Quantitative mass spectrometry-based multiplexing compares the abundance of 5000 S. cerevisiae proteins across 10 carbon sources

Abstract

The budding yeast Saccharomyces cerevisiae is a model system for investigating biological processes. Cellular events are known to be dysregulated due to shifts in carbon sources. However, the comprehensive proteomic alterations thereof have not been fully investigated. Here we examined proteomic alterations in S. cerevisiae due to the adaptation of yeast from glucose to nine different carbon sources - maltose, trehalose, fructose, sucrose, glycerol, acetate, pyruvate, lactic acid, and oleate. Isobaric tag-based mass spectrometry techniques are at the forefront of global proteomic investigations. As such, we used a TMT10-plex strategy to study multiple growth conditions in a single experiment. The SPS-MS3 method on an Orbitrap Fusion Lumos mass spectrometer enabled the quantification of over 5000 yeast proteins across ten carbon sources at a 1% protein-level FDR. On average, the proteomes of yeast cultured in fructose and sucrose deviated the least from those cultured in glucose. As expected, gene ontology classification revealed the major alteration in protein abundances occurred in metabolic pathways and mitochondrial proteins. Our protocol lays the groundwork for further investigation of carbon source-induced protein alterations. Additionally, these data offer a hypothesis-generating resource for future studies aiming to investigate both characterized and uncharacterized genes.

Biological significance: We investigate the proteomic alterations in S. cerevisiae resulting from adaptation of yeast from glucose to nine different carbon sources - maltose, trehalose, fructose, sucrose, glycerol, acetate, pyruvate, lactic acid, and oleate. SPS-MS3 TMT10plex analysis is used for quantitative proteomic analysis. We showcase a technique that allows the quantification of over 5000 yeast proteins, the highest number to date in S. cerevisiae, across 10 growth conditions in a single experiment. As expected, gene ontology classification of proteins with the major alterations in abundances occurred in metabolic pathways and mitochondrial proteins, reflecting the degree of metabolic stress when cellular machinery shifts from growth on glucose to an alternative carbon source. Our protocol lays the groundwork for further investigation of carbon source-induced protein alterations. Improving depth of coverage - measuring abundance changes of over 5000 proteins - increases our understanding of difficult-to-study genes in the model system S. cerevisiae and by homology human cell biology. We submit this highly comprehensive dataset as a hypothesis generating resource for targeted studies on uncharacterized genes.

Keywords: Carbon sources; Metabolism; Orbitrap Fusion Lumos; SPS-MS3; TMT; Yeast.

Figure 1. Workflow for TMT10-plex analysis of S. cerevisiae grown with ten different carbon sources

A starter culture was grown overnight in glucose and cultures were inoculated to OD₆₀₀ 0.05/mL and harvested at OD₆₀₀ 0.6/mL. Cells were disrupted by bead beating, proteins were reduced and alkylated, and subsequently extracted by chloroform-methanol precipitation. Peptides resulting from digestion with LysC and trypsin were labeled with TMT reagents. A total of 50 μg of peptide was pooled from each sample and fractionated via basic pH reversed-phase chromatography into a total of 24 fractions. Mass spectrometry data were collected for 3 h on an Orbitrap Fusion Lumos mass spectrometer.

Figure 2. General characterization of quantified proteins

We show the proportion of the proteins quantified (green) and not quantified (light grey) for A) verified ORFs, B) uncharacterized ORFs, and C) dubious ORFs. In addition, for verified ORFs, we illustrate the proportion of D) transcription factors, E) kinases, and F) phosphatases that were quantified (light green) and those that were not quantified (grey).

Figure 3. Clustering and correlation of the 10 samples in the TMT10-plex dataset

A) Hierarchical clustering of the proteins quantified in each carbon source. The data were displayed with respect to glucose. The value in parenthesis to the right of the carbon source is square root of the average squared deviation from glucose. B) Violin plots illustrating the coefficients of variation (CV) values for identical peptides across biological replicates for each carbon source. White circles show the medians; box limits indicate the 25th and 75th percentiles and whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles. C) Correlation matrix comparing the proteins quantified in the 10 carbon sources against each other. The lower triangle shows the correlation plot for each pair of carbon sources, while the upper panel shows the corresponding Pearson correlation (r).

Figure 4. Comparison of proteins from samples grown with glucose, sucrose, or fructose versus all other carbon sources

A) Volcano plot displaying the p-value and the log2 fold-change of the TMT signal-to-noise values of proteins from yeast grown in glucose, sucrose and fructose to that of the other carbon sources. Highlighted are proteins of unknown function with potential roles in metabolism. P-values were capped at 1E-16. Gene ontology terms for proteins that are B) down -regulated or C) up-regulated in yeast with glucose, sucrose or fructose as the carbon sources.