Molecular and physiological investigation of longevity in yeast

World population has changed in both developed and developing countries due to low birth and death ratio, resulting in relatively elder populations, which is a major challenge. Aging is a risk factor for several diseases such as Alzheimer, Parkinson, diabetes, cancer and cardiovascular diseases, due to declining cellular physiological functions with time. In recent years, researchers have been focusing on this issue due to the increasing socioeconomic concerns. The purpose is to understand the cellular aging process and to provide healthy lifespan. In aging research, the yeast Saccharomyces cerevisiae, the fruit fly Drosophila melanogaster, the worm Caenorhabditis elegans, rodents and primates have been used as model organisms. The yeast S. cerevisiae is a unicellular eukaryotic microorganism and it has been used in industrial processes such as baking, wine and beer production since ancient times. It has also been commonly used as a eukaryotic model organism in genetic and molecular biological research since it does not need expensive and special media and cultivation conditions. The yeast S. cerevisiae is a commonly preferred model organism in aging studies because of the shortness of its life span, availability of its genome sequence data and extensive molecular tools and techniques, and its similarity to mammalian cells. Aging studies can be performed by two ways in S. cerevisiae: by investigating the replicative life span (RLS) or the chronological lifespan (CLS). RLS has been developed because of the asymmetrical division of S. cerevisiae cells. In the method, after mitosis, the daughter cell is separated from the mother cell by using a micromanipulator, and the procedure is continued until the mother cell stops division. At the end of mitosis, the number of buds one mother cell produced are determined and the number of daughter cells is the RLS of S. cerevisiae. CLS is the length of time where non-dividing stationary phase yeast cells survive. In the method, cell viability at the beginning of the stationary phase of growth is determined by viable cell counting method on solid media. After that, viability of non-dividing cells at the stationary phase is monitored. Since (i) stationary phase cells produce energy from mitochondria, (ii) damage accumulates over time and (iii) cells exit from the cell cycle (G0), CLS method is accepted as a convenient model to study aging of non-dividing cells (e.g., neurons) in higher eukaryotic organisms. Long-lived S. cerevisiae, C. elegans and Drosophila mutants have had a major role for understanding cellular aging mechanism. The studies share a similar approach, since the long-lived mutant strain is obtained first, and then it is investigated in detail to understand its molecular basis. These strategies show similarity to inverse metabolic engineering approach. In inverse metabolic engineering approach, first, the desired phenotype is obtained, and then, its genetic mechanism is identified. This approach is a very powerful strategy to obtain complex desired microbial phenotypes with genetic complexity. So far, the approach has not been used to obtain chronologically long-lived yeast. In the present study, inverse metabolic engineering approach was used to obtain chronologically long-lived S. cerevisiae. Furthermore, since previous aging research with model organisms has shown a close relationship between stress resistance and longevity, in the current study, CLS of previously obtained stress-resistant S. cerevisiae mutants was also investigated. To isolate a chronologically long-lived yeast mutant by using inverse metabolic engineering, first, genetic diversity of the initial population for selection was increased by ethyl methane sulfonate (EMS)-mutagenesis. Successive batch cultivation of the initial mutant population was performed under gradually enhanced caloric restriction, since caloric restriction is known to extend lifespan. During successive batch cultivations, glucose concentration of the medium was decreased from 0.5% to 0.02%. In the last batch cultivation, since growth ratio of the mutant population decreased to 10%, the selection procedure was ended. After that, individual mutants were randomly picked from the last population. Semi-quantitative and quantitative CLS analyses were applied to these individual mutants. A single mutant (called SRM11) that has the highest chronological survival among the other mutants was chosen for further physiological and molecular analysis. Quantitative CLS method based on viable cell counting on solid media is tedious for large studies where multiple strains and multiple conditions are tested, as a high number of plates and flasks is required for incubation. It is also labor-intensive. Therefore, in the present study, the semi-quantitative method was used for preliminary determination of CLS in mutants. A culture density adjustment step was introduced to the semi-quantitative method, for a more reliable evaluation of CLS of cultures at significantly different culture densities. Following the semi-quantitative CLS, quantitative CLS analysis was applied to the prominent mutants. The procedure was applied successfully and correctly determined the chronologically long-lived phenotypes in a wide range of strains and conditions. Growth behavior of the mutant SRM11 was investigated by growth curve and biomass analysis. The purpose of these experiments was to determine whether any trade-off in growth physiology occurred or not. Analysis results showed that there was no trade-off in the growth fitness of the mutant SRM11 in both standard medium (2% YMM) and calorie-restricted medium (0.5% YMM). In this experiment, it was observed that 75% reduction of glucose concentration of the medium (from 2% to 0.5% glucose) did not affect growth ratio of the yeast strains when glucose was utilized. High-Pressure Liquid Chromatography (HPLC) analysis was performed to investigate the metabolic profiles of the long-lived mutant SRM11 and the reference strain. For this purpose, glucose, ethanol, glycerol and acetate concentrations in culture samples were monitored by HPLC analysis. Even though there was no significant difference in glucose consumption between the reference strain and the mutant SRM11, there was a significant difference in the production of fermentative metabolites. The mutant SRM11 produced low amounts of ethanol, glycerol and acetate in standard glucose medium (2% YMM). These findings indicated a respirative shift in the metabolism of the mutant SRM11, showing that respirative metabolism is related to chronological longevity in S. cerevisiae. In 0.5% YMM, a limited ethanol and glycerol production was observed in the cultures of the reference strain and the mutant SRM11, which indicates that at 0.5% glucose concentration, fermentation is limited. Aging studies have shown a close relationship between stress resistance and longevity in model organisms. Therefore, stress resistance analysis of the long-lived mutant SRM11 was performed, using the semi-quantitative spot assay method. The results revealed that the mutant SRM11 highly resisted to copper stress. Also, SRM11 resisted to silver, phenylethanol, ethanol and boric acid stresses, indicating that these stress types may be related with chronological longevity. Whole genome transcriptome of the long-lived mutant SRM11 was analyzed by using DNA microarray technology, to understand the molecular mechanisms of chronological longevity in yeast. DNA microarray analysis results revealed that 769 open reading frames (ORFs) were upregulated, 838 ORFs were downregulated by two-fold and higher in the long-lived SRM11 mutant, compared to the reference strain. For the interpretation of the gene expression change, Gene Ontology (GO) analysis was used. GO analysis of the upregulated genes indicated that oligosaccharide-carbohydrate metabolic biological process and response to oxidative stress were induced in SRM11. Furthermore, GO analysis of the downregulated genes revealed that ribosome subunits biogenesis, ribosome assembly and RNA processing were repressed in SRM11. These results suggest that repression of protein synthesis and activation of oligosaccharide-carbohydrate metabolic processes and response to oxidative stress are associated with the extension of chronological longevity in the yeast S. cerevisiae. CLS of various stress-resistant S. cerevisiae mutants was also investigated by semi-quantitative and quantitative methods. Among the tested stress-resistant mutants, silver-resistant, ethanol-resistant, phenylethanol-resistant, oxidative stress-resistant mutants were determined as long-lived mutants. Resistance to oxidative stress has been shown to be related with chronological survival in yeast, however; resistance to silver, ethanol and phenylethanol stress has not been shown, yet. The present study, for the first time, showed that resistance to silver, ethanol and phenylethanol may be related with chronological longevity in the yeast S. cerevisiae. In the current study, a comparative transcriptomic analysis of the long-lived stress resistant mutants (silver-resistant, phenylethanol-resistant and ethanol-resistant mutant and long-lived mutant SRM11) was also carried out to find a relationship between chronological longevity and stress resistance. The comparative transcriptomic analysis showed that 17 downregulated genes and 48 upregulated genes were overlapping in the long-lived and stress-resistant mutants. GO analysis of the overlapping upregulated genes showed that the upregulated genes were enriched in mostly oligosaccharide and carbohydrate metabolic process and generation of precursor metabolites and energy. GO analysis of the overlapping downregulated genes indicated that ribosomal subunits biogenesis and aminoacid transport biological process were repressed. As a conclusion, the present study suggests that increased respirative metabolism, resistance to copper, silver, ethanol and phenylethanol stress, repression of protein synthesis and induction of oligosaccharide and carbohydrate metabolic processes are associated with chronological longevity in the yeast S. cerevisiae. Further detailed analyses at genomic and proteomic levels will help understand the complex molecular basis of the longevity in S. cerevisiae.