Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer

A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism

Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant.

A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells

Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.

Endothelial Cell Phenotypes Demonstrate Different Metabolic Patterns and Predict Mortality in Trauma Patients

In trauma patients, shock-induced endotheliopathy (SHINE) is associated with a poor prognosis. We have previously identified four metabolic phenotypes in a small cohort of trauma patients (N = 20) and displayed the intracellular metabolic profile of the endothelial cell by integrating quantified plasma metabolomic profiles into a genome-scale metabolic model (iEC-GEM). A retrospective observational study of 99 trauma patients admitted to a Level 1 Trauma Center. Mass spectrometry was conducted on admission samples of plasma metabolites. Quantified metabolites were analyzed by computational network analysis of the iEC-GEM. Four plasma metabolic phenotypes (A-D) were identified, of which phenotype D was associated with an increased injury severity score (p < 0.001); 90% (91.6%) of the patients who died within 72 h possessed this phenotype. The inferred EC metabolic patterns were found to be different between phenotype A and D. Phenotype D was unable to maintain adequate redox homeostasis. We confirm that trauma patients presented four metabolic phenotypes at admission. Phenotype D was associated with increased mortality. Different EC metabolic patterns were identified between phenotypes A and D, and the inability to maintain adequate redox balance may be linked to the high mortality.

Metabolic systems analysis identifies a novel mechanism contributing to shock in patients with endotheliopathy of trauma (EoT) involving thromboxane A2 and LTC4

Purpose

Endotheliopathy of trauma (EoT), as defined by circulating levels of syndecan-1 ≥ 40 ng/mL, has been reported to be associated with significantly increased transfusion requirements and a doubled 30-day mortality. Increased shedding of the glycocalyx points toward the endothelial cell membrane composition as important for the clinical outcome being the rationale for this study.

Results

The plasma metabolome of 95 severely injured trauma patients was investigated by mass spectrometry, and patients with EoT vs. non-EoT were compared by partial least square-discriminant analysis, identifying succinic acid as the top metabolite to differentiate EoT and non-EoT patients (VIP score = 3). EoT and non-EoT patients' metabolic flux profile was inferred by integrating the corresponding plasma metabolome data into a genome-scale metabolic network reconstruction analysis and performing a functional study of the metabolic capabilities of each group. Model predictions showed a decrease in cholesterol metabolism secondary to impaired mevalonate synthesis in EoT compared to non-EoT patients. Intracellular task analysis indicated decreased synthesis of thromboxanA2 and leukotrienes, as well as a lower carnitine palmitoyltransferase I activity in EoT compared to non-EoT patients. Sensitivity analysis also showed a significantly high dependence of eicosanoid-associated metabolic tasks on alpha-linolenic acid as unique to EoT patients.

Conclusions

Model-driven analysis of the endothelial cells' metabolism identified potential novel targets as impaired thromboxane A2 and leukotriene synthesis in EoT patients when compared to non-EoT patients. Reduced thromboxane A2 and leukotriene availability in the microvasculature impairs vasoconstriction ability and may thus contribute to shock in EoT patients. These findings are supported by extensive scientific literature; however, further investigations are required on these findings.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

A pan-cancer metabolic atlas of the tumor microenvironment

Tumors are heterogeneous cellular environments with entwined metabolic dependencies. Here, we use a tumor transcriptome deconvolution approach to profile the metabolic states of cancer and non-cancer (stromal) cells in bulk tumors of 20 solid tumor types. We identify metabolic genes and processes recurrently altered in cancer cells across tumor types, highlighting pan-cancer upregulation of deoxythymidine triphosphate (dTTP) production. In contrast, the tryptophan catabolism rate-limiting enzymes IDO1 and TDO2 are highly overexpressed in stroma, raising the hypothesis that kynurenine-mediated suppression of antitumor immunity may be predominantly constrained by the stroma. Oxidative phosphorylation is the most upregulated metabolic process in cancer cells compared to both stromal cells and a large atlas of cancer cell lines, suggesting that the Warburg effect may be less pronounced in cancer cells in vivo. Overall, our analysis highlights fundamental differences in metabolic states of cancer and stromal cells inside tumors and establishes a pan-cancer resource to interrogate tumor metabolism.

Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment

The tumor's physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

The landscape of tiered regulation of breast cancer cell metabolism

Altered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across three different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning, we systematically chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level. We distinguish between two types of reactions, directly and indirectly regulated. Directly-regulated reactions include those whose flux is regulated by transcriptomic alterations (~890) or via proteomic or phospho-proteomics alterations (~140) in the enzymes catalyzing them. We term the reactions that currently lack evidence for direct regulation as (putative) indirectly regulated (~930). Many metabolic pathways are predicted to be regulated at different levels, and those may change at different media conditions. Remarkably, we find that the flux of predicted indirectly regulated reactions is strongly coupled to the flux of the predicted directly regulated ones, uncovering a tiered hierarchical organization of breast cancer cell metabolism. Furthermore, the predicted indirectly regulated reactions are predominantly reversible. Taken together, this architecture may facilitate rapid and efficient metabolic reprogramming in response to the varying environmental conditions incurred by the tumor cells. The approach presented lays a conceptual and computational basis for mapping metabolic regulation in additional cancers.