Interspecies systems biology uncovers metabolites affecting C. elegans gene expression and life history traits.

Diet greatly influences gene expression and physiology. In mammals, elucidating the effects and mechanisms of individual nutrients is challenging due to the complexity of both the animal and its diet. Here, we used an interspecies systems biology approach with Caenorhabditis elegans and two of its bacterial diets, Escherichia coli and Comamonas aquatica, to identify metabolites that affect the animal's gene expression and physiology. We identify vitamin B12 as the major dilutable metabolite provided by Comamonas aq. that regulates gene expression, accelerates development, and reduces fertility but does not affect lifespan. We find that vitamin B12 has a dual role in the animal: it affects development and fertility via the methionine/S-Adenosylmethionine (SAM) cycle and breaks down the short-chain fatty acid propionic acid, preventing its toxic buildup. Our interspecies systems biology approach provides a paradigm for understanding complex interactions between diet and physiology.

Riboneogenesis in yeast.

Glucose is catabolized in yeast via two fundamental routes, glycolysis and the oxidative pentose phosphate pathway, which produces NADPH and the essential nucleotide component ribose-5-phosphate. Here, we describe riboneogenesis, a thermodynamically driven pathway that converts glycolytic intermediates into ribose-5-phosphate without production of NADPH. Riboneogenesis begins with synthesis, by the combined action of transketolase and aldolase, of the seven-carbon bisphosphorylated sugar sedoheptulose-1,7-bisphosphate. In the pathway's committed step, sedoheptulose bisphosphate is hydrolyzed to sedoheptulose-7-phosphate by the enzyme sedoheptulose-1,7-bisphosphatase (SHB17), whose activity we identified based on metabolomic analysis of the corresponding knockout strain. The crystal structure of Shb17 in complex with sedoheptulose-1,7-bisphosphate reveals that the substrate binds in the closed furan form in the active site. Sedoheptulose-7-phosphate is ultimately converted by known enzymes of the nonoxidative pentose phosphate pathway to ribose-5-phosphate. Flux through SHB17 increases when ribose demand is high relative to demand for NADPH, including during ribosome biogenesis in metabolically synchronized yeast cells.

Transposon insertional mutagenesis in Saccharomyces uvarum reveals trans-acting effects influencing species-dependent essential genes.

To understand how complex genetic networks perform and regulate diverse cellular processes, the function of each individual component must be defined. Comprehensive phenotypic studies of mutant alleles have been successful in model organisms in determining what processes depend on the normal function of a gene. These results are often ported to newly sequenced genomes by using sequence homology. However, sequence similarity does not always mean identical function or phenotype, suggesting that new methods are required to functionally annotate newly sequenced species. We have implemented comparative analysis by high-throughput experimental testing of gene dispensability in Saccharomyces uvarum, a sister species of Saccharomyces cerevisiae. We created haploid and heterozygous diploid Tn7 insertional mutagenesis libraries in S. uvarum to identify species-dependent essential genes, with the goal of detecting genes with divergent functions and/or different genetic interactions. Comprehensive gene dispensability comparisons with S. cerevisiae predicted diverged dispensability at 12% of conserved orthologs, and validation experiments confirmed 22 differentially essential genes. Despite their differences in essentiality, these genes were capable of cross-species complementation, demonstrating that trans-acting factors that are background-dependent contribute to differential gene essentiality. This study shows that direct experimental testing of gene disruption phenotypes across species can inform comparative genomic analyses and improve gene annotations. Our method can be widely applied in microorganisms to further our understanding of genome evolution.

Drosophila larvae synthesize the putative oncometabolite L-2-hydroxyglutarate during normal developmental growth.

L-2-hydroxyglutarate (L-2HG) has emerged as a putative oncometabolite that is capable of inhibiting enzymes involved in metabolism, chromatin modification, and cell differentiation. However, despite the ability of L-2HG to interfere with a broad range of cellular processes, this molecule is often characterized as a metabolic waste product. Here, we demonstrate that Drosophila larvae use the metabolic conditions established by aerobic glycolysis to both synthesize and accumulate high concentrations of L-2HG during normal developmental growth. A majority of the larval L-2HG pool is derived from glucose and dependent on the Drosophila estrogen-related receptor (dERR), which promotes L-2HG synthesis by up-regulating expression of the Drosophila homolog of lactate dehydrogenase (dLdh). We also show that dLDH is both necessary and sufficient for directly synthesizing L-2HG and the Drosophila homolog of L-2-hydroxyglutarate dehydrogenase (dL2HGDH), which encodes the enzyme that breaks down L-2HG, is required for stage-specific degradation of the L-2HG pool. In addition, dLDH also indirectly promotes L-2HG accumulation via synthesis of lactate, which activates a metabolic feed-forward mechanism that inhibits dL2HGDH activity and stabilizes L-2HG levels. Finally, we use a genetic approach to demonstrate that dLDH and L-2HG influence position effect variegation and DNA methylation, suggesting that this compound serves to coordinate glycolytic flux with epigenetic modifications. Overall, our studies demonstrate that growing animal tissues synthesize L-2HG in a controlled manner, reveal a mechanism that coordinates glucose catabolism with L-2HG synthesis, and establish the fly as a unique model system for studying the endogenous functions of L-2HG during cell growth and proliferation.

Functional genetic discovery of enzymes using full-scan mass spectrometry metabolomics 1.

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.

Proteomic analysis of the human KEOPS complex identifies C14ORF142 as a core subunit homologous to yeast Gon7.

The KEOPS/EKC complex is a tRNA modification complex involved in the biosynthesis of N6-threonylcarbamoyladenosine (t6A), a universally conserved tRNA modification found on ANN-codon recognizing tRNAs. In archaea and eukaryotes, KEOPS is composed of OSGEP/Kae1, PRPK/Bud32, TPRKB/Cgi121 and LAGE3/Pcc1. In fungi, KEOPS contains an additional subunit, Gon7, whose orthologs outside of fungi, if existent, remain unidentified. In addition to displaying defective t6A biosynthesis, Saccharomyces cerevisiae strains harboring KEOPS mutations are compromised for telomere homeostasis, growth and transcriptional co-activation. To identify a Gon7 ortholog in multicellular eukaryotes as well as to uncover KEOPS-interacting proteins that may link t6A biosynthesis to the diverse set of KEOPS mutant phenotypes, we conducted a proteomic analysis of human KEOPS. This work identified 152 protein interactors, one of which, C14ORF142, interacted strongly with all four KEOPS subunits, suggesting that it may be a core component of human KEOPS. Further characterization of C14ORF142 revealed that it shared a number of biophysical and biochemical features with fungal Gon7, suggesting that C14ORF142 is the human ortholog of Gon7. In addition, our proteomic analysis identified specific interactors for different KEOPS subcomplexes, hinting that individual KEOPS subunits may have additional functions outside of t6A biosynthesis.

Heritability and genetic basis of protein level variation in an outbred population.

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.

Dissection of genetically complex traits with extremely large pools of yeast segregants.

Most heritable traits, including many human diseases, are caused by multiple loci. Studies in both humans and model organisms, such as yeast, have failed to detect a large fraction of the loci that underlie such complex traits. A lack of statistical power to identify multiple loci with small effects is undoubtedly one of the primary reasons for this problem. We have developed a method in yeast that allows the use of much larger sample sizes than previously possible and hence permits the detection of multiple loci with small effects. The method involves generating very large numbers of progeny from a cross between two Saccharomyces cerevisiae strains and then phenotyping and genotyping pools of these offspring. We applied the method to 17 chemical resistance traits and mitochondrial function, and identified loci for each of these phenotypes. We show that the level of genetic complexity underlying these quantitative traits is highly variable, with some traits influenced by one major locus and others by at least 20 loci. Our results provide an empirical demonstration of the genetic complexity of a number of traits and show that it is possible to identify many of the underlying factors using straightforward techniques. Our method should have broad applications in yeast and can be extended to other organisms.

Yeast metabolic and signaling genes are required for heat-shock survival and have little overlap with the heat-induced genes.

Genome-wide gene-expression studies have shown that hundreds of yeast genes are induced or repressed transiently by changes in temperature; many are annotated to stress response on this basis. To obtain a genome-scale assessment of which genes are functionally important for innate and/or acquired thermotolerance, we combined the use of a barcoded pool of ~4,800 nonessential, prototrophic Saccharomyces cerevisiae deletion strains with Illumina-based deep-sequencing technology. As reported in other recent studies that have used deletion mutants to study stress responses, we observed that gene deletions resulting in the highest thermosensitivity generally are not the same as those transcriptionally induced in response to heat stress. Functional analysis of identified genes revealed that metabolism, cellular signaling, and chromatin regulation play roles in regulating thermotolerance and in acquired thermotolerance. However, for most of the genes identified, the molecular mechanism behind this action remains unclear. In fact, a large fraction of identified genes are annotated as having unknown functions, further underscoring our incomplete understanding of the response to heat shock. We suggest that survival after heat shock depends on a small number of genes that function in assessing the metabolic health of the cell and/or regulate its growth in a changing environment.

Reconstitution and characterization of eukaryotic N6-threonylcarbamoylation of tRNA using a minimal enzyme system.

The universally conserved Kae1/Qri7/YgjD and Sua5/YrdC protein families have been implicated in growth, telomere homeostasis, transcription and the N6-threonylcarbamoylation (t(6)A) of tRNA, an essential modification required for translational fidelity by the ribosome. In bacteria, YgjD orthologues operate in concert with the bacterial-specific proteins YeaZ and YjeE, whereas in archaeal and eukaryotic systems, Kae1 operates as part of a larger macromolecular assembly called KEOPS with Bud32, Cgi121, Gon7 and Pcc1 subunits. Qri7 orthologues function in the mitochondria and may represent the most primitive member of the Kae1/Qri7/YgjD protein family. In accordance with previous findings, we confirm that Qri7 complements Kae1 function and uncover that Qri7 complements the function of all KEOPS subunits in growth, t(6)A biosynthesis and, to a partial degree, telomere maintenance. These observations suggest that Kae1 provides a core essential function that other subunits within KEOPS have evolved to support. Consistent with this inference, Qri7 alone is sufficient for t(6)A biosynthesis with Sua5 in vitro. In addition, the 2.9 Å crystal structure of Qri7 reveals a simple homodimer arrangement that is supplanted by the heterodimerization of YgjD with YeaZ in bacteria and heterodimerization of Kae1 with Pcc1 in KEOPS. The partial complementation of telomere maintenance by Qri7 hints that KEOPS has evolved novel functions in higher organisms.

Nucleotide degradation and ribose salvage in yeast.

Nucleotide degradation is a universal metabolic capability. Here we combine metabolomics, genetics and biochemistry to characterize the yeast pathway. Nutrient starvation, via PKA, AMPK/SNF1, and TOR, triggers autophagic breakdown of ribosomes into nucleotides. A protein not previously associated with nucleotide degradation, Phm8, converts nucleotide monophosphates into nucleosides. Downstream steps, which involve the purine nucleoside phosphorylase, Pnp1, and pyrimidine nucleoside hydrolase, Urh1, funnel ribose into the nonoxidative pentose phosphate pathway. During carbon starvation, the ribose-derived carbon accumulates as sedoheptulose-7-phosphate, whose consumption by transaldolase is impaired due to depletion of transaldolase's other substrate, glyceraldehyde-3-phosphate. Oxidative stress increases glyceraldehyde-3-phosphate, resulting in rapid consumption of sedoheptulose-7-phosphate to make NADPH for antioxidant defense. Ablation of Phm8 or double deletion of Pnp1 and Urh1 prevent effective nucleotide salvage, resulting in metabolite depletion and impaired survival of starving yeast. Thus, ribose salvage provides means of surviving nutrient starvation and oxidative stress.

MOB rules: Antibiotic Exposure Reprograms Metabolism to Mobilize Bacillus subtilis in Competitive Interactions.

Antibiotics have dose-dependent effects on exposed bacteria. The medicinal use of antibiotics relies on their growth-inhibitory activities at sufficient concentrations. At subinhibitory concentrations, exposure effects vary widely among different antibiotics and bacteria. Bacillus subtilis responds to bacteriostatic translation inhibitors by mobilizing a population of cells (MOB-Mobilized Bacillus) to spread across a surface. How B. subtilis regulates the antibiotic-induced mobilization is not known. In this study, we used chloramphenicol to identify regulatory functions that B. subtilis requires to coordinate cell mobilization following subinhibitory exposure. We measured changes in gene expression and metabolism and mapped the results to a network of regulatory proteins that direct the mobile response. Our data reveal that several transcriptional regulators coordinately control the reprogramming of metabolism to support mobilization. The network regulates changes in glycolysis, nucleotide metabolism, and amino acid metabolism that are signature features of the mobilized population. Among the hundreds of genes with changing expression, we identified two, pdhA and pucA, where the magnitudes of their changes in expression, and in the abundance of associated metabolites, reveal hallmark metabolic features of the mobilized population. Using reporters of pdhA and pucA expression, we visualized the separation of major branches of metabolism in different regions of the mobilized population. Our results reveal a regulated response to chloramphenicol exposure that enables a population of bacteria in different metabolic states to mount a coordinated mobile response.

Comparative gene expression between two yeast species.

BACKGROUND: Comparative genomics brings insight into sequence evolution, but even more may be learned by coupling sequence analyses with experimental tests of gene function and regulation. However, the reliability of such comparisons is often limited by biased sampling of expression conditions and incomplete knowledge of gene functions across species. To address these challenges, we previously systematically generated expression profiles in Saccharomyces bayanus to maximize functional coverage as compared to an existing Saccharomyces cerevisiae data repository. RESULTS: In this paper, we take advantage of these two data repositories to compare patterns of ortholog expression in a wide variety of conditions. First, we developed a scalable metric for expression divergence that enabled us to detect a significant correlation between sequence and expression conservation on the global level, which previous smaller-scale expression studies failed to detect. Despite this global conservation trend, between-species gene expression neighborhoods were less well-conserved than within-species comparisons across different environmental perturbations, and approximately 4% of orthologs exhibited a significant change in co-expression partners. Furthermore, our analysis of matched perturbations collected in both species (such as diauxic shift and cell cycle synchrony) demonstrated that approximately a quarter of orthologs exhibit condition-specific expression pattern differences. CONCLUSIONS: Taken together, these analyses provide a global view of gene expression patterns between two species, both in terms of the conditions and timing of a gene's expression as well as co-expression partners. Our results provide testable hypotheses that will direct future experiments to determine how these changes may be specified in the genome.

Quantitative measurement of allele-specific protein expression in a diploid yeast hybrid by LC-MS.

Understanding the genetic basis of gene regulatory variation is a key goal of evolutionary and medical genetics. Regulatory variation can act in an allele-specific manner (cis-acting) or it can affect both alleles of a gene (trans-acting). Differential allele-specific expression (ASE), in which the expression of one allele differs from another in a diploid, implies the presence of cis-acting regulatory variation. While microarrays and high-throughput sequencing have enabled genome-wide measurements of transcriptional ASE, methods for measurement of protein ASE (pASE) have lagged far behind. We describe a flexible, accurate, and scalable strategy for measurement of pASE by liquid chromatography-coupled mass spectrometry (LC-MS). We apply this approach to a hybrid between the yeast species Saccharomyces cerevisiae and Saccharomyces bayanus. Our results provide the first analysis of the relative contribution of cis-acting and trans-acting regulatory differences to protein expression divergence between yeast species.

Mitochondrial Aconitase ACO2 Links Iron Homeostasis with Tumorigenicity in Non-Small Cell Lung Cancer.

The ability of a patient tumor to engraft an immunodeficient mouse is the strongest known independent indicator of poor prognosis in early-stage non-small cell lung cancer (NSCLC). Analysis of primary NSCLC proteomes revealed low-level expression of mitochondrial aconitase (ACO2) in the more aggressive, engrafting tumors. Knockdown of ACO2 protein expression transformed immortalized lung epithelial cells, whereas upregulation of ACO2 in transformed NSCLC cells inhibited cell proliferation in vitro and tumor growth in vivo. High level ACO2 increased iron response element binding protein 1 (IRP1) and the intracellular labile iron pool. Impaired cellular proliferation associated with high level ACO2 was reversed by treatment of cells with an iron chelator, whereas increased cell proliferation associated with low level ACO2 was suppressed by treatment of cells with iron. Expression of CDGSH iron-sulfur (FeS) domain-containing protein 1 [CISD1; also known as mitoNEET (mNT)] was modulated by ACO2 expression level and inhibition of mNT by RNA interference or by treatment of cells with pioglitazone also increased iron and cell death. Hence, ACO2 is identified as a regulator of iron homeostasis and mNT is implicated as a target in aggressive NSCLC. IMPLICATIONS: FeS cluster-associated proteins including ACO2, mNT (encoded by CISD1), and IRP1 (encoded by ACO1) are part of an "ACO2-Iron Axis" that regulates iron homeostasis and is a determinant of a particularly aggressive subset of NSCLC.

Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis.

Mitochondria are central to many cellular processes including respiration, ion homeostasis, and apoptosis. Using computational predictions combined with traditional quantitative experiments, we have identified 100 proteins whose deficiency alters mitochondrial biogenesis and inheritance in Saccharomyces cerevisiae. In addition, we used computational predictions to perform targeted double-mutant analysis detecting another nine genes with synthetic defects in mitochondrial biogenesis. This represents an increase of about 25% over previously known participants. Nearly half of these newly characterized proteins are conserved in mammals, including several orthologs known to be involved in human disease. Mutations in many of these genes demonstrate statistically significant mitochondrial transmission phenotypes more subtle than could be detected by traditional genetic screens or high-throughput techniques, and 47 have not been previously localized to mitochondria. We further characterized a subset of these genes using growth profiling and dual immunofluorescence, which identified genes specifically required for aerobic respiration and an uncharacterized cytoplasmic protein required for normal mitochondrial motility. Our results demonstrate that by leveraging computational analysis to direct quantitative experimental assays, we have characterized mutants with subtle mitochondrial defects whose phenotypes were undetected by high-throughput methods.

Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone orbitrap mass spectrometer.

We present a liquid chromatography-mass spectrometry (LC-MS) method that capitalizes on the mass-resolving power of the orbitrap to enable sensitive and specific measurement of known and unanticipated metabolites in parallel, with a focus on water-soluble species involved in core metabolism. The reversed phase LC method, with a cycle time 25 min, involves a water-methanol gradient on a C18 column with tributylamine as the ion pairing agent. The MS portion involves full scans from 85 to 1000 m/z at 1 Hz and 100,000 resolution in negative ion mode on a stand alone orbitrap ("Exactive"). The median limit of detection, across 80 metabolite standards, was 5 ng/mL with the linear range typically >or=100-fold. For both standards and a cellular extract from Saccharomyces cerevisiae (Baker's yeast), the median inter-run relative standard deviation in peak intensity was 8%. In yeast exact, we detected 137 known compounds, whose (13)C-labeling patterns could also be tracked to probe metabolic flux. In yeast engineered to lack a gene of unknown function (YKL215C), we observed accumulation of an ion of m/z 128.0351, which we subsequently confirmed to be oxoproline, resulting in annotation of YKL215C as an oxoprolinase. These examples demonstrate the suitability of the present method for quantitative metabolomics, fluxomics, and discovery metabolite profiling.

The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction.

MOTIVATION: Rapidly expanding repositories of highly informative genomic data have generated increasing interest in methods for protein function prediction and inference of biological networks. The successful application of supervised machine learning to these tasks requires a gold standard for protein function: a trusted set of correct examples, which can be used to assess performance through cross-validation or other statistical approaches. Since gene annotation is incomplete for even the best studied model organisms, the biological reliability of such evaluations may be called into question. RESULTS: We address this concern by constructing and analyzing an experimentally based gold standard through comprehensive validation of protein function predictions for mitochondrion biogenesis in Saccharomyces cerevisiae. Specifically, we determine that (i) current machine learning approaches are able to generalize and predict novel biology from an incomplete gold standard and (ii) incomplete functional annotations adversely affect the evaluation of machine learning performance. While computational approaches performed better than predicted in the face of incomplete data, relative comparison of competing approaches-even those employing the same training data-is problematic with a sparse gold standard. Incomplete knowledge causes individual methods' performances to be differentially underestimated, resulting in misleading performance evaluations. We provide a benchmark gold standard for yeast mitochondria to complement current databases and an analysis of our experimental results in the hopes of mitigating these effects in future comparative evaluations. AVAILABILITY: The mitochondrial benchmark gold standard, as well as experimental results and additional data, is available at http://function.princeton.edu/mitochondria.

Directing experimental biology: a case study in mitochondrial biogenesis.

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.