Computational modeling to determine key regulators of hypoxia effects on the lactate production in the glycolysis pathway

A Boolean network model of hypoxia, mechanosensing and TGF-β signaling captures the role of phenotypic plasticity and mutations in tumor metastasis

The tumor microenvironment aids cancer progression by promoting several cancer hallmarks, independent of cancer-related mutations. Biophysical properties of this environment, such as the stiffness of the matrix cells adhere to and local cell density, impact proliferation, apoptosis, and the epithelial to mesenchymal transition (EMT). The latter is a rate-limiting step for invasion and metastasis, enhanced in hypoxic tumor environments but hindered by soft matrices and/or high cell densities. As these influences are often studied in isolation, the crosstalk between hypoxia, biomechanical signals, and the classic EMT driver TGF-β is not well mapped, limiting our ability to predict and anticipate cancer cell behaviors in changing tumor environments. To address this, we built a Boolean regulatory network model that integrates hypoxic signaling with a mechanosensitive model of EMT, which includes the EMT-promoting crosstalk of mitogens and biomechanical signals, cell cycle control, and apoptosis. Our model reproduces the requirement of Hif-1α for proliferation, the anti-proliferative effects of strong Hif-1α stabilization during hypoxia, hypoxic protection from anoikis, and hypoxia-driven mechanosensitive EMT. We offer experimentally testable predictions about the effect of VHL loss on cancer hallmarks, with or without secondary oncogene activation. Taken together, our model serves as a predictive framework to synthesize the signaling responses associated with tumor progression and metastasis in healthy vs. mutant cells. Our single-cell model is a key step towards more extensive regulatory network models that cover damage-response and senescence, integrating most cell-autonomous cancer hallmarks into a single model that can, in turn, control the behavior of in silico cells within a tissue model of epithelial homeostasis and carcinoma.

Development of hepatocellular carcinoma organoid model recapitulating HIF-1A metabolic signature

Hypoxia is one of the main hallmarks of hepatocellular carcinoma (HCC) resulting from improper oxygenation and insufficient nourishment of the HCC microenvironment. The effect of hypoxia is mediated by hypoxia-inducible factor-1A (HIF-1A) via targeting various downstream pathways, including glycolysis, angiogenesis, and survival signaling. However, HCC cell lines in a 2-dimensional (2D) setting do not resemble the metabolic signature of HCC. Here we aim to overcome these limitations by developing an HCC organoid that recapitulates the HIF-1A metabolic shift. The enrichment analysis of the RNA-Seq data revealed that HIF-1A-driven glycolytic shift is of the significant pathways. The established organoid model, using xeno-free plasma-derived extracellular matrix (ECM) as a scaffold and nutritive biomatrix, maintained its structural integrity and viability for up to 14 days; the comparative analysis of the cobalt (II) chloride (CoCl2)-treated organoids to the untreated ones unveiled reduced size and proliferative capacity. Interestingly, our organoid model showed an elevated expression of HIF-1A and glycolysis enzymes compared to their counterparts in the CoCl2-treated organoids. HIF-1A molecular expression-translated biochemical signature is further assessed in our spontaneously growing organoids showing an increase in glucose uptake, intracellular pyruvate, extracellular lactate dehydrogenase expression, and extracellular lactate production, while hydrogen peroxide (H2O2), a marker for oxidative metabolism, is reduced. Our data confirmed the potency of the established organoid model to mimic the molecular and biochemical HIF-1A-driven metabolism, which validates its potential use as an in vitro HCC model. Our model naturally simulates hypoxic conditions and simultaneous HIF-1A-dependent glycolysis within HCC rather than using of CoCl2-induced hypoxic conditions.

Exploration on anti-hypoxia properties of peptides: a review

Peptides are important components of human nutrition and health, and considered as safe, nontoxic, and easily absorbed potential drugs. Anti-hypoxia peptides are a kind of peptides that can prevent hypoxia or hypoxia damage. In this paper, the sources, preparations, and molecular mechanisms of anti-hypoxia peptides were systemically reviewed. The combination of bioinformatics, chemical synthesis, enzymatic hydrolysis, and microbial fermentation are recommended for efficient productions of anti-hypoxic peptides. The mechanisms of anti-hypoxic peptides include interference with glycolytic process and HIF-1α pathway, mitochondrial apoptosis, and inflammatory response. In addition, bioinformatics analysis, including virtual screening and molecular docking, provides an alternative or auxiliary method for exploring the potential anti-hypoxic activities and mechanisms of peptides. The potential challenges and prospects of anti-hypoxic peptides are also discussed. This paper can provide references for researchers in this field and promote further research and clinical applications of anti-hypoxic peptides in the future.

Modeling of glycosaminoglycan biosynthesis in intervertebral disc cells

Loss of proteoglycan (PG) is a potential factor responsible for degeneration of the intervertebral disc (IVD). PG consists of a core protein with covalently attached glycosaminoglycan (GAG) chains. The objective of this study was to develop a mathematical model of GAG biosynthesis to investigate the effects of glycolytic enzymes on GAG biosynthesis of IVD cells. A new mathematical model of GAG biosynthesis was developed for IVD cells by incorporating biosynthesis of uridine diphosphate-sugars into the glycolytic pathway. This new model showed good agreement between the model predictions of intracellular ATP content and GAG biosynthesis and experimental data measured at different external glucose levels. The quantitative analyses demonstrated that GAG biosynthesis may be sensitive to the activities of hexokinase (HK) and phosphofructokinase (PFK), especially at low glucose supply, with GAG biosynthesis being significantly enhanced by a slight increase in activities of HK and PFK. This suggests that metabolic reprogramming could be a potential strategy for promoting PG biosynthesis in IVD cells. Furthermore, it was shown that GAG biosynthesis may be promoted by increasing intracellular glutamine concentration or activity of glutamine:fructose-6-phosphate amidotransferase in the hexamine pathway. This study provides a better understanding of the relationship between glycolysis and PG biosynthesis in IVD cells. The theoretical framework developed in this study is useful for studying the role of glycolysis in disc degeneration and developing new preventive and treatment strategies for degeneration of the IVD.

Hybrid computational models of multicellular tumour growth considering glucose metabolism

Cancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.

High-Throughput Screening and Molecular Dynamics Simulation of Natural Products for the Identification of Anticancer Agents against MCM7 Protein

Minichromosome maintenance complex component 7 (MCM7) belongs to the minichromosome maintenance family that is necessary for the initiation of eukaryotic DNA replication. Overexpression of the MCM7 protein is linked to cellular proliferation and is accountable for critical malignancy in many cancers. Mechanistically, the suppression of MCM7 greatly lowers the cellular proliferation associated with cancer. Advances in immunotherapy have revolutionized treatments for many types of cancer. To date, no effective small molecular candidate has been found that can stop the advancement of cancer produced by the MCM7 protein. Here, we present the findings of methods that used a combination of structure-assisted drug design, high-throughput virtual screening, and simulations studies to swiftly generate lead compounds against MCM7 protein. In the current study, we designed efficient compounds that may combat all emerging cancer targeting the common MCM7 protein. For this objective, a molecular docking and molecular dynamics (MD) simulation-based virtual screening of 29,000 NPASS library was carried out. As a consequence of using specific pharmacological, physiological, and ADMET criteria, four new prevailing compounds, NPA000018, NPA000111, NPA00305, and NPA014826, were successfully selected. The MD simulations were also used for a time period of 50 ns to evaluate for stability and dynamics behavior of the compounds. Eventually, compounds NPA000111 and NPA014826 were found to be highly potent against MCM7 protein. According to our results, the selected compounds may be effective in treating certain cancer subtypes, for which additional follow-up experimental validation is recommended.

Pharmacoinformatics-based phytochemical screening for anticancer impacts of yellow sweet clover, Melilotus officinalis (Linn.) Pall

To date, much attention has been paid to phytochemicals because of their diverse pharmacological effects on a variety of diseases such as cancer. In this regard, computer-aided drug design, as a cost- and time-effective approach, is primarily applied to investigate the drug candidates before their further costly in vitro and in vivo experimental evaluations. Accordingly, different signaling pathways and proteins can be targeted using such strategies. As a key protein for the initiation of eukaryotic DNA replication, mini-chromosome maintenance complex component 7 (MCM7) overexpression is related to the initiation and progression of aggressive malignancies. The current study was conducted to identify new potential natural compounds from the yellow sweet clover, Melilotus officinalis (Linn.) Pall, by examining the potential of 40 isolated phytochemicals against MCM7 protein. A structure-based pharmacophore model to the protein active site cavity was generated and followed by virtual screening and molecular docking. Overall, four compounds were selected for further evaluation based on their binding affinities. Our analyses revealed that two novel compounds, namely rosmarinic acid (PubChem CID:5281792) and melilotigenin (PubChem CID:14059499) might be druggable and offer safe usage in human. The stability of these two protein-ligand complex structures was confirmed through molecular dynamics simulation. The findings of this study reveal the potential of these two phytochemicals to serve as anticancer agents, while further pharmacological experiments are required to confirm their effectiveness against human cancers.

Recent advances in breast cancer immunotherapy: The promising impact of nanomedicines

Breast cancer (BC) is one of the prevalent cancers among women. Generally, the treatment of BC is mostly based on several prominent strategies, including chemotherapy, surgery, endocrine therapy, molecular targeted therapy, and radiation. Owing to the growing knowledge about the complexity of BC pathobiology, immunotherapy as a promising treatment modality has substantially improved the patients' care in the clinic. Immunotherapy is used to harness the patient's immune system to recognize and battle devious cancer cells. As a novel therapy approach, this emerging strategy targets the key molecular entities of tumor tissue. To achieve maximal therapeutic impacts, the dynamic interplay between cancer and immune cells needs to be fully comprehended. The key molecular machinery of solid tumors can be targeted by nanoscale immunomedicines. While discussing the potential biomarkers involved in the initiation and progression of BC, we aimed to provide comprehensive insights into the immunotherapy and articulate the recent advances in terms of the therapeutic strategies used to control this disease, including immune checkpoint inhibitors, vaccines, chimeric antigen receptor T cells therapy, and nanomedicines.

A control theoretic three timescale model for analyzing energy management in mammalian cancer cells

Interaction among different pathways, such as metabolic, signaling and gene regulatory networks, of cellular system is responsible to maintain homeostasis in a mammalian cell. Malfunctioning of this cooperation may lead to many complex diseases, such as cancer and type 2 diabetes. Timescale differences among these pathways make their integration a daunting task. Metabolic, signaling and gene regulatory networks have three different timescales, such as, ultrafast, fast and slow respectively. The article deals with this problem by developing a support vector regression (SVR) based three timescale model with the application of genetic algorithm based nonlinear controller. The proposed model can successfully capture the nonlinear transient dynamics and regulations of such integrated biochemical pathway under consideration. Besides, the model is quite capable of predicting the effects of certain drug targets for many types of complex diseases. Here, energy and cell proliferation management of mammalian cancer cells have been explored and analyzed with the help of the proposed novel approach. Previous investigations including in silico/in vivo/in vitro experiments have validated the results (the regulations of glucose transporter 1 (glut1), hexokinase (HK), and hypoxia-inducible factor-1 α (HIF-1 α ) among others, and the switching of pyruvate kinase (M2 isoform) between dimer and tetramer) generated by this model proving its effectiveness. Subsequently, the model predicts the effects of six selected drug targets, such as, the deactivation of transketolase and glucose-6-phosphate isomerase among others, in the case of mammalian malignant cells in terms of growth, proliferation, fermentation, and energy supply in the form of adenosine triphosphate (ATP).