In nature, microbes often need to "decide" which of several available nutrients to utilize, a choice that depends on a cell's inherent preference and external nutrient levels. While natural environments can have mixtures of different nutrients, phenotypic variation in microbes' decisions of which nutrient to utilize is poorly studied. Here, we quantified differences in the concentration of glucose and galactose required to induce galactose-responsive (GAL) genes across 36 wild S. cerevisiae strains. Using bulk segregant analysis, we found that a locus containing the galactose sensor GAL3 was associated with differences in GAL signaling in eight different crosses. Using allele replacements, we confirmed that GAL3 is the major driver of GAL induction variation, and that GAL3 allelic variation alone can explain as much as 90% of the variation in GAL induction in a cross. The GAL3 variants we found modulate the diauxic lag, a selectable trait. These results suggest that ecological constraints on the galactose pathway may have led to variation in a single protein, allowing cells to quantitatively tune their response to nutrient changes in the environment.
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http://dx.doi.org/10.1371/journal.pgen.1006766 | DOI Listing |
Nat Metab
January 2025
Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Nutrient sensors allow cells to adapt their metabolisms to match nutrient availability by regulating metabolic pathway expression. Many such sensors are cytosolic receptors that measure intracellular nutrient concentrations. One might expect that inducing the metabolic pathway that degrades a nutrient would reduce intracellular nutrient levels, destabilizing induction.
View Article and Find Full Text PDFFront Mol Biosci
October 2024
Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, United States.
Efficaciously assessing product quality remains time- and resource-intensive. Online Process Analytical Technologies (PATs), encompassing real-time monitoring tools and soft-sensor models, are indispensable for understanding process effects and real-time product quality. This research study evaluated three modeling approaches for predicting CHO cell growth and production, metabolites (extracellular, nucleotide sugar donors (NSD) and glycan profiles): Mechanistic based on first principle Michaelis-Menten kinetics (MMK), data-driven orthogonal partial least square (OPLS) and neural network machine learning (NN).
View Article and Find Full Text PDFJ Biotechnol
November 2024
Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA. Electronic address:
Plant Physiol Biochem
September 2024
Engineering Research Center of National Forestry and Grassland Administration for Rosa roxburghii, Agricultural College, Guizhou University, Guiyang, China. Electronic address:
Biotechnol Appl Biochem
December 2024
Department of Obstetrics (Guoxing), Haikou Hospital of The Maternal and Child Health, Haikou, Hainan Province, China.
Gestational diabetes (GD) is a condition characterized by elevated blood sugar levels during pregnancy. GD poses various health risks, such as serious birth injuries, the need for cesarean delivery, and the necessity of newborn care. Monitoring glucose levels is essential for ensuring safe delivery and reducing the risks to both the mother and fetus.
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