In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer

Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improve in-silico gene essentiality analyses. Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected in in silico cancer metabolic models. In this study, we analysed essential genes for the biosynthesis of polyamines. Our analysis corroborates the importance of previously known regulators of the pathway, such as Adenosylmethionine Decarboxylase 1 (AMD1) and uncovers novel enzymes predicted to be relevant for polyamine homeostasis. We focused on Adenine phosphoribosyltransferase (APRT) and demonstrated the detrimental consequence of APRT gene silencing on diferent leukaemia cell lines. Our results highlight the importance of revisiting the metabolic models used for in-silico gene essentiality analyses in order to maximize the potential for drug target identifcation in cance

Cancertool: A visualization and representation interface to exploit cancer datasets

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.We are grateful to Iñaki Lazaro for the design of the tumor type logos, Evarist Planet and Antoni Berenguer for insightful discussions, and the Carracedo lab for valuable input. V. Torrano is funded by Fundación Vasca de Innovación e Investigación Sanitarias, BIOEF (BIO15/CA/052), the AECC J.P. Bizkaia and the Basque Department of Health (2016111109). The work of A. Carracedo is supported by the Basque Department of Industry, Tourism and Trade (Etortek) and the Department of Education (IKERTALDE IT1106-16, also participated by A. Gomez-Muñoz), the BBVA Foundation, the MINECO [SAF2016-79381-R (FEDER/EU)]; Severo Ochoa Excellence Accreditation SEV-2016-0644; Excellence Networks (SAF2016-81975-REDT), European Training Networks Project (H2020-MSCA-ITN-308 2016 721532), the AECC IDEAS16 (IDEAS175CARR), and the European Research Council (Starting Grant 336343, PoC 754627). CIBERONC was cofunded with FEDER funds. The work of A. Aransay is supported by the Basque Department of Industry, Tourism and Trade (Etortek and Elkartek Programs), the Innovation Technology Department of Bizkaia County, CIBERehd Network, and Spanish MINECO the Severo Ochoa Excellence Accreditation (SEV-2016-0644). I. Apaolaza is funded by a Basque Government predoctoral grant (PRE_2017_2_0028). X.R. Bustelo is supported by grants from the Castilla-León Government (BIO/SA01/15, CSI049U16), Spanish Ministry of Economy and Competitiveness (MINECO; SAF2015-64556-R), Worldwide Cancer Research (14-1248), Ramón Areces Foundation, and the Spanish Society against Cancer (GC16173472GARC). Funding from MINECO to X.R. Bustelo is partially contributed by the European Regional Development Fund. The work of F.J. Planes is supported by the MINECO (BIO2016-77998-R) and ELKARTEK Programme of the Basque Government (KK-2016/00026)

CANCERTOOL: A Visualization and Representation Interface to Exploit Cancer Datasets

[EN] With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design. Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable forma

Novel Constrain-Based Modeling approaches for the identification of metabolic drug targets in cancer.

Metabolic reprogramming has been defined to be a hallmark of cancer. One major question in cancer research is how to exploit these metabolic alterations for the identification of novel therapeutic strategies. With the outbreak of high throughput –omics data and the advances in genomics, novel holistic and integrative approaches are required to address this question. Systems Biology aims at responding to these needs and has provided the scientific community with a large variety of algorithms and approaches. Among different computational approaches in Systems Biology, Constraint-Based Modeling, based on genome-scale metabolic networks, has received much attention in the last years. They have provided different promising tools to predict metabolic targets in cancer, but, so far, with limited predictive power when compared to experimental data. In this doctoral thesis, we present a novel methodology to more accurately predict metabolic targets in cancer. Our approach is radically different to previous approaches in the literature and relies on a novel concept termed genetic Minimal Cut Sets. The relevance of our approach is shown in two different case studies. First, we applied it to explain the role that RRM1 plays in Multiple Myeloma. Second, we aimed at identifying selective therapeutic strategies in tamoxifen-resistant breast cancer.La reprogramación metabólica se ha definido como una de las señas de identidad del cáncer. Una de las preguntas que surgen es cómo explotar estas alteraciones metabólicas para la identificación de estrategias terapéuticas. Debido a la disponibilidad de datos –ómicos de alta resolución molecular y los avances en genómica, surge la necesidad de nuevas aproximaciones holísticas e integrativas para abordar esta cuestión. La Biología de Sistemas pretende responder a estas necesidades y ha proporcionado una gran variedad de algoritmos y metodologías a la comunidad científica. Entre las distintas aproximaciones de la Biología de Sistemas, se ha centrado mucho la atención en el Modelado Basado en Restricciones, que se basa en redes metabólicas a escala genómica. Dichos métodos han proporcionado herramientas prometedoras para la predicción de dianas metabólicas en cáncer, pero, por el momento, tienen una capacidad predictiva limitada comparada con resultados experimentales. En esta tesis doctoral presentamos una metodología novedosa que predice, de una forma más precisa, dianas metabólicas en cáncer. Nuestra aproximación es radicalmente diferente a otras aproximaciones que se encuentran en la literatura y se basa en un nuevo concepto, los genetic Minimal Cut Sets. Mostramos la relevancia de la aproximación en dos estudios. Primero, aplicamos la metodología para explicar el papel que juega RRM1 en el Mieloma Múltiple. Después, intentamos identificar estrategias terapéuticas selectivas en cáncer de mama resistente a tamoxifeno

An in-silico approach to predict and exploit synthetic lethality in cancer metabolism

Exploiting synthetic lethality is a promising approach for cancer therapy. Here, the authors present an approach to identifying such interactions by finding genetic minimal cut sets (gMCSs) that block cancer proliferation, and apply it to study the lethality of RRM1 inhibition in multiple myeloma

COBRA methods and metabolic drug targets in cancer

The identification of therapeutic strategies exploiting the metabolic alterations of malignant cells is a relevant area in cancer research. Here, we discuss a novel computational method, based on the COBRA (COnstraint-Based Reconstruction and Analysis) framework for metabolic networks, to perform this task. Current and future steps are presented

COBRA methods and metabolic drug targets in cancer

The identification of therapeutic strategies exploiting the metabolic alterations of malignant cells is a relevant area in cancer research. Here, we discuss a novel computational method, based on the COBRA (COnstraint-Based Reconstruction and Analysis) framework for metabolic networks, to perform this task. Current and future steps are presented

Effect of Freezing on Gut Microbiota Composition and Functionality for In Vitro Fermentation Experiments

The gut microbiota has a profound effect on human health and is modulated by food and bioactive compounds. To study such interaction, in vitro batch fermentations are performed with fecal material, and some experimental designs may require that such fermentations be performed with previously frozen stools. Although it is known that freezing fecal material does not alter the composition of the microbial community in 16S rRNA gene amplicon and metagenomic sequencing studies, it is not known whether the microbial community in frozen samples could still be used for in vitro fermentations. To explore this, we undertook a pilot study in which in vitro fermentations were performed with fecal material from celiac, cow’s milk allergic, obese, or lean children that was frozen (or not) with 20% glycerol. Before fermentation, the fecal material was incubated in a nutritious medium for 6 days, with the aim of giving the microbial community time to recover from the effects of freezing. An aliquot was taken daily from the stabilization vessel and used for the in vitro batch fermentation of lentils. The microbial community structure was significantly different between fresh and frozen samples, but the variation introduced by freezing a sample was always smaller than the variation among individuals, both before and after fermentation. Moreover, the potential functionality (as determined in silico by a genome-scaled metabolic reconstruction) did not differ significantly, possibly due to functional redundancy. The most affected genus was Bacteroides, a fiber degrader. In conclusion, if frozen fecal material is to be used for in vitro fermentation purposes, our preliminary analyses indicate that the functionality of microbial communities can be preserved after stabilization.“Plan propio de Investigación y Transferencia” - “Intensificación de la Investigación, modalidad B” - University of GranadaEuropean Research Commission (Research Executive Agency) - Stance4Health (Grant Contract No. 816303

Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods

Abstract The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions

A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism

Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies