A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Lotte Witjes, Rik Kooke, Justin J. J. van der Hooft, Ric C. H. de Vos, Joost J. B. Keurentjes, Marnix H. Medema, Harm Nijveen

ABSTRACT

OBJECTIVE: Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed. RESULTS: Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs. More... »

PAGES

194

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13104-019-4222-3

DOI

http://dx.doi.org/10.1186/s13104-019-4222-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113179537

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30940198


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Wageningen University & Research", 
          "id": "https://www.grid.ac/institutes/grid.4818.5", 
          "name": [
            "Bioinformatics Group, Wageningen University & Research, Wageningen, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Witjes", 
        "givenName": "Lotte", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for BioSystems Genomics", 
          "id": "https://www.grid.ac/institutes/grid.450019.9", 
          "name": [
            "Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands", 
            "Centre for BioSystems Genomics, Wageningen, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kooke", 
        "givenName": "Rik", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Wageningen University & Research", 
          "id": "https://www.grid.ac/institutes/grid.4818.5", 
          "name": [
            "Bioinformatics Group, Wageningen University & Research, Wageningen, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "van der Hooft", 
        "givenName": "Justin J. J.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Netherlands Metabolomics Centre", 
          "id": "https://www.grid.ac/institutes/grid.450196.f", 
          "name": [
            "Business Unit Bioscience, Wageningen University & Research, Wageningen, The Netherlands", 
            "Centre for BioSystems Genomics, Wageningen, The Netherlands", 
            "Netherlands Metabolomics Centre, Utrecht, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "de Vos", 
        "givenName": "Ric C. H.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for BioSystems Genomics", 
          "id": "https://www.grid.ac/institutes/grid.450019.9", 
          "name": [
            "Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands", 
            "Centre for BioSystems Genomics, Wageningen, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Keurentjes", 
        "givenName": "Joost J. B.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Wageningen University & Research", 
          "id": "https://www.grid.ac/institutes/grid.4818.5", 
          "name": [
            "Bioinformatics Group, Wageningen University & Research, Wageningen, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Medema", 
        "givenName": "Marnix H.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Wageningen University & Research", 
          "id": "https://www.grid.ac/institutes/grid.4818.5", 
          "name": [
            "Bioinformatics Group, Wageningen University & Research, Wageningen, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nijveen", 
        "givenName": "Harm", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1534/genetics.114.168690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003361484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.114.168690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003361484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-011-0363-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007821767", 
          "https://doi.org/10.1007/s11306-011-0363-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1608041113", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010287214"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1608041113", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010287214"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/tpj.12681", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011138984"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1419547112", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017441468"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1109273108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020389392"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1105/tpc.112.100057", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023331348"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c6np00035e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026924832"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fpls.2016.01012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034194568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.107.080101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035921397"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.107.080101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035921397"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bts444", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036147444"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.pbi.2015.01.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038257122"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/nph.13981", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040662590"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-011-0368-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042363301", 
          "https://doi.org/10.1007/s11306-011-0368-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1319681110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042918621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.plaphy.2013.02.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043736686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.plaphy.2013.02.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043736686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.plaphy.2013.02.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043736686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.plaphy.2013.02.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043736686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.plaphy.2013.02.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043736686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/tpj.12827", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043805645"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt.3597", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045197127", 
          "https://doi.org/10.1038/nbt.3597"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1105/tpc.106.049478", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046677773"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jxb/erw441", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059858808"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1154990", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062457534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1104/pp.16.01942", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083859075"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkx305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084823623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1105/tpc.17.00009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084824156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jgg.2017.05.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085164338"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkx404", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085223183"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11101-017-9524-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090954138", 
          "https://doi.org/10.1007/s11101-017-9524-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11101-017-9524-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090954138", 
          "https://doi.org/10.1007/s11101-017-9524-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molp.2017.08.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091420841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molp.2017.08.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091420841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41557-018-0013-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101674146", 
          "https://doi.org/10.1038/s41557-018-0013-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41557-018-0013-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101674146", 
          "https://doi.org/10.1038/s41557-018-0013-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41557-018-0013-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101674146", 
          "https://doi.org/10.1038/s41557-018-0013-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41557-018-0013-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101674146", 
          "https://doi.org/10.1038/s41557-018-0013-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41557-018-0013-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101674146", 
          "https://doi.org/10.1038/s41557-018-0013-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/323014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104013903"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/323014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104013903"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/323014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104013903"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00299-018-2296-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104130885", 
          "https://doi.org/10.1007/s00299-018-2296-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00299-018-2296-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104130885", 
          "https://doi.org/10.1007/s00299-018-2296-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c8np00028j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104544535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c8np00028j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104544535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c8np00028j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104544535"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "OBJECTIVE: Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed.\nRESULTS: Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s13104-019-4222-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7573588", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1039457", 
        "issn": [
          "1756-0500"
        ], 
        "name": "BMC Research Notes", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "12"
      }
    ], 
    "name": "A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters", 
    "pagination": "194", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13104-019-4222-3"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1113179537"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "54db967832de277b4482a2c4078956c72796a03d3867e232f129df166bd9696c"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101462768"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30940198"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13104-019-4222-3", 
      "https://app.dimensions.ai/details/publication/pub.1113179537"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-16T06:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000377_0000000377/records_106840_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs13104-019-4222-3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13104-019-4222-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13104-019-4222-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13104-019-4222-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13104-019-4222-3'


 

This table displays all metadata directly associated to this object as RDF triples.

216 TRIPLES      21 PREDICATES      61 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13104-019-4222-3 schema:about anzsrc-for:06
2 anzsrc-for:0604
3 schema:author N733229c6da1f4dc2adcbaf0896a38823
4 schema:citation sg:pub.10.1007/s00299-018-2296-3
5 sg:pub.10.1007/s11101-017-9524-2
6 sg:pub.10.1007/s11306-011-0363-7
7 sg:pub.10.1007/s11306-011-0368-2
8 sg:pub.10.1038/nbt.3597
9 sg:pub.10.1038/s41557-018-0013-z
10 https://doi.org/10.1016/j.jgg.2017.05.003
11 https://doi.org/10.1016/j.molp.2017.08.012
12 https://doi.org/10.1016/j.pbi.2015.01.006
13 https://doi.org/10.1016/j.plaphy.2013.02.001
14 https://doi.org/10.1039/c6np00035e
15 https://doi.org/10.1039/c8np00028j
16 https://doi.org/10.1073/pnas.1109273108
17 https://doi.org/10.1073/pnas.1319681110
18 https://doi.org/10.1073/pnas.1419547112
19 https://doi.org/10.1073/pnas.1608041113
20 https://doi.org/10.1093/bioinformatics/bts444
21 https://doi.org/10.1093/jxb/erw441
22 https://doi.org/10.1093/nar/gkx305
23 https://doi.org/10.1093/nar/gkx404
24 https://doi.org/10.1101/323014
25 https://doi.org/10.1104/pp.16.01942
26 https://doi.org/10.1105/tpc.106.049478
27 https://doi.org/10.1105/tpc.112.100057
28 https://doi.org/10.1105/tpc.17.00009
29 https://doi.org/10.1111/nph.13981
30 https://doi.org/10.1111/tpj.12681
31 https://doi.org/10.1111/tpj.12827
32 https://doi.org/10.1126/science.1154990
33 https://doi.org/10.1534/genetics.107.080101
34 https://doi.org/10.1534/genetics.114.168690
35 https://doi.org/10.3389/fpls.2016.01012
36 schema:datePublished 2019-12
37 schema:datePublishedReg 2019-12-01
38 schema:description OBJECTIVE: Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed. RESULTS: Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree true
42 schema:isPartOf N5e658d9ec1c0434eac317cf2d7e94cd6
43 Nb04a8e52c98642faa357242209bb81af
44 sg:journal.1039457
45 schema:name A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters
46 schema:pagination 194
47 schema:productId N0954bc67c30b4d62a3e2bc3f5e75b818
48 N5d9783909f904dceb559244ba149e194
49 N63731f954e744072bfb741a7d3ac5cab
50 N86a407bf657e4961b8c5722644ea58e6
51 N86b969b7c19b462681e1451c7d289093
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113179537
53 https://doi.org/10.1186/s13104-019-4222-3
54 schema:sdDatePublished 2019-04-16T06:25
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher Nb71bcf0932074a0c8cbcb90658d8d23c
57 schema:url https://link.springer.com/10.1186%2Fs13104-019-4222-3
58 sgo:license sg:explorer/license/
59 sgo:sdDataset articles
60 rdf:type schema:ScholarlyArticle
61 N07de83344814478d897ff5fceffb441f schema:affiliation https://www.grid.ac/institutes/grid.4818.5
62 schema:familyName Witjes
63 schema:givenName Lotte
64 rdf:type schema:Person
65 N0954bc67c30b4d62a3e2bc3f5e75b818 schema:name pubmed_id
66 schema:value 30940198
67 rdf:type schema:PropertyValue
68 N353e2d3bdaa94a53bfeabd230d1bebd5 schema:affiliation https://www.grid.ac/institutes/grid.450019.9
69 schema:familyName Kooke
70 schema:givenName Rik
71 rdf:type schema:Person
72 N4f07bba5265a424593f20e8da651a919 schema:affiliation https://www.grid.ac/institutes/grid.4818.5
73 schema:familyName Medema
74 schema:givenName Marnix H.
75 rdf:type schema:Person
76 N58877444201240fb8ae861493e1feaaa schema:affiliation https://www.grid.ac/institutes/grid.450019.9
77 schema:familyName Keurentjes
78 schema:givenName Joost J. B.
79 rdf:type schema:Person
80 N5d9783909f904dceb559244ba149e194 schema:name readcube_id
81 schema:value 54db967832de277b4482a2c4078956c72796a03d3867e232f129df166bd9696c
82 rdf:type schema:PropertyValue
83 N5e658d9ec1c0434eac317cf2d7e94cd6 schema:issueNumber 1
84 rdf:type schema:PublicationIssue
85 N63731f954e744072bfb741a7d3ac5cab schema:name nlm_unique_id
86 schema:value 101462768
87 rdf:type schema:PropertyValue
88 N733229c6da1f4dc2adcbaf0896a38823 rdf:first N07de83344814478d897ff5fceffb441f
89 rdf:rest N940022c02348411a8d2f624823198adc
90 N86a407bf657e4961b8c5722644ea58e6 schema:name dimensions_id
91 schema:value pub.1113179537
92 rdf:type schema:PropertyValue
93 N86b969b7c19b462681e1451c7d289093 schema:name doi
94 schema:value 10.1186/s13104-019-4222-3
95 rdf:type schema:PropertyValue
96 N940022c02348411a8d2f624823198adc rdf:first N353e2d3bdaa94a53bfeabd230d1bebd5
97 rdf:rest Nddb684cf76314117967eb19295e69349
98 Nb03ff2b9c11b433091a557f4a4e6439a schema:affiliation https://www.grid.ac/institutes/grid.4818.5
99 schema:familyName van der Hooft
100 schema:givenName Justin J. J.
101 rdf:type schema:Person
102 Nb04a8e52c98642faa357242209bb81af schema:volumeNumber 12
103 rdf:type schema:PublicationVolume
104 Nb71bcf0932074a0c8cbcb90658d8d23c schema:name Springer Nature - SN SciGraph project
105 rdf:type schema:Organization
106 Nb8e862453d0c4ebb8e3aaa0c0afaf566 rdf:first Ne24452943f7d4119aa735b7a1f52c69c
107 rdf:rest rdf:nil
108 Nc6fa84beda1c48728e789b2f4831cf44 rdf:first N4f07bba5265a424593f20e8da651a919
109 rdf:rest Nb8e862453d0c4ebb8e3aaa0c0afaf566
110 Nddb684cf76314117967eb19295e69349 rdf:first Nb03ff2b9c11b433091a557f4a4e6439a
111 rdf:rest Nf1fded8c86784259b1b10134b1c3e558
112 Ndf6fb1493ec14f688c12fd4506b884df rdf:first N58877444201240fb8ae861493e1feaaa
113 rdf:rest Nc6fa84beda1c48728e789b2f4831cf44
114 Ne24452943f7d4119aa735b7a1f52c69c schema:affiliation https://www.grid.ac/institutes/grid.4818.5
115 schema:familyName Nijveen
116 schema:givenName Harm
117 rdf:type schema:Person
118 Nf1fded8c86784259b1b10134b1c3e558 rdf:first Nfd35b00477194db0a5a7de271dce3744
119 rdf:rest Ndf6fb1493ec14f688c12fd4506b884df
120 Nfd35b00477194db0a5a7de271dce3744 schema:affiliation https://www.grid.ac/institutes/grid.450196.f
121 schema:familyName de Vos
122 schema:givenName Ric C. H.
123 rdf:type schema:Person
124 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
125 schema:name Biological Sciences
126 rdf:type schema:DefinedTerm
127 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
128 schema:name Genetics
129 rdf:type schema:DefinedTerm
130 sg:grant.7573588 http://pending.schema.org/fundedItem sg:pub.10.1186/s13104-019-4222-3
131 rdf:type schema:MonetaryGrant
132 sg:journal.1039457 schema:issn 1756-0500
133 schema:name BMC Research Notes
134 rdf:type schema:Periodical
135 sg:pub.10.1007/s00299-018-2296-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104130885
136 https://doi.org/10.1007/s00299-018-2296-3
137 rdf:type schema:CreativeWork
138 sg:pub.10.1007/s11101-017-9524-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090954138
139 https://doi.org/10.1007/s11101-017-9524-2
140 rdf:type schema:CreativeWork
141 sg:pub.10.1007/s11306-011-0363-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007821767
142 https://doi.org/10.1007/s11306-011-0363-7
143 rdf:type schema:CreativeWork
144 sg:pub.10.1007/s11306-011-0368-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042363301
145 https://doi.org/10.1007/s11306-011-0368-2
146 rdf:type schema:CreativeWork
147 sg:pub.10.1038/nbt.3597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045197127
148 https://doi.org/10.1038/nbt.3597
149 rdf:type schema:CreativeWork
150 sg:pub.10.1038/s41557-018-0013-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1101674146
151 https://doi.org/10.1038/s41557-018-0013-z
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/j.jgg.2017.05.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085164338
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/j.molp.2017.08.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091420841
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.pbi.2015.01.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038257122
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.plaphy.2013.02.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043736686
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1039/c6np00035e schema:sameAs https://app.dimensions.ai/details/publication/pub.1026924832
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1039/c8np00028j schema:sameAs https://app.dimensions.ai/details/publication/pub.1104544535
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1073/pnas.1109273108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020389392
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1073/pnas.1319681110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042918621
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1073/pnas.1419547112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017441468
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1073/pnas.1608041113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010287214
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1093/bioinformatics/bts444 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036147444
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1093/jxb/erw441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059858808
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1093/nar/gkx305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084823623
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1093/nar/gkx404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085223183
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1101/323014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104013903
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1104/pp.16.01942 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083859075
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1105/tpc.106.049478 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046677773
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1105/tpc.112.100057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023331348
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1105/tpc.17.00009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084824156
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1111/nph.13981 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040662590
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1111/tpj.12681 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011138984
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1111/tpj.12827 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043805645
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1126/science.1154990 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062457534
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1534/genetics.107.080101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035921397
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1534/genetics.114.168690 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003361484
202 rdf:type schema:CreativeWork
203 https://doi.org/10.3389/fpls.2016.01012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034194568
204 rdf:type schema:CreativeWork
205 https://www.grid.ac/institutes/grid.450019.9 schema:alternateName Centre for BioSystems Genomics
206 schema:name Centre for BioSystems Genomics, Wageningen, The Netherlands
207 Laboratory of Genetics, Wageningen University & Research, Wageningen, The Netherlands
208 rdf:type schema:Organization
209 https://www.grid.ac/institutes/grid.450196.f schema:alternateName Netherlands Metabolomics Centre
210 schema:name Business Unit Bioscience, Wageningen University & Research, Wageningen, The Netherlands
211 Centre for BioSystems Genomics, Wageningen, The Netherlands
212 Netherlands Metabolomics Centre, Utrecht, The Netherlands
213 rdf:type schema:Organization
214 https://www.grid.ac/institutes/grid.4818.5 schema:alternateName Wageningen University & Research
215 schema:name Bioinformatics Group, Wageningen University & Research, Wageningen, The Netherlands
216 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...