Enchytraeus albidus Microarray: Enrichment, Design, Annotation and Database (EnchyBASE)
Identifieur interne : 000517 ( Pmc/Corpus ); précédent : 000516; suivant : 000518Enchytraeus albidus Microarray: Enrichment, Design, Annotation and Database (EnchyBASE)
Auteurs : Sara C. Novais ; Joel Arrais ; Pedro Lopes ; Tine Vandenbrouck ; Wim De Coen ; Dick Roelofs ; Amadeu M. V. M. Soares ; M Nica J. B. AmorimSource :
- PLoS ONE [ 1932-6203 ] ; 2012.
Abstract
Url:
DOI: 10.1371/journal.pone.0034266
PubMed: 22558086
PubMed Central: 3338728
Links to Exploration step
PMC:3338728Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en"><italic>Enchytraeus albidus</italic> Microarray: Enrichment, Design, Annotation and Database (EnchyBASE)</title><author><name sortKey="Novais, Sara C" sort="Novais, Sara C" uniqKey="Novais S" first="Sara C." last="Novais">Sara C. Novais</name><affiliation><nlm:aff id="aff1"><addr-line>Department of Biology & CESAM, University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author><author><name sortKey="Arrais, Joel" sort="Arrais, Joel" uniqKey="Arrais J" first="Joel" last="Arrais">Joel Arrais</name><affiliation><nlm:aff id="aff2"><addr-line>Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author><author><name sortKey="Lopes, Pedro" sort="Lopes, Pedro" uniqKey="Lopes P" first="Pedro" last="Lopes">Pedro Lopes</name><affiliation><nlm:aff id="aff2"><addr-line>Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author><author><name sortKey="Vandenbrouck, Tine" sort="Vandenbrouck, Tine" uniqKey="Vandenbrouck T" first="Tine" last="Vandenbrouck">Tine Vandenbrouck</name><affiliation><nlm:aff id="aff3"><addr-line>University of Antwerp, Department of Biology - E.B.T., Groenenborgerlaan, Antwerp, Belgium</addr-line></nlm:aff></affiliation></author><author><name sortKey="De Coen, Wim" sort="De Coen, Wim" uniqKey="De Coen W" first="Wim" last="De Coen">Wim De Coen</name><affiliation><nlm:aff id="aff3"><addr-line>University of Antwerp, Department of Biology - E.B.T., Groenenborgerlaan, Antwerp, Belgium</addr-line></nlm:aff></affiliation></author><author><name sortKey="Roelofs, Dick" sort="Roelofs, Dick" uniqKey="Roelofs D" first="Dick" last="Roelofs">Dick Roelofs</name><affiliation><nlm:aff id="aff4"><addr-line>VU University Amsterdam, Institute of Ecological Sciences, De Boelelaan, The Netherlands</addr-line></nlm:aff></affiliation></author><author><name sortKey="Soares, Amadeu M V M" sort="Soares, Amadeu M V M" uniqKey="Soares A" first="Amadeu M. V. M." last="Soares">Amadeu M. V. M. Soares</name><affiliation><nlm:aff id="aff1"><addr-line>Department of Biology & CESAM, University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author><author><name sortKey="Amorim, M Nica J B" sort="Amorim, M Nica J B" uniqKey="Amorim M" first="M Nica J. B." last="Amorim">M Nica J. B. Amorim</name><affiliation><nlm:aff id="aff1"><addr-line>Department of Biology & CESAM, University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22558086</idno><idno type="pmc">3338728</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3338728</idno><idno type="RBID">PMC:3338728</idno><idno type="doi">10.1371/journal.pone.0034266</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000517</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000517</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main"><italic>Enchytraeus albidus</italic> Microarray: Enrichment, Design, Annotation and Database (EnchyBASE)</title><author><name sortKey="Novais, Sara C" sort="Novais, Sara C" uniqKey="Novais S" first="Sara C." last="Novais">Sara C. Novais</name><affiliation><nlm:aff id="aff1"><addr-line>Department of Biology & CESAM, University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author><author><name sortKey="Arrais, Joel" sort="Arrais, Joel" uniqKey="Arrais J" first="Joel" last="Arrais">Joel Arrais</name><affiliation><nlm:aff id="aff2"><addr-line>Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author><author><name sortKey="Lopes, Pedro" sort="Lopes, Pedro" uniqKey="Lopes P" first="Pedro" last="Lopes">Pedro Lopes</name><affiliation><nlm:aff id="aff2"><addr-line>Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author><author><name sortKey="Vandenbrouck, Tine" sort="Vandenbrouck, Tine" uniqKey="Vandenbrouck T" first="Tine" last="Vandenbrouck">Tine Vandenbrouck</name><affiliation><nlm:aff id="aff3"><addr-line>University of Antwerp, Department of Biology - E.B.T., Groenenborgerlaan, Antwerp, Belgium</addr-line></nlm:aff></affiliation></author><author><name sortKey="De Coen, Wim" sort="De Coen, Wim" uniqKey="De Coen W" first="Wim" last="De Coen">Wim De Coen</name><affiliation><nlm:aff id="aff3"><addr-line>University of Antwerp, Department of Biology - E.B.T., Groenenborgerlaan, Antwerp, Belgium</addr-line></nlm:aff></affiliation></author><author><name sortKey="Roelofs, Dick" sort="Roelofs, Dick" uniqKey="Roelofs D" first="Dick" last="Roelofs">Dick Roelofs</name><affiliation><nlm:aff id="aff4"><addr-line>VU University Amsterdam, Institute of Ecological Sciences, De Boelelaan, The Netherlands</addr-line></nlm:aff></affiliation></author><author><name sortKey="Soares, Amadeu M V M" sort="Soares, Amadeu M V M" uniqKey="Soares A" first="Amadeu M. V. M." last="Soares">Amadeu M. V. M. Soares</name><affiliation><nlm:aff id="aff1"><addr-line>Department of Biology & CESAM, University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author><author><name sortKey="Amorim, M Nica J B" sort="Amorim, M Nica J B" uniqKey="Amorim M" first="M Nica J. B." last="Amorim">M Nica J. B. Amorim</name><affiliation><nlm:aff id="aff1"><addr-line>Department of Biology & CESAM, University of Aveiro, Aveiro, Portugal</addr-line></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><italic>Enchytraeus albidus</italic> (Oligochaeta) is an ecologically relevant species used as standard test organisms for risk assessment. Effects of stressors in this species are commonly determined at the population level using reproduction and survival as endpoints. The assessment of transcriptomic responses can be very useful e.g. to understand underlying mechanisms of toxicity with gene expression fingerprinting. In the present paper the following is being addressed: 1) development of suppressive subtractive hybridization (SSH) libraries enriched for differentially expressed genes after metal and pesticide exposures; 2) sequencing and characterization of all generated cDNA inserts; 3) development of a publicly available genomic database on <italic>E. albidus</italic>. A total of 2100 Expressed Sequence Tags (ESTs) were isolated, sequenced and assembled into 1124 clusters (947 singletons and 177 contigs). From these sequences, 41% matched known proteins in GenBank (BLASTX, e-value≤10<sup>-5</sup>) and 37% had at least one Gene Ontology (GO) term assigned. In total, 5.5% of the sequences were assigned to a metabolic pathway, based on KEGG. With this new sequencing information, an Agilent custom oligonucleotide microarray was designed, representing a potential tool for transcriptomic studies. EnchyBASE (<ext-link ext-link-type="uri" xlink:href="http://bioinformatics.ua.pt/enchybase/">http://bioinformatics.ua.pt/enchybase/</ext-link>) was developed as a web freely available database containing genomic information on <italic>E. albidus</italic> and will be further extended in the near future for other enchytraeid species. The database so far includes all ESTs generated for <italic>E. albidus</italic> from three cDNA libraries. This information can be downloaded and applied in functional genomics and transcription studies.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Jeffrey, S" uniqKey="Jeffrey S">S Jeffrey</name></author><author><name sortKey="Gardi, C" uniqKey="Gardi C">C Gardi</name></author><author><name sortKey="Jones, A" uniqKey="Jones A">A Jones</name></author><author><name sortKey="Montanarella, L" uniqKey="Montanarella L">L Montanarella</name></author><author><name sortKey="Marmo, L" uniqKey="Marmo L">L Marmo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Iso" uniqKey="Iso">ISO</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Oecd" uniqKey="Oecd">OECD</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Oecd" uniqKey="Oecd">OECD</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Amorim, Mjb" uniqKey="Amorim 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22558086</article-id><article-id pub-id-type="pmc">3338728</article-id><article-id pub-id-type="publisher-id">PONE-D-11-19704</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0034266</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biology</subject><subj-group><subject>Computational Biology</subject><subj-group><subject>Genomics</subject><subj-group><subject>Functional Genomics</subject><subject>Genome Expression Analysis</subject></subj-group></subj-group><subj-group><subject>Microarrays</subject></subj-group></subj-group><subj-group><subject>Ecology</subject><subj-group><subject>Ecological Environments</subject><subj-group><subject>Terrestrial Environments</subject></subj-group></subj-group><subj-group><subject>Environmental Protection</subject><subject>Soil Ecology</subject><subject>Terrestrial Ecology</subject></subj-group></subj-group><subj-group><subject>Genomics</subject><subj-group><subject>Genome Analysis Tools</subject><subj-group><subject>Gene Ontologies</subject><subject>Sequence Assembly Tools</subject><subject>Transcriptomes</subject></subj-group></subj-group><subj-group><subject>Functional Genomics</subject></subj-group></subj-group><subj-group><subject>Toxicology</subject><subj-group><subject>Genetic Toxicology</subject></subj-group></subj-group></subj-group></article-categories><title-group><article-title><italic>Enchytraeus albidus</italic> Microarray: Enrichment, Design, Annotation and Database (EnchyBASE)</article-title><alt-title alt-title-type="running-head">Microarray and EnchyBASE for <italic>Enchytraeus albidus</italic></alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Novais</surname><given-names>Sara C.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Arrais</surname><given-names>Joel</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Lopes</surname><given-names>Pedro</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Vandenbrouck</surname><given-names>Tine</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>De Coen</surname><given-names>Wim</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Roelofs</surname><given-names>Dick</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author"><name><surname>Soares</surname><given-names>Amadeu M. V. M.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Amorim</surname><given-names>Mónica J. B.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>Department of Biology & CESAM, University of Aveiro, Aveiro, Portugal</addr-line></aff><aff id="aff2"><label>2</label><addr-line>Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal</addr-line></aff><aff id="aff3"><label>3</label><addr-line>University of Antwerp, Department of Biology - E.B.T., Groenenborgerlaan, Antwerp, Belgium</addr-line></aff><aff id="aff4"><label>4</label><addr-line>VU University Amsterdam, Institute of Ecological Sciences, De Boelelaan, The Netherlands</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Gibas</surname><given-names>Cynthia</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1">University of North Carolina at Charlotte, United States of America</aff><author-notes><corresp id="cor1">* E-mail: <email>sara.novais@ua.pt</email></corresp><fn fn-type="con"><p>Conceived and designed the experiments: SN TV WDC AS MA. Performed the experiments: SN TV. Analyzed the data: SN. Wrote the paper: SN MA DR. Designed and implemented the database, web server configuration and web application programming: JA PL.</p></fn></author-notes><pub-date pub-type="collection"><year>2012</year></pub-date><pub-date pub-type="epub"><day>27</day><month>4</month><year>2012</year></pub-date><volume>7</volume><issue>4</issue><elocation-id>e34266</elocation-id><history><date date-type="received"><day>7</day><month>10</month><year>2011</year></date><date date-type="accepted"><day>24</day><month>2</month><year>2012</year></date></history><permissions><copyright-statement>Novais 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.</copyright-statement><copyright-year>2012</copyright-year></permissions><abstract><p><italic>Enchytraeus albidus</italic> (Oligochaeta) is an ecologically relevant species used as standard test organisms for risk assessment. Effects of stressors in this species are commonly determined at the population level using reproduction and survival as endpoints. The assessment of transcriptomic responses can be very useful e.g. to understand underlying mechanisms of toxicity with gene expression fingerprinting. In the present paper the following is being addressed: 1) development of suppressive subtractive hybridization (SSH) libraries enriched for differentially expressed genes after metal and pesticide exposures; 2) sequencing and characterization of all generated cDNA inserts; 3) development of a publicly available genomic database on <italic>E. albidus</italic>. A total of 2100 Expressed Sequence Tags (ESTs) were isolated, sequenced and assembled into 1124 clusters (947 singletons and 177 contigs). From these sequences, 41% matched known proteins in GenBank (BLASTX, e-value≤10<sup>-5</sup>) and 37% had at least one Gene Ontology (GO) term assigned. In total, 5.5% of the sequences were assigned to a metabolic pathway, based on KEGG. With this new sequencing information, an Agilent custom oligonucleotide microarray was designed, representing a potential tool for transcriptomic studies. EnchyBASE (<ext-link ext-link-type="uri" xlink:href="http://bioinformatics.ua.pt/enchybase/">http://bioinformatics.ua.pt/enchybase/</ext-link>) was developed as a web freely available database containing genomic information on <italic>E. albidus</italic> and will be further extended in the near future for other enchytraeid species. The database so far includes all ESTs generated for <italic>E. albidus</italic> from three cDNA libraries. This information can be downloaded and applied in functional genomics and transcription studies.</p></abstract><counts><page-count count="9"></page-count></counts></article-meta></front><body><sec id="s1"><title>Introduction</title><p>Enchytraeids (Oligochaeta), members of the soil mesofauna, play a key role on the regulation of the composition and activity of soil communities; they improve the soil pore structure and are involved in the organic matter decomposition <xref ref-type="bibr" rid="pone.0034266-Jeffrey1">[1]</xref>. <italic>Enchytraeus albidus</italic> is present in a wide range of soils and conditions worldwide. <italic>E. albidus</italic> have been increasingly used as indicators of soil health since the standardization of the ecotoxicological tests, where survival, reproduction and, more recently, bioaccumulation effects are measured <xref ref-type="bibr" rid="pone.0034266-ISO1">[2]</xref>–<xref ref-type="bibr" rid="pone.0034266-OECD2">[4]</xref>. There is ample literature on chemical and natural stress on enchytraeids at these levels e.g. with heavy metals <xref ref-type="bibr" rid="pone.0034266-Amorim1">[5]</xref>, <xref ref-type="bibr" rid="pone.0034266-Lock1">[6]</xref>, organic substances <xref ref-type="bibr" rid="pone.0034266-Novais1">[7]</xref>, <xref ref-type="bibr" rid="pone.0034266-Amorim2">[8]</xref>, chemical mixtures <xref ref-type="bibr" rid="pone.0034266-Loureiro1">[9]</xref> and different soil properties <xref ref-type="bibr" rid="pone.0034266-Dodard1">[10]</xref>–<xref ref-type="bibr" rid="pone.0034266-Amorim3">[12]</xref>. Such information is of extreme importance as they provide the tools for risk assessors, and policy makers at a later stage. However, the current ecotoxicology tests are time consuming (e.g. 6 weeks for reproduction) and there is little mechanistic understanding of the impact caused by such stressors. Complementing existing knowledge with the molecular profiling and genomic studies can help considerably to elucidate modes of action, molecular pathways of response or general biological processes affected by stressors. Furthermore, it has been shown by several authors that responses at gene level can be observed in several invertebrates within short time intervals such as 1 or 2 days <xref ref-type="bibr" rid="pone.0034266-Nota1">[13]</xref>–<xref ref-type="bibr" rid="pone.0034266-Poynton1">[17]</xref>, presenting a clear advantage in comparison to the more time-consuming population studies.</p><p>Promising developments have taken place in this area in soil invertebrates with the establishment of Expressed Sequence Tag (EST) databases and microarrays for a few species of earthworms: <italic>Lumbricus rubellus</italic><xref ref-type="bibr" rid="pone.0034266-Owen1">[18]</xref> and <italic>Eisenia fetida</italic><xref ref-type="bibr" rid="pone.0034266-Pirooznia1">[19]</xref> and the springtail <italic>Folsomia candida</italic><xref ref-type="bibr" rid="pone.0034266-Timmermans1">[20]</xref>. The generation of ESTs is of particular interest when studying non-genomic model organisms, which is the case of the referred invertebrate species and also <italic>E. albidus</italic>. This is an efficient way to retrieve sequence information on the protein coding part of the genome <xref ref-type="bibr" rid="pone.0034266-Timmermans1">[20]</xref>, although not comparable to present next generation sequencing techniques.</p><p>Regarding <italic>E. albidus</italic>, Amorim and co-authors started the EST sequencing project with a normalized cDNA library <xref ref-type="bibr" rid="pone.0034266-Amorim4">[16]</xref>. A cDNA microarray was developed based on this normalized library and has been used to study the effects of phenmedipham, copper, different soil properties or exposure duration <xref ref-type="bibr" rid="pone.0034266-Amorim4">[16]</xref>, <xref ref-type="bibr" rid="pone.0034266-Novais2">[21]</xref>–<xref ref-type="bibr" rid="pone.0034266-Gomes2">[23]</xref>. The existing cDNA library was improved using suppression subtractive hybridization-PCR (SSH-PCR), a technique that combines high subtraction efficiency with a normalization step to generate differentially expressed sequences equally represented in the library <xref ref-type="bibr" rid="pone.0034266-Diatchenko1">[24]</xref>, <xref ref-type="bibr" rid="pone.0034266-Diatchenko2">[25]</xref>.</p><p>In the present paper the following main points were addressed: 1) development of two SSH libraries enriched with genes differentially expressed after exposure to metals and pesticides at different concentrations and exposure times; SSH-metals was developed by exposure to cadmium, zinc, copper and nickel; SSH-pesticides was developed by exposure to dimethoate, atrazine, carbendazim and lindane; 2) sequencing and characterization of all generated cDNA inserts; 3) development of a publicly available genomic database on <italic>E. albidus</italic> including the ESTs, allowing the users to search e.g. for sequence similarity (BLAST), gene ontology terms and for sequence information on the differentially expressed genes at the different conditions. After assembling all the information, the existing microarray was enriched and developed into a denser populated Agilent custom oligonucleotide microarray. The present can be used for studies envisaging mechanistic understanding of stress and soil quality assessment.</p></sec><sec id="s2"><title>Development and Analysis</title><sec id="s2a"><title>cDNA Libraries Construction</title><p>SSH procedure was applied for the development of two cDNA libraries (SSH-metals, SSH-pesticides) <xref ref-type="bibr" rid="pone.0034266-Diatchenko1">[24]</xref>, <xref ref-type="bibr" rid="pone.0034266-Diatchenko2">[25]</xref>. A schematic representation of the exposures and RNA pools made for both SSH enriched cDNA libraries is shown in <xref ref-type="fig" rid="pone-0034266-g001">Figure 1</xref>.</p><fig id="pone-0034266-g001" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0034266.g001</object-id><label>Figure 1</label><caption><title>Exposure experimental setup.</title><p>Schematic representation of the exposures performed and pools of RNA used for the SSH library development: A) Library enriched for genes differentially expressed after metal exposures; B) Library enriched for genes differentially expressed after pesticide exposures. CT = Control; EC50 = Concentration that induces 50% reduction in the number of juveniles (50% effect concentration on reproduction); EC90 = Concentration that induces 90% reduction in the number of juveniles (90% effect concentration on reproduction).</p></caption><graphic xlink:href="pone.0034266.g001"></graphic></fig><p>For the library enriched with differentially expressed genes after metal exposures, 15 adult organisms with well developed clitellum were exposed, in each replicate, to 25 g of LUFA 2.2 standard natural soil <xref ref-type="bibr" rid="pone.0034266-Lkke1">[26]</xref>, moist to 50% of the water holding capacity according to the standard guidelines <xref ref-type="bibr" rid="pone.0034266-ISO1">[2]</xref>, <xref ref-type="bibr" rid="pone.0034266-OECD1">[3]</xref>. Soil was spiked with 4 different metal salts individually: cadmium chloride, zinc chloride, copper chloride and nickel chloride. Enchytraeids were exposed to each metal in two different concentrations in the range of the effective concentrations for 50% (EC<sub>50</sub>) and 90% (EC<sub>90</sub>) reduction in reproduction (known based on previous results <xref ref-type="bibr" rid="pone.0034266-Amorim1">[5]</xref>, <xref ref-type="bibr" rid="pone.0034266-Novais3">[27]</xref>, <xref ref-type="bibr" rid="pone.0034266-Lock3">[28]</xref>) and three time points (2, 4 and 8 days). These concentrations were selected, on the one hand to be able to relate gene effects with known effects at higher levels of biological organization and on the other hand to increase the likelihood of finding effects in gene expression, than would be expected with very low concentrations. Three replicates per condition were used. The total RNA from the organisms in each replicate was extracted using the Trizol extraction method (Invitrogen, Belgium). RNA concentration and purity was determined by spectrophotometry (NanoDrop 1000, Thermo Fisher Scientific) and quality was checked by denaturing formaldehyde agarose gel.</p><p>A pool containing RNA from all the exposure conditions was made using 1 replicate of each condition. A second pool containing RNA from control organisms (organisms exposed to clean LUFA 2.2 soil) was similarly prepared. For the library enriched for differentially expressed genes after pesticide exposures, enchytraeids were exposed to dimethoate, atrazine, lindane and carbendazim. Exposure was performed in the same way as for the SSH-metals in regard to concentrations (EC<sub>50</sub> and EC<sub>90</sub><xref ref-type="bibr" rid="pone.0034266-Novais1">[7]</xref>) and duration (2, 4 and 8 days). Similarly, two different RNA pools were obtained: one from organisms exposed to the pesticides and one from control organisms. The exposure concentrations of all compounds are given in <xref ref-type="table" rid="pone-0034266-t001">table 1</xref>.</p><table-wrap id="pone-0034266-t001" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0034266.t001</object-id><label>Table 1</label><caption><title>Concentrations of the four metals and four pesticides to which <italic>E. albidus</italic> were exposed for the SSH libraries development.</title></caption><alternatives><graphic id="pone-0034266-t001-1" xlink:href="pone.0034266.t001"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">EC<sub>50</sub> (mg/kg)</td><td align="left" rowspan="1" colspan="1">EC<sub>90</sub> (mg/kg)</td><td align="left" rowspan="1" colspan="1">References</td></tr></thead><tbody><tr><td colspan="4" align="left" rowspan="1"><italic>SSH Metals</italic></td></tr><tr><td align="left" rowspan="1" colspan="1">Copper</td><td align="left" rowspan="1" colspan="1">100</td><td align="left" rowspan="1" colspan="1">320</td><td align="left" rowspan="1" colspan="1"><xref ref-type="bibr" rid="pone.0034266-Amorim1">[5]</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Cadmium</td><td align="left" rowspan="1" colspan="1">6</td><td align="left" rowspan="1" colspan="1">150</td><td align="left" rowspan="1" colspan="1"><xref ref-type="bibr" rid="pone.0034266-Novais3">[27]</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Zinc</td><td align="left" rowspan="1" colspan="1">40</td><td align="left" rowspan="1" colspan="1">100</td><td align="left" rowspan="1" colspan="1"><xref ref-type="bibr" rid="pone.0034266-Novais3">[27]</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Nickel</td><td align="left" rowspan="1" colspan="1">50</td><td align="left" rowspan="1" colspan="1">120</td><td align="left" rowspan="1" colspan="1"><xref ref-type="bibr" rid="pone.0034266-Lock3">[28]</xref></td></tr><tr><td colspan="4" align="left" rowspan="1"><italic>SSH Pesticides</italic></td></tr><tr><td align="left" rowspan="1" colspan="1">Dimethoate</td><td align="left" rowspan="1" colspan="1">2</td><td align="left" rowspan="1" colspan="1">25</td><td align="left" rowspan="1" colspan="1"><xref ref-type="bibr" rid="pone.0034266-Novais1">[7]</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Atrazine</td><td align="left" rowspan="1" colspan="1">3</td><td align="left" rowspan="1" colspan="1">50</td><td align="left" rowspan="1" colspan="1"><xref ref-type="bibr" rid="pone.0034266-Novais1">[7]</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Carbendazim</td><td align="left" rowspan="1" colspan="1">0.5</td><td align="left" rowspan="1" colspan="1">3</td><td align="left" rowspan="1" colspan="1"><xref ref-type="bibr" rid="pone.0034266-Novais1">[7]</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Lindane</td><td align="left" rowspan="1" colspan="1">40</td><td align="left" rowspan="1" colspan="1">130</td><td align="left" rowspan="1" colspan="1"><xref ref-type="bibr" rid="pone.0034266-Novais1">[7]</xref></td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt101"><label></label><p>EC<sub>50</sub> = Concentration that induces 50% reduction in the number of juveniles; EC<sub>90</sub> = Concentration that induces 90% reduction in the number of juveniles.</p></fn><fn id="nt102"><label></label><p>Concentrations of exposure are based on the effect concentrations on reproduction, available on the literature.</p></fn></table-wrap-foot></table-wrap><p>To each RNA pool, 0.1 volumes of 3 M sodium acetate and 3 volumes of 96% ethanol were added and the pairs of pools were shipped at room temperature to Evrogen (Moscow, Russia). Amplification of the double stranded cDNAs (using SMART approach <xref ref-type="bibr" rid="pone.0034266-Zhu1">[29]</xref>) and the subtraction procedures were performed by Evrogen for both libraries. The cDNA was SMART-amplified (19 cycles), starting from 0.5 µg of each RNA pool, and used for subtractive hybridization using SSH method in both directions <xref ref-type="bibr" rid="pone.0034266-Diatchenko1">[24]</xref>, <xref ref-type="bibr" rid="pone.0034266-Diatchenko2">[25]</xref>. Prior to the library construction, the samples were subjected to the mirror orientation selection (MOS) procedure <xref ref-type="bibr" rid="pone.0034266-Rebrikov1">[30]</xref> to eliminate false positive clones resulting from the SSH procedures (Evrogen). The treated samples were then handled by us for the libraries construction. Briefly, the subtracted cDNAs were ligated in a TA-vector system (pGEM-T easy vector, Promega). <italic>Escherichia coli</italic> calcium competent cells (JM109, Promega) were transformed through heat shock. The recombinant clones were picked and grown in 96-well plates. Glycerol stocks were made (12.5%) and stored at −80°C. Clones were amplified with vector-specific primers (T7 and SP6 primers, Promega), and purified by an exosap reaction <xref ref-type="bibr" rid="pone.0034266-Werle1">[31]</xref> based on exonuclease I and shrimp alkaline phosphatase (Fermentas).</p></sec><sec id="s2b"><title>EST Sequencing and Comparative Sequence Analysis</title><p>From the SSH libraries, 1920 clones were selected (960 from each library). After checking the quality of the PCR inserts on an agarose gel, 67 clones had no inserts or had more than one insert and were excluded. Therefore, the remaining 1853 purified clones were sent to be sequenced with primers SP6 and T7 (VIB service, Flemish Institute for Biotechnology).</p><p>CodonCode Aligner software (<ext-link ext-link-type="uri" xlink:href="http://www.codoncode.com/aligner">www.codoncode.com/aligner</ext-link>) was used to remove vectors and screen for low-quality sequence regions. From the 1853, 101 sequences were shorter than 50 base pairs (bp) or did not pass the quality control (low-quality sequence regions only) and were thus removed from further analysis. In sum, from the 921 clones sequenced from the metals enriched library, we obtained 875 good quality sequences (95%) and from the 932 clones sequenced from the pesticides enriched library, we obtained 877 good quality sequences (94%). All good quality sequences were submitted to GenBank dbEST (accession numbers: JK309883-JK310757; JK474167 - JK475043).</p><p>Sequences from the first cDNA library developed by Amorim and co-authors <xref ref-type="bibr" rid="pone.0034266-Amorim4">[16]</xref> were added for further analysis. In total 2100 ESTs were retrieved from the three libraries and aligned and assembled using Cap3 program (<ext-link ext-link-type="uri" xlink:href="http://www.genome.clemson.edu/cgi-bin/cugi_cap3">http://www.genome.clemson.edu/cgi-bin/cugi_cap3</ext-link>). This procedure resulted in 1124 unique sequences (clusters): 947 singletons (338 ESTs from the normalized library, 370 ESTs from the metals enriched library and 239 EST from the pesticides enriched library) and 177 contigs. The 45% singletons obtained in this study is inferior to the 80% observed in the EST sequencing project for <italic>Eisenia fetida</italic><xref ref-type="bibr" rid="pone.0034266-Pirooznia1">[19]</xref> but similar to the percentages of singletons observed in the EST sequencing projects of other terrestrial invertebrate species: 49% for <italic>Eisenia andrei</italic><xref ref-type="bibr" rid="pone.0034266-Lee1">[32]</xref>, 52% for <italic>Folsomia candida</italic><xref ref-type="bibr" rid="pone.0034266-Timmermans1">[20]</xref> or 53% for <italic>Lumbricus rubelus</italic><xref ref-type="bibr" rid="pone.0034266-Owen1">[18]</xref>. From the 177 contigs, nearly 85% were assembled from 2 to 5 sequences and more than half were assembled from only 2 sequences (<xref ref-type="fig" rid="pone-0034266-g002">Figure 2</xref>).</p><fig id="pone-0034266-g002" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0034266.g002</object-id><label>Figure 2</label><caption><title>Expressed Sequence Tags distribution over the 177 contigs.</title></caption><graphic xlink:href="pone.0034266.g002"></graphic></fig><p>The length of the 177 contigs varied from 69 to 1630 bp with an average of 735 bp. The highest number of sequences in one contig was by far observed on the pesticides enriched library with 449 ESTs, whereas the highest depth among the contigs in the metals enriched library was 24 ESTs and in the normalized library was 4 ESTs. In terms of redundancy [total number of sequences divided by the number of clusters <xref ref-type="bibr" rid="pone.0034266-Timmermans1">[20]</xref>], the pesticides library was the most redundant (3.22) followed by the metals library (1.80) and the least one, the normalized library (1.02). Overall, data had a redundancy of 1.87. Interestingly, also Timmermans et al. <xref ref-type="bibr" rid="pone.0034266-Timmermans1">[20]</xref> refer a similar difference in redundancy obtained for the phenantrene (3.18) library in comparison to the cadmium (1.62) and normalized (1.32) ones.</p><p>The overlaping ESTs from the different cDNA libraries is represented in <xref ref-type="fig" rid="pone-0034266-g003">Figure 3</xref>. Interestingly, very little overlap occurred, with only one contig containing sequences from the three libraries (cluster EAC00169, <xref ref-type="table" rid="pone-0034266-t002">Table 2</xref>). This was also observed in other studies (e.g. <xref ref-type="bibr" rid="pone.0034266-Timmermans1">[20]</xref>) confirming the relevance of the enrichment with as much varied conditions as possible.</p><fig id="pone-0034266-g003" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0034266.g003</object-id><label>Figure 3</label><caption><title>Cluster overlaps between the three different libraries.</title><p>The common clusters represent contigs assembled from ESTs with different library origins. Normalized: normalized library; SSH Metals: Library enriched for genes differentially expressed after metal exposures; SSH Pesticides: Library enriched for genes differentially expressed after pesticide exposures.</p></caption><graphic xlink:href="pone.0034266.g003"></graphic></fig><table-wrap id="pone-0034266-t002" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0034266.t002</object-id><label>Table 2</label><caption><title>The most represented sequenced transcripts in <italic>E. albidus</italic> cDNA libraries.</title></caption><alternatives><graphic id="pone-0034266-t002-2" xlink:href="pone.0034266.t002"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1">Cluster ID</td><td align="left" rowspan="1" colspan="1">ESTs</td><td align="left" rowspan="1" colspan="1">Length</td><td align="left" rowspan="1" colspan="1">Library</td><td align="left" rowspan="1" colspan="1">Blast Hit</td><td align="left" rowspan="1" colspan="1">E-value</td><td align="left" rowspan="1" colspan="1">GeneBank AccNumber</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">EAC00048</td><td align="left" rowspan="1" colspan="1">449</td><td align="left" rowspan="1" colspan="1">660</td><td align="left" rowspan="1" colspan="1">Pest</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00024</td><td align="left" rowspan="1" colspan="1">25</td><td align="left" rowspan="1" colspan="1">480</td><td align="left" rowspan="1" colspan="1">Met+Pest</td><td align="left" rowspan="1" colspan="1">Actin</td><td align="left" rowspan="1" colspan="1">7.25E-12</td><td align="left" rowspan="1" colspan="1">ADJ56346</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00129</td><td align="left" rowspan="1" colspan="1">22</td><td align="left" rowspan="1" colspan="1">960</td><td align="left" rowspan="1" colspan="1">Met+Pest</td><td align="left" rowspan="1" colspan="1">Sarcoplasmic calcium-binding protein</td><td align="left" rowspan="1" colspan="1">1.01E-09</td><td align="left" rowspan="1" colspan="1">P04572</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00065</td><td align="left" rowspan="1" colspan="1">16</td><td align="left" rowspan="1" colspan="1">720</td><td align="left" rowspan="1" colspan="1">Pest</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00139</td><td align="left" rowspan="1" colspan="1">15</td><td align="left" rowspan="1" colspan="1">1620</td><td align="left" rowspan="1" colspan="1">Met</td><td align="left" rowspan="1" colspan="1">Actin</td><td align="left" rowspan="1" colspan="1">1.04E-17</td><td align="left" rowspan="1" colspan="1">NP_001003349</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00074</td><td align="left" rowspan="1" colspan="1">15</td><td align="left" rowspan="1" colspan="1">660</td><td align="left" rowspan="1" colspan="1">Pest</td><td align="left" rowspan="1" colspan="1">myosin heavy chain</td><td align="left" rowspan="1" colspan="1">5.51E-07</td><td align="left" rowspan="1" colspan="1">CAC28360</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00083</td><td align="left" rowspan="1" colspan="1">15</td><td align="left" rowspan="1" colspan="1">840</td><td align="left" rowspan="1" colspan="1">Pest</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00094</td><td align="left" rowspan="1" colspan="1">13</td><td align="left" rowspan="1" colspan="1">1260</td><td align="left" rowspan="1" colspan="1">Met</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00138</td><td align="left" rowspan="1" colspan="1">13</td><td align="left" rowspan="1" colspan="1">1140</td><td align="left" rowspan="1" colspan="1">Met</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00108</td><td align="left" rowspan="1" colspan="1">12</td><td align="left" rowspan="1" colspan="1">720</td><td align="left" rowspan="1" colspan="1">Met+Pest</td><td align="left" rowspan="1" colspan="1">myosin heavy chain</td><td align="left" rowspan="1" colspan="1">3.11E-06</td><td align="left" rowspan="1" colspan="1">AAD52842</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00109</td><td align="left" rowspan="1" colspan="1">12</td><td align="left" rowspan="1" colspan="1">840</td><td align="left" rowspan="1" colspan="1">Met</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00157</td><td align="left" rowspan="1" colspan="1">12</td><td align="left" rowspan="1" colspan="1">720</td><td align="left" rowspan="1" colspan="1">Met</td><td align="left" rowspan="1" colspan="1">ADP/ATP carrier protein 3</td><td align="left" rowspan="1" colspan="1">1.34E-11</td><td align="left" rowspan="1" colspan="1">NP_001187478</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00066</td><td align="left" rowspan="1" colspan="1">12</td><td align="left" rowspan="1" colspan="1">720</td><td align="left" rowspan="1" colspan="1">Pest</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00070</td><td align="left" rowspan="1" colspan="1">11</td><td align="left" rowspan="1" colspan="1">540</td><td align="left" rowspan="1" colspan="1">Pest</td><td align="left" rowspan="1" colspan="1">MADS FLC-like protein 2</td><td align="left" rowspan="1" colspan="1">4.33E-05</td><td align="left" rowspan="1" colspan="1">ACL54966</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00035</td><td align="left" rowspan="1" colspan="1">10</td><td align="left" rowspan="1" colspan="1">660</td><td align="left" rowspan="1" colspan="1">Met+Pest</td><td align="left" rowspan="1" colspan="1">hemoglobin c chain precursor</td><td align="left" rowspan="1" colspan="1">3.30E-06</td><td align="left" rowspan="1" colspan="1">CAA09958</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00092</td><td align="left" rowspan="1" colspan="1">10</td><td align="left" rowspan="1" colspan="1">780</td><td align="left" rowspan="1" colspan="1">Met</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00143</td><td align="left" rowspan="1" colspan="1">10</td><td align="left" rowspan="1" colspan="1">660</td><td align="left" rowspan="1" colspan="1">Met+Pest</td><td align="left" rowspan="1" colspan="1">ribosomal protein s7</td><td align="left" rowspan="1" colspan="1">8.78E-14</td><td align="left" rowspan="1" colspan="1">AAW50967</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00169</td><td align="left" rowspan="1" colspan="1">9</td><td align="left" rowspan="1" colspan="1">1020</td><td align="left" rowspan="1" colspan="1">Met+Pest+Norm</td><td align="left" rowspan="1" colspan="1">Myosin regulatory light chain</td><td align="left" rowspan="1" colspan="1">4.12E-12</td><td align="left" rowspan="1" colspan="1">P80164</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00015</td><td align="left" rowspan="1" colspan="1">9</td><td align="left" rowspan="1" colspan="1">900</td><td align="left" rowspan="1" colspan="1">Met</td><td align="left" rowspan="1" colspan="1">No significant hit</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">EAC00082</td><td align="left" rowspan="1" colspan="1">9</td><td align="left" rowspan="1" colspan="1">720</td><td align="left" rowspan="1" colspan="1">Pest</td><td align="left" rowspan="1" colspan="1">myosin heavy chain cg17927</td><td align="left" rowspan="1" colspan="1">3.44E-05</td><td align="left" rowspan="1" colspan="1">EFA08290</td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt103"><label></label><p>Met: sequences from the SSH library enriched for genes differentially expressed after metal exposures; Pest: sequences from the SSH library enriched for genes differentially expressed after pesticide exposures; Norm: sequences from the normalized library.</p></fn></table-wrap-foot></table-wrap><p>The sequenced unique fragments (singletons and consensus sequences of assembled contigs) were identified based on their similarity to sequences in the National Centre for Biotechnology Information (NCBI) database as determined by the Basic Local Alignment Search Tool (BLAST) <xref ref-type="bibr" rid="pone.0034266-Altschul1">[33]</xref>. The sequences were submitted to Blast2GO <xref ref-type="bibr" rid="pone.0034266-Conesa1">[34]</xref> being compared with peptide sequence databases using BLASTX analysis. From the 1124 clusters, a total of 459 sequences (41%) matched known proteins in the database with an e-value ≤ 10<sup>−5</sup>. Among these, 72 sequences (16%) had e-values between 10<sup>−123</sup> and 10<sup>−50</sup>.</p><p>The most abundant sequenced transcripts identified were actin, myosin, Sarcoplasmic calcium-binding protein, ADP/ATP carrier protein 3, MADS FLC-like protein 2, hemoglobin c and ribosomal protein s7 (<xref ref-type="table" rid="pone-0034266-t002">Table 2</xref>).</p><p>As can be seen in <xref ref-type="table" rid="pone-0034266-t002">table 2</xref>, housekeeping genes like actin were highly represented in the SSH libraries but not in the normalized library, indicating that the normalization method was efficient <xref ref-type="bibr" rid="pone.0034266-Timmermans1">[20]</xref>, <xref ref-type="bibr" rid="pone.0034266-Sturzenbaum1">[35]</xref>.</p><p>From the 459 blast hits, 46 (10%) matched sequences from earthworms, soil organisms phylogenetically close to <italic>E. albidus</italic> (<italic>Eisenia fetida</italic>, <italic>Lumbricus rubellus</italic>, <italic>Lumbricus terrestris</italic>, <italic>Lumbricus variegatus</italic>).</p></sec><sec id="s2c"><title>Functional Annotation</title><p>Gene ontology terms (GO) were assigned to the predicted proteins by homology blast using the same Blast2GO software <xref ref-type="bibr" rid="pone.0034266-Conesa1">[34]</xref>. A total of 415 sequences had at least one GO term assigned (37% of the 1124 clusters).</p><p>The summary of GO terms showing the representation of the higher-level terms (GO-slim), assigned to 5 or more sequences is given in <xref ref-type="table" rid="pone-0034266-t003">table 3</xref>.</p><table-wrap id="pone-0034266-t003" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0034266.t003</object-id><label>Table 3</label><caption><title>GO-slim terms represented by more than 5 sequences in the combined library datasets for <italic>E. albidus</italic>.</title></caption><alternatives><graphic id="pone-0034266-t003-3" xlink:href="pone.0034266.t003"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Gene Ontology ID</td><td align="left" rowspan="1" colspan="1">No. Sequences</td><td align="left" rowspan="1" colspan="1">Library(no. sequences)</td></tr></thead><tbody><tr><td colspan="4" align="left" rowspan="1"><bold>Biological process</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Lipid metabolic process</td><td align="left" rowspan="1" colspan="1">GO:0006629</td><td align="left" rowspan="1" colspan="1">5</td><td align="left" rowspan="1" colspan="1">Met(3)+Norm(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cellular amino acid and derivative metabolic process</td><td align="left" rowspan="1" colspan="1">GO:0006519</td><td align="left" rowspan="1" colspan="1">5</td><td align="left" rowspan="1" colspan="1">Met(4)+Pest(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Reproduction</td><td align="left" rowspan="1" colspan="1">GO:0000003</td><td align="left" rowspan="1" colspan="1">6</td><td align="left" rowspan="1" colspan="1">Pest(2)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Carbohydrate metabolic process</td><td align="left" rowspan="1" colspan="1">GO:0005975</td><td align="left" rowspan="1" colspan="1">7</td><td align="left" rowspan="1" colspan="1">Met(4)+Pest(3)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cell proliferation</td><td align="left" rowspan="1" colspan="1">GO:0008283</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">Met(5)+Norm(3)</td></tr><tr><td align="left" rowspan="1" colspan="1">Ion Transport</td><td align="left" rowspan="1" colspan="1">GO:0006811</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">Met(8)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cell death</td><td align="left" rowspan="1" colspan="1">GO:0008219</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">Met(6)+Pest(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Signal transduction</td><td align="left" rowspan="1" colspan="1">GO:0007165</td><td align="left" rowspan="1" colspan="1">9</td><td align="left" rowspan="1" colspan="1">Met(3)+Pest(2)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Protein modification process</td><td align="left" rowspan="1" colspan="1">GO:0006464</td><td align="left" rowspan="1" colspan="1">11</td><td align="left" rowspan="1" colspan="1">Met(4)+Pest(3)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Growth</td><td align="left" rowspan="1" colspan="1">GO:0040007</td><td align="left" rowspan="1" colspan="1">12</td><td align="left" rowspan="1" colspan="1">Met(5)+Pest(3)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cell cycle</td><td align="left" rowspan="1" colspan="1">GO:0007049</td><td align="left" rowspan="1" colspan="1">12</td><td align="left" rowspan="1" colspan="1">Met(6)+Pest(4)+Norm(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Response to stress</td><td align="left" rowspan="1" colspan="1">GO:0006950</td><td align="left" rowspan="1" colspan="1">13</td><td align="left" rowspan="1" colspan="1">Met(8)+Pest(1)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Embryonic development</td><td align="left" rowspan="1" colspan="1">GO:0009790</td><td align="left" rowspan="1" colspan="1">14</td><td align="left" rowspan="1" colspan="1">Met(7)+Pest(3)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Catabolic process</td><td align="left" rowspan="1" colspan="1">GO:0009056</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">Met(11)+Pest(5)+Norm(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cytoskeleton organization</td><td align="left" rowspan="1" colspan="1">GO:0007010</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">Met(14)+Pest(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cell differentiation</td><td align="left" rowspan="1" colspan="1">GO:0030154</td><td align="left" rowspan="1" colspan="1">22</td><td align="left" rowspan="1" colspan="1">Met(17)+Pest(3)+Norm(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Generation of precursor metabolites and energy</td><td align="left" rowspan="1" colspan="1">GO:0006091</td><td align="left" rowspan="1" colspan="1">23</td><td align="left" rowspan="1" colspan="1">Met(13)+Pest(6)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Anatomical structure morphogenesis</td><td align="left" rowspan="1" colspan="1">GO:0009653</td><td align="left" rowspan="1" colspan="1">28</td><td align="left" rowspan="1" colspan="1">Met(21)+Pest(4)+Norm(3)</td></tr><tr><td align="left" rowspan="1" colspan="1">Translation</td><td align="left" rowspan="1" colspan="1">GO:0006412</td><td align="left" rowspan="1" colspan="1">35</td><td align="left" rowspan="1" colspan="1">Met(20)+Pest(7)+Norm(8)</td></tr><tr><td align="left" rowspan="1" colspan="1">Transcription</td><td align="left" rowspan="1" colspan="1">GO:0006350</td><td align="left" rowspan="1" colspan="1">72</td><td align="left" rowspan="1" colspan="1">Met(5)+Pest(65)+Norm(2)</td></tr><tr><td colspan="4" align="left" rowspan="1"><bold>Molecular function</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Kinase activity</td><td align="left" rowspan="1" colspan="1">GO:0016301</td><td align="left" rowspan="1" colspan="1">5</td><td align="left" rowspan="1" colspan="1">Met(2)+Pest(2)+Norm(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Translation factor activity, nucleic acid binding</td><td align="left" rowspan="1" colspan="1">GO:0008135</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">Met(3)+Pest(3)+Norm(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Electron carrier activity</td><td align="left" rowspan="1" colspan="1">GO:0009055</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">Met(4)+Pest(1)+Norm(3)</td></tr><tr><td align="left" rowspan="1" colspan="1">Transcription regulator activity</td><td align="left" rowspan="1" colspan="1">GO:0030528</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">Met(5)+Pest(2)+Norm(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Actin binding</td><td align="left" rowspan="1" colspan="1">GO:0003779</td><td align="left" rowspan="1" colspan="1">12</td><td align="left" rowspan="1" colspan="1">Met(4)+Pest(8)</td></tr><tr><td align="left" rowspan="1" colspan="1">Peptidase activity</td><td align="left" rowspan="1" colspan="1">GO:0008233</td><td align="left" rowspan="1" colspan="1">13</td><td align="left" rowspan="1" colspan="1">Met(7)+Pest(4)+Norm(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Transporter activity</td><td align="left" rowspan="1" colspan="1">GO:0005215</td><td align="left" rowspan="1" colspan="1">16</td><td align="left" rowspan="1" colspan="1">Met(12)+Pest(1)+Norm(3)</td></tr><tr><td align="left" rowspan="1" colspan="1">Calcium ion binding</td><td align="left" rowspan="1" colspan="1">GO:0005509</td><td align="left" rowspan="1" colspan="1">17</td><td align="left" rowspan="1" colspan="1">Met(9)+Pest(6)+Norm(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Motor activity</td><td align="left" rowspan="1" colspan="1">GO:0003774</td><td align="left" rowspan="1" colspan="1">18</td><td align="left" rowspan="1" colspan="1">Met(4)+Pest(13)+Norm(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">RNA binding</td><td align="left" rowspan="1" colspan="1">GO:0003723</td><td align="left" rowspan="1" colspan="1">21</td><td align="left" rowspan="1" colspan="1">Met(10)+Pest(3)+Norm(8)</td></tr><tr><td align="left" rowspan="1" colspan="1">Structural molecule activity</td><td align="left" rowspan="1" colspan="1">GO:0005198</td><td align="left" rowspan="1" colspan="1">45</td><td align="left" rowspan="1" colspan="1">Met(31)+Pest(5)+Norm(9)</td></tr><tr><td align="left" rowspan="1" colspan="1">DNA binding</td><td align="left" rowspan="1" colspan="1">GO:0003677</td><td align="left" rowspan="1" colspan="1">68</td><td align="left" rowspan="1" colspan="1">Met(3)+Pest(64)+Norm(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Nucleotide binding</td><td align="left" rowspan="1" colspan="1">GO:0000166</td><td align="left" rowspan="1" colspan="1">82</td><td align="left" rowspan="1" colspan="1">Met(51)+Pest(28)+Norm(3)</td></tr><tr><td colspan="4" align="left" rowspan="1"><bold>Cellular Component</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Nucleolus</td><td align="left" rowspan="1" colspan="1">GO:0005730</td><td align="left" rowspan="1" colspan="1">7</td><td align="left" rowspan="1" colspan="1">Met(4)+Norm(3)</td></tr><tr><td align="left" rowspan="1" colspan="1">Nucleoplasm</td><td align="left" rowspan="1" colspan="1">GO:0005654</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">Met(4)+Pest(2)+Norm(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Extracellular region</td><td align="left" rowspan="1" colspan="1">GO:0005576</td><td align="left" rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">Met(7)+Norm(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Plasma membrane</td><td align="left" rowspan="1" colspan="1">GO:0005886</td><td align="left" rowspan="1" colspan="1">10</td><td align="left" rowspan="1" colspan="1">Met(6)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Mitochondrion</td><td align="left" rowspan="1" colspan="1">GO:0005739</td><td align="left" rowspan="1" colspan="1">26</td><td align="left" rowspan="1" colspan="1">Met(15)+Pest(4)+Norm(7)</td></tr><tr><td align="left" rowspan="1" colspan="1">Ribosome</td><td align="left" rowspan="1" colspan="1">GO:0005840</td><td align="left" rowspan="1" colspan="1">29</td><td align="left" rowspan="1" colspan="1">Met(18)+Pest(3)+Norm(8)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cytosol</td><td align="left" rowspan="1" colspan="1">GO:0005829</td><td align="left" rowspan="1" colspan="1">34</td><td align="left" rowspan="1" colspan="1">Met(21)+Pest(6)+Norm(7)</td></tr><tr><td align="left" rowspan="1" colspan="1">Protein complex</td><td align="left" rowspan="1" colspan="1">GO:0043234</td><td align="left" rowspan="1" colspan="1">64</td><td align="left" rowspan="1" colspan="1">Met(35)+Pest(22)+Norm(7)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cytoskeleton</td><td align="left" rowspan="1" colspan="1">GO:0005856</td><td align="left" rowspan="1" colspan="1">82</td><td align="left" rowspan="1" colspan="1">Met(50)+Pest(29)+Norm(3)</td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt104"><label></label><p>Met: sequences from the SSH library enriched for genes differentially expressed after metal exposures; Pest: sequences from the SSH library enriched for genes differentially expressed after pesticide exposures; Norm: sequences from the normalized library.</p></fn></table-wrap-foot></table-wrap><p>Transcription and translation are the most represented biological processes in the developed libraries. As for the molecular functions and cellular components, the most represented are the nucleotide and DNA binding and the cytoskeleton and protein complex, respectively. Most of the biological functions have a higher representation in the metals enriched library, with the exception of reproduction which is not represented in this library and transcription which is mainly represented in the pesticides enriched library.</p><p>Molecular pathways were assigned to the clusters using the Kyoto Encyclopedia of Genes and Genomes (KEGG) <xref ref-type="bibr" rid="pone.0034266-Ogata1">[36]</xref> based on their Enzyme Commission numbers (EC). In total, 61 sequences matched enzymes with an EC number (5.5% of the 1124 clusters), belonging to 25 different pathways, all related to metabolism. The metabolisms with more pathways where the enzymes coded for <italic>E. albidus</italic> sequences are involved were the energy and amino acid metabolisms, followed by e.g. the metabolisms of carbohydrates and nucleotides (<xref ref-type="table" rid="pone-0034266-t004">Table 4</xref>).</p><table-wrap id="pone-0034266-t004" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0034266.t004</object-id><label>Table 4</label><caption><title>KEGG pathways in the combined library datasets for <italic>E. albidus</italic>.</title></caption><alternatives><graphic id="pone-0034266-t004-4" xlink:href="pone.0034266.t004"></graphic><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="center" span="1"></col><col align="center" span="1"></col></colgroup><thead><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">No. Sequences</td><td align="left" rowspan="1" colspan="1">Library (no. sequences)</td></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1"><bold>Amino acid metabolism</bold></td><td align="left" rowspan="1" colspan="1"><bold>12</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Alanine, aspartate and glutamate metabolism</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Arginine and proline metabolism</td><td align="left" rowspan="1" colspan="1">2</td><td align="left" rowspan="1" colspan="1">Met(1)+Pest(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Cysteine and methionine metabolism</td><td align="left" rowspan="1" colspan="1">2</td><td align="left" rowspan="1" colspan="1">Met(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Glycine, serine and threonine metabolism</td><td align="left" rowspan="1" colspan="1">3</td><td align="left" rowspan="1" colspan="1">Met(2)+Pest(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Phenylalanine metabolism</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Phenylalanine, tyrosine and tryptophan biosynthesis</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Tyrosine metabolism</td><td align="left" rowspan="1" colspan="1">2</td><td align="left" rowspan="1" colspan="1">Met(2)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Energy metabolism</bold></td><td align="left" rowspan="1" colspan="1"><bold>25</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Carbon fixation in photosynthetic organisms</td><td align="left" rowspan="1" colspan="1">3</td><td align="left" rowspan="1" colspan="1">Met(2)+Pest(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Methane metabolism</td><td align="left" rowspan="1" colspan="1">4</td><td align="left" rowspan="1" colspan="1">Met(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Oxidative phosphorylation</td><td align="left" rowspan="1" colspan="1">14</td><td align="left" rowspan="1" colspan="1">Met(9)+Pest(1)+Norm(4)</td></tr><tr><td align="left" rowspan="1" colspan="1">Photosynthesis</td><td align="left" rowspan="1" colspan="1">4</td><td align="left" rowspan="1" colspan="1">Met(4)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Carbohydrate metabolism</bold></td><td align="left" rowspan="1" colspan="1"><bold>3</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Glycolysis/Gluconeogenesis</td><td align="left" rowspan="1" colspan="1">2</td><td align="left" rowspan="1" colspan="1">Pest(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Pentose phosphate pathway</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Nucleotide metabolism</bold></td><td align="left" rowspan="1" colspan="1"><bold>3</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Purine metabolism</td><td align="left" rowspan="1" colspan="1">2</td><td align="left" rowspan="1" colspan="1">Met(2)</td></tr><tr><td align="left" rowspan="1" colspan="1">Pyrimidine metabolism</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Lipid metabolism</bold></td><td align="left" rowspan="1" colspan="1"><bold>1</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Glycerophospholipid metabolism</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Glycan Biosynthesis and Metabolism</bold></td><td align="left" rowspan="1" colspan="1"><bold>2</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">N-Glycan biosynthesis</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Various types of N-glycan biosynthesis</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Metabolism of Cofactors and Vitamins</bold></td><td align="left" rowspan="1" colspan="1"><bold>2</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Nicotinate and nicotinamide metabolism</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Thiamine metabolism</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Biosynthesis of Other Secondary Metabolites</bold></td><td align="left" rowspan="1" colspan="1"><bold>3</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Isoquinoline alkaloid biosynthesis</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Novobiocin biosynthesis</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1">Tropane, piperidine and pyridine alkaloid biosynthesis</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Metabolism of Terpenoids and Polyketides</bold></td><td align="left" rowspan="1" colspan="1"><bold>1</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Biosynthesis of ansamycins</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Xenobiotics Biodegradation and Metabolism</bold></td><td align="left" rowspan="1" colspan="1"><bold>1</bold></td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Styrene degradation</td><td align="left" rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">Met(1)</td></tr></tbody></table></alternatives><table-wrap-foot><fn id="nt105"><label></label><p>Met: sequences from the SSH library enriched for genes differentially expressed after metal exposures; Pest: sequences from the SSH library enriched for genes differentially expressed after pesticide exposures; Norm: sequences from the normalized library.</p></fn></table-wrap-foot></table-wrap></sec><sec id="s2d"><title>Development of the Database</title><p>EnchyBASE development required the integration of diverse bioinformatics software. Four intertwined components were needed to deploy the whole system: a web application server, a database management system and a local BLAST tool.</p><p>The ESTs and associated annotation information led to deployment of EnchyBASE in an Apache Web Server with PostgreSQL for the database backend. PartiGene <xref ref-type="bibr" rid="pone.0034266-Parkinson1">[37]</xref>, the chosen gene sequence-clustering tool, is the key responsible for the adopted solutions. Its web component, wwwPartiGene, requires serving dynamic PHP pages and a connection to a PostgreSQL database. ViroBLAST <xref ref-type="bibr" rid="pone.0034266-Deng1">[38]</xref> was selected as a local BLAST tool as it provides an eased setup process for executing various distinct BLASTs against local sequence clusters.</p><p>Whereas the miscellaneous system components were relatively easy to adapt or implement, the constructed integration pipeline was a more complex task. EnchyBASE deployment workflow involved three key steps: 1) sequence annotation, 2) sequence clustering and annotation of clusters, and 3) BLAST database migration.</p><p>The first step involved the annotation of the obtained ESTs for <italic>E. albidus</italic> using the BLAST2GO bioinformatics tool <xref ref-type="bibr" rid="pone.0034266-Conesa1">[34]</xref>. Sequences were clustered using Cap3 program and the retrieved clusters were also annotated. Generated data was then moved on to PartiGene. At last, annotated sequences were used to generate a BLASTable database using NCBI BLAST toolkit <xref ref-type="bibr" rid="pone.0034266-Gertz1">[39]</xref>. The resulting dataset was made available to ViroBLAST for real time BLAST against <italic>E. albidus</italic> sequence data.</p><p>For end-users, the system provides three main key features: sequence download, annotation search and BLAST. Researchers are able to download the entire sequence dataset or specific clustered sequences. The search engine allows browsing data through multiple queries. Users can search for specific clusters or sequences, common BLAST annotations, ontology annotations and primer features. At last, BLASTing can be performed against <italic>E. albidus</italic> data. Available BLAST functions are blastn, blastx, tblastn and tblastx. BLAST parameters may be easily configured in EnchyBASE’s BLAST interface.</p></sec></sec><sec sec-type="discussion" id="s3"><title>Discussion</title><p>Various advantages may be pointed in regard to the genomic information gathered in the course of this study. With the development of EnchyBASE users can access the sequences present in each of the libraries, as well as all the information related to each sequence (BLAST homologies and GO terms) when available. This information can be downloaded after simple search queries by Cluster ID, GenBank accession number, BLAST annotation or GO term. Furthermore, designed primers with tested efficiencies for some of the sequences are provided. The database also enables the users to run blasts with their own sequences and look for homologies with the enchytraeids species.</p><p>In the present work it was possible to observe that only one gene was shared by the three libraries and, in general the gene overlap between libraries was low (<xref ref-type="fig" rid="pone-0034266-g003">Figure 3</xref>). Also, the exposure to the two different groups of chemicals (metals and pesticides) affected distinct biological functions e.g. reproduction or lipid metabolic processes were only affected by pesticides or metals, respectively (<xref ref-type="table" rid="pone-0034266-t003">Table 3</xref>).</p><p>These findings suggest that the exposure to pesticides triggered a different set of genes in comparison to metals exposure. However, the actual expression profiles of <italic>E. albidus</italic>, when exposed to the individual chemicals or natural stressors, require confirmation through experiments of gene expression analysis.</p><p>A new custom Agilent microarray was developed, with printed 60-mer oligonucleotides designed from the unique sequences in the database. All transcription data generated with this microarray will be stored in enchyBASE similarly to what is presently done with the data gathered with the former cDNA microarray, where information on the differentially expressed genes to each stress condition is available. This information, along with the respective differentially expressed gene sequences, can be used by the scientific community in functional genomics studies and quantitative polymerase chain reaction (qPCR) experiments.</p><p>In the near future and through hybridizations on this microarray we expect to increase knowledge on the molecular pathways involved in response to stress factors. This information can improve the current understanding of chemicals mode of action on soil invertebrates, which along with data on other organisms can help to develop predictive models of toxic effects. Additionally, generating specific stress signature fingerprints would be of particular interest to classify different types of stressors, levels of toxicity, or chemical groups.</p><p>The obtained sequence information can be potentially used to answer questions regarding chemical exposure as e.g. adaptation to chemical stress. It is commonly agreed that soil invertebrates can genetically adapt to metal stress, modifying metal toxicity and gaining resistance to contaminated soils <xref ref-type="bibr" rid="pone.0034266-Holmstrup1">[40]</xref>–<xref ref-type="bibr" rid="pone.0034266-Timmermans2">[42]</xref>. Some known mechanisms of detoxification like the storage of metal ions in membrane enclosed cellular granules or in metallothionein complexes <xref ref-type="bibr" rid="pone.0034266-Holmstrup1">[40]</xref>–<xref ref-type="bibr" rid="pone.0034266-Vijver1">[44]</xref> have been associated with changes in the energy metabolism due to the need of energy for these detoxification processes <xref ref-type="bibr" rid="pone.0034266-Holmstrup1">[40]</xref>. The use of transcriptomics to determine differential gene expression in metal tolerant populations has recently been successfully applied in the soil arthropod <italic>Orchesella cincta</italic><xref ref-type="bibr" rid="pone.0034266-Roelofs1">[45]</xref>, <xref ref-type="bibr" rid="pone.0034266-Roelofs2">[46]</xref>. Roelofs and co-authors observed different gene expression patterns between reference and tolerant populations of this species after cadmium exposure, confirming the micro-evolutionary processes occurring in this soil species’ populations <xref ref-type="bibr" rid="pone.0034266-Roelofs2">[46]</xref>. Mechanisms of genetic adaptation to metal stress in enchytraeids has never been observed but can now be investigated with similar transcription studies using enchyBASE and the newly developed microarray.</p><p>These new tools can also be potentially used to answer other ecological questions, e.g. drought tolerance. Maraldo and co-authors <xref ref-type="bibr" rid="pone.0034266-Maraldo1">[47]</xref>, <xref ref-type="bibr" rid="pone.0034266-Maraldo2">[48]</xref> found that <italic>E. albidus</italic> is able to adapt to environments with strong fluctuations in humidity, being able to keep its water content stable during moist and relatively dry conditions. It is known that <italic>E. albidus</italic> can even tolerate incredibly low temperature (−20°C), probably related to the ability to synthesise high concentrations of glucose <xref ref-type="bibr" rid="pone.0034266-Slotsbo1">[49]</xref>. Mechanisms of tolerance to drought, freeze and other environmental conditions, have been currently studied for the springtails <italic>Onychiurus arcticus</italic><xref ref-type="bibr" rid="pone.0034266-Clark1">[50]</xref> and <italic>Folsomia candida</italic><xref ref-type="bibr" rid="pone.0034266-Timmermans3">[51]</xref>–<xref ref-type="bibr" rid="pone.0034266-deBoer2">[53]</xref>. Such can also be further studied in enchytraeids using transcriptomic tools.</p><p>Also, <italic>E. albidus</italic> is known to be able to avoid unfavourable conditions such as natural stressors like inadequate soil properties (e.g. pH, clay content) <xref ref-type="bibr" rid="pone.0034266-Amorim3">[12]</xref> or chemicals <xref ref-type="bibr" rid="pone.0034266-Amorim2">[8]</xref>, <xref ref-type="bibr" rid="pone.0034266-Amorim5">[54]</xref>. Interestingly, not all chemicals are equally avoided, and some are even not perceived despite their high toxicity. The underlying mechanisms of these differences can also be pursued with transcription studies. Among other potential utility of EnchyBASE is the study of the mechanisms behind chemical mixtures toxicity or combinations of environmental stressors, relevant issues in soil ecotoxicology.</p><p>The microarray and EnchyBASE provide the scientific community information with potentially multiple applications, constituting a stepping stone for ecotoxicology, genomics and molecular ecological studies with enchytraeids.</p></sec></body><back><ack><p>The authors would like to thank Bieke Van Dorst for the practical help provided during the cDNA cloning processes.</p></ack><fn-group><fn fn-type="conflict"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type="financial-disclosure"><p><bold>Funding: </bold>This study was sponsored by Fundação para a Ciência e Tecnologia (Portugal), through a PhD grant to Sara Novais (SFRH/BD/36253/2007) and through the project references PTDC/BIA-BCM/64745/2006 (REDE)and PTDC/BIA-BEC/103716/2008 (NANOkA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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