Tomer Shlomi

Associating Genes and Protein Complexes with Disease via Network Propagation

A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCEs predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.

Network-based prediction of human tissue-specific metabolism.

Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.

Quantitative flux analysis reveals folate-dependent NADPH production

ATP is the dominant energy source in animals for mechanical and electrical work (for example, muscle contraction or neuronal firing). For chemical work, there is an equally important role for NADPH, which powers redox defence and reductive biosynthesis. The most direct route to produce NADPH from glucose is the oxidative pentose phosphate pathway, with malic enzyme sometimes also important. Although the relative contribution of glycolysis and oxidative phosphorylation to ATP production has been extensively analysed, similar analysis of NADPH metabolism has been lacking. Here we demonstrate the ability to directly track, by liquid chromatography–mass spectrometry, the passage of deuterium from labelled substrates into NADPH, and combine this approach with carbon labelling and mathematical modelling to measure NADPH fluxes. In proliferating cells, the largest contributor to cytosolic NADPH is the oxidative pentose phosphate pathway. Surprisingly, a nearly comparable contribution comes from serine-driven one-carbon metabolism, in which oxidation of methylene tetrahydrofolate to 10-formyl-tetrahydrofolate is coupled to reduction of NADP+ to NADPH. Moreover, tracing of mitochondrial one-carbon metabolism revealed complete oxidation of 10-formyl-tetrahydrofolate to make NADPH. As folate metabolism has not previously been considered an NADPH producer, confirmation of its functional significance was undertaken through knockdown of methylenetetrahydrofolate dehydrogenase (MTHFD) genes. Depletion of either the cytosolic or mitochondrial MTHFD isozyme resulted in decreased cellular NADPH/NADP+ and reduced/oxidized glutathione ratios (GSH/GSSG) and increased cell sensitivity to oxidative stress. Thus, although the importance of folate metabolism for proliferating cells has been long recognized and attributed to its function of producing one-carbon units for nucleic acid synthesis, another crucial function of this pathway is generating reducing power.

Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase

Fumarate hydratase (FH) is an enzyme of the tricarboxylic acid cycle (TCA cycle) that catalyses the hydration of fumarate into malate. Germline mutations of FH are responsible for hereditary leiomyomatosis and renal-cell cancer (HLRCC). It has previously been demonstrated that the absence of FH leads to the accumulation of fumarate, which activates hypoxia-inducible factors (HIFs) at normal oxygen tensions. However, so far no mechanism that explains the ability of cells to survive without a functional TCA cycle has been provided. Here we use newly characterized genetically modified kidney mouse cells in which Fh1 has been deleted, and apply a newly developed computer model of the metabolism of these cells to predict and experimentally validate a linear metabolic pathway beginning with glutamine uptake and ending with bilirubin excretion from Fh1-deficient cells. This pathway, which involves the biosynthesis and degradation of haem, enables Fh1-deficient cells to use the accumulated TCA cycle metabolites and permits partial mitochondrial NADH production. We predicted and confirmed that targeting this pathway would render Fh1-deficient cells non-viable, while sparing wild-type Fh1-containing cells. This work goes beyond identifying a metabolic pathway that is induced in Fh1-deficient cells to demonstrate that inhibition of haem oxygenation is synthetically lethal when combined with Fh1 deficiency, providing a new potential target for treating HLRCC patients.

A key role for mitochondrial gatekeeper pyruvate dehydrogenase in oncogene-induced senescence

In response to tenacious stress signals, such as the unscheduled activation of oncogenes, cells can mobilize tumour suppressor networks to avert the hazard of malignant transformation. A large body of evidence indicates that oncogene-induced senescence (OIS) acts as such a break, withdrawing cells from the proliferative pool almost irreversibly, thus crafting a vital pathophysiological mechanism that protects against cancer. Despite the widespread contribution of OIS to the cessation of tumorigenic expansion in animal models and humans, we have only just begun to define the underlying mechanism and identify key players. Although deregulation of metabolism is intimately linked to the proliferative capacity of cells, and senescent cells are thought to remain metabolically active, little has been investigated in detail about the role of cellular metabolism in OIS. Here we show, by metabolic profiling and functional perturbations, that the mitochondrial gatekeeper pyruvate dehydrogenase (PDH) is a crucial mediator of senescence induced by BRAFV600E, an oncogene commonly mutated in melanoma and other cancers. BRAFV600E-induced senescence was accompanied by simultaneous suppression of the PDH-inhibitory enzyme pyruvate dehydrogenase kinase 1 (PDK1) and induction of the PDH-activating enzyme pyruvate dehydrogenase phosphatase 2 (PDP2). The resulting combined activation of PDH enhanced the use of pyruvate in the tricarboxylic acid cycle, causing increased respiration and redox stress. Abrogation of OIS, a rate-limiting step towards oncogenic transformation, coincided with reversion of these processes. Further supporting a crucial role of PDH in OIS, enforced normalization of either PDK1 or PDP2 expression levels inhibited PDH and abrogated OIS, thereby licensing BRAFV600E-driven melanoma development. Finally, depletion of PDK1 eradicated melanoma subpopulations resistant to targeted BRAF inhibition, and caused regression of established melanomas. These results reveal a mechanistic relationship between OIS and a key metabolic signalling axis, which may be exploited therapeutically.

Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism

The computational study of human metabolism has been advanced with the advent of the first generic (non‐tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue‐specific genome‐scale models of human metabolism. The algorithm generates a tissue‐specific model from the generic human model by integrating a variety of tissue‐specific molecular data sources, including literature‐based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome‐scale stoichiometric model of hepatic metabolism. The model is verified using standard cross‐validation procedures, and through its ability to carry out hepatic metabolic functions. The models flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue‐specific models, and be applied to other organisms.

A genome-scale computational study of the interplay between transcriptional regulation and metabolism.

This paper presents a new method, steady‐state regulatory flux balance analysis (SR‐FBA), for predicting gene expression and metabolic fluxes in a large‐scale integrated metabolic–regulatory model. Using SR‐FBA to study the metabolism of Escherichia coli, we quantify the extent to which the different levels of metabolic and transcriptional regulatory constraints determine metabolic behavior: metabolic constraints determine the flux activity state of 45–51% of metabolic genes, depending on the growth media, whereas transcription regulation determines the flux activity state of 13–20% of the genes. A considerable number of 36 genes are redundantly expressed, that is, they are expressed even though the fluxes of their associated reactions are zero, indicating that they are not optimally tuned for cellular flux demands. The undetermined state of the remaining ∼30% of the genes suggests that they may represent metabolic variability within a given growth medium. Overall, SR‐FBA enables one to address a host of new questions concerning the interplay between regulation and metabolism.

Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity.

Plant metabolic engineering is commonly used in the production of functional foods and quality trait improvement. However, to date, computational model-based approaches have only been scarcely used in this important endeavor, in marked contrast to their prominent success in microbial metabolic engineering. In this study we present a computational pipeline for the reconstruction of fully compartmentalized tissue-specific models of Arabidopsis thaliana on a genome scale. This reconstruction involves automatic extraction of known biochemical reactions in Arabidopsis for both primary and secondary metabolism, automatic gap-filling, and the implementation of methods for determining subcellular localization and tissue assignment of enzymes. The reconstructed tissue models are amenable for constraint-based modeling analysis, and significantly extend upon previous model reconstructions. A set of computational validations (i.e., cross-validation tests, simulations of known metabolic functionalities) and experimental validations (comparison with experimental metabolomics datasets under various compartments and tissues) strongly testify to the predictive ability of the models. The utility of the derived models was demonstrated in the prediction of measured fluxes in metabolically engineered seed strains and the design of genetic manipulations that are expected to increase vitamin E content, a significant nutrient for human health. Overall, the reconstructed tissue models are expected to lay down the foundations for computational-based rational design of plant metabolic engineering. The reconstructed compartmentalized Arabidopsis tissue models are MIRIAM-compliant and are available upon request.

QPath: a method for querying pathways in a protein-protein interaction network

BackgroundSequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks.ResultsWe present a comprehensive framework for protein network searches using pathway queries. Given a linear query pathway and a network of interest, our algorithm, QPath, efficiently searches the network for homologous pathways, allowing both insertions and deletions of proteins in the identified pathways. Matched pathways are automatically scored according to their variation from the query pathway in terms of the protein insertions and deletions they employ, the sequence similarity of their constituent proteins to the query proteins, and the reliability of their constituent interactions. We applied QPath to systematically infer protein pathways in fly using an extensive collection of 271 putative pathways from yeast. QPath identified 69 conserved pathways whose members were both functionally enriched and coherently expressed. The resulting pathways tended to preserve the function of the original query pathways, allowing us to derive a first annotated map of conserved protein pathways in fly.ConclusionPathway homology searches using QPath provide a powerful approach for identifying biologically significant pathways and inferring their function. The growing amounts of protein interactions in public databases underscore the importance of our network querying framework for mining protein network data.

Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model

Motivation: The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations. Results: IOMA is shown to successfully predict the metabolic state of human erythrocytes (compared to kinetic model simulations), showing a significant advantage over the commonly used methods flux balance analysis and minimization of metabolic adjustment. Thereafter, IOMA is shown to correctly predict metabolic fluxes in Escherichia coli under different gene knockouts for which both metabolomic and proteomic data is available, achieving higher prediction accuracy over the extant methods. Considering the lack of high-throughput flux measurements, while high-throughput metabolomic and proteomic data are becoming readily available, we expect IOMA to significantly contribute to future research of cellular metabolism. Contacts: kerenyiz@post.tau.ac.il; tomersh@cs.technion.ac.il