Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Genetic-metabolic coupling for targeted metabolic engineering

View ORCID ProfileStefano Cardinale, View ORCID ProfileFelipe Gonzalo Tueros, View ORCID ProfileMorten Otto Alexander Sommer
doi: https://doi.org/10.1101/156927
Stefano Cardinale
1NNF-CFB, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
2Lead Contact
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Stefano Cardinale
  • For correspondence: stefca@biosustain.dtu.dk msom@bio.dtu.dk
Felipe Gonzalo Tueros
1NNF-CFB, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Felipe Gonzalo Tueros
Morten Otto Alexander Sommer
1NNF-CFB, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Morten Otto Alexander Sommer
  • For correspondence: stefca@biosustain.dtu.dk msom@bio.dtu.dk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

SUMMARY

To produce chemicals, microbes typically employ potent biosynthetic enzymes that interact with native metabolism to affect cell fitness as well as chemical production. However, production optimization largely relies on data collected from wild type strains in the absence of metabolic perturbations, thus limiting their relevance to specific process scenarios. Here, we address this issue by coupling cell fitness to the production of thiamine diphosphate in Escherichia coli using a synthetic RNA biosensor.

We apply this system to interrogate a library of transposon mutants to elucidate the native gene network influencing both cell fitness and thiamine production. Specifically, we identify uncharacterized effectors of the OxyR-SoxR stress response that limit thiamine biosynthesis via alternative regulation of iron storage and Fe-S-cluster inclusion in enzymes.

Our study represents a new generalizable approach for the reliable high-throughput identification of genetic targets of both known and unknown function that are directly relevant to a specific biosynthetic process.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted July 02, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Genetic-metabolic coupling for targeted metabolic engineering
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Genetic-metabolic coupling for targeted metabolic engineering
Stefano Cardinale, Felipe Gonzalo Tueros, Morten Otto Alexander Sommer
bioRxiv 156927; doi: https://doi.org/10.1101/156927
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Genetic-metabolic coupling for targeted metabolic engineering
Stefano Cardinale, Felipe Gonzalo Tueros, Morten Otto Alexander Sommer
bioRxiv 156927; doi: https://doi.org/10.1101/156927

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioengineering
Subject Areas
All Articles
  • Animal Behavior and Cognition (4838)
  • Biochemistry (10729)
  • Bioengineering (8006)
  • Bioinformatics (27169)
  • Biophysics (13930)
  • Cancer Biology (11080)
  • Cell Biology (15984)
  • Clinical Trials (138)
  • Developmental Biology (8757)
  • Ecology (13228)
  • Epidemiology (2067)
  • Evolutionary Biology (17308)
  • Genetics (11663)
  • Genomics (15879)
  • Immunology (10986)
  • Microbiology (25979)
  • Molecular Biology (10600)
  • Neuroscience (56318)
  • Paleontology (417)
  • Pathology (1727)
  • Pharmacology and Toxicology (2998)
  • Physiology (4528)
  • Plant Biology (9583)
  • Scientific Communication and Education (1610)
  • Synthetic Biology (2668)
  • Systems Biology (6956)
  • Zoology (1507)