Genome-Scale Modeling of NADPH-Driven β-Lapachone Sensitization in Head and Neck Squamous Cell Carcinoma

Computational models as catalysts for investigating redoxin systems

Thioredoxin, glutaredoxin and peroxiredoxin systems play central roles in redox regulation, signaling and metabolism in cells. In these systems, reducing equivalents from NAD(P)H are transferred by coupled thiol-disulfide exchange reactions to redoxins which then reduce a wide array of targets. However, the characterization of redoxin activity has been unclear, with redoxins regarded as enzymes in some studies and redox metabolites in others. Consequently, redoxin activities have been quantified by enzyme kinetic parameters in vitro, and redox potentials or redox ratios within cells. By analyzing all the reactions within these systems, computational models showed that many kinetic properties attributed to redoxins were due to system-level effects. Models of cellular redoxin networks have also been used to estimate intracellular hydrogen peroxide levels, analyze redox signaling and couple omic and kinetic data to understand the regulation of these networks in disease. Computational modeling has emerged as a powerful complementary tool to traditional redoxin enzyme kinetic and cellular assays that integrates data from a number of sources into a single quantitative framework to accelerate the analysis of redoxin systems.

Cytosolic malic enzyme and glucose-6-phosphate dehydrogenase modulate redox balance in NSCLC with acquired drug resistance

Lung cancer cells often show elevated levels of reactive oxygen species (ROS) and nicotinamide adenine dinucleotide phosphate (NADPH). However, the connections between deregulated redox homeostasis in different subtypes of lung cancer and acquired drug resistance in lung cancer have not yet been fully established. Herein, we analyzed different subtypes of lung cancer data reported in the Cancer Cell Line Encyclopedia (CCLE) database, the Cancer Genome Atlas program (TCGA), and the sequencing data obtained from a gefitinib-resistant non-small-cell lung cancer (NSCLC) cell line (H1975GR). Using flux balance analysis (FBA) model integrated with multiomics data and gene expression profiles, we identified cytosolic malic enzyme 1 (ME1) and glucose-6-phosphate dehydrogenase as the major contributors to the significantly upregulated NADPH flux in NSCLC tissues as compared with normal lung tissues, and gefitinib-resistant NSCLC cell line as compared with the parental cell line. Silencing the gene expression of either of these two enzymes in two osimertinib-resistant NSCLC cell lines (H1975OR and HCC827OR) exhibited strong antiproliferative effects. Our findings not only underscored the pivotal roles of cytosolic ME1 and glucose-6-phosphate dehydrogenase in regulating redox states in NSCLC cells but also provided novel insights into their potential roles in drug-resistant NSCLC cells with disturbed redox states.

Single-Cell Kinetic Modeling of β-Lapachone Metabolism in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) cells are highly heterogeneous in their metabolism and typically experience elevated reactive oxygen species (ROS) levels such as superoxide and hydrogen peroxide (H2O2) in the tumor microenvironment. Tumor cells survive under these chronic oxidative conditions by upregulating antioxidant systems. To investigate the heterogeneity of cellular responses to chemotherapeutic H2O2 generation in tumor and healthy tissue, we leveraged single-cell RNA-sequencing (scRNA-seq) data to perform redox systems-level simulations of quinone-cycling β-lapachone treatment as a source of NQO1-dependent rapid superoxide and hydrogen peroxide (H2O2) production. Transcriptomic data from 10 HNSCC patient tumors was used to populate over 4000 single-cell antioxidant enzymatic network models of drug metabolism. The simulations reflected significant systems-level differences between the redox states of healthy and cancer cells, demonstrating in some patient samples a targetable cancer cell population or in others statistically indistinguishable effects between non-malignant and malignant cells. Subsequent multivariate analyses between healthy and malignant cellular models pointed to distinct contributors of redox responses between these phenotypes. This model framework provides a mechanistic basis for explaining mixed outcomes of NAD(P)H:quinone oxidoreductase 1 (NQO1)-bioactivatable therapeutics despite the tumor specificity of these drugs as defined by NQO1/catalase expression and highlights the role of alternate antioxidant components in dictating drug-induced oxidative stress.

Redox integration of signaling and metabolism in a head and neck cancer model of radiation resistance using COSMRO

Redox metabolism is increasingly investigated in cancer as driving regulator of tumor progression, response to therapies and long-term patients' quality of life. Well-established cancer therapies, such as radiotherapy, either directly impact redox metabolism or have redox-dependent mechanisms of action defining their clinical efficacy. However, the ability to integrate redox information across signaling and metabolic networks to facilitate discovery and broader investigation of redox-regulated pathways in cancer remains a key unmet need limiting the advancement of new cancer therapies. To overcome this challenge, we developed a new constraint-based computational method (COSMro) and applied it to a Head and Neck Squamous Cell Cancer (HNSCC) model of radiation resistance. This novel integrative approach identified enhanced capacity for H2S production in radiation resistant cells and extracted a key relationship between intracellular redox state and cholesterol metabolism; experimental validation of this relationship highlights the importance of redox state in cellular metabolism and response to radiation.

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

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

[18F]Fluoro-DCP, a first generation PET radiotracer for monitoring protein sulfenylation in vivo

Redox metabolism plays essential functions in the pathology of cancer and many other diseases. While several radiotracers for imaging redox metabolism have been developed, there are no reports of radiotracers for in vivo imaging of protein oxidation. Here we take the first step towards this goal and describe the synthesis and kinetic properties of a new positron emission tomography (PET) [18F]Fluoro-DCP radiotracer for in vivo imaging of protein sulfenylation. Time course biodistribution and PET/CT studies using xenograft animal models of Head and Neck Squamous Cell Cancer (HNSCC) demonstrate its capability to distinguish between tumors with radiation sensitive and resistant phenotypes consistent with previous reports of decreased protein sulfenylation in clinical specimens of radiation resistant HNSCC. We envision further development of this technology to aid research efforts towards improving diagnosis of patients with radiation resistant tumors.

Can thiol-based redox systems be utilized as parts for synthetic biology applications?

OBJECTIVES

Synthetic biology has emerged from molecular biology and engineering approaches and aims to develop novel, biologically-inspired systems for industrial and basic research applications ranging from biocomputing to drug production. Surprisingly, redoxin (thioredoxin, glutaredoxin, peroxiredoxin) and other thiol-based redox systems have not been widely utilized in many of these synthetic biology applications.

METHODS

We reviewed thiol-based redox systems and the development of synthetic biology applications that have used thiol-dependent parts.

RESULTS

The development of circuits to facilitate cytoplasmic disulfide bonding, biocomputing and the treatment of intestinal bowel disease are amongst the applications that have used thiol-based parts. We propose that genetically encoded redox sensors, thiol-based biomaterials and intracellular hydrogen peroxide generators may also be valuable components for synthetic biology applications.

DISCUSSION

Thiol-based systems play multiple roles in cellular redox metabolism, antioxidant defense and signaling and could therefore offer a vast and diverse portfolio of components, parts and devices for synthetic biology applications. However, factors limiting the adoption of redoxin systems for synthetic biology applications include the orthogonality of thiol-based components, limitations in the methods to characterize thiol-based systems and an incomplete understanding of the design principles of these systems.

Measuring NQO1 Bioactivation Using [2H7]Glucose

Treatment of cancers with β-lapachone causes NAD(P)H: quinone oxidoreductase 1 (NQO1) to generate an unstable hydroquinone that regenerates itself in a futile cycle while producing reactive oxygen species (ROS) in the form of superoxide and subsequently hydrogen peroxide. Rapid accumulation of ROS damages DNA, hyperactivates poly-ADP-ribose polymerase-I, causes massive depletion of NAD+/ATP, and hampers glycolysis. Cells overexpressing NQO1 subsequently die rapidly through an NAD+-keresis mechanism. Assessing changes in glycolytic rates caused by NQO1 bioactivation would provide a means of assessing treatment efficacy, potentially lowering the chemotherapeutic dosage, and reducing off-target toxicities. NQO1-mediated changes in glycolytic flux were readily detected in A549 (lung), MiaPaCa2 (pancreatic), and HCT-116 (colon) cancer cell lines by 2H-NMR after administration of [2H7]glucose. The deuterated metabolic products 2H-lactate and HDO were quantified, and linear relationships with glucose consumption for both products were observed. The higher concentration of HDO compared to 2H-lactate allows for more sensitive measurement of the glycolytic flux in cancer. Gas chromatography-mass spectrometry analysis agreed with the NMR results and confirmed downregulated energy metabolism in NQO1+ cells after β-lapachone treatment. The demonstrated method is ideal for measuring glycolytic rates, the effects of chemotherapeutics that target glycolysis, and has the potential for in vivo translation.

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors

Redox cofactor production is integral toward antioxidant generation, clearance of reactive oxygen species, and overall tumor response to ionizing radiation treatment. To identify systems-level alterations in redox metabolism that confer resistance to radiation therapy, we developed a bioinformatics pipeline for integrating multi-omics data into personalized genome-scale flux balance analysis models of 716 radiation-sensitive and 199 radiation-resistant tumors. These models collectively predicted that radiation-resistant tumors reroute metabolic flux to increase mitochondrial NADPH stores and reactive oxygen species (ROS) scavenging. Simulated genome-wide knockout screens agreed with experimental siRNA gene knockdowns in matched radiation-sensitive and radiation-resistant cancer cell lines, revealing gene targets involved in mitochondrial NADPH production, central carbon metabolism, and folate metabolism that allow for selective inhibition of glutathione production and H₂O₂ clearance in radiation-resistant cancers. This systems approach represents a significant advancement in developing quantitative genome-scale models of redox metabolism and identifying personalized metabolic targets for improving radiation sensitivity in individual cancer patients.

MTHFD2 Blockade Enhances the Efficacy of β-Lapachone Chemotherapy With Ionizing Radiation in Head and Neck Squamous Cell Cancer

Head and Neck Squamous Cell Cancer (HNSCC) presents with multiple treatment challenges limiting overall survival rates and affecting patients' quality of life. Amongst these, resistance to radiation therapy constitutes a major clinical problem in HNSCC patients compounded by origin, location, and tumor grade that limit tumor control. While cisplatin is considered the standard radiosensitizing agent for definitive or adjuvant radiotherapy, in recurrent tumors or for palliative care other chemotherapeutics such as the antifolates methotrexate or pemetrexed are also being utilized as radiosensitizers. These drugs inhibit the enzyme dihydrofolate reductase, which is essential for DNA synthesis and connects the 1-C/folate metabolism to NAD(P)H and NAD(P)⁺ balance in cells. In previous studies, we identified MTHFD2, a mitochondrial enzyme involved in folate metabolism, as a key contributor to NAD(P)H levels in the radiation-resistant cells and HNSCC tumors. In the study presented here, we investigated the role of MTHFD2 in the response to radiation alone and in combination with β-lapachone, a NQO1 bioactivatable drug, which generates reactive oxygen species concomitant with NAD(P)H oxidation to NAD(P)⁺. These studies are performed in a matched HNSCC cell model of response to radiation: the radiation resistant rSCC-61 and radiation sensitive SCC-61 cells reported earlier by our group. Radiation resistant rSCC-61 cells had increased sensitivity to β-lapachone compared to SCC-61 and knockdown of MTHFD2 in rSCC-61 cells further potentiated the cytotoxicity of β-lapachone with radiation in a dose and time-dependent manner. rSCC-61 MTHFD2 knockdown cells irradiated and treated with β-lapachone showed increased PARP1 activation, inhibition of mitochondrial respiration, decreased respiration-linked ATP production, and increased mitochondrial superoxide and protein oxidation as compared to control rSCC-61 scrambled shRNA. Thus, these studies point to MTHFD2 as a potential target for development of radiosensitizing chemotherapeutics and potentiator of β-lapachone cytotoxicity.

Leveraging genome-scale metabolic models for human health applications

Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.

An Integrative Gene Expression and Mathematical Flux Balance Analysis Identifies Targetable Redox Vulnerabilities in Melanoma Cells

Melanomas harboring BRAF mutations can be treated with BRAF inhibitors (BRAFi), but responses are varied and tumor recurrence is inevitable. Here we used an integrative approach of experimentation and mathematical flux balance analyses in BRAF-mutated melanoma cells to discover that elevated antioxidant capacity is linked to BRAFi sensitivity in melanoma cells. High levels of antioxidant metabolites in cells with reduced BRAFi sensitivity confirmed this conclusion. By extending our analyses to other melanoma subtypes in The Cancer Genome Atlas, we predict that elevated redox capacity is a general feature of melanomas, not previously observed. We propose that redox vulnerabilities could be exploited for therapeutic benefits and identify unsuspected combination targets to enhance the effects of BRAFi in any melanoma, regardless of mutational status. SIGNIFICANCE: An integrative bioinformatics, flux balance analysis, and experimental approach identify targetable redox vulnerabilities and show the potential for modulation of cancer antioxidant defense to augment the benefits of existing therapies in melanoma.

Oxidative pentose phosphate pathway and glucose anaplerosis support maintenance of mitochondrial NADPH pool under mitochondrial oxidative stress

Mitochondrial NADPH protects cells against mitochondrial oxidative stress by serving as an electron donor to antioxidant defense systems. However, due to technical challenges, it still remains unknown as to the pool size of mitochondrial NADPH, its dynamics, and NADPH/NADP+ ratio. Here, we have systemically modulated production rates of H2O2 in mitochondria and assessed mitochondrial NADPH metabolism using iNap sensors, 13C glucose isotopic tracers, and a mathematical model. Using sensors, we observed decreases in mitochondrial NADPH caused by excessive generation of mitochondrial H2O2, whereas the cytosolic NADPH was maintained upon perturbation. We further quantified the extent of mitochondrial NADPH/NADP+ based on the mathematical analysis. Utilizing 13C glucose isotopic tracers, we found increased activity in the pentose phosphate pathway (PPP) accompanied small decreases in the mitochondrial NADPH pool, whereas larger decreases induced both PPP activity and glucose anaplerosis. Thus, our integrative and quantitative approach provides insight into mitochondrial NADPH metabolism during mitochondrial oxidative stress.

Redox Systems Biology: Harnessing the Sentinels of the Cysteine Redoxome

Significance: Cellular redox processes are highly interconnected, yet not in equilibrium, and governed by a wide range of biochemical parameters. Technological advances continue refining how specific redox processes are regulated, but broad understanding of the dynamic interconnectivity between cellular redox modules remains limited. Systems biology investigates multiple components in complex environments and can provide integrative insights into the multifaceted cellular redox state. This review describes the state of the art in redox systems biology as well as provides an updated perspective and practical guide for harnessing thousands of cysteine sensors in the redoxome for multiparameter characterization of cellular redox networks. Recent Advances: Redox systems biology has been applied to genome-scale models and large public datasets, challenged common conceptions, and provided new insights that complement reductionist approaches. Advances in public knowledge and user-friendly tools for proteome-wide annotation of cysteine sentinels can now leverage cysteine redox proteomics datasets to provide spatial, functional, and protein structural information. Critical Issues: Careful consideration of available analytical approaches is needed to broadly characterize the systems-level properties of redox signaling networks and be experimentally feasible. The cysteine redoxome is an informative focal point since it integrates many aspects of redox biology. The mechanisms and redox modules governing cysteine redox regulation, cysteine oxidation assays, proteome-wide annotation of the biophysical and biochemical properties of individual cysteines, and their clinical application are discussed. Future Directions: Investigating the cysteine redoxome at a systems level will uncover new insights into the mechanisms of selectivity and context dependence of redox signaling networks.

Targeting NAD+ Metabolism to Enhance Radiation Therapy Responses

Nicotinamide adenine dinucleotide (NAD⁺) metabolism is integrally connected with the mechanisms of action of radiation therapy and is altered in many radiation-resistant tumors. This makes NAD⁺ metabolism an ideal target for therapies that increase radiation sensitivity and improve patient outcomes. This review provides an overview of NAD⁺ metabolism in the context of the cellular response to ionizing radiation, as well as current therapies that target NAD⁺ metabolism to enhance radiation therapy responses. Additionally, we summarize state-of-the-art methods for measuring, modeling, and manipulating NAD⁺ metabolism, which are being used to identify novel targets in the NAD⁺ metabolic network for therapeutic interventions in combination with radiation therapy.

A pH/ROS Cascade-Responsive Charge-Reversal Nanosystem with Self-Amplified Drug Release for Synergistic Oxidation-Chemotherapy

Poor cell uptake of drugs is one of the major challenges for anticancer therapy. Moreover, the inability to release adequate drug at tumor sites and inherent multidrug resistance (MDR) may further limit the therapeutic effect. Herein, a delivery nanosystem with a charge-reversal capability and self-amplifiable drug release pattern is constructed by encapsulating β-lapachone in pH/ROS cascade-responsive polymeric prodrug micelle. The surface charge of this micellar system would be converted from negative to positive for enhanced tumor cell uptake in response to the weakly acidic tumor microenvironment. Subsequently, the cascade-responsive micellar system could be dissociated in a reactive oxygen species (ROS)-rich intracellular environment, resulting in cytoplasmic release of β-lapachone and camptothecin (CPT). Furthermore, the released β-lapachone is capable of producing ROS under the catalysis of nicotinamide adenine dinucleotide (NAD)(P)H:quinone oxidoreductase-1 (NQO1), which induces the self-amplifiable disassembly of the micelles and drug release to consume adenosine triphosphate (ATP) and downregulate P-glycoprotein (P-gp), eventually overcoming MDR. Moreover, the excessive ROS produced from β-lapachone could synergize with CPT and further propagate tumor cell apoptosis. The studies in vitro and in vivo consistently demonstrate that the combination of the pH-responsive charge-reversal, upregulation of tumoral ROS level, and self-amplifying ROS-responsive drug release achieves potent antitumor efficacy via the synergistic oxidation-chemotherapy.

Regulation of Cancer and Cancer-Related Genes via NAD. Antioxid Redox Signal 2019;30:906-923. [PMID: 29334761 DOI: 10.1089/ars.2017.7478]

SIGNIFICANCE

NAD⁺ is an essential redox cofactor in cellular metabolism and has emerged as an important regulator of a wide spectrum of disease conditions, most notably, cancers. As such, various strategies targeting NAD⁺ synthesis in cancers are in clinical trials. Recent Advances: Being a substrate required for the activity of various enzyme families, especially sirtuins and poly(adenosine diphosphate [ADP]-ribose) polymerases, NAD⁺-mediated signaling plays an important role in gene expression, calcium release, cell cycle progression, DNA repair, and cell proliferation. Many strategies exploring the potential of interfering with NAD⁺ metabolism to sensitize cancer cells to achieve anticancer benefits are highly promising, and are being pursued.

CRITICAL ISSUES

With the multifaceted roles of NAD⁺ in cancer, it is important to understand how cellular processes are reliant on NAD⁺. This review summarizes how NAD⁺ metabolism regulates various pathophysiological processes in cancer, and how this knowledge can be exploited to devise effective anticancer therapies in clinical settings.

FUTURE DIRECTIONS

In line with the redundant pathways that facilitate NAD⁺ metabolism, further studies should comprehensively understand the roles of the various NAD⁺-synthesizing as well as NAD⁺-utilizing biomolecules to understand its true potential in cancer treatment.