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- Hong, Kuk-ki, 1976, et al.
(författare)
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Dynamic (13) C-labelling experiments prove important differences in protein turnover rate between two Saccharomyces cerevisiae strains
- 2012
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Ingår i: FEMS Yeast Research. - : Oxford University Press (OUP). - 1567-1356 .- 1567-1364. ; 12:7, s. 741-747
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Tidskriftsartikel (refereegranskat)abstract
- We developed a method for quantification of protein turnover using (13) C-labelled substrates combined with analysis of the labeling pattern of proteinogenic amino acids. Using this method the specific amino acid turnover rates between proteins and the pool of free amino acids were determined for eight different amino acids (alanine, valine, proline, aspartic acid, glycine, leucine, isoleucine and threonine) in two Saccharomyces cerevisiae strains (CEN.PK 113-7D and YSBN2). Furthermore, proteasome activities were compared for both strains. Both results confirmed the hypothesis of a higher protein turnover rates in CEN.PK 113-7D, which was generated in a previous comparative systems biology study of these two yeast strains. The ATP costs associated with the observed differences in protein turnover were quantified and could explain accurately the differences in biomass yield between both strains that are observed in chemostat cultures. The percent of maintenance ATP associated to protein polymerization (polymerization for growth and re-polymerization due to turnover) and degradation was estimated to be 72% for YSBN2 and 79% for CEN.PK 113-7D, which makes these processes the dominant non-biosynthetic drain of ATP in living cells, and hence it represents an energetic parameter of great relevance.
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| 2. |
- Ågren, Rasmus, 1982, et al.
(författare)
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Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT
- 2012
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment.
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