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      Comparative transcriptome analysis reveals significant metabolic alterations in eri-silkworm ( Samia cynthia ricini) haemolymph in response to 1-deoxynojirimycin

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          Abstract

          Samia cynthia ricini (Lepidoptera: Saturniidae) is an important commercial silk-producing insect; however, in contrast to the silkworm, mulberry leaves are toxic to this insect because the leaves contain the component 1-deoxynojirimycin (DNJ). A transcriptomic analysis of eri-silkworm haemolymph was conducted to examine the genes related to different metabolic pathways and to elucidate the molecular mechanism underlying eri-silkworm haemolymph responses to DNJ. Eight hundred sixty-five differentially expressed genes (DEGs) were identified, among which 577 DEGs were up-regulated and 288 DEGs were down-regulated in the 2% DNJ group compared to control (ddH 2O) after 12h. Based on the results of the functional analysis, these DEGs were associated with ribosomes, glycolysis, N-glycan biosynthesis, and oxidative phosphorylation. In particular, according to the KEGG analysis, 138 DEGs were involved in energy metabolism, glycometabolism and lipid metabolism, and the changes in the expression of nine DEGs were confirmed by reverse transcription quantitative PCR (RT-qPCR). Thus, DNJ induced significant metabolic alterations in eri-silkworm haemolymph. These results will lay the foundation for research into the toxic effects of DNJ on eri-silkworm as a model and provide a reference for the exploitation of new drugs in humans.

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          The KEGG databases at GenomeNet.

          The Kyoto Encyclopedia of Genes and Genomes (KEGG) is the primary database resource of the Japanese GenomeNet service (http://www.genome.ad.jp/) for understanding higher order functional meanings and utilities of the cell or the organism from its genome information. KEGG consists of the PATHWAY database for the computerized knowledge on molecular interaction networks such as pathways and complexes, the GENES database for the information about genes and proteins generated by genome sequencing projects, and the LIGAND database for the information about chemical compounds and chemical reactions that are relevant to cellular processes. In addition to these three main databases, limited amounts of experimental data for microarray gene expression profiles and yeast two-hybrid systems are stored in the EXPRESSION and BRITE databases, respectively. Furthermore, a new database, named SSDB, is available for exploring the universe of all protein coding genes in the complete genomes and for identifying functional links and ortholog groups. The data objects in the KEGG databases are all represented as graphs and various computational methods are developed to detect graph features that can be related to biological functions. For example, the correlated clusters are graph similarities which can be used to predict a set of genes coding for a pathway or a complex, as summarized in the ortholog group tables, and the cliques in the SSDB graph are used to annotate genes. The KEGG databases are updated daily and made freely available (http://www.genome.ad.jp/kegg/).
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            The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

            The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.
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              The P-glycoprotein multidrug transporter.

              F J Sharom (2011)
              Pgp (P-glycoprotein) (ABCB1) is an ATP-powered efflux pump which can transport hundreds of structurally unrelated hydrophobic amphipathic compounds, including therapeutic drugs, peptides and lipid-like compounds. This 170 kDa polypeptide plays a crucial physiological role in protecting tissues from toxic xenobiotics and endogenous metabolites, and also affects the uptake and distribution of many clinically important drugs. It forms a major component of the blood-brain barrier and restricts the uptake of drugs from the intestine. The protein is also expressed in many human cancers, where it probably contributes to resistance to chemotherapy treatment. Many chemical modulators have been identified that block the action of Pgp, and may have clinical applications in improving drug delivery and treating cancer. Pgp substrates are generally lipid-soluble, and partition into the membrane before the transporter expels them into the aqueous phase, much like a 'hydrophobic vacuum cleaner'. The transporter may also act as a 'flippase', moving its substrates from the inner to the outer membrane leaflet. An X-ray crystal structure shows that drugs interact with Pgp within the transmembrane regions by fitting into a large flexible binding pocket, which can accommodate several substrate molecules simultaneously. The nucleotide-binding domains of Pgp appear to hydrolyse ATP in an alternating manner; however, it is still not clear whether transport is driven by ATP hydrolysis or ATP binding. Details of the steps involved in the drug-transport process, and how it is coupled to ATP hydrolysis, remain the object of intensive study.
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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – original draft
                Role: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: ResourcesRole: Software
                Role: Formal analysisRole: Investigation
                Role: InvestigationRole: Software
                Role: InvestigationRole: Software
                Role: Investigation
                Role: Funding acquisitionRole: Resources
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 January 2018
                2018
                : 13
                : 1
                : e0191080
                Affiliations
                [1 ] School of Life Sciences, Anhui Agricultural University, Hefei, Anhui, People’s Republic of China
                [2 ] Analytical and Testing Center of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China
                Institute of Plant Physiology and Ecology Shanghai Institutes for Biological Sciences, CHINA
                Author notes

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

                Author information
                http://orcid.org/0000-0001-9020-7248
                Article
                PONE-D-17-36603
                10.1371/journal.pone.0191080
                5764371
                29324893
                e922aa80-727f-4372-8303-f617d92eab73
                © 2018 Zhang 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
                : 11 October 2017
                : 27 December 2017
                Page count
                Figures: 7, Tables: 1, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31472148
                Award Recipient :
                Funded by: International Science & Technology Cooperation Plan of Anhui Province
                Award ID: 1503062033
                Award Recipient :
                This work was also supported by the National Science Foundation of China (No. 31472148) ( http://www.nsfc.gov.cn/) (JPX) and the International Science & Technology Cooperation Plan of Anhui Province (1503062033) ( http://www.ahkjt.gov.cn/) (YM).
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Lipids
                Lipid Metabolism
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Lipid Metabolism
                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
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                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
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                Genetics
                Gene Expression
                Biology and Life Sciences
                Organisms
                Eukaryota
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                Xenobiotic Metabolism
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