A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect

Lactate and lactylation: Behind the development of tumors

There is growing evidence that lactate can have a wide range of biological impacts in addition to being a waste product of metabolism. Because of the Warburg effect, tumors generate lots of lactate, which create a tumor microenvironment (TME) with low nutrition, hypoxia, and low pH. As a result, the immunosuppressive network is established to gain immune escape potential and regulate tumor growth. Consequently, the tumor lactate pathway is emerging as a possible therapeutic target for tumor. Importantly, Zhao et al. first discovered histone lysine lactylation (Kla) in 2019, which links gene regulation to cell metabolism through dysmetabolic activity and epigenetic modifications, influencing TME and tumor development. Therefore, the aim of this paper is to explore the effects of lactate and lactylation on the TME and tumors, and provide theoretical basis for further research on potential therapeutic targets and biomarkers, with the view to providing new ideas and methods for tumor treatment and prognosis evaluation.

Complex I activity in hypoxia: implications for oncometabolism

Certain cancer cells within solid tumors experience hypoxia, rendering them incapable of oxidative phosphorylation (OXPHOS). Despite this oxygen deficiency, these cells exhibit biochemical pathway activity that relies on NAD+. This mini-review scrutinizes the persistent, residual Complex I activity that oxidizes NADH in the absence of oxygen as the electron acceptor. The resulting NAD+ assumes a pivotal role in fueling the α-ketoglutarate dehydrogenase complex, a critical component in the oxidative decarboxylation branch of glutaminolysis - a hallmark oncometabolic pathway. The proposition is that through glutamine catabolism, high-energy phosphate intermediates are produced via substrate-level phosphorylation in the mitochondrial matrix substantiated by succinyl-CoA ligase, partially compensating for an OXPHOS deficiency. These insights provide a rationale for exploring Complex I inhibitors in cancer treatment, even when OXPHOS functionality is already compromised.

Dietary approaches for exploiting metabolic vulnerabilities in cancer

Renewed interest in tumor metabolism sparked an enthusiasm for dietary interventions to prevent and treat cancer. Changes in diet impact circulating nutrient levels in the plasma and the tumor microenvironment, and preclinical studies suggest that dietary approaches, including caloric and nutrient restrictions, can modulate tumor initiation, progression, and metastasis. Cancers are heterogeneous in their metabolic dependencies and preferred energy sources and can be addicted to glucose, fructose, amino acids, or lipids for survival and growth. This dependence is influenced by tumor type, anatomical location, tissue of origin, aberrant signaling, and the microenvironment. This review summarizes nutrient dependencies and the related signaling pathway activations that provide targets for nutritional interventions. We examine popular dietary approaches used as adjuvants to anticancer therapies, encompassing caloric restrictions, including time-restricted feeding, intermittent fasting, fasting-mimicking diets (FMDs), and nutrient restrictions, notably the ketogenic diet. Despite promising results, much of the knowledge on dietary restrictions comes from in vitro and animal studies, which may not accurately reflect real-life situations. Further research is needed to determine the optimal duration, timing, safety, and efficacy of dietary restrictions for different cancers and treatments. In addition, well-designed human trials are necessary to establish the link between specific metabolic vulnerabilities and targeted dietary interventions. However, low patient compliance in clinical trials remains a significant challenge.

Adjusting for false discoveries in constraint-based differential metabolic flux analysis

One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint-based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks. While many algorithms have been proposed and compared for efficiently and uniformly sampling the feasible region of metabolic networks, their impact on the risk of making false discoveries when comparing different samples has not been investigated yet, and no sampling strategy has been so far specifically designed to mitigate the problem. To be able to precisely assess the False Discovery Rate (FDR), in this work we compared different samples obtained from the very same metabolic model. We compared the FDR obtained for different model scales, sample sizes, parameters of the sampling algorithm, and strategies to filter out non-significant variations. To be able to compare the largely used hit-and-run strategy with the much less investigated corner-based strategy, we first assessed the intrinsic capability of current corner-based algorithms and of a newly proposed one to visit all vertices of a constraint-based region. We show that false discoveries can occur at high rates even for large samples of small-scale networks. However, we demonstrate that a statistical test based on the empirical null distribution of Kullback-Leibler divergence can effectively correct for false discoveries. We also show that our proposed corner-based algorithm is more efficient than state-of-the-art alternatives and much less prone to false discoveries than hit-and-run strategies. We report that the differences in the marginal distributions obtained with the two strategies are related to but not fully explained by differences in sample standard deviation, as previously thought. Overall, our study provides insights into the impact of sampling strategies on FDR in metabolic network analysis and offers new guidelines for more robust and reproducible analyses.

On paradoxes between optimal growth, metabolic control analysis, and flux balance analysis

In Microbiology it is often assumed that growth rate is maximal. This may be taken to suggest that the dependence of the growth rate on every enzyme activity is at the top of an inverse-parabolic function, i.e. that all flux control coefficients should equal zero. This might seem to imply that the sum of these flux control coefficients equals zero. According to the summation law of Metabolic Control Analysis (MCA) the sum of flux control coefficients should equal 1 however. And in Flux Balance Analysis (FBA) catabolism is often limited by a hard bound, causing catabolism to fully control the fluxes, again in apparent contrast with a flux control coefficient of zero. Here we resolve these paradoxes (apparent contradictions) in an analysis that uses the 'Edinburgh pathway', the 'Amsterdam pathway', as well as a generic metabolic network providing the building blocks or Gibbs energy for microbial growth. We review and show that (i) optimization depends on so-called enzyme control coefficients rather than the 'catalytic control coefficients' of MCA's summation law, (ii) when optimization occurs at fixed total protein, the former differ from the latter to the extent that they may all become equal to zero in the optimum state, (iii) in more realistic scenarios of optimization where catalytically inert biomass is compensating or maintenance metabolism is taken into consideration, the optimum enzyme concentrations should not be expected to equal those that maximize the specific growth rate, (iv) optimization may be in terms of yield rather than specific growth rate, which resolves the paradox because the sum of catalytic control coefficients on yield equals 0, (v) FBA effectively maximizes growth yield, and for yield the summation law states 0 rather than 1, thereby removing the paradox, (vi) furthermore, FBA then comes more often to a 'hard optimum' defined by a maximum catabolic flux and a catabolic-enzyme control coefficient of 1. The trade-off between maintenance metabolism and growth is highlighted as worthy of further analysis.

'Social' versus 'asocial' cells-dynamic competition flux balance analysis

In multicellular organisms cells compete for resources or growth factors. If any one cell type wins, the co-existence of diverse cell types disappears. Existing dynamic Flux Balance Analysis (dFBA) does not accommodate changes in cell density caused by competition. Therefore we here develop 'dynamic competition Flux Balance Analysis' (dcFBA). With total biomass synthesis as objective, lower-growth-yield cells were outcompeted even when cells synthesized mutually required nutrients. Signal transduction between cells established co-existence, which suggests that such 'socialness' is required for multicellularity. Whilst mutants with increased specific growth rate did not outgrow the other cell types, loss of social characteristics did enable a mutant to outgrow the other cells. We discuss that 'asocialness' rather than enhanced growth rates, i.e., a reduced sensitivity to regulatory factors rather than enhanced growth rates, may characterize cancer cells and organisms causing ecological blooms. Therapies reinforcing cross-regulation may therefore be more effective than those targeting replication rates.

LAT1 (SLC7A5) catalyzes copper(histidinate) transport switching from antiport to uniport mechanism

LAT1 (SLC7A5) is one of the most studied membrane transporters due to its relevance to physiology in supplying essential amino acids to brain and fetus, and to pathology being linked to nervous or embryo alterations; moreover, LAT1 over-expression is always associated with cancer development. Thus, LAT1 is exploited as a pro-drug vehicle and as a target for anti-cancer therapy. We here report the identification of a new substrate with pathophysiological implications, i.e., Cu-histidinate, and an unconventional uniport mechanism exploited for the Cu-histidinate transport. Crystals of the monomeric species Cu(His)2 were obtained in our experimental conditions and the actual transport of the complex was evaluated by a combined strategy of bioinformatics, site-directed mutagenesis, radiolabeled transport, and mass spectrometry analysis. The LAT1-mediated transport of Cu(His)2 may have profound implications for both the treatment of copper dysmetabolism diseases, such as the rare Menkes disease, and of cancer as an alternative to platinum-based therapies.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Gear Shifting in Biological Energy Transduction

Confronted with thermodynamically adverse output processes, free-energy transducers may shift to lower gears, thereby reducing output per unit input. This option is well known for inanimate machines such as automobiles, but unappreciated in biology. The present study extends existing non-equilibrium thermodynamic principles to underpin biological gear shifting and identify possible mechanisms. It shows that gear shifting differs from altering the degree of coupling and that living systems may use it to optimize their performance: microbial growth is ultimately powered by the Gibbs energy of catabolism, which is partially transformed into Gibbs energy ('output force') in the ATP that is produced. If this output force is high, the cell may turn to a catabolic pathway with a lower ATP stoichiometry. Notwithstanding the reduced stoichiometry, the ATP synthesis flux may then actually increase as compared to that in a system without gear shift, in which growth might come to a halt. A 'variomatic' gear switching strategy should be optimal, explaining why organisms avail themselves of multiple catabolic pathways, as these enable them to shift gears when the growing gets tough.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

Lactate is useful for the efficient replication of porcine epidemic diarrhea virus in cell culture

Porcine epidemic diarrhea virus (PEDV) is a deadly pathogen infecting pig herds, and has caused significant economic losses around the world. Vaccination remains the most effective way of keeping the PEDV epidemic under control. Previous studies have shown that the host metabolism has a significant impact on viral replication. In this study, we have demonstrated that two substrates of metabolic pathway, glucose and glutamine, play a key role in PEDV replication. Interestingly, the boosting effect of these compounds toward viral replication appeared to be dose-independent. Furthermore, we found that lactate, which is a downstream metabolite, promotes PEDV replication, even when added in excess to the cell culture medium. Moreover, the role of lactate in promoting PEDV was independent of the genotype of PEDV and the multiplicity of infection (MOI). Our findings suggest that lactate is a promising candidate for use as a cell culture additive for promoting PEDV replication. It could improve the efficiency of vaccine production and provide the basis for designing novel antiviral strategies.

Research progress of abnormal lactate metabolism and lactate modification in immunotherapy of hepatocellular carcinoma

Tumors meet their energy, biosynthesis, and redox demands through metabolic reprogramming. This metabolic abnormality results in elevated levels of metabolites, particularly lactate, in the tumor microenvironment. Immune cell reprogramming and cellular plasticity mediated by lactate and lactylation increase immunosuppression in the tumor microenvironment and are emerging as key factors in regulating tumor development, metastasis, and the effectiveness of immunotherapies such as immune checkpoint inhibitors. Reprogramming of glucose metabolism and the "Warburg effect" in hepatocellular carcinoma (HCC) lead to the massive production and accumulation of lactate, so lactate modification in tumor tissue is likely to be abnormal as well. This article reviews the immune regulation of abnormal lactate metabolism and lactate modification in hepatocellular carcinoma and the therapeutic strategy of targeting lactate-immunotherapy, which will help to better guide the medication and treatment of patients with hepatocellular carcinoma.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Lactate in the tumor microenvironment: A rising star for targeted tumor therapy

Metabolic reprogramming is one of fourteen hallmarks of tumor cells, among which aerobic glycolysis, often known as the "Warburg effect," is essential to the fast proliferation and aggressive metastasis of tumor cells. Lactate, on the other hand, as a ubiquitous molecule in the tumor microenvironment (TME), is generated primarily by tumor cells undergoing glycolysis. To prevent intracellular acidification, malignant cells often remove lactate along with H⁺, yet the acidification of TME is inevitable. Not only does the highly concentrated lactate within the TME serve as a substrate to supply energy to the malignant cells, but it also works as a signal to activate multiple pathways that enhance tumor metastasis and invasion, intratumoral angiogenesis, as well as immune escape. In this review, we aim to discuss the latest findings on lactate metabolism in tumor cells, particularly the capacity of extracellular lactate to influence cells in the tumor microenvironment. In addition, we examine current treatment techniques employing existing medications that target and interfere with lactate generation and transport in cancer therapy. New research shows that targeting lactate metabolism, lactate-regulated cells, and lactate action pathways are viable cancer therapy strategies.

Targeting glycolysis in non-small cell lung cancer: Promises and challenges

Metabolic disturbance, particularly of glucose metabolism, is a hallmark of tumors such as non-small cell lung cancer (NSCLC). Cancer cells tend to reprogram a majority of glucose metabolism reactions into glycolysis, even in oxygen-rich environments. Although glycolysis is not an efficient means of ATP production compared to oxidative phosphorylation, the inhibition of tumor glycolysis directly impedes cell survival and growth. This review focuses on research advances in glycolysis in NSCLC and systematically provides an overview of the key enzymes, biomarkers, non-coding RNAs, and signaling pathways that modulate the glycolysis process and, consequently, tumor growth and metastasis in NSCLC. Current medications, therapeutic approaches, and natural products that affect glycolysis in NSCLC are also summarized. We found that the identification of appropriate targets and biomarkers in glycolysis, specifically for NSCLC treatment, is still a challenge at present. However, LDHB, PDK1, MCT2, GLUT1, and PFKM might be promising targets in the treatment of NSCLC or its specific subtypes, and DPPA4, NQO1, GAPDH/MT-CO1, PGC-1α, OTUB2, ISLR, Barx2, OTUB2, and RFP180 might be prognostic predictors of NSCLC. In addition, natural products may serve as promising therapeutic approaches targeting multiple steps in glycolysis metabolism, since natural products always present multi-target properties. The development of metabolic intervention that targets glycolysis, alone or in combination with current therapy, is a potential therapeutic approach in NSCLC treatment. The aim of this review is to describe research patterns and interests concerning the metabolic treatment of NSCLC.

Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells

Background

Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem.

Methods

We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts.

Results

We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions.

Conclusions

Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.

Tumor acidity: From hallmark of cancer to target of treatment

Tumor acidity is one of the cancer hallmarks and is associated with metabolic reprogramming and the use of glycolysis, which results in a high intracellular lactic acid concentration. Cancer cells avoid acid stress major by the activation and expression of proton and lactate transporters and exchangers and have an inverted pH gradient (extracellular and intracellular pHs are acid and alkaline, respectively). The shift in the tumor acid–base balance promotes proliferation, apoptosis avoidance, invasiveness, metastatic potential, aggressiveness, immune evasion, and treatment resistance. For example, weak-base chemotherapeutic agents may have a substantially reduced cellular uptake capacity due to “ion trapping”. Lactic acid negatively affects the functions of activated effector T cells, stimulates regulatory T cells, and promotes them to express programmed cell death receptor 1. On the other hand, the inversion of pH gradient could be a cancer weakness that will allow the development of new promising therapies, such as tumor-targeted pH-sensitive antibodies and pH-responsible nanoparticle conjugates with anticancer drugs. The regulation of tumor pH levels by pharmacological inhibition of pH-responsible proteins (monocarboxylate transporters, H⁺-ATPase, etc.) and lactate dehydrogenase A is also a promising anticancer strategy. Another idea is the oral or parenteral use of buffer systems, such as sodium bicarbonate, to neutralize tumor acidity. Buffering therapy does not counteract standard treatment methods and can be used in combination to increase effectiveness. However, the mechanisms of the anticancer effect of buffering therapy are still unclear, and more research is needed. We have attempted to summarize the basic knowledge about tumor acidity.

Nonlinear multi-objective flux balance analysis of the Warburg Effect

Due to its implication in cancer treatment, the Warburg Effect has received extensive in silico investigation. Flux Balance Analysis (FBA), based on constrained optimization, was successfully applied in the Warburg Effect modelling. Yet, the assumption that cell types have one invariant cellular objective severely limits the applicability of the previous FBA models. Meanwhile, we note that cell types with different objectives show different extents of the Warburg Effect. To extend the applicability of the previous model and model the disparate cellular pathway preferences in different cell types, we built a Nonlinear Multi-Objective FBA (NLMOFBA) model by including three key objective terms (ATP production rate, lactate generation rate and ATP yield) into one objective function through linear scalarization. By constructing a cellular objective map and iteratively varying the objective weights, we showed disparate cellular pathway preferences manifested by different cell types driven by their unique cellular objectives, and we gained insights about the causal relationship between cellular objectives and the Warburg Effect. In addition, we obtained other biologically consistent results by using our NLMOFBA model. For example, augmented with the constraint associated with inefficient mitochondria function, low oxygen availability, or limited substrate, NLMOFBA predicts cellular pathways supported by the biology literature. Collectively, our NLMOFBA model can help build a complete understanding towards the Warburg Effect in different cell types. Finally, we investigated the impact of glutaminolysis, an important pathway related to glycolysis, on the occurrence of the Warburg Effect by using linear programming.

Cytoskeleton disruption by the metabolic inhibitor 3-bromopyruvate: implications in cancer therapy

The small molecule 3-bromopyruvate (3BP), is an anticancer molecule that acts by hindering glycolysis and mitochondrial function leading to energy depletion and consequently, to cell death. In this work we have focused on understanding how the glycolytic inhibition affects cancer cell structural features. We showed that 3BP leads to a drastic decrease in the levels of β-actin and α-tubulin followed by disorganization and shrinkage of the cytoskeleton in breast cancer cells. 3BP inhibits cell migration and colony formation independently of the activity of metalloproteinases. To disclose if these structural alterations occurred prior to 3BP toxic effect, non-toxic concentrations of 3BP were used and we could observe that 3BP was able to inhibit energy production and induce loss of β-actin and α-tubulin proteins. This was accompanied with alterations in cytoskeleton organization and an increase in E-cadherin levels which may indicate a decrease in cancer cells aggressiveness. In this study we demonstrate that 3BP glycolytic inhibition of breast cancer cells is accompanied by cytoskeleton disruption and consequently loss of migration ability, suggesting that 3BP can potentially be explored for metastatic breast cancer therapy.

Metabolic flux analysis of 3D spheroids reveals significant differences in glucose metabolism from matched 2D cultures of colorectal cancer and pancreatic ductal adenocarcinoma cell lines

BACKGROUND

Most in vitro cancer cell experiments have been performed using 2D models. However, 3D spheroid cultures are increasingly favored for being more representative of in vivo tumor conditions. To overcome the translational challenges with 2D cell cultures, 3D systems better model more complex cell-to-cell contact and nutrient levels present in a tumor, improving our understanding of cancer complexity. Despite this need, there are few reports on how 3D cultures differ metabolically from 2D cultures.

METHODS

Well-described cell lines from colorectal cancer (HCT116 and SW948) and pancreatic ductal adenocarcinoma (Panc-1 and MIA-Pa-Ca-2) were used to investigate metabolism in 3D spheroid models. The metabolic variation under normal glucose conditions were investigated comparing 2D and 3D cultures by metabolic flux analysis and expression of key metabolic proteins.

RESULTS

We find significant differences in glucose metabolism of 3D cultures compared to 2D cultures, both related to glycolysis and oxidative phosphorylation. Spheroids have higher ATP-linked respiration in standard nutrient conditions and higher non-aerobic ATP production in the absence of supplemented glucose. In addition, ATP-linked respiration is significantly inversely correlated with OCR/ECAR (p = 0.0096). Mitochondrial transport protein, TOMM20, expression decreases in all spheroid models compared to 2D, and monocarboxylate transporter (MCT) expression increases in 3 of the 4 spheroid models.

CONCLUSIONS

In this study of CRC and PDAC cell lines, we demonstrate that glucose metabolism in 3D spheroids differs significantly from 2D cultures, both in terms of glycolytic and oxidative phosphorylation metrics. The metabolic phenotype shift from 2D to 3D culture in one cell line is greater than the phenotypic differences between each cell line and tumor source. The results herein emphasize the need to use 3D cell models for investigating nutrient utilization and metabolic flux for a better understanding of tumor metabolism and potential metabolic therapeutic targets.

The Nutraceutical Alliin From Garlic Is a Novel Substrate of the Essential Amino Acid Transporter LAT1 (SLC7A5)

The plasma membrane transporter LAT1 (SLC7A5) is a crucial player for cell homeostasis because it is responsible for providing cells with essential amino acids and hormones. LAT1 forms a functional heterodimer with the cell surface antigen heavy chain CD98 (also known as 4F2hc and SLC3A2), a type II membrane glycoprotein, which is essential for LAT1 stability and localization to the plasma membrane. The relevance of LAT1 for human metabolism is also related to its altered expression in human diseases, such as cancer and diabetes. These features boosted research toward molecules that are able to interact with LAT1; in this respect, the recent resolution of the LAT1-CD98 3D structure by Cryo-EM has opened important perspectives in the study of the interaction with different molecules in order to identify new drugs to be used in therapy or new substrates of natural origin to be employed as adjuvants and food supplements. In this work, the interaction of LAT1 with alliin, a garlic derivative, has been investigated by using a combined approach of bioinformatics and in vitro transport assays. Alliin is a nutraceutical that has several beneficial effects on human health, such as antidiabetic, anticarcinogenic, antioxidant, and anti-inflammatory properties. The computational analysis suggested that alliin interacts with the substrate binding site of LAT1, to which alliin was docked. These data were then confirmed by the competitive type inhibition measured in proteoliposomes. Interestingly, in the same experimental model, alliin was also revealed to be a substrate of LAT1.

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power

MOTIVATION

Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods can not infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution.

RESULTS

We introduce PMCE, an expressive framework that leverages mutational profiles from cross-sectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-of-the-art in terms of accuracy and robustness to noise, on simulations. The application of PMCE to 7866 samples from the TCGA database allows us to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups.

AVAILABILITY AND IMPLEMENTATION

PMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptomics and Metabolomics Integration Reveals Redox-Dependent Metabolic Rewiring in Breast Cancer Cells

Rewiring glucose metabolism toward aerobic glycolysis provides cancer cells with a rapid generation of pyruvate, ATP, and NADH, while pyruvate oxidation to lactate guarantees refueling of oxidized NAD⁺ to sustain glycolysis. CtPB2, an NADH-dependent transcriptional co-regulator, has been proposed to work as an NADH sensor, linking metabolism to epigenetic transcriptional reprogramming. By integrating metabolomics and transcriptomics in a triple-negative human breast cancer cell line, we show that genetic and pharmacological down-regulation of CtBP2 strongly reduces cell proliferation by modulating the redox balance, nucleotide synthesis, ROS generation, and scavenging. Our data highlight the critical role of NADH in controlling the oncogene-dependent crosstalk between metabolism and the epigenetically mediated transcriptional program that sustains energetic and anabolic demands in cancer cells.

Galectins in Cancer and the Microenvironment: Functional Roles, Therapeutic Developments, and Perspectives

Changes in cell growth and metabolism are affected by the surrounding environmental factors to adapt to the cell’s most appropriate growth model. However, abnormal cell metabolism is correlated with the occurrence of many diseases and is accompanied by changes in galectin (Gal) performance. Gals were found to be some of the master regulators of cell–cell interactions that reconstruct the microenvironment, and disordered expression of Gals is associated with multiple human metabolic-related diseases including cancer development. Cancer cells can interact with surrounding cells through Gals to create more suitable conditions that promote cancer cell aggressiveness. In this review, we organize the current understanding of Gals in a systematic way to dissect Gals’ effect on human disease, including how Gals’ dysregulated expression affects the tumor microenvironment’s metabolism and elucidating the mechanisms involved in Gal-mediated diseases. This information may shed light on a more precise understanding of how Gals regulate cell biology and facilitate the development of more effective therapeutic strategies for cancer treatment by targeting the Gal family.

Glutamine Availability Controls BCR/Abl Protein Expression and Functional Phenotype of Chronic Myeloid Leukemia Cells Endowed with Stem/Progenitor Cell Potential

Simple Summary

In chronic myeloid leukemia (CML), a neoplasm brilliantly taken care of by a molecularly targeted therapeutic approach, the achievement of cure is nevertheless prevented by the maintenance of a small subset of treatment-resistant leukemia stem cells (LSCs), sustaining the so-called minimal residual disease of CML. The phenotypical and functional characterization of this LSC subset is, therefore, crucial to aim at the eradication of disease. Such a characterization includes the acquisition of information relative to the metabolic profile of treatment-resistant LSCs, which is functional to their maintenance in bone marrow. A number of metabolic features of LSCs were shown to determine their sensitivity or resistance to therapy. Glutamine metabolism emerged from this study as a potential target to overcome the persistence of therapy-resistant LSCs.

Abstract

This study was directed to characterize the role of glutamine in the modulation of the response of chronic myeloid leukemia (CML) cells to low oxygen, a main condition of hematopoietic stem cell niches of bone marrow. Cells were incubated in atmosphere at 0.2% oxygen in the absence or the presence of glutamine. The absence of glutamine markedly delayed glucose consumption, which had previously been shown to drive the suppression of BCR/Abl oncoprotein (but not of the fusion oncogene BCR/abl) in low oxygen. Glutamine availability thus emerged as a key regulator of the balance between the pools of BCR/Abl protein-expressing and -negative CML cells endowed with stem/progenitor cell potential and capable to stand extremely low oxygen. These findings were confirmed by the effects of the inhibitors of glucose or glutamine metabolism. The BCR/Abl-negative cell phenotype is the best candidate to sustain the treatment-resistant minimal residual disease (MRD) of CML because these cells are devoid of the molecular target of the BCR/Abl-active tyrosine kinase inhibitors (TKi) used for CML therapy. Therefore, the treatments capable of interfering with glutamine action may result in the reduction in the BCR/Abl-negative cell subset sustaining MRD and in the concomitant rescue of the TKi sensitivity of CML stem cell potential. The data obtained with glutaminase inhibitors seem to confirm this perspective.

Regulation of Eukaryote Metabolism: An Abstract Model Explaining the Warburg/Crabtree Effect

Adaptation of metabolism is a response of many eukaryotic cells to nutrient heterogeneity in the cell microenvironment. One of these adaptations is the shift from respiratory to fermentative metabolism, also called the Warburg/Crabtree effect. It is a response to a very high nutrient increase in the cell microenvironment, even in the presence of oxygen. Understanding whether this metabolic transition can result from basic regulation signals between components of the central carbon metabolism are the the core question of this work. We use an extension of the René Thomas modeling framework for representing the regulations between the main catabolic and anabolic pathways of eukaryotic cells, and formal methods for confronting models with known biological properties in different microenvironments. The formal model of the regulation of eukaryote metabolism defined and validated here reveals the conditions under which this metabolic phenotype switch occurs. It clearly proves that currently known regulating signals within the main components of central carbon metabolism can be sufficient to bring out the Warburg/Crabtree effect. Moreover, this model offers a general perspective of the regulation of the central carbon metabolism that can be used to study other biological questions.

Metabolic control of nitrogen fixation in rhizobium-legume symbioses

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which catabolize plant-derived dicarboxylates to reduce atmospheric N₂ into ammonia. Despite the agricultural importance of this symbiosis, the mechanisms that govern carbon and nitrogen allocation in bacteroids and promote ammonia secretion to the plant are largely unknown. Using a metabolic model derived from genome-scale datasets, we show that carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules. Catabolism of dicarboxylates induces not only a higher oxygen demand but also a higher NADH/NAD⁺ ratio than sugars. Modeling and ¹³C metabolic flux analysis indicate that oxygen limitation restricts the decarboxylating arm of the tricarboxylic acid cycle, which limits ammonia assimilation into glutamate. By tightly controlling oxygen supply and providing dicarboxylates as the energy and electron source donors for N₂ fixation, legumes promote ammonia secretion by bacteroids. This is a defining feature of rhizobium-legume symbioses.

Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models

Background

Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of reactions that sink nutrients into the system. To mitigate possible errors introduced by a poor selection of such boundaries, a rational approach suggests to focus the modeling efforts on the pivotal ones.

Methods

In this work, we present a methodology for the automatic identification of the key fluxes in genome-wide constraint-based models, by means of variance-based sensitivity analysis. The goal is to identify the parameters for which a small perturbation entails a large variation of the model outcomes, also referred to as sensitive parameters. Due to the high number of FBA simulations that are necessary to assess sensitivity coefficients on genome-wide models, our method exploits a master-slave methodology that distributes the computation on massively multi-core architectures. We performed the following steps: (1) we determined the putative parameterizations of the genome-wide metabolic constraint-based model, using Saltelli’s method; (2) we applied FBA to each parameterized model, distributing the massive amount of calculations over multiple nodes by means of MPI; (3) we then recollected and exploited the results of all FBA runs to assess a global sensitivity analysis.

Results

We show a proof-of-concept of our approach on latest genome-wide reconstructions of human metabolism Recon2.2 and Recon3D. We report that most sensitive parameters are mainly associated with the intake of essential amino acids in Recon2.2, whereas in Recon 3D they are associated largely with phospholipids. We also illustrate that in most cases there is a significant contribution of higher order effects.

Conclusion

Our results indicate that interaction effects between different model parameters exist, which should be taken into account especially at the stage of calibration of genome-wide models, supporting the importance of a global strategy of sensitivity analysis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04002-0.

Simultaneous Integration of Gene Expression and Nutrient Availability for Studying the Metabolism of Hepatocellular Carcinoma Cell Lines

How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes.

Modeling the energy metabolism in immune cells

In this review, we summarize and briefly discuss various approaches to modeling the metabolism in human immune cells, with a focus on energy metabolism. These approaches include metabolic reconstruction, elementary modes, and flux balance analysis, which are often subsumed under constraint-based modeling. Further approaches are evolutionary game theory and kinetic modeling. Many immune cells such as macrophages show the Warburg effect, meaning that glycolysis is upregulated upon activation. We outline a minimal model for explaining that effect using optimization. The effect of a confrontation with pathogen cells on immunometabolism is highlighted. Models describing the differences between M1 and M2 macrophages, ROS production in neutrophils, and tryptophan metabolism are discussed. Obstacles and future prospects are outlined.

Glutaminolysis is a metabolic route essential for survival and growth of prostate cancer cells and a target of 5α-dihydrotestosterone regulation

PURPOSE

Resistance to androgen-deprivation therapies and progression to so-called castrate-resistant prostate cancer (CRPC) remain challenges in prostate cancer (PCa) management and treatment. Among other alterations, CRPC has been associated with metabolic reprogramming driven by androgens. Here, we investigated the role of androgens in regulating glutaminolysis in PCa cells and determined the relevance of this metabolic route in controlling the survival and growth of androgen-sensitive (LNCaP) and CRPC (DU145 and PC3) cells.

METHODS

PCa cells (LNCaP, DU145 and PC3) and 3-month old rats were treated with 5α-dihydrotestosterone (DHT). Alternatively, LNCaP cells were exposed to the glutaminase inhibitor BPTES, alone or in combination with the anti-androgen bicalutamide. Biochemical, Western blot and extracellular flux assays were used to evaluate the viability, proliferation, migration and metabolism of PCa cells in response to DHT treatment or glutaminase inhibition.

RESULTS

We found that DHT up-regulated the expression of the glutamine transporter ASCT2 and glutaminase, both in vitro in LNCaP cells and in vivo in rat prostate cells. BPTES diminished the viability and migration of PCa cells, while increasing caspase-3 activity. CRPC cells were found to be more dependent on glutamine and more sensitive to glutaminase inhibition. BPTES and bicalutamide co-treatment had an additive effect on suppressing LNCaP cell viability. Finally, we found that inhibition of glutaminolysis differentially affected glycolysis and lipid metabolism in both androgen-sensitive and CRPC cells.

CONCLUSION

Our data reveal glutaminolysis as a central metabolic route controlling PCa cell fate and highlight the relevance of targeting glutaminase for CRPC treatment.

PluriMetNet: A dynamic electronic model decrypting the metabolic variations in human embryonic stem cells (hESCs) at fluctuating oxygen concentrations

Stem cells are an excellent resource in translational medicine however much is known only in terms of transcriptional and epigenetic regulation of human embryonic stem cells (hESCs). Metabolic regulation of hESCs is still unexplored in many ways, particularly the role of energy metabolism, which is intrinsic to the maintenance of cell viability, however, is very little explored in the past years. Also, there exists no hESC specific core metabolic model of pluripotency as per our knowledge. Through our work, we establish such a metabolic model of hESC using combinatorial in-silico approach of genome scale model reduction and literature curation. Further, through perturbations taking oxygen as a parameter we propose that under lower levels of oxygen concentration there is a significant dynamic change in the energy metabolism of the hESC. We further investigated energy subsystem pathways and their respective reactions in order to locate the direction of energy production along with the dynamic of nutrient metabolites like glucose and glutamine. The output shows a steep increment/decrement at a certain oxygen range. These sharp increments/decrements under hypoxic conditions are termed here as a critical range for hESC metabolic pathway. The data also resonates with the previous experimental studies on hESC energy metabolism confirming the robustness of our model. The model helps to extract range for different pathways in the energy subsystem, making us a little closer in understanding the metabolism of hESC. We also demonstrated the possible range of pathway changes in hESC's energy metabolism that can serve as the crucial preliminary data for further prospective studies. The model also offers a promise in the prediction of the flux behaviour of various metabolites in hESC.Communicated by Ramaswamy H. Sarma.

Profiling and Targeting of Energy and Redox Metabolism in Grade 2 Bladder Cancer Cells with Different Invasiveness Properties

Bladder cancer is one of the most prevalent deadly diseases worldwide. Grade 2 tumors represent a good window of therapeutic intervention, whose optimization requires high resolution biomarker identification. Here we characterize energy metabolism and cellular properties associated with spreading and tumor progression of RT112 and 5637, two Grade 2 cancer cell lines derived from human bladder, representative of luminal-like and basal-like tumors, respectively. The two cell lines have similar proliferation rates, but only 5637 cells show efficient lateral migration. In contrast, RT112 cells are more prone to form spheroids. RT112 cells produce more ATP by glycolysis and OXPHOS, present overall higher metabolic plasticity and are less sensitive than 5637 to nutritional perturbation of cell proliferation and migration induced by treatment with 2-deoxyglucose and metformin. On the contrary, spheroid formation is less sensitive to metabolic perturbations in 5637 than RT112 cells. The ability of metformin to reduce, although with different efficiency, cell proliferation, sphere formation and migration in both cell lines, suggests that OXPHOS targeting could be an effective strategy to reduce the invasiveness of Grade 2 bladder cancer cells.

Metabolic Reprogramming of Cancer by Chemicals that Target Glutaminase Isoenzymes

BACKGROUND

Metabolic reprogramming of tumours is a hallmark of cancer. Among the changes in the metabolic network of cancer cells, glutaminolysis is a key reaction altered in neoplasms. Glutaminase proteins control the first step in glutamine metabolism and their expression correlates with malignancy and growth rate of a great variety of cancers. The two types of glutaminase isoenzymes, GLS and GLS2, differ in their expression patterns and functional roles: GLS has oncogenic properties and GLS2 has been described as a tumour suppressor factor.

RESULTS

We have focused on glutaminase connections with key oncogenes and tumour suppressor genes. Targeting glutaminase isoenzymes includes different strategies aimed at deactivating the rewiring of cancer metabolism. In addition, we found a long list of metabolic enzymes, transcription factors and signalling pathways dealing with glutaminase. On the other hand, a number of chemicals have been described as isoenzyme-specific inhibitors of GLS and/or GLS2 isoforms. These molecules are being characterized as synergic and therapeutic agents in many types of tumours.

CONCLUSION

This review states the metabolic pathways that are rewired in cancer, the roles of glutaminase isoforms in cancer, as well as the metabolic circuits regulated by glutaminases. We also show the plethora of anticancer drugs that specifically inhibit glutaminase isoenzymes for treating several sets of cancer.

Lactate in the Tumor Microenvironment: An Essential Molecule in Cancer Progression and Treatment

Simple Summary

The role of lactate in cancer described by Otto Warburg in 1927 states that cancer cells uptake high amount of glucose with a marked increase in lactate production, this is known as the “Warburg effect”. Since then lactate turn out to be a major signaling molecule in cancer progression. Its release from tumor cells is accompanied by acidification ranging from 6.3 to 6.9 in the tumor microenvironment (TME) which favors processes such as tumor promotion, angiogenesis, metastasis, tumor resistance and more importantly, immunosuppression which has been associated with a poor outcome. The goal of this review is to examine and discuss in deep detail the recent studies that address the role of lactate in all these cancerous processes. Lastly, we explore the efforts to target the lactate production and its transport as a promising approach for cancer therapeutics.

Abstract

Cancer is a complex disease that includes the reprogramming of metabolic pathways by malignant proliferating cells, including those affecting the tumor microenvironment (TME). The “TME concept” was introduced in recognition of the roles played by factors other than tumor cells in cancer progression. In response to the hypoxic or semi-hypoxic characteristic of the TME, cancer cells generate a large amount of lactate via the metabolism of glucose and glutamine. Export of this newly generated lactate by the tumor cells together with H+ prevents intracellular acidification but acidifies the TME. In recent years, the importance of lactate and acidosis in carcinogenesis has gained increasing attention, including the role of lactate as a tumor-promoting metabolite. Here we review the existing literature on lactate metabolism in tumor cells and the ability of extracellular lactate to direct the metabolic reprogramming of those cells. Studies demonstrating the roles of lactate in biological processes that drive or sustain carcinogenesis (tumor promotion, angiogenesis, metastasis and tumor resistance) and lactate’s role as an immunosuppressor that contributes to tumor evasion are also considered. Finally, we consider recent therapeutic efforts using available drugs directed at and interfering with lactate production and transport in cancer treatment.

Disruption of redox homeostasis for combinatorial drug efficacy in K-Ras tumors as revealed by metabolic connectivity profiling

Abstract

Background

Rewiring of metabolism induced by oncogenic K-Ras in cancer cells involves both glucose and glutamine utilization sustaining enhanced, unrestricted growth. The development of effective anti-cancer treatments targeting metabolism may be facilitated by the identification and rational combinatorial targeting of metabolic pathways.

Methods

We performed mass spectrometric metabolomics analysis in vitro and in vivo experiments to evaluate the efficacy of drugs and identify metabolic connectivity.

Results

We show that K-Ras-mutant lung and colon cancer cells exhibit a distinct metabolic rewiring, the latter being more dependent on respiration. Combined treatment with the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 more consistently reduces cell growth of tumor xenografts. Maximal growth inhibition correlates with the disruption of redox homeostasis, involving loss of reduced glutathione regeneration, redox cofactors, and a decreased connectivity among metabolites primarily involved in nucleic acid metabolism.

Conclusions

Our findings open the way to develop metabolic connectivity profiling as a tool for a selective strategy of combined drug repositioning in precision oncology.

Systems metabolomics: from metabolomic snapshots to design principles

Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.

Glucose as a Major Antioxidant: When, What for and Why It Fails?

A human organism depends on stable glucose blood levels in order to maintain its metabolic needs. Glucose is considered to be the most important energy source, and glycolysis is postulated as a backbone pathway. However, when the glucose supply is limited, ketone bodies and amino acids can be used to produce enough ATP. In contrast, for the functioning of the pentose phosphate pathway (PPP) glucose is essential and cannot be substituted by other metabolites. The PPP generates and maintains the levels of nicotinamide adenine dinucleotide phosphate (NADPH) needed for the reduction in oxidized glutathione and protein thiols, the synthesis of lipids and DNA as well as for xenobiotic detoxification, regulatory redox signaling and counteracting infections. The flux of glucose into a PPP—particularly under extreme oxidative and toxic challenges—is critical for survival, whereas the glycolytic pathway is primarily activated when glucose is abundant, and there is lack of NADP⁺ that is required for the activation of glucose-6 phosphate dehydrogenase. An important role of glycogen stores in resistance to oxidative challenges is discussed. Current evidences explain the disruptive metabolic effects and detrimental health consequences of chronic nutritional carbohydrate overload, and provide new insights into the positive metabolic effects of intermittent fasting, caloric restriction, exercise, and ketogenic diet through modulation of redox homeostasis.

Is Lactate an Oncometabolite? Evidence Supporting a Role for Lactate in the Regulation of Transcriptional Activity of Cancer-Related Genes in MCF7 Breast Cancer Cells

Lactate is a ubiquitous molecule in cancer. In this exploratory study, our aim was to test the hypothesis that lactate could function as an oncometabolite by evaluating whether lactate exposure modifies the expression of oncogenes, or genes encoding transcription factors, cell division, and cell proliferation in MCF7 cells, a human breast cancer cell line. Gene transcription was compared between MCF7 cells incubated in (a) glucose/glutamine-free media (control), (b) glucose-containing media to stimulate endogenous lactate production (replicating some of the original Warburg studies), and (c) glucose-containing media supplemented with L-lactate (10 and 20 mM). We found that both endogenous, glucose-derived lactate and exogenous, lactate supplementation significantly affected the transcription of key oncogenes (MYC, RAS, and PI3KCA), transcription factors (HIF1A and E2F1), tumor suppressors (BRCA1, BRCA2) as well as cell cycle and proliferation genes involved in breast cancer (AKT1, ATM, CCND1, CDK4, CDKN1A, CDK2B) (0.001 < p < 0.05 for all genes). Our findings support the hypothesis that lactate acts as an oncometabolite in MCF7 cells. Further research is necessary on other cell lines and biopsy cultures to show generality of the findings and reveal the mechanisms by which dysregulated lactate metabolism could act as an oncometabolite in carcinogenesis.

Single-cell Digital Twins for Cancer Preclinical Investigation

Laboratory models derived from clinical samples represent a solid platform in preclinical research for drug testing and investigation of disease mechanisms. The integration of these laboratory models with their digital counterparts (i.e., predictive mathematical models) allows to set up digital twins essential to fully exploit their potential to face the enormous molecular complexity of human organisms. In particular, due to the close integration of cell metabolism with all other cellular processes, any perturbation in cellular physiology typically reflect on altered cells metabolic profiling. In this regard, changes in metabolism have been shown, also in our laboratory, to drive a causal role in the emergence of cancer disease. Nevertheless, a unique metabolic program does not describe the altered metabolic profile of all tumour cells due to many causes from genetic variability to intratumour heterogeneous dependency on nutrients consumption and metabolism by multiple co-existing subclones. Currently, fluxomics approaches just match with the necessity of characterizing the overall flux distribution of cells within given samples, by disregarding possible heterogeneous behaviors. For the purpose of stratifying cancer heterogeneous subpopulations, quantification of fluxes at the single-cell level is needed. To this aim, we here present a new computational framework called single-cell Flux Balance Analysis (scFBA) that aims to set up digital metabolic twins in the perspective of being better exploited within a framework that makes also use of laboratory patient cell models. In particular, scFBA aims at integrating single-cell RNA-seq data within computational population models in order to depict a snapshot of the corresponding single-cell metabolic phenotypes at a given moment, together with an unsupervised identification of metabolic subpopulations.

Circulatory Glutamine/Glucose ratio for evaluating disease activity in Takayasu arteritis: A NMR based serum metabolomics study

Quantitative assessment of disease activity is important for effective care of patients with Takayasu arteritis (TA). Activated glutaminolysis and reduced glycolytic flux is the hallmark of active inflammation. Based on this, we hypothesize that the circulatory Glutamine/Glucose ratio (QGR) can serve as an indicant of active inflammation in TA. To probe this hypothesis, the serum samples were collected from 45 active and 53 inactive TA patients fulfilling American College of Rheumatology (ACR) criteria and assessed for disease activity according to Indian Takayasu Clinical Activity Score (ITAS) using acute phase reactant-erythrocyte sedimentation rate [ITAS-A (ESR)]. The quantitative profiles of circulatory metabolites implicated in glutaminolysis (Glutamine and Glutamate) and those which estimate glycolytic flux (i.e. glucose and lactate) were measured using high field (800 MHz) NMR spectroscopy. The recorded spectra were analyzed using CHENOMX NMR Suite and the estimated concentration profiles were compared and evaluated for their diagnostic potential using Metaboanalyst. Compared to inactive-TA patients, the sera of active-TA patients were characterized by significantly decreased serum levels of glutamine and lactate suggesting that these patients exhibit activated glutaminolysis and reduced glycolytic activity. This is further supported by significantly decreased QGR and lactate to glucose ratio (LGR) levels in active compared to inactive TA patients. The receiver operating characteristic (ROC) curve analysis revealed satisfactory accuracy, sensitivity and specificity for QGR [with area under ROC curve (AUROC) = 0.76 and 95% confidence interval (CI) = 0.66-0.84) compared to that for LGR (with AUROC = 0.67 and CI = 0.561-0.77). Therefore, we believe that the circulatory QGR has the potential to serve as surrogate marker for the assessment of disease activity in TA patients. However, the use of this ratio in clinical settings will require future studies on large patient cohorts and procedural optimization as well to improve accuracy.

Pan-cancer analysis of the metabolic reaction network

Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other or from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumor samples across 13 types based on the probability of expression for 3765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. These findings were subsequently replicated in an independent validation set of 3388 genome-scale metabolic models. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.

Flux sampling is a powerful tool to study metabolism under changing environmental conditions

The development of high-throughput 'omic techniques has sparked a rising interest in genome-scale metabolic models, with applications ranging from disease diagnostics to crop adaptation. Efficient and accurate methods are required to analyze large metabolic networks. Flux sampling can be used to explore the feasible flux solutions in metabolic networks by generating probability distributions of steady-state reaction fluxes. Unlike other methods, flux sampling can be used without assuming a particular cellular objective. We have undertaken a rigorous comparison of several sampling algorithms and concluded that the coordinate hit-and-run with rounding (CHRR) algorithm is the most efficient based on both run-time and multiple convergence diagnostics. We demonstrate the power of CHRR by using it to study the metabolic changes that underlie photosynthetic acclimation to cold of Arabidopsis thaliana plant leaves. In combination with experimental measurements, we show how the regulated interplay between diurnal starch and organic acid accumulation defines the plant acclimation process. We confirm fumarate accumulation as a requirement for cold acclimation and further predict γ-aminobutyric acid to have a key role in metabolic signaling under cold conditions. These results demonstrate how flux sampling can be used to analyze the feasible flux solutions across changing environmental conditions, whereas eliminating the need to make assumptions which introduce observer bias.

Exploiting Cysteine Residues of SLC Membrane Transporters as Targets for Drugs

The observation that cysteine is the top gainer amino acid during evolution attracted the attention of scientists dealing with protein chemistry. The thiol group of cysteine, indeed, is a potential site for several types of reactions with variable specificity and strength. This feature proved to be promising also in the field of membrane transporters that represent boundary proteins fundamental for cell homeostasis. These proteins are classified, according to the driving force for transport, in primary or secondary active transporters. Another frequently used classification is nowadays based on phylogenesis. Two major groups are identified that take into account both criteria: the ABC and the SLC transporters, the second being much more numerous. The cellular localization of the transporters makes them very attractive for drug design. Moreover, the presence of at least one cysteine residue in all the annotated SLC transporters, so far, highlights the possibility of using the thiol (SH) residue for covalent drug targeting. Even if a delay exists in this research field due to the scarce knowledge of structure/function relationships, the setup of novel experimental tools for studying SLC proteins of plasma and organelle membranes opens an important perspective in pharmacology.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

Cytotoxicity of chemotherapeutic agents and resistance to targeted treatments are the main reasons why cancer is still one of the top causes of death. As tumor cells are intrinsically resistant to therapies that target signaling pathways, targeting the metabolic hallmarks of cancer holds promise for more incisive treatments. Regrettably, the heterogeneity of cancer metabolism hinders the identification of effective treatments. To fully uncover the metabolic heterogeneity within tumors, characterization of metabolic programs (metabolic flux distributions) at the single-cell level is required. To fill the gap between current technologies for genomics and future technologies for fluxomics, both at the single-cell and the genome-wide scale, we propose to integrate cancer data from: 1) single-cell transcriptomics and 2) bulk metabolomics, into a multi-scale stoichiometric model, to deliver for the first time metabolic fluxomes at the single-cell level. To this end, we introduce a new paradigm for flux balance analysis and data integration in cancer metabolism to: 1) characterize metabolic heterogeneity, not only at the inter-, but also at the intra-tumor level 2) identify the metabolic interactions between cancer populations, whose role in resistance to metabolic treatments has been recently recognized 3) predict the collective response to drug targeting of metabolism.

Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power

Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall expression of central carbon metabolism as biomarker to characterize groups of patients with important characteristics, such as response to ad-hoc therapeutic strategies and survival expectancy. To this end, we here introduce the data integration framework named Metabolic Reaction Enrichment Analysis (MaREA), which strives to characterize the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks, without requiring metabolic measurements. MaREA computes a score for each network reaction, based on the expression of the set of genes encoding for the associated enzyme(s). The scores are first used as features for cluster analysis and then to rank and visualize in an organized fashion the metabolic deregulations that distinguish cancer sub-types. We applied our method to recent lung and breast cancer RNA-seq datasets from The Cancer Genome Atlas and we were able to identify subgroups of patients with significant differences in survival expectancy. We show how the prognostic power of MaREA improves when an extracted and further curated core model focusing on central carbon metabolism is used rather than the genome-wide reference network. The visualization of the metabolic differences between the groups with best and worst prognosis allowed to identify and analyze key metabolic properties related to cancer aggressiveness. Some of these properties are shared across different cancer (sub) types, e.g., the up-regulation of nucleic acid and amino acid synthesis, whereas some other appear to be tumor-specific, such as the up- or down-regulation of the phosphoenolpyruvate carboxykinase reaction, which display different patterns in distinct tumor (sub)types. These results might be soon employed to deliver highly automated diagnostic and prognostic strategies for cancer patients.