Feasibility of protein turnover studies in prototroph Saccharomyces cerevisiae strains

Degron masking outlines degronons, co-degrading functional modules in the proteome

Effective organization of proteins into functional modules (networks, pathways) requires systems-level coordination between transcription, translation and degradation. Whereas the cooperation between transcription and translation was extensively studied, the cooperative degradation regulation of protein complexes and pathways has not been systematically assessed. Here we comprehensively analyzed degron masking, a major mechanism by which cellular systems coordinate degron recognition and protein degradation. For over 200 substrates with characterized degrons (E3 ligase targeting motifs, ubiquitination sites and disordered proteasomal entry sequences), we demonstrate that degrons extensively overlap with protein-protein interaction sites. Analysis of binding site information and protein abundance comparisons show that regulatory partners effectively outcompete E3 ligases, masking degrons from the ubiquitination machinery. Protein abundance variations between normal and cancer cells highlight the dynamics of degron masking components. Finally, integrative analysis of gene co-expression, half-life correlations and functional relationships between interacting proteins point towards higher-order, co-regulated degradation modules (‘degronons’) in the proteome.

Systematic bioinformatics analysis of cooperative degradation of protein complexes indicates that degrons extensively overlap with protein-protein interaction sites, hiding degrons from ubiquitination machinery and suggesting the existence of co-degrading functional modules in the proteome.

N-terminal protein acetylation by NatB modulates the levels of Nmnats, the NAD+ biosynthetic enzymes in Saccharomyces cerevisiae

NAD+ is an essential metabolite participating in cellular biochemical processes and signaling. The regulation and interconnection among multiple NAD+ biosynthesis pathways are incompletely understood. Yeast (Saccharomyces cerevisiae) cells lacking the N-terminal (Nt) protein acetyltransferase complex NatB exhibit an approximate 50% reduction in NAD+ levels and aberrant metabolism of NAD+ precursors, changes that are associated with a decrease in nicotinamide mononucleotide adenylyltransferase (Nmnat) protein levels. Here, we show that this decrease in NAD+ and Nmnat protein levels is specifically due to the absence of Nt-acetylation of Nmnat (Nma1 and Nma2) proteins and not of other NatB substrates. Nt-acetylation critically regulates protein degradation by the N-end rule pathways, suggesting that the absence of Nt-acetylation may alter Nmnat protein stability. Interestingly, the rate of protein turnover (t½) of non-Nt-acetylated Nmnats did not significantly differ from those of Nt-acetylated Nmnats. Accordingly, deletion or depletion of the N-end rule pathway ubiquitin E3 ligases in NatB mutants did not restore NAD+ levels. Next, we examined whether the status of Nt-acetylation would affect the translation of Nmnats, finding that the absence of Nt-acetylation does not significantly alter the polysome formation rate on Nmnat mRNAs. However, we observed that NatB mutants have significantly reduced Nmnat protein maturation. Our findings indicate that the reduced Nmnat levels in NatB mutants are mainly due to inefficient protein maturation. Nmnat activities are essential for all NAD+ biosynthesis routes, and understanding the regulation of Nmnat protein homeostasis may improve our understanding of the molecular basis and regulation of NAD+ metabolism.

Mass Spectrometry-Based Analysis of Time-Resolved Proteome Quantification

The aspect of time is essential in biological processes and thus it is important to be able to monitor signaling molecules through time. Proteins are key players in cellular signaling and they respond to many stimuli and change their expression in many time-dependent processes. Mass spectrometry (MS) is an important tool for studying proteins, including their posttranslational modifications and their interaction partners-both in qualitative and quantitative ways. In order to distinguish the different trends over time, proteins, modification sites, and interacting proteins must be compared between different time points, and therefore relative quantification is preferred. In this review, the progress and challenges for MS-based analysis of time-resolved proteome dynamics are discussed. Further, aspects on model systems, technologies, sampling frequencies, and presentation of the dynamic data are discussed.

Complete Native Stable Isotope Labeling by Amino Acids of Saccharomyces cerevisiae for Global Proteomic Analysis

Knowledge about the functions of individual proteins on a system-wide level is crucial to fully understand molecular mechanisms underlying cellular processes. A considerable part of the proteome across all organisms is still poorly characterized. Mass spectrometry is an efficient technology for the global study of proteins. One of the most prominent methods for accurate proteome-wide comparative quantification is stable isotope labeling by amino acids in cell culture (SILAC). However, application of SILAC to prototrophic organisms such as Saccharomyces cerevisiae, also known as baker's yeast, is compromised since they are able to synthesize all amino acids on their own. Here, we describe an advanced strategy, termed 2nSILAC, that allows for in vivo labeling of prototrophic baker's yeast using heavy arginine and lysine under fermentable and respiratory growth conditions, making it a suitable tool for the global study of protein functions. This generic 2nSILAC strategy allows for directly using and systematically screening yeast mutant strain collections available to the scientific community. We exemplarily demonstrate its high potential by analyzing the effects of mitochondrial gene deletions in mitochondrial fractions using quantitative mass spectrometry revealing the role of Coi1 for the assembly of cytochrome c oxidase (respiratory chain complex IV).

Quantitative Proteomic Analysis in Candida albicans Using SILAC-Based Mass Spectrometry

Stable isotope labelling by amino acids in cell culture (SILAC) in conjunction with MS analysis is a sensitive and reliable technique for quantifying relative differences in protein abundance and posttranslational modifications between cell populations. We develop and utilise SILAC-MS workflows for quantitative proteomics in the fungal pathogen Candida albicans. Arginine metabolism provides important cues for escaping host defences during pathogenesis, which limits the use of auxotrophs in Candida research. Our strategy eliminates the need for engineering arginine auxotrophs for SILAC experiments and allows the use of ARG4 as selectable marker during strain construction. Cells that are auxotrophic for lysine are successfully labelled with both lysine and arginine stable isotopes. We find that prototrophic C. albicans preferentially uses exogenous arginine and down-regulates internal production, which allow it to achieve high incorporation rates. However, similar to other yeast, C. albicans is able to metabolise heavy arginine to heavy proline, which compromised the accuracy of protein quantification. A computational method is developed to correct for the incorporation of heavy proline. In addition, we utilise the developed SILAC labelling in C. albicans for the global quantitative proteomic analysis of a strain expressing a phosphatase-dead mutant Cdc14^PD .

Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome

Protein activity is the ultimate arbiter of function in most cellular pathways, and protein concentration is fundamentally connected to protein action. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukaryote, existing datasets are difficult to compare, and there is no consensus abundance value for each protein. We evaluated 21 quantitative analyses of the S. cerevisiae proteome, normalizing and converting all measurements of protein abundance into the intuitive measurement of absolute molecules per cell. We estimate the cellular abundance of 92% of the proteins in the yeast proteome and assess the variation in each abundance measurement. Using our protein abundance dataset, we find that a global response to diverse environmental stresses is not detected at the level of protein abundance, we find that protein tags have only a modest effect on protein abundance, and we identify proteins that are differentially regulated at the mRNA abundance, mRNA translation, and protein abundance levels.

Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability

Temperature-induced cell death is thought to be due to protein denaturation, but the determinants of thermal sensitivity of proteomes remain largely uncharacterized. We developed a structural proteomic strategy to measure protein thermostability on a proteome-wide scale and with domain-level resolution. We applied it to Escherichia coli, Saccharomyces cerevisiae, Thermus thermophilus, and human cells, yielding thermostability data for more than 8000 proteins. Our results (i) indicate that temperature-induced cellular collapse is due to the loss of a subset of proteins with key functions, (ii) shed light on the evolutionary conservation of protein and domain stability, and (iii) suggest that natively disordered proteins in a cell are less prevalent than predicted and (iv) that highly expressed proteins are stable because they are designed to tolerate translational errors that would lead to the accumulation of toxic misfolded species.

Determinants and Regulation of Protein Turnover in Yeast

Protein turnover maintains the recycling needs of the proteome, and its malfunction has been linked to aging and age-related diseases. However, not all proteins turnover equally, and the factors that contribute to accelerate or slow down turnover are mostly unknown. We measured turnover rates for 3,160 proteins in exponentially growing yeast and analyzed their dependence on physical, functional, and genetic properties. We found that functional characteristics, including protein localization, complex membership, and connectivity, have greater effect on turnover than sequence elements. We also found that protein turnover and mRNA turnover are correlated. Analysis under nutrient perturbation and osmotic stress revealed that protein turnover highly depends on cellular state and is faster when proteins are being actively used. Finally, stress-induced changes in protein and transcript abundance correlated with changes in protein turnover. This study provides a resource of protein turnover rates and principles to understand the recycling needs of the proteome under basal conditions and perturbation.

A Simple Light Isotope Metabolic Labeling (SLIM-labeling) Strategy: A Powerful Tool to Address the Dynamics of Proteome Variations In Vivo

Many quantitative proteomics strategies rely on in vivo metabolic incorporation of amino acids with modified stable isotope profiles into proteins. These methods give rise to multiple ions for each peptide, with possible distortion of the isotopolog distribution, making the overall analytical process complex. We validated an alternative strategy, simple light isotope metabolic labeling (SLIM-labeling), which alleviates many of these problems. SLIM-labeling is based on the in vivo reduction of the isotopic composition of proteins using metabolic precursors with a unique light isotope composition to label all amino acids. This brings a new dimension to in-depth, high resolution MS-based quantitative proteomics. Here, we describe a ¹²C-based SLIM-labeling strategy using U-[¹²C]-glucose as the metabolic precursor of all amino acids in the pathogenic yeast Candida albicans Monoisotopic ion intensity increased exponentially following ¹²C enrichment, substantially improving peptide identification scores and protein sequence coverage in bottom-up analyses. Multiplexing samples of ¹²C composition varying from natural abundance (98.93%) to 100% makes it possible to address relative quantification issues, keeping all the critical information for each peptide within a single isotopolog cluster. We applied this method to measure, for the first time, protein turnover at the proteome scale in Candida albicans and its modulation by inhibitors of the proteasome and vacuolar protein degradation systems.