Evidence map›Paper›PMID 29463788›Full record

ArticleNature communications2018

Diverse genetic error modes constrain large-scale bio-based production.

Peter Rugbjerg, Nils Myling-Petersen, Andreas Porse, Kira Sarup-Lytzen, Morten O A Sommer

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed, 1 pooled it
15.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

85 citing papers in PubMed, 1 synthesis or guideline pooled it, 181 citations in OpenAlex.

  1. A Guideline to Set Up Cascaded Continuous Cultivation with E. coli Bl21 (DE3).Methods in molecular biology (Clifton, N.J.) · 2022
    Guideline
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Microbial Production of High-Performance Fibers from Muscle Protein Titin.Methods in molecular biology (Clifton, N.J.) · 2025
    Article

25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Peter RugbjergThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, DK-2800, Kongens Lyngby, Denmark.ORCID http://orcid.org/0000-0003-2561-5063
Nils Myling-PetersenThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, DK-2800, Kongens Lyngby, Denmark.
Andreas PorseThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, DK-2800, Kongens Lyngby, Denmark.
Kira Sarup-LytzenThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, DK-2800, Kongens Lyngby, Denmark.
Morten O A SommerThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, DK-2800, Kongens Lyngby, Denmark. msom@bio.dtu.dk.ORCID http://orcid.org/0000-0003-4005-5674
Novo Nordisk Foundation · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A transition toward sustainable bio-based chemical production is important for green growth. However, productivity and yield frequently decrease as large-scale microbial fermentation progresses, commonly ascribed to phenotypic variation. Yet, given the high metabolic burden and toxicities, evolutionary processes may also constrain bio-based production. We experimentally simulate large-scale fermentation with mevalonic acid-producing Escherichia coli. By tracking growth rate and production, we uncover how populations fully sacrifice production to gain fitness within 70 generations. Using ultra-deep (>1000×) time-lapse sequencing of the pathway populations, we identify multiple recurring intra-pathway genetic error modes. This genetic heterogeneity is only detected using deep-sequencing and new population-level bioinformatics, suggesting that the problem is underestimated. A quantitative model explains the population dynamics based on enrichment of spontaneous mutant cells. We validate our model by tuning production load and escape rate of the production host and apply multiple orthogonal strategies for postponing genetically driven production declines.

Indexed as

Escherichia coliFermentationGenetic VariationMetabolic EngineeringMevalonic AcidModels, GeneticMevalonic Acid

Identifiers

PMID29463788
PMCPMC5820350
OpenAlexW2788004430

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.