Rui Benfeitas

A pathology atlas of the human cancer transcriptome.

Modeling the cancer transcriptome Recent initiatives such as The Cancer Genome Atlas have mapped the genome-wide effect of individual genes on tumor growth. By unraveling genomic alterations in tumors, molecular subtypes of cancers have been identified, which is improving patient diagnostics and treatment. Uhlen et al. developed a computer-based modeling approach to examine different cancer types in nearly 8000 patients. They provide an open-access resource for exploring how the expression of specific genes influences patient survival in 17 different types of cancer. More than 900,000 patient survival profiles are available, including for tumors of colon, prostate, lung, and breast origin. This interactive data set can also be used to generate personalized patient models to predict how metabolic changes can influence tumor growth. Science, this issue p. eaan2507 A systems biology approach should allow genome-wide exploration of the effect of individual proteins on cancer clinical outcomes. INTRODUCTION Cancer is a leading cause of death worldwide, and there is great need to define the molecular mechanisms driving the development and progression of individual tumors. The Hallmarks of Cancer has provided a framework for a deeper molecular understanding of cancer, and the focus so far has been on the genetic alterations in individual cancers, including genome rearrangements, gene amplifications, and specific cancer-driving mutations. Using systems-level approaches, it is now also possible to define downstream effects of individual genetic alterations in a genome-wide manner. RATIONALE In our study, we used a systems-level approach to analyze the transcriptome of 17 major cancer types with respect to clinical outcome, based on a genome-wide transcriptomics analysis of ~8000 individual patients with clinical metadata. The study was made possible through the availability of large open-access knowledge-based efforts such as the Cancer Genome Atlas and the Human Protein Atlas. Here, we used the data to perform a systems-level analysis of 17 major human cancer types, describing both interindividual and intertumor variation patterns. RESULTS The analysis identified candidate prognostic genes associated with clinical outcome for each tumor type; the results show that a large fraction of cancer protein-coding genes are differentially expressed and, in many cases, have an impact on overall patient survival. Systems biology analyses revealed that gene expression of individual tumors within a particular cancer varied considerably and could exceed the variation observed between distinct cancer types. No general prognostic gene necessary for clinical outcome was applicable to all cancers. Shorter patient survival was generally associated with up-regulation of genes involved in mitosis and cell growth and down-regulation of genes involved in cellular differentiation. The data allowed us to generate personalized genome-scale metabolic models for cancer patients to identify key genes involved in tumor growth. In addition, we explored tissue-specific genes associated with the dedifferentiation of tumor cells and the role of specific cancer testis antigens on a genome-wide scale. For lung and colorectal cancer, a selection of prognostic genes identified by the systems biology effort were analyzed in independent, prospective cancer cohorts using immunohistochemistry to validate the gene expression patterns at the protein level. CONCLUSION A Human Pathology Atlas has been created as part of the Human Protein Atlas program to explore the prognostic role of each protein-coding gene in 17 different cancers. Our atlas uses transcriptomics and antibody-based profiling to provide a standalone resource for cancer precision medicine. The results demonstrate the power of large systems biology efforts that make use of publicly available resources. Using genome-scale metabolic models, cancer patients are shown to have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. With more than 900,000 Kaplan-Meier plots, this resource allows exploration of the specific genes influencing clinical outcome for major cancers, paving the way for further in-depth studies incorporating systems-level analyses of cancer. All data presented are available in an interactive open-access database (www.proteinatlas.org/pathology) to allow for genome-wide exploration of the impact of individual proteins on clinical outcome in major human cancers. Schematic overview of the Human Pathology Atlas. A systems-level approach enables analysis of the protein-coding genes of 17 different cancer types from ~8000 patients. Results are available in an interactive open-access database. Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

Hydrogen peroxide metabolism and sensing in human erythrocytes: A validated kinetic model and reappraisal of the role of peroxiredoxin II

Hydrogen peroxide (H2O2) metabolism in human erythrocytes has been thoroughly investigated, but unclear points persist. By integrating the available data into a mathematical model that accurately represents the current understanding and comparing computational predictions to observations we sought to (a) identify inconsistencies in present knowledge, (b) propose resolutions, and (c) examine their functional implications. The systematic confrontation of computational predictions with experimental observations of the responses of intact erythrocytes highlighted the following important discrepancy. The high rate constant (10(7)-10(8) M(-1) s(-1)) for H2O2 reduction determined for purified peroxiredoxin II (Prx2) and the high abundance of this protein indicate that under physiological conditions it consumes practically all the H2O2. However, this is inconsistent with extensive evidence that Prx2s contribution to H2O2 elimination is comparable to that of catalase. Models modified such that Prx2s effective peroxidase activity is just 10(5) M(-1) s(-1) agree near quantitatively with extensive experimental observations. This low effective activity is probably due to a strong but readily reversible inhibition of Prx2s peroxidatic activity in intact cells, implying that the main role of Prx2 in human erythrocytes is not to eliminate peroxide substrates. Simulations of the responses to physiological H2O2 stimuli highlight that a design combining abundant Prx2 with a low effective peroxidase activity spares NADPH while improving potential signaling properties of the Prx2/thioredoxin/thioredoxin reductase system.

New challenges to study heterogeneity in cancer redox metabolism

Reactive oxygen species (ROS) are important pathophysiological molecules involved in vital cellular processes. They are extremely harmful at high concentrations because they promote the generation of radicals and the oxidation of lipids, proteins, and nucleic acids, which can result in apoptosis. An imbalance of ROS and a disturbance of redox homeostasis are now recognized as a hallmark of complex diseases. Considering that ROS levels are significantly increased in cancer cells due to mitochondrial dysfunction, ROS metabolism has been targeted for the development of efficient treatment strategies, and antioxidants are used as potential chemotherapeutic drugs. However, initial ROS-focused clinical trials in which antioxidants were supplemented to patients provided inconsistent results, i.e., improved treatment or increased malignancy. These different outcomes may result from the highly heterogeneous redox responses of tumors in different patients. Hence, population-based treatment strategies are unsuitable and patient-tailored therapeutic approaches are required for the effective treatment of patients. Moreover, due to the crosstalk between ROS, reducing equivalents [e.g., NAD(P)H] and central metabolism, which is heterogeneous in cancer, finding the best therapeutic target requires the consideration of system-wide approaches that are capable of capturing the complex alterations observed in all of the associated pathways. Systems biology and engineering approaches may be employed to overcome these challenges, together with tools developed in personalized medicine. However, ROS- and redox-based therapies have yet to be addressed by these methodologies in the context of disease treatment. Here, we review the role of ROS and their coupled redox partners in tumorigenesis. Specifically, we highlight some of the challenges in understanding the role of hydrogen peroxide (H2O2), one of the most important ROS in pathophysiology in the progression of cancer. We also discuss its interplay with antioxidant defenses, such as the coupled peroxiredoxin/thioredoxin and glutathione/glutathione peroxidase systems, and its reducing equivalent metabolism. Finally, we highlight the need for system-level and patient-tailored approaches to clarify the roles of these systems and identify therapeutic targets through the use of the tools developed in personalized medicine.

Identification of line-specific strategies for improving carotenoid production in synthetic maize through data-driven mathematical modeling.

Plant synthetic biology is still in its infancy. However, synthetic biology approaches have been used to manipulate and improve the nutritional and health value of staple food crops such as rice, potato and maize. With current technologies, production yields of the synthetic nutrients are a result of trial and error, and systematic rational strategies to optimize those yields are still lacking. Here, we present a workflow that combines gene expression and quantitative metabolomics with mathematical modeling to identify strategies for increasing production yields of nutritionally important carotenoids in the seed endosperm synthesized through alternative biosynthetic pathways in synthetic lines of white maize, which is normally devoid of carotenoids. Quantitative metabolomics and gene expression data are used to create and fit parameters of mathematical models that are specific to four independent maize lines. Sensitivity analysis and simulation of each model is used to predict which gene activities should be further engineered in order to increase production yields for carotenoid accumulation in each line. Some of these predictions (e.g. increasing Zmlycb/Gllycb will increase accumulated β-carotenes) are valid across the four maize lines and consistent with experimental observations in other systems. Other predictions are line specific. The workflow is adaptable to any other biological system for which appropriate quantitative information is available. Furthermore, we validate some of the predictions using experimental data from additional synthetic maize lines for which no models were developed.

TCSBN: a database of tissue and cancer specific biological networks

Abstract Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.

Metabolic Network-Based Identification and Prioritization of Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a deadly form of liver cancer with high mortality worldwide. Unfortunately, the large heterogeneity of this disease makes it difficult to develop effective treatment strategies. Cellular network analyses have been employed to study heterogeneity in cancer, and to identify potential therapeutic targets. However, the existing approaches do not consider metabolic growth requirements, i.e., biological network functionality, to rank candidate targets while preventing toxicity to non-cancerous tissues. Here, we developed an algorithm to overcome these issues based on integration of gene expression data, genome-scale metabolic models, network controllability, and dispensability, as well as toxicity analysis. This method thus predicts and ranks potential anticancer non-toxic controlling metabolite and gene targets. Our algorithm encompasses both objective-driven and—independent tasks, and uses network topology to finally rank the predicted therapeutic targets. We employed this algorithm to the analysis of transcriptomic data for 50 HCC patients with both cancerous and non-cancerous samples. We identified several potential targets that would prevent cell growth, including 74 anticancer metabolites, and 3 gene targets (PRKACA, PGS1, and CRLS1). The predicted anticancer metabolites showed good agreement with existing FDA-approved cancer drugs, and the 3 genes were experimentally validated by performing experiments in HepG2 and Hep3B liver cancer cell lines. Our observations indicate that our novel approach successfully identifies therapeutic targets for effective treatment of cancer. This approach may also be applied to any cancer type that has tumor and non-tumor gene or protein expression data.

Is the Peroxiredoxin 2/Thioredoxin/Thioredoxin Reductase system in human erythrocytes designed for redox signaling?

In human erythrocytes H2O2 is mainly consumed by glutathione peroxidase, catalase and peroxiredoxin 2 (Prx2). Our previous analyses indicate that Prx2s peroxidase activity is subjected to a strong but quickly reversible inhibition (see companion abstract). If this activity is inhibited then the main role of Prx2 cannot be to eliminate H2O2. What functional advantages could then such an inhibition confer?We set up and validated a kinetic model of H2O2 metabolism human erythrocytes that shows quantitative agreement with extensive experimental observations. We then applied it to analyze the behavior of Prx2 and Trx under the H2O2 exposure dynamics that erythrocytes face in circulation. The significance of Prx2 inhibition was assessed by comparing the behavior of this model with that of an otherwise identical model lacking inhibition.Our analysis shows that Prx2 inhibition leads to 25-40% lower NADPH consumption under low to moderately high H2O2 supply (<0.8µM H2O2/s). Further, the inhibition extends the range where the concentrations of potential redox signaling readouts - H2O2, Prx2 sulfenic acid, Prx2 disulfide and Trx disulfide- show a proportional response to changes in H2O2 supply, covering practically the whole physiological range of the latter. This is desirable for analogic signal transduction and allows the Prx2/Trx/TrxR system to reliably transduce changes in H2O2 supply as changes in thiol oxidation. Finally, the inhibition allows other less abundant peroxiredoxins in the erythrocyte to be oxidized by H2O2 at physiological H2O2 supplies.Altogether, these results suggest that the postulated reversible inhibition of Prx2s peroxidase facilitates signal transduction by the peroxiredoxins and spares NADPH.We acknowledge: fellowship SFRH/BD/51199/2010, grants PEst-C/SAU/LA0001/2013-2014, PEst-OE/QUI/UI0612/2013, PEst-OE/QUI/UI0313/2014, and FCOMP-01-0124-FEDER-020978 co-financed by FEDER through the COMPETE program and by FCT (project PTDC/QUI-BIQ/119657/2010).

Characterization of different fat depots in NAFLD using inflammation-associated proteome, lipidome and metabolome

Non-alcoholic fatty liver disease (NAFLD) is recognized as a liver manifestation of metabolic syndrome, accompanied with excessive fat accumulation in the liver and other vital organs. Ectopic fat accumulation was previously associated with negative effects at the systemic and local level in the human body. Thus, we aimed to identify and assess the predictive capability of novel potential metabolic biomarkers for ectopic fat depots in non-diabetic men with NAFLD, using the inflammation-associated proteome, lipidome and metabolome. Myocardial and hepatic triglycerides were measured with magnetic spectroscopy while function of left ventricle, pericardial and epicardial fat, subcutaneous and visceral adipose tissue were measured with magnetic resonance imaging. Measured ectopic fat depots were profiled and predicted using a Random Forest algorithm, and by estimating the Area Under the Receiver Operating Characteristic curves. We have identified distinct metabolic signatures of fat depots in the liver (TAG50:1, glutamate, diSM18:0 and CE20:3), pericardium (N-palmitoyl-sphinganine, HGF, diSM18:0, glutamate, and TNFSF14), epicardium (sphingomyelin, CE20:3, PC38:3 and TNFSF14), and myocardium (CE20:3, LAPTGF-β1, glutamate and glucose). Our analyses highlighted non-invasive biomarkers that accurately predict ectopic fat depots, and reflect their distinct metabolic signatures in subjects with NAFLD.

ESS: a tool for genome-scale quantification of essentiality score for reaction/genes in constraint-based modeling

Genome-scale metabolic models (GEMs) are comprehensive descriptions of cell metabolism and have been extensively used to understand biological responses in health and disease. One such application is in determining metabolic adaptation to the absence of a gene or reaction, i.e., essentiality analysis. However, current methods do not permit efficiently and accurately quantifying reaction/gene essentiality. Here, we present Essentiality Score Simulator (ESS), a tool for quantification of gene/reaction essentialities in GEMs. ESS quantifies and scores essentiality of each reaction/gene and their combinations based on the stoichiometric balance using synthetic lethal analysis. This method provides an option to weight metabolic models which currently rely mostly on topologic parameters, and is potentially useful to investigate the metabolic pathway differences between different organisms, cells, tissues, and/or diseases. We benchmarked the proposed method against multiple network topology parameters, and observed that our method displayed higher accuracy based on experimental evidence. In addition, we demonstrated its application in the wild-type and ldh knock-out E. coli core model, as well as two human cell lines, and revealed the changes of essentiality in metabolic pathways based on the reactions essentiality score. ESS is available without any limitation at https://sourceforge.net/projects/essentiality-score-simulator.

Understanding the representative gut microbiota dysbiosis in metformin-treated Type 2 diabetes patients using genome-scale metabolic modeling

Dysbiosis in the gut microbiome composition may be promoted by therapeutic drugs such as metformin, the world’s most prescribed antidiabetic drug. Under metformin treatment, disturbances of the intestinal microbes lead to increased abundance of Escherichia spp., Akkermansia muciniphila, Subdoligranulum variabile and decreased abundance of Intestinibacter bartlettii. This alteration may potentially lead to adverse effects on the host metabolism, with the depletion of butyrate producer genus. However, an increased production of butyrate and propionate was verified in metformin-treated Type 2 diabetes (T2D) patients. The mechanisms underlying these nutritional alterations and their relation with gut microbiota dysbiosis remain unclear. Here, we used Genome-scale Metabolic Models of the representative gut bacteria Escherichia spp., I. bartlettii, A. muciniphila, and S. variabile to elucidate their bacterial metabolism and its effect on intestinal nutrient pool, including macronutrients (e.g., amino acids and short chain fatty acids), minerals and chemical elements (e.g., iron and oxygen). We applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify combinations of reactions and extracellular nutrients whose absence prevents growth. Our analyses suggest that Escherichia sp. is the bacteria least vulnerable to nutrient availability. We have also examined bacterial contribution to extracellular nutrients including short chain fatty acids, amino acids, and gasses. For instance, Escherichia sp. and S. variabile may contribute to the production of important short chain fatty acids (e.g., acetate and butyrate, respectively) involved in the host physiology under aerobic and anaerobic conditions. We have also identified pathway susceptibility to nutrient availability and reaction changes among the four bacteria using both FBA and flux variability analysis. For instance, lipopolysaccharide synthesis, nucleotide sugar metabolism, and amino acid metabolism are pathways susceptible to changes in Escherichia sp. and A. muciniphila. Our observations highlight important commensal and competing behavior, and their association with cellular metabolism for prevalent gut microbes. The results of our analysis have potential important implications for development of new therapeutic approaches in T2D patients through the development of prebiotics, probiotics, or postbiotics.