Francesco Gatto

Genome‐scale modeling of human metabolism – a systems biology approach

Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease‐associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome‐scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models – one of the fundamental aspects of systems biology – have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type‐ and cancer‐specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome‐scale reconstructions can be employed to simulate metabolic flux distributions and how high‐throughput omics data can be analyzed in a context‐dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome‐scale modeling and the future challenge of developing a model of whole‐body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed.

Chromosome 3p loss of heterozygosity is associated with a unique metabolic network in clear cell renal carcinoma

Significance It is suggested that regulation of metabolism is a point of convergence of many different cancer-associated pathways. Here we challenged the validity of this assertion and verified that a transversal metabolic signature in cancer emerges chiefly in the regulation of nucleotide metabolism. However, the most common form of renal cancer deviates from this behavior and presents some defects in its metabolic network not present in the normal kidney and unseen in other tumors. Notably, reduced copy number in key metabolic genes located adjacent to VHL (a tumor suppressor gene frequently deleted in this cancer) recapitulates these defects. These results are suggestive that recurrent chromosomal loss of heterozygosity in cancer may uniquely shape the metabolic network. Several common oncogenic pathways have been implicated in the emergence of renowned metabolic features in cancer, which in turn are deemed essential for cancer proliferation and survival. However, the extent to which different cancers coordinate their metabolism to meet these requirements is largely unexplored. Here we show that even in the heterogeneity of metabolic regulation a distinct signature encompassed most cancers. On the other hand, clear cell renal cell carcinoma (ccRCC) strongly deviated in terms of metabolic gene expression changes, showing widespread down-regulation. We observed a metabolic shift that associates differential regulation of enzymes in one-carbon metabolism with high tumor stage and poor clinical outcome. A significant yet limited set of metabolic genes that explained the partial divergence of ccRCC metabolism correlated with loss of von Hippel-Lindau tumor suppressor (VHL) and a potential activation of signal transducer and activator of transcription 1. Further network-dependent analyses revealed unique defects in nucleotide, one-carbon, and glycerophospholipid metabolism at the transcript and protein level, which contrasts findings in other tumors. Notably, this behavior is recapitulated by recurrent loss of heterozygosity in multiple metabolic genes adjacent to VHL. This study therefore shows how loss of heterozygosity, hallmarked by VHL deletion in ccRCC, may uniquely shape tumor metabolism.

Flux balance analysis predicts essential genes in clear cell renal cell carcinoma metabolism

Flux balance analysis is the only modelling approach that is capable of producing genome-wide predictions of gene essentiality that may aid to unveil metabolic liabilities in cancer. Nevertheless, a systemic validation of gene essentiality predictions by flux balance analysis is currently missing. Here, we critically evaluated the accuracy of flux balance analysis in two cancer types, clear cell renal cell carcinoma (ccRCC) and prostate adenocarcinoma, by comparison with large-scale experiments of gene essentiality in vitro. We found that in ccRCC, but not in prostate adenocarcinoma, flux balance analysis could predict essential metabolic genes beyond random expectation. Five of the identified metabolic genes, AGPAT6, GALT, GCLC, GSS, and RRM2B, were predicted to be dispensable in normal cell metabolism. Hence, targeting these genes may selectively prevent ccRCC growth. Based on our analysis, we discuss the benefits and limitations of flux balance analysis for gene essentiality predictions in cancer metabolism, and its use for exposing metabolic liabilities in ccRCC, whose emergent metabolic network enforces outstanding anabolic requirements for cellular proliferation.

JRC GMO-Matrix: a web application to support Genetically Modified Organisms detection strategies

BackgroundThe polymerase chain reaction (PCR) is the current state of the art technique for DNA-based detection of Genetically Modified Organisms (GMOs). A typical control strategy starts by analyzing a sample for the presence of target sequences (GM-elements) known to be present in many GMOs. Positive findings from this “screening” are then confirmed with GM (event) specific test methods. A reliable knowledge of which GMOs are detected by combinations of GM-detection methods is thus crucial to minimize the verification efforts.DescriptionIn this article, we describe a novel platform that links the information of two unique databases built and maintained by the European Union Reference Laboratory for Genetically Modified Food and Feed (EU-RL GMFF) at the Joint Research Centre (JRC) of the European Commission, one containing the sequence information of known GM-events and the other validated PCR-based detection and identification methods. The new platform compiles in silico determinations of the detection of a wide range of GMOs by the available detection methods using existing scripts that simulate PCR amplification and, when present, probe binding. The correctness of the information has been verified by comparing the in silico conclusions to experimental results for a subset of forty-nine GM events and six methods.ConclusionsThe JRC GMO-Matrix is unique for its reliance on DNA sequence data and its flexibility in integrating novel GMOs and new detection methods. Users can mine the database using a set of web interfaces that thus provide a valuable support to GMO control laboratories in planning and evaluating their GMO screening strategies. The platform is accessible at http://gmo-crl.jrc.ec.europa.eu/jrcgmomatrix/.

Development and applicability of a ready-to-use PCR system for GMO screening.

With the growing number of GMOs introduced to the market, testing laboratories have seen their workload increase significantly. Ready-to-use multi-target PCR-based detection systems, such as pre-spotted plates (PSP), reduce analysis time while increasing capacity. This paper describes the development and applicability to GMO testing of a screening strategy involving a PSP and its associated web-based Decision Support System. The screening PSP was developed to detect all GMOs authorized in the EU in one single PCR experiment, through the combination of 16 validated assays. The screening strategy was successfully challenged in a wide inter-laboratory study on real-life food/feed samples. The positive outcome of this study could result in the adoption of a PSP screening strategy across the EU; a step that would increase harmonization and quality of GMO testing in the EU. Furthermore, this system could represent a model for other official control areas where high-throughput DNA-based detection systems are needed.

Glycosaminoglycan Profiling in Patients’ Plasma and Urine Predicts the Occurrence of Metastatic Clear Cell Renal Cell Carcinoma

Metabolic reprogramming is a hallmark of clear cell renal cell carcinoma (ccRCC) progression. Here, we used genome-scale metabolic modeling to elucidate metabolic reprogramming in 481 ccRCC samples and discovered strongly coordinated regulation of glycosaminoglycan (GAG) biosynthesis at the transcript and protein levels. Extracellular GAGs are implicated in metastasis, so we speculated that such regulation might translate into a non-invasive biomarker for metastatic ccRCC (mccRCC). We measured 18 GAG properties in 34 mccRCC samples versus 16 healthy plasma and/or urine samples. The GAG profiles were distinctively altered in mccRCC. We derived three GAG scores that distinguished mccRCC patients with 93.1%-100% accuracy. We validated the score accuracies in an independent cohort (up to 18 mccRCC versus nine healthy) and verified that the scores normalized in eight patients with no evidence of disease. In conclusion, coordinated regulation of GAG biosynthesis occurs in ccRCC, and non-invasive GAG profiling is suitable for mccRCC diagnosis.

Recon3D enables a three-dimensional view of gene variation in human metabolism

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

Systematic Analysis Reveals that Cancer Mutations Converge on Deregulated Metabolism of Arachidonate and Xenobiotics

Mutations are the basis of the clonal evolution of most cancers. Nevertheless, a systematic analysis of whether mutations are selected in cancer because they lead to the deregulation of specific biological processes independent of the type of cancer is still lacking. In this study, we correlated the genome and transcriptome of 1,082 tumors. We found that nine commonly mutated genes correlated with substantial changes in gene expression, which primarily converged on metabolism. Further network analyses circumscribed the convergence to a network of reactions, termed AraX, that involves the glutathione- and oxygen-mediated metabolism of arachidonic acid and xenobiotics. In an independent cohort of 4,462 samples, all nine mutated genes were consistently correlated with the deregulation of AraX. Among all of the metabolic pathways, AraX deregulation represented the strongest predictor of patient survival. These findings suggest that oncogenic mutations drive a selection process that converges on the deregulation of the AraX network.

Kiwi: a tool for integration and visualization of network topology and gene-set analysis

BackgroundThe analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures.ResultsHere we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network.ConclusionsKiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.

Exploiting off‐targeting in guide‐RNAs for CRISPR systems for simultaneous editing of multiple genes

Bioinformatics tools to design guide‐RNAs (gRNAs) in Clustered Regularly Interspaced Short Palindromic Repeats systems mostly focused on minimizing off‐targeting to enhance efficacy of genome editing. However, there are circumstances in which off‐targeting might be desirable to target multiple genes simultaneously with a single gRNA. We termed these gRNAs as promiscuous gRNAs. Here, we present a computational workflow to identify promiscuous gRNAs that putatively bind to the region of interest for a defined list of genes in a genome. We experimentally validated two promiscuous gRNA for gene deletion, one targeting FAA1 and FAA4 and one targeting PLB1 and PLB2, thus demonstrating that multiplexed genome editing through design of promiscuous gRNA can be performed in a time and cost‐effective manner.