IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v5y2014i1d10.1038_ncomms6792.html
   My bibliography  Save this article

Prediction of antibiotic resistance by gene expression profiles

Author

Listed:
  • Shingo Suzuki

    (Quantitative Biology Center (QBiC), RIKEN)

  • Takaaki Horinouchi

    (Quantitative Biology Center (QBiC), RIKEN)

  • Chikara Furusawa

    (Quantitative Biology Center (QBiC), RIKEN)

Abstract

Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype–genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype–genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances.

Suggested Citation

  • Shingo Suzuki & Takaaki Horinouchi & Chikara Furusawa, 2014. "Prediction of antibiotic resistance by gene expression profiles," Nature Communications, Nature, vol. 5(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6792
    DOI: 10.1038/ncomms6792
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms6792
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/ncomms6792?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6792. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.