Untargeted metabolomics unravels functionalities of phosphorylation sites in Saccharomyces cerevisiae

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

A non-pheromone GPCR is essential for meiosis and ascosporogenesis in the wheat scab fungus

Meiosis is essential for generating genetic diversity and sexual spores, but the regulation of meiosis and ascosporogenesis is not clear in filamentous fungi, in which dikaryotic and diploid cells formed inside fruiting bodies are not free living and independent of pheromones or pheromone receptors. In this study, Gia1, a non-pheromone GPCR (G protein-coupled receptor) with sexual-specific expression in Fusarium graminearum, is found to be essential for ascosporogenesis. The gia1 mutant was normal in perithecium development, crozier formation, and karyogamy but failed to undergo meiosis, which could be partially rescued by a dominant active mutation in GPA1 and activation of the Gpmk1 pathway. GIA1 orthologs have conserved functions in regulating meiosis and ascosporogenesis in Sordariomycetes. GIA1 has a paralog, GIP1, in F. graminearum and other Hypocreales species which is essential for perithecium formation. GIP1 differed from GIA1 in expression profiles and downstream signaling during sexual reproduction. Whereas the C-terminal tail and IR3 were important for intracellular signaling, the N-terminal region and EL3 of Gia1 were responsible for recognizing its ligand, which is likely a protein enriched in developing perithecia, particularly in the gia1 mutant. Taken together, these results showed that GIA1 encodes a non-pheromone GPCR that regulates the entry into meiosis and ascosporogenesis via the downstream Gpmk1 MAP kinase pathway in F. graminearum and other filamentous ascomycetes.

High-throughput metabolomics for the design and validation of a diauxic shift model

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

High-throughput functional characterization of protein phosphorylation sites in yeast

Phosphorylation is a critical post-translational modification involved in the regulation of almost all cellular processes. However, fewer than 5% of thousands of recently discovered phosphosites have been functionally annotated. In this study, we devised a chemical genetic approach to study the functional relevance of phosphosites in Saccharomyces cerevisiae. We generated 474 yeast strains with mutations in specific phosphosites that were screened for fitness in 102 conditions, along with a gene deletion library. Of these phosphosites, 42% exhibited growth phenotypes, suggesting that these are more likely functional. We inferred their function based on the similarity of their growth profiles with that of gene deletions and validated a subset by thermal proteome profiling and lipidomics. A high fraction exhibited phenotypes not seen in the corresponding gene deletion, suggestive of a gain-of-function effect. For phosphosites conserved in humans, the severity of the yeast phenotypes is indicative of their human functional relevance. This high-throughput approach allows for functionally characterizing individual phosphosites at scale.

Extensive regulation of enzyme activity by phosphorylation in Escherichia coli

Protein serine/threonine/tyrosine (S/T/Y) phosphorylation is an essential and frequent post-translational modification in eukaryotes, but historically has been considered less prevalent in bacteria because fewer proteins were found to be phosphorylated and most proteins were modified to a lower degree. Recent proteomics studies greatly expanded the phosphoproteome of Escherichia coli to more than 2000 phosphorylation sites (phosphosites), yet mechanisms of action were proposed for only six phosphosites and fitness effects were described for 38 phosphosites upon perturbation. By systematically characterizing functional relevance of S/T/Y phosphorylation in E. coli metabolism, we found 44 of the 52 mutated phosphosites to be functional based on growth phenotypes and intracellular metabolome profiles. By effectively doubling the number of known functional phosphosites, we provide evidence that protein phosphorylation is a major regulation process in bacterial metabolism. Combining in vitro and in vivo experiments, we demonstrate how single phosphosites modulate enzymatic activity and regulate metabolic fluxes in glycolysis, methylglyoxal bypass, acetate metabolism and the split between pentose phosphate and Entner-Doudoroff pathways through mechanisms that include shielding the substrate binding site, limiting structural dynamics, and disrupting interactions relevant for activity in vivo.

Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics

Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.

The functional landscape of the human phosphoproteome

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Cells sense and respond to stressful environments by utilizing complex signaling networks that integrate diverse signals to coordinate a multi-faceted physiological response. Much of this response is controlled by post-translational protein phosphorylation. Although many regulators that mediate changes in protein phosphorylation are known, how these regulators inter-connect in a single regulatory network that can transmit cellular signals is not known. It is also unclear how regulators that promote growth and regulators that activate the stress response interconnect to reorganize resource allocation during stress. Here, we developed an integrated experimental and computational workflow to infer the signaling network that regulates phosphorylation changes during osmotic stress in the budding yeast Saccharomyces cerevisiae. The workflow integrates data measuring protein phosphorylation changes in response to osmotic stress with known physical interactions between yeast proteins from large-scale datasets, along with other information about how regulators recognize their targets. The resulting network suggested new signaling connections between regulators and pathways, including those involved in regulating growth and defense, and predicted new regulators involved in stress defense. Our work highlights the power of using network inference to deliver new insight on how cells coordinate a diverse adaptive strategy to stress.

Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells

Deregulated signal transduction and energy metabolism are hallmarks of cancer and both play a fundamental role in tumorigenesis. While it is increasingly recognised that signalling and metabolism are highly interconnected, the underpinning mechanisms of their co-regulation are still largely unknown. Here we designed and acquired proteomics, phosphoproteomics, and metabolomics experiments in fumarate hydratase (FH) deficient cells and developed a computational modelling approach to identify putative regulatory phosphorylation-sites of metabolic enzymes. We identified previously reported functionally relevant phosphosites and potentially novel regulatory residues in enzymes of the central carbon metabolism. In particular, we showed that pyruvate dehydrogenase (PDHA1) enzymatic activity is inhibited by increased phosphorylation in FH-deficient cells, restricting carbon entry from glucose to the tricarboxylic acid cycle. Moreover, we confirmed PDHA1 phosphorylation in human FH-deficient tumours. Our work provides a novel approach to investigate how post-translational modifications of enzymes regulate metabolism and could have important implications for understanding the metabolic transformation of FH-deficient cancers with potential clinical applications.

Estimating the total number of phosphoproteins and phosphorylation sites in eukaryotic proteomes

Background

Phosphorylation is the most frequent post-translational modification made to proteins and may regulate protein activity as either a molecular digital switch or a rheostat. Despite the cornucopia of high-throughput (HTP) phosphoproteomic data in the last decade, it remains unclear how many proteins are phosphorylated and how many phosphorylation sites (p-sites) can exist in total within a eukaryotic proteome. We present the first reliable estimates of the total number of phosphoproteins and p-sites for four eukaryotes (human, mouse, Arabidopsis, and yeast).

Results

In all, 187 HTP phosphoproteomic datasets were filtered, compiled, and studied along with two low-throughput (LTP) compendia. Estimates of the number of phosphoproteins and p-sites were inferred by two methods: Capture-Recapture, and fitting the saturation curve of cumulative redundant vs. cumulative non-redundant phosphoproteins/p-sites. Estimates were also adjusted for different levels of noise within the individual datasets and other confounding factors. We estimate that in total, 13 000, 11 000, and 3000 phosphoproteins and 230 000, 156 000, and 40 000 p-sites exist in human, mouse, and yeast, respectively, whereas estimates for Arabidopsis were not as reliable.

Conclusions

Most of the phosphoproteins have been discovered for human, mouse, and yeast, while the dataset for Arabidopsis is still far from complete. The datasets for p-sites are not as close to saturation as those for phosphoproteins. Integration of the LTP data suggests that current HTP phosphoproteomics appears to be capable of capturing 70 % to 95 % of total phosphoproteins, but only 40 % to 60 % of total p-sites.