The methylation of cytosine to 5-methylcytosine (5-meC) is an important epigenetic DNA modification in many bacteria, plants, and mammals, but its relevance for important model organisms, including Caenorhabditis elegans and Drosophila melanogaster, is still equivocal. By reporting the presence of 5-meC in a broad variety of wild, laboratory, and industrial yeasts, a recent study also challenged the dogma about the absence of DNA methylation in yeast species. We would like to bring to attention that the protocol used for gas chromatography/mass spectrometry involved hydrolysis of the DNA preparations. As this process separates cytosine and 5-meC from the sugar phosphate backbone, this method is unable to distinguish DNA- from RNA-derived 5-meC. We employed an alternative LC–MS/MS protocol where by targeting 5-methyldeoxycytidine moieties after enzymatic digestion, only 5-meC specifically derived from DNA is quantified. This technique unambiguously identified cytosine DNA methylation in Arabidopsis thaliana (14.0% of cytosines methylated), Mus musculus (7.6%), and Escherichia coli (2.3%). Despite achieving a detection limit at 250 attomoles (corresponding to <0.00002 methylated cytosines per nonmethylated cytosine), we could not confirm any cytosine DNA methylation in laboratory and industrial strains of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Saccharomyces boulardii, Saccharomyces paradoxus, or Pichia pastoris. The protocol however unequivocally confirmed DNA methylation in adult Drosophila melanogaster at a value (0.034%) that is up to 2 orders of magnitude below the detection limit of bisulphite sequencing. Thus, 5-meC is a rare DNA modification in drosophila but absent in yeast.

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SummaryA challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

The regulation of gene expression in response to nutrient availability is fundamental to the genotype-phenotype relationship. The metabolic-genetic make-up of the cell, as reflected in auxotrophy, is hence likely to be a determinant of gene expression. Here, we address the importance of the metabolic-genetic background by monitoring transcriptome, proteome and metabolome in a repertoire of 16 Saccharomyces cerevisiae laboratory backgrounds, combinatorially perturbed in histidine, leucine, methionine and uracil biosynthesis. The metabolic background affected up to 85% of the coding genome. Suggesting widespread confounding, these transcriptional changes show, on average, 83% overlap between unrelated auxotrophs and 35% with previously published transcriptomes generated for non-metabolic gene knockouts. Background-dependent gene expression correlated with metabolic flux and acted, predominantly through masking or suppression, on 88% of transcriptional interactions epistatically. As a consequence, the deletion of the same metabolic gene in a different background could provoke an entirely different transcriptional response. Propagating to the proteome and scaling up at the metabolome, metabolic background dependencies reveal the prevalence of metabolism-dependent epistasis at all regulatory levels. Urging a fundamental change of the prevailing laboratory practice of using auxotrophs and nutrient supplemented media, these results reveal epistatic intertwining of metabolism with gene expression on the genomic scale.

The combination of qualitative analysis with label-free quantification has greatly facilitated the throughput and flexibility of novel proteomic techniques. However, such methods rely heavily on robust and reproducible sample preparation procedures. Here, we benchmark a selection of in gel, on filter, and in solution digestion workflows for their application in label-free proteomics. Each procedure was associated with differing advantages and disadvantages. The in gel methods interrogated were cost effective, but were limited in throughput and digest efficiency. Filter-aided sample preparations facilitated reasonable processing times and yielded a balanced representation of membrane proteins, but led to a high signal variation in quantification experiments. Two in solution digest protocols, however, gave optimal performance for label-free proteomics. A protocol based on the detergent RapiGest led to the highest number of detected proteins at second-best signal stability, while a protocol based on acetonitrile-digestion, RapidACN, scored best in throughput and signal stability but came second in protein identification. In addition, we compared label-free data dependent (DDA) and data independent (SWATH) acquisition on a TripleTOF 5600 instrument. While largely similar in protein detection, SWATH outperformed DDA in quantification, reducing signal variation and markedly increasing the number of precisely quantified peptides.