Evidence map›Paper›PMID 25435910›Full record

ArticleBiotechnology for biofuels2014

Leveraging transcription factors to speed cellobiose fermentation by Saccharomyces cerevisiae.

Yuping Lin, Kulika Chomvong, Ligia Acosta-Sampson, Raíssa Estrela, Jonathan M Galazka, Soo Rin Kim, Yong-Su Jin, Jamie Hd Cate

Open access · goldAbstract read
In one paragraph

Article in Biotechnology for biofuels, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

27 citing papers in PubMed, 46 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Yuping LinDepartments of Molecular and Cell Biology, University of California, Berkeley, CA 94720 USA.
Kulika ChomvongPlant and Microbial Biology, University of California, Berkeley, CA 94720 USA.
Ligia Acosta-SampsonDepartments of Molecular and Cell Biology, University of California, Berkeley, CA 94720 USA.
Raíssa EstrelaDepartments of Molecular and Cell Biology, University of California, Berkeley, CA 94720 USA.
Jonathan M GalazkaDepartments of Molecular and Cell Biology, University of California, Berkeley, CA 94720 USA.
Soo Rin KimDepartment of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 USA ; Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 USA.
Yong-Su JinDepartment of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 USA ; Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 USA.
Jamie Hd CateDepartments of Molecular and Cell Biology, University of California, Berkeley, CA 94720 USA ; Chemistry, University of California, Berkeley, CA 94720 USA ; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA.
University of California, Berkeley · USUniversity of Illinois Urbana-Champaign · USLawrence Berkeley National Laboratory · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSaccharomyces cerevisiae, a key organism used for the manufacture of renewable fuels and chemicals, has been engineered to utilize non-native sugars derived from plant cell walls, such as cellobiose and xylose. However, the rates and efficiencies of these non-native sugar fermentations pale in comparison with those of glucose. Systems biology methods, used to understand biological networks, hold promise for rational microbial strain development in metabolic engineering. Here, we present a systematic strategy for optimizing non-native sugar fermentation by recombinant S. cerevisiae, using cellobiose as a model.

resultsDifferences in gene expression between cellobiose and glucose metabolism revealed by RNA deep sequencing indicated that cellobiose metabolism induces mitochondrial activation and reduces amino acid biosynthesis under fermentation conditions. Furthermore, glucose-sensing and signaling pathways and their target genes, including the cAMP-dependent protein kinase A pathway controlling the majority of glucose-induced changes, the Snf3-Rgt2-Rgt1 pathway regulating hexose transport, and the Snf1-Mig1 glucose repression pathway, were at most only partially activated under cellobiose conditions. To separate correlations from causative effects, the expression levels of 19 transcription factors perturbed under cellobiose conditions were modulated, and the three strongest promoters under cellobiose conditions were applied to fine-tune expression of the heterologous cellobiose-utilizing pathway. Of the changes in these 19 transcription factors, only overexpression of SUT1 or deletion of HAP4 consistently improved cellobiose fermentation. SUT1 overexpression and HAP4 deletion were not synergistic, suggesting that SUT1 and HAP4 may regulate overlapping genes important for improved cellobiose fermentation. Transcription factor modulation coupled with rational tuning of the cellobiose consumption pathway significantly improved cellobiose fermentation.

conclusionsWe used systems-level input to reveal the regulatory mechanisms underlying suboptimal metabolism of the non-glucose sugar cellobiose. By identifying key transcription factors that cause suboptimal cellobiose fermentation in engineered S. cerevisiae, and by fine-tuning the expression of a heterologous cellobiose consumption pathway, we were able to greatly improve cellobiose fermentation by engineered S. cerevisiae. Our results demonstrate a powerful strategy for applying systems biology methods to rapidly identify metabolic engineering targets and overcome bottlenecks in performance of engineered strains.

Indexed as

BiofuelsCellobioseGlycolysisMetabolic engineeringSystems biologyTranscription factor

Identifiers

PMID25435910
PMCPMC4243952
OpenAlexW2155044396

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.