Abstract
Omics analysis is a versatile approach for understanding the conservation and diversity of molecular systems across multiple taxa. In this study, we compared the proteome expression profiles of four yeast species (Saccharomyces cerevisiae, Saccharomyces mikatae, Kluyveromyces waltii, and Kluyveromyces lactis) grown on glucose- or glycerol-containing media. Conserved expression changes across all species were observed only for a small proportion of all proteins differentially expressed between the two growth conditions. Two Kluyveromyces species, both of which exhibited a high growth rate on glycerol, a nonfermentative carbon source, showed distinct species-specific expression profiles. In K. waltii grown on glycerol, proteins involved in the glyoxylate cycle and gluconeogenesis were expressed in high abundance. In K. lactis grown on glycerol, the expression of glycolytic and ethanol metabolic enzymes was unexpectedly low, whereas proteins involved in cytoplasmic translation, including ribosomal proteins and elongation factors, were highly expressed. These marked differences in the types of predominantly expressed proteins suggest that K. lactis optimizes the balance of proteome resource allocation between metabolism and protein synthesis giving priority to cellular growth. In S. cerevisiae, about 450 duplicate gene pairs were retained after whole-genome duplication. Intriguingly, we found that in the case of duplicates with conserved sequences, the total abundance of proteins encoded by a duplicate pair in S. cerevisiae was similar to that of protein encoded by nonduplicated ortholog in Kluyveromyces yeast. Given the frequency of haploinsufficiency, this observation suggests that conserved duplicate genes, even though minor cases of retained duplicates, do not exhibit a dosage effect in yeast, except for ribosomal proteins. Thus, comparative proteomic analyses across multiple species may reveal not only species-specific characteristics of metabolic processes under nonoptimal culture conditions but also provide valuable insights into intriguing biological principles, including the balance of proteome resource allocation and the role of gene duplication in evolutionary history.
Highlights
Cases of retained duplicates, do not exhibit a dosage effect in yeast, except for ribosomal proteins
Differences in Growth Rate on Fermentative and Nonfermentative Carbon Sources among Yeast Species—We examined the growth rate on glucose and glycerol of multiple yeast species, including Saccharomyces sensu stricto (Saccharomyces cerevisiae, S. paradoxus, S. mikatae, and S. bayanus) and related yeast species within the Saccharomycetaceae family (Candida glabrata, Saccharomyces kluyveri, Kluyveromyces waltii, and Kluyveromyces lactis)
Even though some differences were observed across species, the growth rate on glucose was not associated with phylogenic distance among the eight yeast species examined (Fig. 1)
Summary
Yeast Strains and Cultivation—S. cerevisiae strain S288C was used in this study. Other yeast strains were obtained from National Bio Resource Project (NBRP, Japan; http://www.nbrp.jp/): Saccharomyces paradoxus strain FSP2–3C (NBRP ID, BY20701), S. mikatae strain IFO1815 (NBRP ID, BY20110), Saccharomyces bayanus strain Su1A (NBRP ID, BY20703), Candida glabrata strain YAT3377 (NBRP ID, BY23876), Saccharomyces kluyveri strain SK125 (NBRP ID, BY21541), K. waltii strain IFO1666 (NBRP ID, BY20700), and K. lactis strain PM6 –7A (NBRP ID, BY21799). A data set of orthologous protein families for nine yeast species was downloaded from the Genolevures hemiascomycete yeast database [22, 23] (http://www.genolevures.org/proteinfamilies.html), from which all ortholog relationships between S. cerevisiae and K. lactis were retrieved We reconstructed these ortholog relationship data sets, including both one-to-one and one-to-many connections, to make them amenable to consistent comparisons of ortholog protein abundance across all four yeast species examined, because the original data sets were independently created by individual research groups based on different criteria. For pair-wise comparisons between each proteome data set, the logarithm value of the abundance ratio for individual proteins or orthologous groups was divided by the standard deviation calculated as a function of their averaged PSM counts (using the fitting curve shown in supplemental Fig. 4B), to produce a “standardized z-score” (supplemental Tables S11 and S12). All data points were included for statistical analyses and p values less than 0.05 were considered as a cutoff of significant difference
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