The p53 pathway is functionally inactivated in most, if not all, human cancers. The p53 protein is a central effector of numerous stress-related molecular cascades. p53 controls a safeguard mechanism that prevents accumulation of abnormal cells and their transformation by regulating DNA repair, cell cycle progression, cell death, or senescence. The multiple cellular processes regulated by p53 were more recently extended to the control of metabolism and many studies support the notion that perturbations of p53-associated metabolic activities are linked to cancer development, as well as to other pathophysiological conditions including aging, type II diabetes, and liver disease. Although much less documented than p53 metabolic activities, converging lines of evidence indicate that other key components of this tumor suppressor pathway are also involved in cellular metabolism through p53-dependent as well as p53-independent mechanisms. Thus, at least from a metabolic standpoint, the p53 pathway must be considered as a non-linear pathway, but the complex metabolic network controlled by these p53 regulators and the mechanisms by which their activities are coordinated with p53 metabolic functions remain poorly understood. In this review, we highlight some of the metabolic pathways controlled by several central components of the p53 pathway and their role in tissue homeostasis, metabolic diseases, and cancer.

Growing evidence supports the importance of the p53 tumor suppressor in metabolism but the mechanisms underlying p53-mediated control of metabolism remain poorly understood. Here, we identify the multifunctional E4F1 protein as a key regulator of p53 metabolic functions in adipocytes. While E4F1 expression is upregulated during obesity, E4f1 inactivation in mouse adipose tissue results in a lean phenotype associated with insulin resistance and protection against induced obesity. Adipocytes lacking E4F1 activate a p53-dependent transcriptional program involved in lipid metabolism. The direct interaction between E4F1 and p53 and their co-recruitment to the Steaoryl-CoA Desaturase-1 locus play an important role to regulate monounsaturated fatty acids synthesis in adipocytes. Consistent with the role of this E4F1-p53-Steaoryl-CoA Desaturase-1 axis in adipocytes, p53 inactivation or diet complementation with oleate partly restore adiposity and improve insulin sensitivity in E4F1-deficient mice. Altogether, our findings identify a crosstalk between E4F1 and p53 in the control of lipid metabolism in adipocytes that is relevant to obesity and insulin resistance.

The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduceDEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes inSaccharomyces cerevisiaeusing ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provideDEXOMas an open-source library compatible with COBRA Toolbox 3.0, available athttps://github.com/MetExplore/dexom.

Pyruvate metabolism defects lead to severe neuropathies such as the Leigh syndrome (LS) but the molecular mechanisms underlying neuronal cell death remain poorly understood. Here, we unravel a connection between pyruvate metabolism and the regulation of the epitranscriptome that plays an essential role during brain development. Using genetically engineered mouse model and primary neuronal cells, we identify the transcription factor E4F1 as a key coordinator of AcetylCoenzyme A (AcCoA) production by the pyruvate dehydrogenase complex (PDC) and its utilization as an essential co-factor by the Elongator complex to acetylate tRNAs at the wobble position uridine 34 (U₃₄). E4F1-mediated direct transcriptional regulation of Dlat and Elp3, two genes encoding key subunits of the PDC and of the Elongator complex, respectively, ensures proper translation fidelity and cell survival in the central nervous system (CNS) during mouse embryonic development. Furthermore, analysis of PDH-deficient cells highlight a crosstalk linking the PDC to ELP3 expression that is perturbed in LS patients.

The correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM , a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub ¹ .