Similar temperature dependencies of glycolytic enzymes: an evolutionary adaptation to temperature dynamics? View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-12

AUTHORS

AnaLuisa B Cruz, Marit Hebly, Giang-Huong Duong, Sebastian A Wahl, Jack T Pronk, Joseph J Heijnen, Pascale Daran-Lapujade, Walter M van Gulik

ABSTRACT

BACKGROUND: Temperature strongly affects microbial growth, and many microorganisms have to deal with temperature fluctuations in their natural environment. To understand regulation strategies that underlie microbial temperature responses and adaptation, we studied glycolytic pathway kinetics in Saccharomyces cerevisiae during temperature changes. RESULTS: Saccharomyces cerevisiae was grown under different temperature regimes and glucose availability conditions. These included glucose-excess batch cultures at different temperatures and glucose-limited chemostat cultures, subjected to fast linear temperature shifts and circadian sinoidal temperature cycles. An observed temperature-independent relation between intracellular levels of glycolytic metabolites and residual glucose concentration for all experimental conditions revealed that it is the substrate availability rather than temperature that determines intracellular metabolite profiles. This observation corresponded with predictions generated in silico with a kinetic model of yeast glycolysis, when the catalytic capacities of all glycolytic enzymes were set to share the same normalized temperature dependency. CONCLUSIONS: From an evolutionary perspective, such similar temperature dependencies allow cells to adapt more rapidly to temperature changes, because they result in minimal perturbations of intracellular metabolite levels, thus circumventing the need for extensive modification of enzyme levels. More... »

PAGES

151

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1752-0509-6-151

DOI

http://dx.doi.org/10.1186/1752-0509-6-151

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008784589

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23216813


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-6-151'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-6-151'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-6-151'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-6-151'


 

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