Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome

Genetic effects on molecular network states explain complex traits

The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.

Dynamic metabolome profiling uncovers potential TOR signaling genes

Although the genetic code of the yeast Saccharomyces cerevisiae was sequenced 25 years ago, the characterization of the roles of genes within it is far from complete. The lack of a complete mapping of functions to genes hampers systematic understanding of the biology of the cell. The advent of high-throughput metabolomics offers a unique approach to uncovering gene function with an attractive combination of cost, robustness, and breadth of applicability. Here, we used flow-injection time-of-flight mass spectrometry to dynamically profile the metabolome of 164 loss-of-function mutants in TOR and receptor or receptor-like genes under a time course of rapamycin treatment, generating a dataset with >7000 metabolomics measurements. In order to provide a resource to the broader community, those data are made available for browsing through an interactive data visualization app hosted at https://rapamycin-yeast.ethz.ch. We demonstrate that dynamic metabolite responses to rapamycin are more informative than steady-state responses when recovering known regulators of TOR signaling, as well as identifying new ones. Deletion of a subset of the novel genes causes phenotypes and proteome responses to rapamycin that further implicate them in TOR signaling. We found that one of these genes, CFF1, was connected to the regulation of pyrimidine biosynthesis through URA10. These results demonstrate the efficacy of the approach for flagging novel potential TOR signaling-related genes and highlight the utility of dynamic perturbations when using functional metabolomics to deliver biological insight.

TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle

Recent studies have revealed that the growth rate of budding yeast and mammalian cells varies during the cell cycle. By linking a multitude of signals to cell growth, the highly conserved Target of Rapamycin Complex 1 (TORC1) and Protein Kinase A (PKA) pathways are prime candidates for mediating the dynamic coupling between growth and division. However, measurements of TORC1 and PKA activity during the cell cycle are still lacking. Following the localization dynamics of two TORC1 and PKA targets via time-lapse microscopy in hundreds of yeast cells, we found that the activity of these pathways towards ribosome biogenesis fluctuates in synchrony with the cell cycle even under constant external conditions. Mutations of upstream TORC1 and PKA regulators suggested that internal metabolic signals partially mediate these activity changes. Our study reveals a new aspect of TORC1 and PKA signaling, which will be important for understanding growth regulation during the cell cycle.

Metabolic signatures of regulation by phosphorylation and acetylation

Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.

Real-Time Single-Cell Monitoring of Drug Effects Using Droplet-Based Microfluidic Technology: A Proof-of-Concept Study

Drugs that act on ribosome biogenesis and cell proliferation play important roles in treatment of human diseases. Moreover, measurement of drug effects at a single-cell level would create vast opportunities for pharmaceutical innovation. We present in this study an original proof-of-concept study of single-cell measurement of drug effects with a focus on inhibition of ribosome biogenesis and cell proliferation, and using yeast (Saccharomyces cerevisiae) as a model eukaryotic organism. We employed a droplet-based microfluidic technology and nucleolar protein-tagged strain of the yeast for real-time monitoring of the cells. We report a comprehensive account of the ways in which interrelated pathways are impacted by drug treatment in a single-cell level. Self-organizing maps, transcription factor, and Gene Ontology enrichment analyses were utilized to these ends. This article makes a contribution to advance single-cell measurement of drug effects. We anticipate the microfluidic technology platform presented herein is well poised for future applications in personalized/precision medicine research as well as in industrial settings for drug discovery and clinical development. In addition, the study offers new insights on ribosome biogenesis and cell proliferation that should prove useful in cancer research and other complex human diseases impacted by these key cellular processes.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BACKGROUND

The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.

RESULTS

We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.

CONCLUSIONS

SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

Cellular quiescence in budding yeast

Cellular quiescence, the temporary and reversible exit from proliferative growth, is the predominant state of all cells. However, our understanding of the biological processes and molecular mechanisms that underlie cell quiescence remains incomplete. As with the mitotic cell cycle, budding and fission yeast are preeminent model systems for studying cellular quiescence owing to their rich experimental toolboxes and the evolutionary conservation across eukaryotes of pathways and processes that control quiescence. Here, we review current knowledge of cell quiescence in budding yeast and how it pertains to cellular quiescence in other organisms, including multicellular animals. Quiescence entails large-scale remodeling of virtually every cellular process, organelle, gene expression, and metabolic state that is executed dynamically as cells undergo the initiation, maintenance, and exit from quiescence. We review these major transitions, our current understanding of their molecular bases, and highlight unresolved questions. We summarize the primary methods employed for quiescence studies in yeast and discuss their relative merits. Understanding cell quiescence has important consequences for human disease as quiescent single-celled microbes are notoriously difficult to kill and quiescent human cells play important roles in diseases such as cancer. We argue that research on cellular quiescence will be accelerated through the adoption of common criteria, and methods, for defining cell quiescence. An integrated approach to studying cell quiescence, and a focus on the behavior of individual cells, will yield new insights into the pathways and processes that underlie cell quiescence leading to a more complete understanding of the life cycle of cells. TAKE AWAY: Quiescent cells are viable cells that have reversibly exited the cell cycle Quiescence is induced in response to a variety of nutrient starvation signals Quiescence is executed dynamically through three phases: initiation, maintenance, and exit Quiescence entails large-scale remodeling of gene expression, organelles, and metabolism Single-cell approaches are required to address heterogeneity among quiescent cells.

KAE1 Allelic Variants Affect TORC1 Activation and Fermentation Kinetics in Saccharomyces cerevisiae

The eukaryotic domain-conserved TORC1 signalling pathway connects growth with nutrient sufficiency, promoting anabolic processes such as ribosomal biogenesis and protein synthesis. In Saccharomyces cerevisiae, TORC1 is activated mainly by the nitrogen sources. Recently, this pathway has gotten renewed attention but now in the context of the alcoholic fermentation, due to its key role in nitrogen metabolism regulation. Although the distal and proximal effectors downstream TORC1 are well characterised in yeast, the mechanism by which TORC1 is activated by nitrogen sources is not fully understood. In this work, we took advantage of a previously developed microculture-based methodology, which indirectly evaluates TORC1 activation in a nitrogen upshift experiment, to identify genetic variants affecting the activation of this pathway. We used this method to phenotype a recombinant population derived from two strains (SA and WE) with different geographic origins, which show opposite phenotypes for TORC1 activation by glutamine. Using this phenotypic information, we performed a QTL mapping that allowed us to identify several QTLs for TORC1 activation. Using a reciprocal hemizygous analysis, we validated GUS1, KAE1, PIB2, and UTH1 as genes responsible for the natural variation in the TORC1 activation. We observed that reciprocal hemizygous strains for KAE1 (ATPase required for t⁶A tRNA modification) gene showed the greatest phenotypic differences for TORC1 activation, with the hemizygous strain carrying the SA allele (KAE1^SA) showing the higher TORC1 activation. In addition, we evaluated the fermentative capacities of the hemizygous strains under low nitrogen conditions, observing an antagonistic effect for KAE1^SA allele, where the hemizygous strain containing this allele presented the lower fermentation rate. Altogether, these results highlight the importance of the tRNA processing in TORC1 activation and connects this pathway with the yeasts fermentation kinetics under nitrogen-limited conditions.

Indirect monitoring of TORC1 signalling pathway reveals molecular diversity among different yeast strains

Saccharomyces cerevisiae is the main species responsible for the alcoholic fermentation in wine production. One of the main problems in this process is the deficiency of nitrogen sources in the grape must, which can lead to stuck or sluggish fermentations. Currently, yeast nitrogen consumption and metabolism are under active inquiry, with emphasis on the study of the TORC1 signalling pathway, given its central role responding to nitrogen availability and influencing growth and cell metabolism. However, the mechanism by which different nitrogen sources activates TORC1 is not completely understood. Existing methods to evaluate TORC1 activation by nitrogen sources are time-consuming, making difficult the analyses of large numbers of strains. In this work, a new indirect method for monitoring TORC1 pathway was developed on the basis of the luciferase reporter gene controlled by the promoter region of RPL26A gene, a gene known to be expressed upon TORC1 activation. The method was tested in strains representative of the clean lineages described so far in S. cerevisiae. The activation of the TORC1 pathway by a proline-to-glutamine upshift was indirectly evaluated using our system and the traditional direct methods based on immunoblot (Sch9 and Rps6 phosphorylation). Regardless of the different molecular readouts obtained with both methodologies, the general results showed a wide phenotypic variation between the representative strains analysed. Altogether, this easy-to-use assay opens the possibility to study the molecular basis for the differential TORC1 pathway activation, allowing to interrogate a larger number of strains in the context of nitrogen metabolism phenotypic differences.

Sporadic cases of lumpy skin disease among cattle in Sharkia province, Egypt: Genetic characterization of lumpy skin disease virus isolates and pathological findings

BACKGROUND AND AIM

Lumpy skin disease (LSD) is a highly infectious viral disease upsetting cattle, caused by LSD virus (LSDV) within the family Poxviridae. Sporadic cases of LSD have been observed in cattle previously vaccinated with the Romanian sheep poxvirus (SPPV) vaccine during the summer of 2016 in Sharkia province, Egypt. The present study was undertaken to perform molecular characterization of LSDV strains which circulated in this period as well as investigate their phylogenetic relatedness with published reference capripoxvirus genome sequences.

MATERIALS AND METHODS

A total of 82 skin nodules, as well as 5 lymph nodes, were collected from suspect LSD cases, and the virus was isolated in embryonated chicken eggs (ECEs). LSD was confirmed by polymerase chain reactions amplification of the partial and full-length sequences of the attachment and G-protein-coupled chemokine receptor (GPCR) genes, respectively, as well as a histopathological examination of the lesions. Molecular characterization of the LSDV isolates was conducted by sequencing the GPCR gene.

RESULTS

Characteristic skin nodules that covered the whole intact skin, as well as lymphadenopathy, were significant clinical signs in all suspected cases. LSDV isolation in ECEs revealed the characteristic focal white pock lesions dispersed on the chorioallantoic membranes. Histopathologic examination showed characteristic eosinophilic intracytoplasmic inclusion bodies within inflammatory cell infiltration. Phylogenetic analysis revealed that the LSDV isolates were clustered together with other African and European LSDV strains. In addition, the LSDV isolates have a unique signature of LSDVs (A11, T12, T34, S99, and P199).

CONCLUSION

LSDV infections have been detected in cattle previously vaccinated with Romanian SPPV vaccine during the summer of 2016 and making the evaluation of vaccine efficacy under field conditions necessary.

Gtr/Ego-independent TORC1 activation is achieved through a glutamine-sensitive interaction with Pib2 on the vacuolar membrane

TORC1 is a central regulator of cell growth in response to amino acids. The role of the evolutionarily conserved Gtr/Rag pathway in the regulation of TORC1 is well-established. Recent genetic studies suggest that an additional regulatory pathway, depending on the activity of Pib2, plays a role in TORC1 activation independently of the Gtr/Rag pathway. However, the interplay between the Pib2 pathway and the Gtr/Rag pathway remains unclear. In this study, we show that Pib2 and Gtr/Ego form distinct complexes with TORC1 in a mutually exclusive manner, implying dedicated functional relationships between TORC1 and Pib2 or Gtr/Rag in response to specific amino acids. Furthermore, simultaneous depletion of Pib2 and the Gtr/Ego system abolishes TORC1 activity and completely compromises the vacuolar localization of TORC1. Thus, the amino acid-dependent activation of TORC1 is achieved through the Pib2 and Gtr/Ego pathways alone. Finally, we show that glutamine induces a dose-dependent increase in Pib2-TORC1 complex formation, and that glutamine binds directly to the Pib2 complex. These data provide strong preliminary evidence for Pib2 functioning as a putative glutamine sensor in the regulation of TORC1.

TORC1 is a central regulator of cell growth in response to amino acids. The evolutionarily conserved Gtr/Rag pathway is a well-established TORC1 regulatory pathway. In this study, we show that two molecular machineries, Pib2 and Gtr/Ego, form distinct complexes with TORC1 in a mutually exclusive manner, implying an exclusive functional relationship between TORC1 and Pib2 or Gtr/Rag in response to various amino acids. We also show that the amino acid-dependent activation of TORC1 is achieved through the Pib2 and Gtr/Ego pathways by anchoring them to the vacuolar membrane. Finally, we show that glutamine binds directly to the Pib2 complex and that glutamine enhances Pib2-TORC1 complex formation. Collectively we provide evidence supporting a role for Pib2 as an element of a putative glutamine sensor.

Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism

The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.

Real Talk: The Inter-play Between the mTOR, AMPK, and Hexosamine Biosynthetic Pathways in Cell Signaling

O-linked N-acetylglucosamine, better known as O-GlcNAc, is a sugar post-translational modification participating in a diverse range of cell functions. Disruptions in the cycling of O-GlcNAc mediated by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), respectively, is a driving force for aberrant cell signaling in disease pathologies, such as diabetes, obesity, Alzheimer's disease, and cancer. Production of UDP-GlcNAc, the metabolic substrate for OGT, by the Hexosamine Biosynthetic Pathway (HBP) is controlled by the input of amino acids, fats, and nucleic acids, making O-GlcNAc a key nutrient-sensor for fluctuations in these macromolecules. The mammalian target of rapamycin (mTOR) and AMP-activated protein kinase (AMPK) pathways also participate in nutrient-sensing as a means of controlling cell activity and are significant factors in a variety of pathologies. Research into the individual nutrient-sensitivities of the HBP, AMPK, and mTOR pathways has revealed a complex regulatory dynamic, where their unique responses to macromolecule levels coordinate cell behavior. Importantly, cross-talk between these pathways fine-tunes the cellular response to nutrients. Strong evidence demonstrates that AMPK negatively regulates the mTOR pathway, but O-GlcNAcylation of AMPK lowers enzymatic activity and promotes growth. On the other hand, AMPK can phosphorylate OGT leading to changes in OGT function. Complex sets of interactions between the HBP, AMPK, and mTOR pathways integrate nutritional signals to respond to changes in the environment. In particular, examining these relationships using systems biology approaches might prove a useful method of exploring the complex nature of cell signaling. Overall, understanding the complex interactions of these nutrient pathways will provide novel mechanistic information into how nutrients influence health and disease.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

Phosphorylation is a broad regulatory mechanism with implications in nearly all processes of the cell. However, a global understanding of possible regulatory mechanisms remains elusive. In this study, we examined the potential regulatory role of kinases, phosphatases and transcription-factors in yeast metabolism across a variety of steady-state and dynamic conditions. The main novelty of our analysis was to infer putative regulatory interactions from in silico estimated activity of transcription-factors and kinases/phosphatases. This provided functional information about the proteins important for the experimental conditions at hand that had not been uncovered before. We showed that activity profiles are predictive features to estimate metabolite changes in dynamic experiments, while the same was not visible in steady-state conditions. We also showed that dynamic experiments could be used to recapitulate and provide novel TFs-metabolite and K/Ps-metabolite regulatory associations. We believe these findings illustrates the usefulness of this approach for future integrative studies interested in studying metabolic regulation.

Frontiers of high-throughput metabolomics

Large scale metabolomics studies are increasingly used to investigate genetically different individuals and time-dependent responses to environmental stimuli. New mass spectrometric approaches with at least an order of magnitude more rapid analysis of small molecules within the cell's metabolome are now paving the way towards true high-throughput metabolomics, opening new opportunities in systems biology, functional genomics, drug discovery, and personalized medicine. Here we discuss the impact and advantages of the progress made in profiling large cohorts and dynamic systems with high temporal resolution and automated sampling. In both areas, high-throughput metabolomics is gaining traction because it can generate hypotheses on molecular mechanisms and metabolic regulation. We conclude with the current status of the less mature single cell analyses where high-throughput analytics will be indispensable to resolve metabolic heterogeneity in populations and compartmentalization of metabolites.

Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach

Kinetic models are essential to quantitatively understand and predict the behaviour of metabolic networks. Detailed and thermodynamically feasible kinetic models of metabolism are inherently difficult to formulate and fit. They have a large number of heterogeneous parameters, are non-linear and have complex interactions. Many powerful fitting strategies are ruled out by the intractability of the likelihood function. Here, we have developed a computational framework capable of fitting feasible and accurate kinetic models using Approximate Bayesian Computation. This framework readily supports advanced modelling features such as model selection and model-based experimental design. We illustrate this approach on the tightly-regulated mammalian methionine cycle. Sampling from the posterior distribution, the proposed framework generated thermodynamically feasible parameter samples that converged on the true values, and displayed remarkable prediction accuracy in several validation tests. Furthermore, a posteriori analysis of the parameter distributions enabled appraisal of the systems properties of the network (e.g., control structure) and key metabolic regulations. Finally, the framework was used to predict missing allosteric interactions.

Network-based approaches for analysis of complex biological systems

Cells function and respond to changes in their environment by the coordinated activity of their molecular components, including mRNAs, proteins and metabolites. At the heart of proper cellular function are molecular networks connecting these components to process extra-cellular environmental signals and drive dynamic, context-specific cellular responses. Network-based computational approaches aim to systematically integrate measurements from high-throughput experiments to gain a global understanding of cellular function under changing environmental conditions. We provide an overview of recent methodological developments toward solving two major computational problems within this field in the past two years (2013-2015): network reconstruction and network-based interpretation. Looking forward, we envision development of methods that can predict phenotypes with high accuracy as well as provide biologically plausible mechanistic hypotheses.

Elucidation of Genetic Interactions in the Yeast GATA-Factor Network Using Bayesian Model Selection

Understanding the structure and function of complex gene regulatory networks using classical genetic assays is an error-prone procedure that frequently generates ambiguous outcomes. Even some of the best-characterized gene networks contain interactions whose validity is not conclusively proven. Founded on dynamic experimental data, mechanistic mathematical models are able to offer detailed insights that would otherwise require prohibitively large numbers of genetic experiments. Here we attempt mechanistic modeling of the transcriptional network formed by the four GATA-factor proteins, a well-studied system of central importance for nitrogen-source regulation of transcription in the yeast Saccharomyces cerevisiae. To resolve ambiguities in the network organization, we encoded a set of five interactions hypothesized in the literature into a set of 32 mathematical models, and employed Bayesian model selection to identify the most plausible set of interactions based on dynamic gene expression data. The top-ranking model was validated on newly generated GFP reporter dynamic data and was subsequently used to gain a better understanding of how yeast cells organize their transcriptional response to dynamic changes of nitrogen sources. Our work constitutes a necessary and important step towards obtaining a holistic view of the yeast nitrogen regulation mechanisms; on the computational side, it provides a demonstration of how powerful Monte Carlo techniques can be creatively combined and used to address the great challenges of large-scale dynamical system inference.

Gene regulatory networks underlie all key processes that enable a cell to maintain long-term homeostasis in a changing environment. Understanding the structure and function of complex gene networks is an experimentally difficult and error-prone procedure. Mechanistic mathematical modeling promises to alleviate these problems, as we demonstrate here for the yeast GATA-factor network, the central controller of the cellular response to nitrogen source quality. Despite years of targeted studies, the interaction pattern of this network is still not known precisely. To resolve several still-remaining ambiguities, we generated a set of alternative mathematical models, and compared them against each other using Bayesian model selection based on dynamic gene expression data. The top-ranking model was then validated on a separate, newly generated dataset. Our work thus provides new insights to the mechanism of nitrogen regulation in yeast, while at the same time overcoming some key computational inference problems for large models in systems biology.

Integration of omics: more than the sum of its parts

Genome scale data on biological systems has increasingly become available by sequencing of DNA and RNA, and by mass spectrometric quantification of proteins and metabolites. The cellular components from which these -omics regimes are derived act as one integrated system in vivo; thus, there is a natural instinct to integrate -omics data types. Statistical analyses, the use of previous knowledge in the form of networks, and the use of time-resolved measurements are three key design elements for life scientists to consider in planning integrated -omics studies. These design elements are reviewed in the context of multiple recent systems biology studies that leverage data from different types of -omics analyses. While most of these studies rely on well-established model organisms, the concepts for integrating -omics data that were developed in these studies can help to enable systems research in the field of cancer biology.

Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations

Cells respond to environmental and/or genetic perturbations in order to survive and proliferate. Characterization of the changes after various stimuli at different -omics levels is crucial to comprehend the adaptation of cells to the changing conditions. Genome-wide quantification and analysis of transcript levels, the genes affected by perturbations, extends our understanding of cellular metabolism by pointing out the mechanisms that play role in sensing the stress caused by those perturbations and related signaling pathways, and in this way guides us to achieve endeavors, such as rational engineering of cells or interpretation of disease mechanisms. Saccharomyces cerevisiae as a model system has been studied in response to different perturbations and corresponding transcriptional profiles were followed either statically or/and dynamically, short and long term. This review focuses on response of yeast cells to diverse stress inducing perturbations, including nutritional changes, ionic stress, salt stress, oxidative stress, osmotic shock, and to genetic interventions such as deletion and overexpression of genes. It is aimed to conclude on common regulatory phenomena that allow yeast to organize its transcriptomic response after any perturbation under different external conditions.

Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple 'Omic' Layers

We propose 'trans-omic' analysis for reconstructing global biochemical networks across multiple omic layers by use of both multi-omic measurements and computational data integration. We introduce technologies for connecting multi-omic data based on prior knowledge of biochemical interactions and characterize a biochemical trans-omic network by concepts of a static and dynamic nature. We introduce case studies of metabolism-centric trans-omic studies to show how to reconstruct a biochemical trans-omic network by connecting multi-omic data and how to analyze it in terms of the static and dynamic nature. We propose a trans-ome-wide association study (trans-OWAS) connecting phenotypes with trans-omic networks that reflect both genetic and environmental factors, which can characterize several complex lifestyle diseases as breakdowns in the trans-omic system.

Emerging flow injection mass spectrometry methods for high-throughput quantitative analysis

Where does flow injection analysis mass spectrometry (FIA-MS) stand relative to ambient mass spectrometry (MS) and chromatography-MS? Improvements in FIA-MS methods have resulted in fast-expanding uses of this technique. Key advantages of FIA-MS over chromatography-MS are fast analysis (typical run time <60 s) and method simplicity, and FIA-MS offers high-throughput without compromising sensitivity, precision and accuracy as much as ambient MS techniques. Consequently, FIA-MS is increasingly becoming recognized as a suitable technique for applications where quantitative screening of chemicals needs to be performed rapidly and reliably. The FIA-MS methods discussed herein have demonstrated quantitation of diverse analytes, including pharmaceuticals, pesticides, environmental contaminants, and endogenous compounds, at levels ranging from parts-per-billion (ppb) to parts-per-million (ppm) in very complex matrices (such as blood, urine, and a variety of foods of plant and animal origin), allowing successful applications of the technique in clinical diagnostics, metabolomics, environmental sciences, toxicology, and detection of adulterated/counterfeited goods. The recent boom in applications of FIA-MS for high-throughput quantitative analysis has been driven in part by (1) the continuous improvements in sensitivity and selectivity of MS instrumentation, (2) the introduction of novel sample preparation procedures compatible with standalone mass spectrometric analysis such as salting out assisted liquid-liquid extraction (SALLE) with volatile solutes and NH4(+) QuEChERS, and (3) the need to improve efficiency of laboratories to satisfy increasing analytical demand while lowering operational cost. The advantages and drawbacks of quantitative analysis by FIA-MS are discussed in comparison to chromatography-MS and ambient MS (e.g., DESI, LAESI, DART). Generally, FIA-MS sits 'in the middle' between ambient MS and chromatography-MS, offering a balance between analytical capability and sample analysis throughput suitable for broad applications in life sciences, agricultural chemistry, consumer safety, and beyond.

Dynamic phosphoproteomics reveals TORC1-dependent regulation of yeast nucleotide and amino acid biosynthesis

Phosphoproteomics studies have unraveled the extent of protein phosphorylation as a key cellular regulation mechanism, but assigning functionality to specific phosphorylation events remains a major challenge. TORC1 (target of rapamycin complex 1) is a kinase-containing protein complex that transduces changes in nutrient availability into phosphorylation signaling events that alter cell growth and proliferation. To resolve the temporal sequence of phosphorylation responses to nutritionally and chemically induced changes in TORC1 signaling and to identify previously unknown kinase-substrate relationships in Saccharomyces cerevisiae, we performed quantitative mass spectrometry-based phosphoproteomic analyses after shifts in nitrogen sources and rapamycin treatment. From early phosphorylation events that were consistent over at least two experimental perturbations, we identified 51 candidate and 10 known proximal targets of TORC1 that were direct substrates of TORC1 or of one of its kinase or phosphatase substrates. By correlating these phosphoproteomics data with dynamic metabolomics data, we inferred the functional role of phosphorylation on the metabolic activity of 12 enzymes, including three candidate TORC1-proximal targets: Amd1, which is involved in nucleotide metabolism; Hom3, which is involved in amino acid metabolism; and Tsl1, which mediates carbohydrate storage. Finally, we identified the TORC1 substrates Sch9 and Atg1 as candidate kinases that phosphorylate Amd1 and Hom3, respectively.