Our understanding of metabolic networks is incomplete, and new enzymatic activities await discovery in well-studied organisms. Mass spectrometric measurement of cellular metabolites reveals compounds ...inside cells that are unexplained by current maps of metabolic reactions, and existing computational models are unable to account for all activities observed within cells. Additional large-scale genetic and biochemical approaches are required to elucidate metabolic gene function. We have used full-scan mass spectrometry metabolomics of polar small molecules to examine deletion mutants of candidate enzymes in the model yeast Saccharomyces cerevisiae. We report the identification of 25 genes whose deletion results in focal metabolic changes consistent with loss of enzymatic activity and describe the informatic approaches used to enrich for candidate enzymes from uncharacterized open reading frames. Triumphs and pitfalls of metabolic phenotyping screens are discussed, including estimates of the frequency of uncharacterized eukaryotic genes that affect metabolism and key issues to consider when searching for new enzymatic functions in other organisms.
The ability of transcriptional activation domains (TADs) to signal ubiquitin-mediated proteolysis suggests an involvement of the ubiquitin-proteasome pathway in transcription. To probe this ...involvement, we asked how ubiquitylation regulates the activity of a transcription factor containing the VP16 TAD. We show that the VP16 TAD signals ubiquitylation through the Met30 ubiquitin-ligase and that Met30 is also required for the VP16 TAD to activate transcription. The requirement for Met30 in transcription is circumvented by fusion of ubiquitin to the VP16 activator, demonstrating that activator ubiquitylation is essential for transcriptional activation. We propose that ubiquitylation regulates TAD function by serving as a dual signal for activation and activator destruction.
The genetic basis of heritable traits has been studied for decades. Although recent mapping efforts have elucidated genetic determinants of transcript levels, mapping of protein abundance has lagged. ...Here, we analyze levels of 4084 GFP-tagged yeast proteins in the progeny of a cross between a laboratory and a wild strain using flow cytometry and high-content microscopy. The genotype of trans variants contributed little to protein level variation between individual cells but explained >50% of the variance in the population's average protein abundance for half of the GFP fusions tested. To map trans-acting factors responsible, we performed flow sorting and bulk segregant analysis of 25 proteins, finding a median of five protein quantitative trait loci (pQTLs) per GFP fusion. Further, we find that cis-acting variants predominate; the genotype of a gene and its surrounding region had a large effect on protein level six times more frequently than the rest of the genome combined. We present evidence for both shared and independent genetic control of transcript and protein abundance: More than half of the expression QTLs (eQTLs) contribute to changes in protein levels of regulated genes, but several pQTLs do not affect their cognate transcript levels. Allele replacements of genes known to underlie trans eQTL hotspots confirmed the correlation of effects on mRNA and protein levels. This study represents the first genome-scale measurement of genetic contribution to protein levels in single cells and populations, identifies more than a hundred trans pQTLs, and validates the propagation of effects associated with transcript variation to protein abundance.
RNA interference (RNAi) regulates gene expression by the cleavage of messenger RNA, by mRNA degradation and by preventing protein synthesis. These effects are mediated by a ribonucleoprotein complex ...known as RISC (RNA-induced silencing complex). We have previously identified four Drosophila components (short interfering RNAs, Argonaute 2 (ref. 2), VIG and FXR) of a RISC enzyme that degrades specific mRNAs in response to a double-stranded-RNA trigger. Here we show that Tudor-SN (tudor staphylococcal nuclease)-a protein containing five staphylococcal/micrococcal nuclease domains and a tudor domain-is a component of the RISC enzyme in Caenorhabditis elegans, Drosophila and mammals. Although Tudor-SN contains non-canonical active-site sequences, we show that purified Tudor-SN exhibits nuclease activity similar to that of other staphylococcal nucleases. Notably, both purified Tudor-SN and RISC are inhibited by a specific competitive inhibitor of micrococcal nuclease. Tudor-SN is the first RISC subunit to be identified that contains a recognizable nuclease domain, and could therefore contribute to the RNA degradation observed in RNAi.
Prior to mass spectrometric analysis, cellular small molecules must be extracted and separated from interfering components such as salts and culture medium. To ensure minimal perturbation of ...metabolism, yeast cells grown in liquid culture are rapidly harvested by filtration as described here. Simultaneous quenching of metabolism and extraction is afforded by immediate immersion in low-temperature organic solvent. Samples prepared using this method are suitable for a range of downstream liquid chromatography-mass spectrometry analyses and are stable in solvent for >1 yr at -80°C.
Histone demethylation by Jumonji-family proteins is coupled with the decarboxylation of α-ketoglutarate (αKG) to yield succinate, prompting hypotheses that their activities are responsive to levels ...of these metabolites in the cell. Consistent with this paradigm we show here that the Saccharomyces cerevisiae Jumonji demethylase Jhd2 opposes the accumulation of H3K4me3 in fermenting cells only when they are nutritionally manipulated to contain an elevated αKG/succinate ratio. We also find that Jhd2 opposes H3K4me3 in respiratory cells that do not exhibit such an elevated αKG/succinate ratio. While jhd2∆ caused only limited gene expression defects in fermenting cells, transcript profiling and physiological measurements show that JHD2 restricts mitochondrial respiratory capacity in cells grown in non-fermentable carbon in an H3K4me-dependent manner. In association with these phenotypes, we find that JHD2 limits yeast proliferative capacity under physiologically challenging conditions as measured by both replicative lifespan and colony growth on non-fermentable carbon. JHD2's impact on nutrient response may reflect an ancestral role of its gene family in mediating mitochondrial regulation.
Computational approaches have promised to organize collections of functional genomics data into testable predictions of gene and protein involvement in biological processes and pathways. However, few ...such predictions have been experimentally validated on a large scale, leaving many bioinformatic methods unproven and underutilized in the biology community. Further, it remains unclear what biological concerns should be taken into account when using computational methods to drive real-world experimental efforts. To investigate these concerns and to establish the utility of computational predictions of gene function, we experimentally tested hundreds of predictions generated from an ensemble of three complementary methods for the process of mitochondrial organization and biogenesis in Saccharomyces cerevisiae. The biological data with respect to the mitochondria are presented in a companion manuscript published in PLoS Genetics (doi:10.1371/journal.pgen.1000407). Here we analyze and explore the results of this study that are broadly applicable for computationalists applying gene function prediction techniques, including a new experimental comparison with 48 genes representing the genomic background. Our study leads to several conclusions that are important to consider when driving laboratory investigations using computational prediction approaches. While most genes in yeast are already known to participate in at least one biological process, we confirm that genes with known functions can still be strong candidates for annotation of additional gene functions. We find that different analysis techniques and different underlying data can both greatly affect the types of functional predictions produced by computational methods. This diversity allows an ensemble of techniques to substantially broaden the biological scope and breadth of predictions. We also find that performing prediction and validation steps iteratively allows us to more completely characterize a biological area of interest. While this study focused on a specific functional area in yeast, many of these observations may be useful in the contexts of other processes and organisms.
Complex biological samples include thousands of metabolites that range widely in both physiochemical properties and concentration. Simultaneously analyzing metabolites with different properties using ...a single analytical method is very challenging. The analytical process for metabolites comprises multiple steps including sampling, quenching, sample preparation, separation and detection. Each step can have a significant effect on the reliability and precision of ultimate analytic results. The aim of review is a discussion of considerations and challenges for the simultaneous analysis of metabolites using LC- and GC-MS systems. The review discusses available methodology for each analytical step, and presents the limitations and advantages of each method for the large-scale targeted metabolomics analysis of human and animal biological samples.
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