Evidence map›Paper›PMID 32811554›Full record

ArticleMicrobial cell factories2020

Investigation of Bar-seq as a method to study population dynamics of Saccharomyces cerevisiae deletion library during bioreactor cultivation.

Maren Wehrs, Mitchell G Thompson, Deepanwita Banerjee, Jan-Philip Prahl, Norma M Morella, Carolina A Barcelos, Jadie Moon, Zak Costello, Jay D Keasling, Patrick M Shih and 2 more

Open access · goldAbstract read
In one paragraph

Article in Microbial cell factories, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Advances inJournal of fungi (Basel, Switzerland) · 2023
    Review
  2. Article
  3. Review
  4. Microbial production of advanced biofuels.Nature reviews. Microbiology · 2021
    Review
  5. Omics-Driven Biotechnology for Industrial Applications.Frontiers in bioengineering and biotechnology · 2021
    Review
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

12 authors at 2 institutions in 3 countries.

Maren WehrsBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Mitchell G ThompsonBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Deepanwita BanerjeeBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Jan-Philip PrahlBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Norma M MorellaFred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
Carolina A BarcelosBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Jadie MoonBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Zak CostelloBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Jay D KeaslingBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Patrick M ShihJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.
Deepti TanjoreBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. dtanjore@lbl.gov.
Aindrila MukhopadhyayBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. amukhopadhyay@lbl.gov.ORCID http://orcid.org/0000-0002-6513-7425
Lawrence Berkeley National Laboratory · USFred Hutch Cancer Center · US

Funding

U.S. Department of Energy DE-AC02-05CH11231
6 · The paper itself

Abstract

backgroundDespite the latest advancements in metabolic engineering for genome editing and characterization of host performance, the successful development of robust cell factories used for industrial bioprocesses and accurate prediction of the behavior of microbial systems, especially when shifting from laboratory-scale to industrial conditions, remains challenging. To increase the probability of success of a scale-up process, data obtained from thoroughly performed studies mirroring cellular responses to typical large-scale stimuli may be used to derive crucial information to better understand potential implications of large-scale cultivation on strain performance. This study assesses the feasibility to employ a barcoded yeast deletion library to assess genome-wide strain fitness across a simulated industrial fermentation regime and aims to understand the genetic basis of changes in strain physiology during industrial fermentation, and the corresponding roles these genes play in strain performance.

resultsWe find that mutant population diversity is maintained through multiple seed trains, enabling large scale fermentation selective pressures to act upon the community. We identify specific deletion mutants that were enriched in all processes tested in this study, independent of the cultivation conditions, which include MCK1, RIM11, MRK1, and YGK3 that all encode homologues of mammalian glycogen synthase kinase 3 (GSK-3). Ecological analysis of beta diversity between all samples revealed significant population divergence over time and showed feed specific consequences of population structure. Further, we show that significant changes in the population diversity during fed-batch cultivations reflect the presence of significant stresses. Our observations indicate that, for this yeast deletion collection, the selection of the feeding scheme which affects the accumulation of the fermentative by-product ethanol impacts the diversity of the mutant pool to a higher degree as compared to the pH of the culture broth. The mutants that were lost during the time of most extreme population selection suggest that specific biological processes may be required to cope with these specific stresses.

conclusionsOur results demonstrate the feasibility of Bar-seq to assess fermentation associated stresses in yeast populations under industrial conditions and to understand critical stages of a scale-up process where variability emerges, and selection pressure gets imposed. Overall our work highlights a promising avenue to identify genetic loci and biological stress responses required for fitness under industrial conditions.

Indexed as

FermentationBiodiversityBioreactorsBiotechnologyGene DeletionGenes, FungalGlycogen Synthase Kinase 3Industrial MicrobiologyMetabolic EngineeringSaccharomyces cerevisiaeStress, PhysiologicalGlycogen Synthase Kinase 3

Identifiers

PMID32811554
PMCPMC7437010
OpenAlexW3073848486

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.