Defining the signalling determinants of a posterior ventral spinal cord identity in human neuromesodermal progenitor derivatives.

The anteroposterior axial identity of motor neurons (MNs) determines their functionality and vulnerability to neurodegeneration. Thus, it is a crucial parameter in the design of strategies aiming to produce MNs from human pluripotent stem cells (hPSCs) for regenerative medicine/disease modelling applications. However, the in vitro generation of posterior MNs corresponding to the thoracic/lumbosacral spinal cord has been challenging. Although the induction of cells resembling neuromesodermal progenitors (NMPs), the bona fide precursors of the spinal cord, offers a promising solution, the progressive specification of posterior MNs from these cells is not well defined. Here, we determine the signals guiding the transition of human NMP-like cells toward thoracic ventral spinal cord neurectoderm. We show that combined WNT-FGF activities drive a posterior dorsal pre-/early neural state, whereas suppression of TGFß-BMP signalling pathways promotes a ventral identity and neural commitment. Based on these results, we define an optimised protocol for the generation of thoracic MNs that can efficiently integrate within the neural tube of chick embryos. We expect that our findings will facilitate the comparison of hPSC-derived spinal cord cells of distinct axial identities.

Glycosphingolipid metabolic reprogramming drives neural differentiation.

Neural development is accomplished by differentiation events leading to metabolic reprogramming. Glycosphingolipid metabolism is reprogrammed during neural development with a switch from globo- to ganglio-series glycosphingolipid production. Failure to execute this glycosphingolipid switch leads to neurodevelopmental disorders in humans, indicating that glycosphingolipids are key players in this process. Nevertheless, both the molecular mechanisms that control the glycosphingolipid switch and its function in neurodevelopment are poorly understood. Here, we describe a self-contained circuit that controls glycosphingolipid reprogramming and neural differentiation. We find that globo-series glycosphingolipids repress the epigenetic regulator of neuronal gene expression AUTS2. AUTS2 in turn binds and activates the promoter of the first and rate-limiting ganglioside-producing enzyme GM3 synthase, thus fostering the synthesis of gangliosides. By this mechanism, the globo-AUTS2 axis controls glycosphingolipid reprogramming and neural gene expression during neural differentiation, which involves this circuit in neurodevelopment and its defects in neuropathology.

16S rRNA of Mucosal Colon Microbiome and CCL2 Circulating Levels Are Potential Biomarkers in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common malignancies in the Western world and intestinal dysbiosis might contribute to its pathogenesis. The mucosal colon microbiome and C-C motif chemokine 2 (CCL2) were investigated in 20 healthy controls (HC) and 20 CRC patients using 16S rRNA sequencing and immunoluminescent assay, respectively. A total of 10 HC subjects were classified as overweight/obese (OW/OB_HC) and 10 subjects were normal weight (NW_HC); 15 CRC patients were classified as OW/OB_CRC and 5 patients were NW_CRC. Results: Fusobacterium nucleatum and Escherichia coli were more abundant in OW/OB_HC than in NW_HC microbiomes. Globally, Streptococcus intermedius, Gemella haemolysans, Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were significantly increased in CRC patient tumor/lesioned tissue (CRC_LT) and CRC patient unlesioned tissue (CRC_ULT) microbiomes compared to HC microbiomes. CCL2 circulating levels were associated with tumor presence and with the abundance of Fusobacterium nucleatum, Bacteroides fragilis and Gemella haemolysans. Our data suggest that mucosal colon dysbiosis might contribute to CRC pathogenesis by inducing inflammation. Notably, Fusobacterium nucleatum, which was more abundant in the OW/OB_HC than in the NW_HC microbiomes, might represent a putative link between obesity and increased CRC risk.

Clustering analysis of tumor metabolic networks.

BACKGROUND: Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. RESULTS: We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. CONCLUSIONS: We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

Correction to: Clustering analysis of tumor metabolic networks.

An amendment to this paper has been published and can be accessed via the original article.

From trash to treasure: detecting unexpected contamination in unmapped NGS data.

BACKGROUND: Next Generation Sequencing (NGS) experiments produce millions of short sequences that, mapped to a reference genome, provide biological insights at genomic, transcriptomic and epigenomic level. Typically the amount of reads that correctly maps to the reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. This 'misalignment' can be ascribed to low quality bases or sequence differences between the sample reads and the reference genome. Investigating the source of the unmapped reads is definitely important to better assess the quality of the whole experiment and to check for possible downstream or upstream 'contamination' from exogenous nucleic acids. RESULTS: Here we propose DecontaMiner, a tool to unravel the presence of contaminating sequences among the unmapped reads. It uses a subtraction approach to identify bacteria, fungi and viruses genome contamination. DecontaMiner generates several output files to track all the processed reads, and to provide a complete report of their characteristics. The good quality matches on microorganism genomes are counted and compared among samples. DecontaMiner builds an offline HTML page containing summary statistics and plots. The latter are obtained using the state-of-the-art D3 javascript libraries. DecontaMiner has been mainly used to detect contamination in human RNA-Seq data. The software is freely available at http://www-labgtp.na.icar.cnr.it/decontaminer . CONCLUSIONS: DecontaMiner is a tool designed and developed to investigate the presence of contaminating sequences in unmapped NGS data. It can suggest the presence of contaminating organisms in sequenced samples, that might derive either from laboratory contamination or from their biological source, and in both cases can be considered as worthy of further investigation and experimental validation. The novelty of DecontaMiner is mainly represented by its easy integration with the standard procedures of NGS data analysis, while providing a complete, reliable, and automatic pipeline.

Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer.

BACKGROUND: Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states. RESULTS: In this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study. CONCLUSIONS: Metabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena.

Design and expression of peptides with antimicrobial activity against Salmonella typhimurium.

We showed previously that insertion of Synechocystis Δ12 -desaturase in salmonella's membrane alters membrane physical state (MPS), followed by the expression of stress genes causing inability to survive within murine macrophages (MΦ). Recently, we showed that expression of one membrane lipid domain (MLD) of Δ12 -desaturase (ORF200) interferes with salmonella MPS, causing loss of virulence in mice and immunoprotection. Here, we postulate that an α-antimicrobial peptide (α-AMP) intercalates within membrane lipids, and depending on its amino acid sequence, it does so within specific key sensors of MLD. In this study, we choose as target for a putative synthetic AMP, PhoP/PhoQ, a sensor that responds to low Mg2+ concentration. We synthesised a modified DNA fragment coding for an amino acid sequence (NUF) similar to that fragment and expressed it in salmonella typhimurium. We showed that the pattern of gene expression controlled by PhoP/PhoQ highlights dysregulation of pathways involving phospholipids biosynthesis, stress proteins and genes coding for antigens. RNA-Seq of strain expressing ORF200 showed that the pattern of those genes is also altered here. Accumulation of NUF conferred temporary immunoprotection. This represents a powerful procedure to address synthetic α-AMPs to a specific MLD generating live non-virulent bacterial strains.

A hnRNP K⁻AR-Related Signature Reflects Progression toward Castration-Resistant Prostate Cancer.

The major challenge in castration-resistant prostate cancer (CRPC) remains the ability to predict the clinical responses to improve patient selection for appropriate treatments. The finding that androgen deprivation therapy (ADT) induces alterations in the androgen receptor (AR) transcriptional program by AR coregulators activity in a context-dependent manner, offers the opportunity for identifying signatures discriminating different clinical states of prostate cancer (PCa) progression. Gel electrophoretic analyses combined with western blot showed that, in androgen-dependent PCa and CRPC in vitro models, the subcellular distribution of spliced and serine-phosphorylated heterogeneous nuclear ribonucleoprotein K (hnRNP K) isoforms can be associated with different AR activities. Using mass spectrometry and bioinformatic analyses, we showed that the protein sets of androgen-dependent (LNCaP) and ADT-resistant cell lines (PDB and MDB) co-immunoprecipitated with hnRNP K varied depending on the cell type, unravelling a dynamic relationship between hnRNP K and AR during PCa progression to CRPC. By comparing the interactome of LNCaP, PDB, and MDB cell lines, we identified 51 proteins differentially interacting with hnRNP K, among which KLK3, SORD, SPON2, IMPDH2, ACTN4, ATP1B1, HSPB1, and KHDRBS1 were associated with AR and differentially expressed in normal and tumor human prostate tissues. This hnRNP K⁻AR-related signature, associated with androgen sensitivity and PCa progression, may help clinicians to better manage patients with CRPC.

Adaptive phenotype drives resistance to androgen deprivation therapy in prostate cancer.

BACKGROUND: Prostate cancer (PCa), the second most common cancer affecting men worldwide, shows a broad spectrum of biological and clinical behaviour representing the epiphenomenon of an extreme heterogeneity. Androgen deprivation therapy is the mainstay of treatment for advanced forms but after few years the majority of patients progress to castration-resistant prostate cancer (CRPC), a lethal form that poses considerable therapeutic challenges. METHODS: Western blotting, immunocytochemistry, invasion and reporter assays, and in vivo studies were performed to characterize androgen resistant sublines phenotype in comparison to the parental cell line LNCaP. RNA microarray, mass spectrometry, integrative transcriptomic and proteomic differential analysis coupled with GeneOntology and multivariate analyses were applied to identify deregulated genes and proteins involved in CRPC evolution. RESULTS: Treating the androgen-responsive LNCaP cell line for over a year with 10 µM bicalutamide both in the presence and absence of 0.1 nM 5-α-dihydrotestosterone (DHT) we obtained two cell sublines, designated PDB and MDB respectively, presenting several analogies with CRPC. Molecular and functional analyses of PDB and MDB, compared to the parental cell line, showed that both resistant cell lines were PSA low/negative with comparable levels of nuclear androgen receptor devoid of activity due to altered phosphorylation; cell growth and survival were dependent on AKT and p38MAPK activation and PARP-1 overexpression; their malignant phenotype increased both in vitro and in vivo. Performing bioinformatic analyses we highlighted biological processes related to environmental and stress adaptation supporting cell survival and growth. We identified 15 proteins that could direct androgen-resistance acquisition. Eleven out of these 15 proteins were closely related to biological processes involved in PCa progression. CONCLUSIONS: Our models suggest that environmental factors and epigenetic modulation can activate processes of phenotypic adaptation driving drug-resistance. The identified key proteins of these adaptive phenotypes could be eligible targets for innovative therapies as well as molecules of prognostic and predictive value.

Var2GO: a web-based tool for gene variants selection.

BACKGROUND: One of the most challenging issue in the variant calling process is handling the resulting data, and filtering the genes retaining only the ones strictly related to the topic of interest. Several tools permit to gather annotations at different levels of complexity for the detected genes and to group them according to the pathways and/or processes they belong to. However, it might be a time consuming and frustrating task. This is partly due to the size of the file, that might contain many thousands of genes, and to the search of associated variants that requires a gene-by-gene investigation and annotation approach. As a consequence, the initial gene list is often reduced exploiting the knowledge of variants effect, novelty and genotype, with the potential risk of losing meaningful pieces of information. RESULTS: Here we present Var2GO, a new web-based tool to support the annotation and filtering of variants and genes coming from variant calling of high-throughput sequencing data. Var2GO permits to upload either the unprocessed Variant Calling Format file or a table containing the annotated variants. The raw data undergo a preliminary step of variants annotation, using the SnpEff tool, and are converted to a table format. The table is then uploaded into an on the fly generated database. Genes associated to the variants are automatically annotated with the corresponding Gene Ontology terms covering the three GO domains. Using the web interface it is then possible to filter and extract, from the whole list, genes having annotations in the domain of interest, by simply specifying filtering parameters and one or more keywords. The relevance of this tool is demonstrated on exome sequencing data. CONCLUSIONS: Var2GO is a novel tool that implements a topic-based approach, expressly designed to help biologists in narrowing the search of relevant genes coming from variant calling analysis. Its main purpose is to support non-bioinformaticians in handling and processing raw variant calling data through an intuitive web interface. Furthermore, Var2GO offers a complete pipeline that, starting from the raw VCF file, allows to annotate both variants and associated genes and supports the extraction of relevant biological knowledge.

Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach.

Prostate cancer (PCa) is characterised by androgen dependency. Unfortunately, under anti-androgen treatment pressure, castration-resistant prostate cancer (CRPC) emerges, characterised by heterogeneous cell populations that, over time, lead to the development of different androgen-dependent or -independent phenotypes. Despite important advances in therapeutic strategies, CRPC remains incurable. Context-specific essential genes represent valuable candidates for targeted anti-cancer therapies. Through the investigation of gene and protein annotations and the integration of published transcriptomic data, we identified two consensus lists to stratify PCa patients' risk and discriminate CRPC phenotypes based on androgen receptor activity. ROC and Kaplan-Meier survival analyses were used for gene set validation in independent datasets. We further evaluated these genes for their association with cancer dependency. The deregulated expression of the PCa-related genes was associated with overall and disease-specific survival, metastasis and/or high recurrence risk, while the CRPC-related genes clearly discriminated between adeno and neuroendocrine phenotypes. Some of the genes showed context-specific essentiality. We further identified candidate drugs through a computational repositioning approach for targeting these genes and treating lethal variants of PCa. This work provides a proof-of-concept for the use of an integrative approach to identify candidate biomarkers involved in PCa progression and CRPC pathogenesis within the goal of precision medicine.

Antifungal activity of azole compounds CPA18 and CPA109 against azole-susceptible and -resistant strains of Candida albicans.

OBJECTIVES: In this study we investigated the in vitro fungistatic and fungicidal activities of CPA18 and CPA109, two azole compounds with original structural features, alone and in combination with fluconazole against fluconazole-susceptible and -resistant Candida albicans strains. METHODS: Antifungal activities were measured by MIC evaluation and time-kill studies. Azole binding analysis was performed by UV-Vis spectroscopy. Hyphal growth inhibition and filipin and propidium iodide staining assays were used for morphological analysis. An analysis of membrane lipids was also performed to gauge alterations in membrane composition and integrity. Synergism was calculated using fractional inhibitory concentration indices (FICIs). Evaluation of cytotoxicity towards murine macrophages was performed to verify selective antifungal activity. RESULTS: Even though their binding affinity to C. albicans Erg11p is comparable to that of fluconazole, CPA compounds are active against resistant strains of C. albicans with a mutation in ERG11 sequences and/or overexpressing the ABC transporter genes CDR1 and CDR2, which encode ATP-dependent efflux pumps. Moreover, CPA18 is fungistatic, even against the two resistant strains, and was found to be synergistic with fluconazole. Differently from fluconazole and other related azoles, CPA compounds induced marked changes in membrane permeability and dramatic alterations in membrane lipid composition. CONCLUSIONS: Our outcomes suggest that CPA compounds are able to overcome major mechanisms of resistance in C. albicans. Also, they are promising candidates for combination treatment that could reduce the toxicity caused by high fluconazole doses, particularly in immunocompromised patients.

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience.

Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.

Bifidobacterium affects antitumor efficacy of oncolytic adenovirus in a mouse model of melanoma.

Gut microbiota plays a key role in modulating responses to cancer immunotherapy in melanoma patients. Oncolytic viruses (OVs) represent emerging tools in cancer therapy, inducing a potent immunogenic cancer cell death (ICD) and recruiting immune cells in tumors, poorly infiltrated by T cells. We investigated whether the antitumoral activity of oncolytic adenovirus Ad5D24-CpG (Ad-CpG) was gut microbiota-mediated in a syngeneic mouse model of melanoma and observed that ICD was weakened by vancomycin-mediated perturbation of gut microbiota. Ad-CpG efficacy was increased by oral supplementation with Bifidobacterium, reducing melanoma progression and tumor-infiltrating regulatory T cells. Fecal microbiota was enriched in bacterial species belonging to the Firmicutes phylum in mice treated with both Bifidobacterium and Ad-CpG; furthermore, our data suggest that molecular mimicry between melanoma and Bifidobacterium-derived epitopes may favor activation of cross-reactive T cells and constitutes one of the mechanisms by which gut microbiota modulates OVs response.

Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge.

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed Taxonomy- and Function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants' predictions performed better than random predictions in classifying IBD versus nonIBD, Ulcerative Colitis (UC) versus nonIBD, and Crohn's Disease (CD) versus nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.

TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models.

Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses. This repository has an extended scope for use in graph classification, clustering, community detection, and graph embedding studies. Along with the data, we developed and provided Met2Graph, an R package for creating three different types of metabolic graphs, depending on the desired nodes and edges: Metabolites-, Enzymes-, and Reactions-based graphs. This package allows the easy generation of datasets for downstream analysis.

Netpro2vec: A Graph Embedding Framework for Biomedical Applications.

The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec. The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.

Early anteroposterior regionalisation of human neural crest is shaped by a pro-mesodermal factor.

The neural crest (NC) is an important multipotent embryonic cell population and its impaired specification leads to various developmental defects, often in an anteroposterior (A-P) axial level-specific manner. The mechanisms underlying the correct A-P regionalisation of human NC cells remain elusive. Recent studies have indicated that trunk NC cells, the presumed precursors of childhood tumour neuroblastoma, are derived from neuromesodermal-potent progenitors of the postcranial body. Here we employ human embryonic stem cell differentiation to define how neuromesodermal progenitor (NMP)-derived NC cells acquire a posterior axial identity. We show that TBXT, a pro-mesodermal transcription factor, mediates early posterior NC/spinal cord regionalisation together with WNT signalling effectors. This occurs by TBXT-driven chromatin remodelling via its binding in key enhancers within HOX gene clusters and other posterior regulator-associated loci. This initial posteriorisation event is succeeded by a second phase of trunk HOX gene control that marks the differentiation of NMPs toward their TBXT-negative NC/spinal cord derivatives and relies predominantly on FGF signalling. Our work reveals a previously unknown role of TBXT in influencing posterior NC fate and points to the existence of temporally discrete, cell type-dependent modes of posterior axial identity control.

Learning from Metabolic Networks: Current Trends and Future Directions for Precision Medicine.

BACKGROUND: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype- phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition- specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning. METHODS: This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies that exploited metabolic networks to study several pathological conditions, not only those directly related to metabolism. CONCLUSION: We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).