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      Integrated application of transcriptomics and metabolomics yields insights into population-asynchronous ovary development in Coilia nasus

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          Abstract

          Populations of Coilia nasus demonstrate asynchronous ovarian development, which severely restricts artificial breeding and large-scale cultivation. In this study, we used a combination of transcriptomic and metabolomic methods to identify the key signaling pathways and genes regulation affecting ovarian development. We identified 565 compounds and generated 47,049 unigenes from ovary tissue. Fifteen metabolites and 830 genes were significantly up-regulated, while 27 metabolites and 642 genes were significantly down-regulated from stage III to stage IV of ovary development. Meanwhile, 31 metabolites and 1,932 genes were significantly up-regulated, and four metabolites and 764 genes were down-regulated from stage IV to stage V. These differentially expressed genes and metabolites were enriched by MetScape. Forty-three and 50 signaling pathways had important functions from stage III–IV and from stage IV–V in the ovary, respectively. Among the above signaling pathways, 39 played important roles from ovarian stage III–V, including “squalene and cholesterol biosynthesis”, “steroid hormone biosynthesis”, and “arachidonate metabolism and prostaglandin formation” pathways which may thus have key roles in regulating asynchronous development. These results shed new light on our understanding of the mechanisms responsible for population-asynchronous development in fish.

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          Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing.

          We present a de novo assembly of a eukaryote transcriptome using 454 pyrosequencing data. The Glanville fritillary butterfly (Melitaea cinxia; Lepidoptera: Nymphalidae) is a prominent species in population biology but had no previous genomic data. Sequencing runs using two normalized complementary DNA collections from a genetically diverse pool of larvae, pupae, and adults yielded 608,053 expressed sequence tags (mean length = 110 nucleotides), which assembled into 48,354 contigs (sets of overlapping DNA segments) and 59,943 singletons. BLAST comparisons confirmed the accuracy of the sequencing and assembly, and indicated the presence of c. 9000 unique genes, along with > 6000 additional microarray-confirmed unannotated contigs. Average depth of coverage was 6.5-fold for the longest 4800 contigs (348-2849 bp in length), sufficient for detecting large numbers of single nucleotide polymorphisms. Oligonucleotide microarray probes designed from the assembled sequences showed highly repeatable hybridization intensity and revealed biological differences among individuals. We conclude that 454 sequencing, when performed to provide sufficient coverage depth, allows de novo transcriptome assembly and a fast, cost-effective, and reliable method for development of functional genomic tools for nonmodel species. This development narrows the gap between approaches based on model organisms with rich genetic resources vs. species that are most tractable for ecological and evolutionary studies.
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            Thermal performance curves, phenotypic plasticity, and the time scales of temperature exposure.

            Thermal performance curves (TPCs) describe the effects of temperature on biological rate processes. Here, we use examples from our work on common killifish (Fundulus heteroclitus) to illustrate some important conceptual issues relating to TPCs in the context of using these curves to predict the responses of organisms to climate change. Phenotypic plasticity has the capacity to alter the shape and position of the TPCs for acute exposures, but these changes can be obscured when rate processes are measured only following chronic exposures. For example, the acute TPC for mitochondrial respiration in killifish is exponential in shape, but this shape changes with acclimation. If respiration rate is measured only at the acclimation temperature, the TPC is linear, concealing the underlying mechanistic complexity at an acute time scale. These issues are particularly problematic when attempting to use TPCs to predict the responses of organisms to temperature change in natural environments. Many TPCs are generated using laboratory exposures to constant temperatures, but temperature fluctuates in the natural environment, and the mechanisms influencing performance at acute and chronic time scales, and the responses of the performance traits at these time scales may be quite different. Unfortunately, our current understanding of the mechanisms underlying the responses of organisms to temperature change is incomplete, particularly with respect to integrating from processes occurring at the level of single proteins up to whole-organism functions across different time scales, which is a challenge for the development of strongly grounded mechanistic models of responses to global climate change. © The Author 2011. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved.
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              Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.

              Metabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions. We describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation. Metscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager. metscape-help@umich.edu; akarnovs@umich.edu Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                22 August 2016
                2016
                : 6
                : 31835
                Affiliations
                [1 ]Wuxi Fisheries College, Nanjing Agricultural University , Wuxi, Jiangsu, 214081, China
                [2 ]Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences , Wuxi, Jiangsu, 214081, China
                Author notes
                Article
                srep31835
                10.1038/srep31835
                4992829
                27545088
                fbc04ca6-b6f6-4ade-abb7-841568e24418
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 13 April 2016
                : 27 July 2016
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