Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data

Metaboverse enables automated discovery and visualization of diverse metabolic regulatory patterns

Metabolism is intertwined with various cellular processes, including controlling cell fate, influencing tumorigenesis, participating in stress responses and more. Metabolism is a complex, interdependent network, and local perturbations can have indirect effects that are pervasive across the metabolic network. Current analytical and technical limitations have long created a bottleneck in metabolic data interpretation. To address these shortcomings, we developed Metaboverse, a user-friendly tool to facilitate data exploration and hypothesis generation. Here we introduce algorithms that leverage the metabolic network to extract complex reaction patterns from data. To minimize the impact of missing measurements within the network, we introduce methods that enable pattern recognition across multiple reactions. Using Metaboverse, we identify a previously undescribed metabolite signature that correlated with survival outcomes in early stage lung adenocarcinoma patients. Using a yeast model, we identify metabolic responses suggesting an adaptive role of citrate homeostasis during mitochondrial dysfunction facilitated by the citrate transporter, Ctp1. We demonstrate that Metaboverse augments the user's ability to extract meaningful patterns from multi-omics datasets to develop actionable hypotheses.

Quantitative modeling of pentose phosphate pathway response to oxidative stress reveals a cooperative regulatory strategy

Living cells use signaling and regulatory mechanisms to adapt to environmental stresses. Adaptation to oxidative stress involves the regulation of many enzymes in both glycolysis and pentose phosphate pathways (PPP), so as to support PPP-driven NADPH recycling for antioxidant defense. The underlying regulatory logic is investigated by developing a kinetic modeling approach fueled with metabolomics and 13C-fluxomics datasets from human fibroblast cells. Bayesian parameter estimation and phenotypic analysis of models highlight complementary roles for several metabolite-enzyme regulations. Specifically, carbon flux rerouting into PPP involves a tight coordination between the upregulation of G6PD activity concomitant to a decreased NADPH/NADP+ ratio and the differential control of downward and upward glycolytic fluxes through the joint inhibition of PGI and GAPD enzymes. Such functional interplay between distinct regulatory feedbacks promotes efficient detoxification and homeostasis response over a broad range of stress level, but can also explain paradoxical pertubation phenotypes for instance reported for 6PGD modulation in mammalian cells.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

Using Pathway Covering to Explore Connections among Metabolites

Interpreting changes in metabolite abundance in response to experimental treatments or disease states remains a major challenge in metabolomics. Pathway Covering is a new algorithm that takes a list of metabolites (compounds) and determines a minimum-cost set of metabolic pathways in an organism that includes (covers) all the metabolites in the list. We used five functions for assigning costs to pathways, including assigning a constant for all pathways, which yields a solution with the smallest pathway count; two methods that penalize large pathways; one that prefers pathways based on the pathway's assigned function, and one that loosely corresponds to metabolic flux. The pathway covering set computed by the algorithm can be displayed as a multi-pathway diagram ("pathway collage") that highlights the covered metabolites. We investigated the pathway covering algorithm by using several datasets from the Metabolomics Workbench. The algorithm is best applied to a list of metabolites with significant statistics and fold-changes with a specified direction of change for each metabolite. The pathway covering algorithm is now available within the Pathway Tools software and BioCyc website.

A scoring approach for multi-platform acquisition in metabolomics

Since the ultimate goal of untargeted metabolomics is the analysis of the broadest possible range of metabolites, some new metrics have to be used by researchers to evaluate and select different analytical strategies when multi-platform analyses are considered. In this context, we aimed at developing a scoring approach allowing to compare the performance of different LC-MS conditions for metabolomics studies. By taking into account both chromatographic and MS attributes of the analytes' peaks (i.e. retention, signal-to-noise ratio, peak intensity and shape), the newly proposed score reflects the potential of a set of LC-MS operating conditions to provide useful analytical information for a given compound. A chemical library containing 597 metabolites was used as a benchmark to apply this approach on two RPLC and three HILIC methods hyphenated to high resolution mass spectrometry (HRMS) in positive and negative ionization modes. The scores not only allowed to evaluate each analytical platform, but also to optimize the number of analytical methods needed for the analysis of metabolomics samples. As a result, the most informative combination of three LC methods and ionization modes was found, leading to a coverage of nearly 95% of the detected compounds. It was therefore demonstrated that the overall performance reached with three selected methods was almost equivalent to the performance reached when five LC-MS conditions were used.

Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease

The ability to efficiently adapt metabolism by substrate sensing, trafficking, storage, and utilization, dependent on availability and requirement, is known as metabolic flexibility. In this review, we discuss the breadth and depth of metabolic flexibility and its impact on health and disease. Metabolic flexibility is essential to maintain energy homeostasis in times of either caloric excess or caloric restriction, and in times of either low or high energy demand, such as during exercise. The liver, adipose tissue, and muscle govern systemic metabolic flexibility and manage nutrient sensing, uptake, transport, storage, and expenditure by communication via endocrine cues. At a molecular level, metabolic flexibility relies on the configuration of metabolic pathways, which are regulated by key metabolic enzymes and transcription factors, many of which interact closely with the mitochondria. Disrupted metabolic flexibility, or metabolic inflexibility, however, is associated with many pathological conditions including metabolic syndrome, type 2 diabetes mellitus, and cancer. Multiple factors such as dietary composition and feeding frequency, exercise training, and use of pharmacological compounds, influence metabolic flexibility and will be discussed here. Last, we outline important advances in metabolic flexibility research and discuss medical horizons and translational aspects.