AI delivers Michaelis constants as fuel for genome-scale metabolic models

Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic, genome-wide modeling of metabolism

Unveiling a key role of oxaloacetate-glutamate interaction in regulation of respiration and ROS generation in nonsynaptic brain mitochondria using a kinetic model.

Glutamate plays diverse roles in neuronal cells, affecting cell energetics and reactive oxygen species (ROS) generation. These roles are especially vital for neuronal cells, which deal with high amounts of glutamate as a neurotransmitter. Our analysis explored neuronal glutamate implication in cellular energy metabolism and ROS generation, using a kinetic model that simulates electron transport details in respiratory complexes, linked ROS generation and metabolic reactions. The analysis focused on the fact that glutamate attenuates complex II inhibition by oxaloacetate, stimulating the latter's transformation into aspartate. Such a mechanism of complex II activation by glutamate could cause almost complete reduction of ubiquinone and deficiency of oxidized form (Q), which closes the main stream of electron transport and opens a way to massive ROS generating transfer in complex III from semiquinone radicals to molecular oxygen. In this way, under low workload, glutamate triggers the respiratory chain (RC) into a different steady state characterized by high ROS generation rate. The observed stepwise dependence of ROS generation on glutamate concentration experimentally validated this prediction. However, glutamate's attenuation of oxaloacetate's inhibition accelerates electron transport under high workload. Glutamate-oxaloacetate interaction in complex II regulation underlies the observed effects of uncouplers and inhibitors and acceleration of Ca2+ uptake. Thus, this theoretical analysis uncovered the previously unknown roles of oxaloacetate as a regulator of ROS generation and glutamate as a modifier of this regulation. The model predicted that this mechanism of complex II activation by glutamate might be operative in situ and responsible for excitotoxicity. Spatial-time gradients of synthesized hydrogen peroxide concentration, calculated in the reaction-diffusion model with convection under a non-uniform local approximation of nervous tissue, have shown that overproduction of H2O2 in a cell causes excess of its level in neighbor cells

A new clinically applicable immune-metabolic signature (IMMETCOLS) reveals metabolic singularities in consensus molecular subtypes (CMS) in colorectal cancer

Background: In the last years, a great effort has been made to unify different independent colorectal cancer (CRC) molecular classification systems into four consensus molecular subtypes (CMS). The four subtypes are found to be associated with distinct microenvironmental features and clinical outcome, although metabolic singularities are not well established. Metabolic dysregulation has been reported as a hallmark of CMS3, but metabolic heterogeneity among other subtypes has not been investigated. Here, taking into account the increasing evidence on the importance, for determining response to therapies, of the metabolic crosstalk between cancer cells, tumor microenvironment and immune cells, we investigated the metabolic singularities in the four CMS using a genetic immune-metabolic signature (IMMETCOLS). Conclusions: IMMETCOLS signature refines CMS prognosis in CRC patients and potentially allows specific metabolic interventions in CMS subtypes

Glucose-6-phosphate dehydrogenase and transketolase modulate breast cancer cell metabolic reprogramming and correlate with poor patient outcome

The pentose phosphate pathway is a fundamental metabolic pathway that provides cells with ribose and NADPH required for anabolic reactions - synthesis of nucleotides and fatty acids - and maintenance of intracellular redox homeostasis. It plays a key role in tumor metabolic reprogramming and has been reported to be deregulated in different types of tumors. Herein, we silenced the most important enzymes of this pathway - glucose-6-phosphate dehydrogenase (G6PD) and transketolase (TKT) - in the human breast cancer cell line MCF7. We demonstrated that inhibition of G6PD, the oxidative branch-controlling enzyme, reduced proliferation, cell survival and increased oxidative stress. At the metabolic level, silencing of both enzymes reduced ribose synthesis. G6PD silencing in particular, augmented the glycolytic flux, reduced lipid synthesis and increased glutamine uptake, whereas silencing of TKT reduced the glycolytic flux. Importantly, we showed using breast cancer patient datasets that expression of both enzymes is positively correlated and that high expression levels of G6PD and TKT are associated with decreased overall and relapse-free survival. Altogether, our results suggest that this metabolic pathway could be subjected to therapeutic intervention to treat breast tumors and warrant further investigation

Effect of crowding by dextrans on the hydrolysis of N-Succinyl-L-phenyl-Ala-p-nitroanilide catalyzed by α-chymotrypsin

Traditionally, studies on the diffusion-controlled reaction of biological macromolecules have been carried out in dilute solutions (in vitro). However, in an intracellular environment (in vivo), there is a high concentration of macromolecules, which results in nonspecific interactions (macromolecular crowding). This affects the kinetics and thermodynamics of the reactions that occur in these systems. In this paper, we study the crowding effect of large macromolecules on the reaction rates of the hydrolysis of N-succinyl-L-phenyl-Ala-p-nitroanilide catalyzed by R-chymo- trypsin, by adding dextrans of various molecular weights to the reaction solutions. The results indicate that the volume occupied by the crowding agent, but not its size, plays an important role in the rate of this reaction. A vmax decay and a Km increase were obtained when the dextran concentration in the sample was increased. The increase in Km can be attributed to the slowing of protein diffusion, due to the presence of crowding. Whereas the decrease in vmax could be explained by the effect of mixed inhibition by product, which is enhanced in crowded media. As far as we know, this is the first reported experiment on the crowding effect in an enzymatic reaction with a mixed inhibition by product

Diffusion of alpha-Chymotrypsin in solution-crowded media. A fluorescence recovery after photobleaching study

Fluorescence Recovery after Photobleaching (FRAP) is one of the most powerful and used techniques to study diffusion processes of macromolecules in membranes or in bulk. Here, we study the diffusion of alpha-chymotrypsin in different crowded (Dextran) in vitro solutions using a confocal laser scanning microscope. In the considered experimental conditions, confocal FRAP images could be analyzed applying the uniform circular disc approximation described for a nonscanning microscope generalized to take into account anomalous diffusion. Considering the slow diffusion of macromolecules in crowded media, we compare the fitting of confocal FRAP curves analyzed with the equations provided by the Gaussian and the uniform circular disc profile models for nonscanning microscopes. As the fitted parameter variation with the size and concentration of crowders is qualitatively similar for both models, the use of the uniform circular disc or the Gaussian model is justified for these experiments. Moreover, in our experimental conditions, alpha-chymotrypsin shows anomalous diffusion (a < 1), depending on the size and concentration of Dextran molecules, until a high concentration and high size of crowding agent are achieved. This result indicates a range of validity of the idealized fitting expressions used, beyond of which other physical phenomena must be considered

Oncogenic regulation of tumor metabolic reprogramming

Development of malignancy is accompanied by a complete metabolic reprogramming closely related to the acquisition of most of cancer hallmarks. In fact, key oncogenic pathways converge to adapt the metabolism of carbohydrates, proteins, lipids and nucleic acids to the dynamic tumor microenvironment, conferring a selective advantage to cancer cells. Therefore, metabolic properties of tumor cells are significantly different from those of non-transformed cells. In addition, tumor metabolic reprogramming is linked to drug resistance in cancer treatment. Accordingly, metabolic adaptations are specific vulnerabilities that can be used in different therapeutic approaches for cancer therapy. In this review, we discuss the dysregulation of the main metabolic pathways that enable cell transformation and its association with oncogenic signaling pathways, focusing on the effects of c-MYC, hypoxia inducible factor 1 (HIF1), phosphoinositide-3-kinase (PI3K), and the mechanistic target of rapamycin (mTOR) on cancer cell metabolism. Elucidating these connections is of crucial importance to identify new targets and develop selective cancer treatments that improve response to therapy and overcome the emerging resistance to chemotherapeutics

Synergy-COPD: a systems approach for understanding and managing chronic diseases.

Chronic diseases (CD) are generating a dramatic societal burden worldwide that is expected to persist over the next decades. The challenges posed by the epidemics of CD have triggered a novel health paradigm with major consequences on the traditional concept of disease and with a profound impact on key aspects of healthcare systems. We hypothesized that the development of a systems approach to understand CD together with the generation of an ecosystem to transfer the acquired knowledge into the novel healthcare scenario may contribute to a cost-effective enhancement of health outcomes. To this end, we designed the Synergy-COPD project wherein the heterogeneity of chronic obstructive pulmonary disease (COPD) was addressed as a use case representative of CD. The current manuscript describes main features of the project design and the strategies put in place for its development, as well the expected outcomes during the project life-span. Moreover, the manuscript serves as introductory and unifying chapter of the different papers associated to the Supplement describing the characteristics, tools and the objectives of Synergy-COP

Sampling with poling-based flux balance analysis: optimal versus sub-optimal flux space analysis of Actinobacillus succinogenes

Flux balance analysis is traditionally implemented to identify the maximum theoretical flux for some specified reaction and a single distribution of flux values for all the reactions present which achieve this maximum value. However it is well known that the uncertainty in reaction networks due to branches, cycles and experimental errors results in a large number of combinations of internal reaction fluxes which can achieve the same optimal flux value. In this work, we have modified the applied linear objective of flux balance analysis to include a poling penalty function, which pushes each new set of reaction fluxes away from previous solutions generated. Repeated poling-based flux balance analysis generates a sample of different solutions (a characteristic set), which represents all the possible functionality of the reaction network. Compared to existing sampling methods, for the purpose of generating a relatively ¿small¿ characteristic set, our new method is shown to obtain a higher coverage than competing methods under most conditions. The influence of the linear objective function on the sampling (the linear bias) constrains optimisation results to a subspace of optimal solutions all producing the same maximal fluxes. Visualisation of reaction fluxes plotted against each other in 2 dimensions with and without the linear bias indicates the existence of correlations between fluxes. This method of sampling is applied to the organism Actinobacillus succinogenes for the production of succinic acid from glycerol. A new method of sampling for the generation of different flux distributions (sets of individual fluxes satisfying constraints on the steady-state mass balances of intermediates) has been developed using a relatively simple modification of flux balance analysis to include a poling penalty function inside the resulting optimisation objective function. This new methodology can achieve a high coverage of the possible flux space and can be used with and without linear bias to show optimal versus sub-optimal solution spaces. Basic analysis of the Actinobacillus succinogenes system using sampling shows that in order to achieve the maximal succinic acid production CO2 must be taken into the system. Solutions involving release of CO2 all give sub-optimal succinic acid production

Predictive medicine: outcomes, challenges and opportunities in the Synergy-COPD project

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a major challenge for healthcare. Heterogeneities in clinical manifestations and in disease progression are relevant traits in COPD with impact on patient management and prognosis. It is hypothesized that COPD heterogeneity results from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering. OBJECTIVES: To assess the potential of systems medicine to better understand non-pulmonary determinants of COPD heterogeneity. To transfer acquired knowledge to healthcare enhancing subject-specific health risk assessment and stratification to improve management of chronic patients. METHOD: Underlying mechanisms of skeletal muscle dysfunction and of co-morbidity clustering in COPD patients were explored with strategies combining deterministic modelling and network medicine analyses using the Biobridge dataset. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was done (ICD9-CM data from Medicare, 13 million people). A targeted network analysis using the two studies: skeletal muscle dysfunction and co-morbidity clustering explored shared pathways between them. RESULTS: (1) Evidence of abnormal regulation of pivotal skeletal muscle biological pathways and increased risk for co-morbidity clustering was observed in COPD; (2) shared abnormal pathway regulation between skeletal muscle dysfunction and co-morbidity clustering; and, (3) technological achievements of the projects were: (i) COPD Knowledge Base; (ii) novel modelling approaches; (iii) Simulation Environment; and, (iv) three layers of Clinical Decision Support Systems. CONCLUSIONS: The project demonstrated the high potential of a systems medicine approach to address COPD heterogeneity. Limiting factors for the project development were identified. They were relevant to shape strategies fostering 4P Medicine for chronic patients. The concept of Digital Health Framework and the proposed roadmap for its deployment constituted relevant project outcomes