Evidence map›Paper›PMID 24453942›Full record

ArticlePLoS biology2014

Different levels of catabolite repression optimize growth in stable and variable environments.

Aaron M New, Bram Cerulus, Sander K Govers, Gemma Perez-Samper, Bo Zhu, Sarah Boogmans, Joao B Xavier, Kevin J Verstrepen

Open access · goldAbstract read
In one paragraph

Article in PLoS biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed
11.5field-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

105 citing papers in PubMed, 217 citations in OpenAlex.

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  18. Effective bet-hedging through growth rate dependent stability.Proceedings of the National Academy of Sciences of the United States of America · 2023
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45 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

8 authors at 2 institutions in 2 countries.

Aaron M NewVIB Laboratory of Systems Biology, Leuven, Belgium ; CMPG Laboratory of Genetics and Genomics, KU Leuven, Leuven, Belgium.
Bram CerulusVIB Laboratory of Systems Biology, Leuven, Belgium ; CMPG Laboratory of Genetics and Genomics, KU Leuven, Leuven, Belgium.
Sander K GoversVIB Laboratory of Systems Biology, Leuven, Belgium ; CMPG Laboratory of Genetics and Genomics, KU Leuven, Leuven, Belgium.
Gemma Perez-SamperVIB Laboratory of Systems Biology, Leuven, Belgium ; CMPG Laboratory of Genetics and Genomics, KU Leuven, Leuven, Belgium.
Bo ZhuVIB Laboratory of Systems Biology, Leuven, Belgium ; CMPG Laboratory of Genetics and Genomics, KU Leuven, Leuven, Belgium.
Sarah BoogmansVIB Laboratory of Systems Biology, Leuven, Belgium ; CMPG Laboratory of Genetics and Genomics, KU Leuven, Leuven, Belgium.
Joao B XavierProgram in Computational Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States.
Kevin J VerstrepenVIB Laboratory of Systems Biology, Leuven, Belgium ; CMPG Laboratory of Genetics and Genomics, KU Leuven, Leuven, Belgium.
KU Leuven · BEMemorial Sloan Kettering Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Organisms respond to environmental changes by adapting the expression of key genes. However, such transcriptional reprogramming requires time and energy, and may also leave the organism ill-adapted when the original environment returns. Here, we study the dynamics of transcriptional reprogramming and fitness in the model eukaryote Saccharomyces cerevisiae in response to changing carbon environments. Population and single-cell analyses reveal that some wild yeast strains rapidly and uniformly adapt gene expression and growth to changing carbon sources, whereas other strains respond more slowly, resulting in long periods of slow growth (the so-called "lag phase") and large differences between individual cells within the population. We exploit this natural heterogeneity to evolve a set of mutants that demonstrate how the frequency and duration of changes in carbon source can favor different carbon catabolite repression strategies. At one end of this spectrum are "specialist" strategies that display high rates of growth in stable environments, with more stringent catabolite repression and slower transcriptional reprogramming. The other mutants display less stringent catabolite repression, resulting in leaky expression of genes that are not required for growth in glucose. This "generalist" strategy reduces fitness in glucose, but allows faster transcriptional reprogramming and shorter lag phases when the cells need to shift to alternative carbon sources. Whole-genome sequencing of these mutants reveals that mutations in key regulatory genes such as HXK2 and STD1 adjust the regulation and transcriptional noise of metabolic genes, with some mutations leading to alternative gene regulatory strategies that allow "stochastic sensing" of the environment. Together, our study unmasks how variable and stable environments favor distinct strategies of transcriptional reprogramming and growth.

Indexed as

Gene-Environment InteractionGene Expression Regulation, FungalGenome, FungalAdaptation, PhysiologicalCarbonGenetic FitnessGlucoseHexokinaseHigh-Throughput Nucleotide SequencingIntracellular Signaling Peptides and ProteinsMutationSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsTranscription, GeneticCarbonGlucoseHexokinaseHXK2 protein, S cerevisiaeIntracellular Signaling Peptides and ProteinsSaccharomyces cerevisiae ProteinsSTD1 protein, S cerevisiae

Identifiers

PMID24453942
PMCPMC3891604
OpenAlexW1971866274

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.