hybrid oysters under low salinity stress.

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          Most cited references49

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          MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity

          Background MTML-msBayes uses hierarchical approximate Bayesian computation (HABC) under a coalescent model to infer temporal patterns of divergence and gene flow across codistributed taxon-pairs. Under a model of multiple codistributed taxa that diverge into taxon-pairs with subsequent gene flow or isolation, one can estimate hyper-parameters that quantify the mean and variability in divergence times or test models of migration and isolation. The software uses multi-locus DNA sequence data collected from multiple taxon-pairs and allows variation across taxa in demographic parameters as well as heterogeneity in DNA mutation rates across loci. The method also allows a flexible sampling scheme: different numbers of loci of varying length can be sampled from different taxon-pairs. Results Simulation tests reveal increasing power with increasing numbers of loci when attempting to distinguish temporal congruence from incongruence in divergence times across taxon-pairs. These results are robust to DNA mutation rate heterogeneity. Estimating mean divergence times and testing simultaneous divergence was less accurate with migration, but improved if one specified the correct migration model. Simulation validation tests demonstrated that one can detect the correct migration or isolation model with high probability, and that this HABC model testing procedure was greatly improved by incorporating a summary statistic originally developed for this task (Wakeley's ΨW ). The method is applied to an empirical data set of three Australian avian taxon-pairs and a result of simultaneous divergence with some subsequent gene flow is inferred. Conclusions To retain flexibility and compatibility with existing bioinformatics tools, MTML-msBayes is a pipeline software package consisting of Perl, C and R programs that are executed via the command line. Source code and binaries are available for download at http://msbayes.sourceforge.net/ under an open source license (GNU Public License).
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            Intracellular NOD-like receptors in host defense and disease.

            The innate immune system comprises several classes of pattern recognition receptors, including Toll-like receptors (TLRs), NOD-like receptors (NLRs), and RIG-1-like receptors (RLRs). TLRs recognize microbes on the cell surface and in endosomes, whereas NLRs and RLRs detect microbial components in the cytosol. Here we discuss the recent understanding in NLRs. Two NLRs, NOD1 and NOD2, sense the cytosolic presence of the peptidoglycan fragments meso-DAP and muramyl dipeptide, respectively, and drive the activation of mitogen-activated protein kinase (MAPK) and the transcription factor NF-kappaB. A different set of NLRs induces caspase-1 activation through the assembly of large protein complexes named inflammasomes. Genetic variations in several NLR members are associated with the development of inflammatory disorders. Further understanding of NLRs should provide new insights into the mechanisms of host defense and the pathogenesis of inflammatory diseases.
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              Ubiquitin-dependent protein degradation.

              A growing number of cellular regulatory mechanisms are being linked to protein modification by the polypeptide ubiquitin. These include key transitions in the cell cycle, class I antigen processing, signal transduction pathways, and receptor-mediated endocytosis. In most, but not all, of these examples, ubiquitination of a protein leads to its degradation by the 26S proteasome. Following attachment of ubiquitin to a substrate and binding of the ubiquitinated protein to the proteasome, the bound substrate must be unfolded (and eventually deubiquitinated) and translocated through a narrow set of channels that leads to the proteasome interior, where the polypeptide is cleaved into short peptides. Protein ubiquitination and deubiquitination are both mediated by large enzyme families, and the proteasome itself comprises a family of related but functionally distinct particles. This diversity underlies both the high substrate specificity of the ubiquitin system and the variety of regulatory mechanisms that it serves.
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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
                9 February 2017
                2017
                : 12
                : 2
                : e0171483
                Affiliations
                [1 ]Fisheries College, Ocean University of China, Qingdao, Shandong, China
                [2 ]The Key Lab of South China Sea Fishery Resources Exploitation & Utilization, Ministry of Agriculture, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou, Guangdong, China
                [3 ]Engineering Research Center of Shellfish Culture and Breeding of Liaoning Province, College of Fisheries and Life Science, Dalian Ocean University, Dalian, Liaoning, China
                Xiamen University, CHINA
                Author notes

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

                • Conceptualization: LY ZW JS.

                • Data curation: LY.

                • Formal analysis: LY ZW JS.

                • Investigation: PM YL JD RY.

                • Methodology: LY JS.

                • Resources: XY.

                • Software: LY.

                Article
                PONE-D-16-35970
                10.1371/journal.pone.0171483
                5300195
                28182701
                44b6a657-e927-4c8e-ae97-36f56adee36f
                © 2017 Yan 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
                : 7 September 2016
                : 21 January 2017
                Page count
                Figures: 2, Tables: 5, Pages: 20
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31172403
                Award Recipient :
                This research was supported by the National Natural Science Foundation of China (grant no.31172403). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Chemistry
                Chemical Properties
                Salinity
                Physical Sciences
                Chemistry
                Physical Chemistry
                Chemical Properties
                Salinity
                Biology and Life Sciences
                Organisms
                Animals
                Invertebrates
                Molluscs
                Bivalves
                Oysters
                Biology and Life Sciences
                Cell Biology
                Cell Processes
                Cell Death
                Apoptosis
                Biology and Life Sciences
                Cell Biology
                Osmotic Shock
                Research and analysis methods
                Extraction techniques
                RNA extraction
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Gene Ontologies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Gene Ontologies
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Energy Metabolism
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Energy Metabolism
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Energy Metabolism
                Custom metadata
                All reads of transcriptome files are available from the NCBI database (accession number(s) SRP070594, PRJNA312561).

                Uncategorized
                Uncategorized

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