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13 pages, 6013 KiB  
Article
Development and Characterization of 3D-Printed PLA/Exfoliated Graphite Composites for Enhanced Electrochemical Performance in Energy Storage Applications
by Ananias Lima dos Santos, Francisco Cezar Ramos de Souza, João Carlos Martins da Costa, Daniel Araújo Gonçalves, Raimundo Ribeiro Passos and Leandro Aparecido Pocrifka
Polymers 2024, 16(22), 3131; https://doi.org/10.3390/polym16223131 - 9 Nov 2024
Viewed by 665
Abstract
This research introduces a new way to create a composite material (PLA/EG) for 3D printing. It combines polylactic acid (PLA) with exfoliated graphite (EG) using a physical mixing method, followed by direct mixing in a single-screw extruder. Structural and vibrational analyses using X-ray [...] Read more.
This research introduces a new way to create a composite material (PLA/EG) for 3D printing. It combines polylactic acid (PLA) with exfoliated graphite (EG) using a physical mixing method, followed by direct mixing in a single-screw extruder. Structural and vibrational analyses using X-ray diffraction and Fourier transform infrared spectroscopy confirmed the PLA/EG’s formation (composite). The analysis also suggests physical adsorption as the primary interaction between the two materials. The exfoliated graphite acts as a barrier (thermal behavior), reducing heat transfer via TG. Electrochemical measurements reveal redox activity (cyclic voltammetry) with a specific capacitance of ~ 6 F g−1, low solution resistance, and negligible charge transfer resistance, indicating ion movement through a Warburg diffusion process. Additionally, in terms of complex behavior (electrochemical impedance spectroscopy), the PLA/EG’s actual capacitance C’(ω) displayed a value greater than 1000 μF cm−2, highlighting the composite’s effectiveness in storing charge. These results demonstrate that PLA/EG composites hold significant promise as electrodes in electronic devices. The methodology used in this study not only provides a practical way to create functional composites but also opens doors for new applications in electronics and energy storage. Full article
(This article belongs to the Special Issue Advanced Analytical Methods for Applied Polymeric Science)
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Figure 1
<p>XRD standards of PLA, PLA/EG, and exfoliated graphite (EG).</p>
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<p>Bands observed in the FTIR spectra of PLA and PLA/EG.</p>
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<p>(<b>A</b>) Thermal behavior according to thermogravimetric analysis and (<b>B</b>) thermal behavior according to the thermogravimetric derivative.</p>
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<p>(<b>A</b>) Thermal behavior according to thermogravimetric analysis and (<b>B</b>) thermal behavior according to the thermogravimetric derivative.</p>
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<p>Electrochemical behavior of PLA/EG according to cyclic voltammetry.</p>
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<p>Plots of peak currents vs. the square root of scan rate, with scan rates ranging from 1 to 100 mV s<sup>−1</sup>.</p>
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<p>(<b>A</b>) Electrochemical behavior according to EIS via Nyquist; (<b>B</b>) electrochemical behavior according to EIS via Bode; and (<b>C</b>) real part of the complex capacitance C’(ω) of PLA/EG.</p>
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<p>(<b>A</b>) Electrochemical behavior according to EIS via Nyquist; (<b>B</b>) electrochemical behavior according to EIS via Bode; and (<b>C</b>) real part of the complex capacitance C’(ω) of PLA/EG.</p>
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<p>Preparation and production of PLA/EG filaments via 3D printing.</p>
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16 pages, 3654 KiB  
Article
Re-Examination of the Sel’kov Model of Glycolysis and Its Symmetry-Breaking Instability Due to the Impact of Diffusion with Implications for Cancer Imitation Caused by the Warburg Effect
by Miljko V. Satarić, Tomas Nemeš and Jack A. Tuszynski
Biophysica 2024, 4(4), 545-560; https://doi.org/10.3390/biophysica4040036 - 6 Nov 2024
Viewed by 400
Abstract
We revisit the seminal model of glycolysis first proposed by Sel’kov more than fifty years ago. We investigate the onset of instabilities in biological systems described by the Sel’kov model in order to determine the conditions of the model parameters that lead to [...] Read more.
We revisit the seminal model of glycolysis first proposed by Sel’kov more than fifty years ago. We investigate the onset of instabilities in biological systems described by the Sel’kov model in order to determine the conditions of the model parameters that lead to bifurcations. We analyze the glycolysis reaction under the circumstances when the diffusivity of both ATP and ADP reactants are taken into account. We estimate the critical value of the model’s single compact dimensionless parameter, which is responsible for the onset of reaction instability and the system’s symmetry breaking. It appears that it leads to spatial inhomogeneities of reactants, leading to the formation of standing waves instead of a homogeneous distribution of ATP molecules. The consequences of this model and its results are discussed in the context of the Warburg effect, which signifies a transition from oxidative phosphorylation to glycolysis that is correlated with the initiation and progression of cancer. Our analysis may lead to the selection of therapeutic interventions in order to prevent the symmetry-breaking phenomenon described in our work. Full article
(This article belongs to the Collection Feature Papers in Biophysics)
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<p>A 3D representation of the ATP molecule with the total number of atoms.</p>
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<p>The function <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>ρ</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </semantics></math>, Equation (<a href="#FD16-biophysica-04-00036" class="html-disp-formula">16</a>), represented in a 3D plot. It is obvious that this function is bound by 1 for <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>→</mo> <mo>∞</mo> <mo>;</mo> <msub> <mi>ρ</mi> <mn>2</mn> </msub> <mo>→</mo> <mo>∞</mo> </mrow> </semantics></math>.</p>
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<p>Nullclines given by Equation (<a href="#FD20-biophysica-04-00036" class="html-disp-formula">20</a>). The intersection of nullclines ∘ is the steady-state point of the Sel’kov system given by Equation (<a href="#FD19-biophysica-04-00036" class="html-disp-formula">19</a>).</p>
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<p><math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>(</mo> <mi>α</mi> <mo>)</mo> </mrow> </semantics></math> given by Equation (<a href="#FD32-biophysica-04-00036" class="html-disp-formula">32</a>). Both eigenvalues are increasing functions of <math display="inline"><semantics> <mi>α</mi> </semantics></math>. The horizontal and vertical coordinates represent dimensionless variables.</p>
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<p>The shape of the function given by Equation (<a href="#FD45-biophysica-04-00036" class="html-disp-formula">45</a>) for <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. The horizontal and vertical coordinates represent dimensionless variables.</p>
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<p>The distributions of the perturbed concentration of reactant <span class="html-italic">X</span> (ATP) for two leading modes.</p>
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26 pages, 2890 KiB  
Review
The Pivotal Role of One-Carbon Metabolism in Neoplastic Progression During the Aging Process
by Avisek Majumder, Shabana Bano and Kasturi Bala Nayak
Biomolecules 2024, 14(11), 1387; https://doi.org/10.3390/biom14111387 - 31 Oct 2024
Viewed by 689
Abstract
One-carbon (1C) metabolism is a complex network of metabolic reactions closely related to producing 1C units (as methyl groups) and utilizing them for different anabolic processes, including nucleotide synthesis, methylation, protein synthesis, and reductive metabolism. These pathways support the high proliferative rate of [...] Read more.
One-carbon (1C) metabolism is a complex network of metabolic reactions closely related to producing 1C units (as methyl groups) and utilizing them for different anabolic processes, including nucleotide synthesis, methylation, protein synthesis, and reductive metabolism. These pathways support the high proliferative rate of cancer cells. While drugs that target 1C metabolism (like methotrexate) have been used for cancer treatment, they often have significant side effects. Therefore, developing new drugs with minimal side effects is necessary for effective cancer treatment. Methionine, glycine, and serine are the main three precursors of 1C metabolism. One-carbon metabolism is vital not only for proliferative cells but also for non-proliferative cells in regulating energy homeostasis and the aging process. Understanding the potential role of 1C metabolism in aging is crucial for advancing our knowledge of neoplastic progression. This review provides a comprehensive understanding of the molecular complexities of 1C metabolism in the context of cancer and aging, paving the way for researchers to explore new avenues for developing advanced therapeutic interventions for cancer. Full article
(This article belongs to the Special Issue Homocysteine and H2S in Health and Disease)
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<p>This cartoon diagram illustrates the enzymatic reactions and compartmentalization of 1C metabolism. Cells utilize 1C units from methionine, serine, and glycine to produce various compounds that work as building blocks for the biosynthesis of nucleic acids and proteins, regulate methylation reactions, and help maintain a cellular redox status. Serine and glycine can enter cells from the outside or be synthesized de novo from the glycolysis intermediate, 3-phosphoglycerate (3-PG). Methionine and folate always come from diet, are carried over the methionine cycle, and can operate in both the cytoplasm and mitochondria (all abbreviations are given at the end).</p>
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<p>One-carbon (1C) metabolism through the methionine cycle and folate cycle and its utilization in other closely linked pathways (like polyamine synthesis and the transsulfuration pathway) (all abbreviations are given at the end).</p>
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<p>Schematic representation showing the inputs of 1C units from dietary sources and their processing and utilization in different biosynthesis processes as output. In this process, methionine, glucose, serine, and glycine can be used as inputs to carry over 1C metabolism. Serine can be obtained from the diet or produced from glucose via the de novo process. Folate from the diet is converted to THF, which accepts a 1C unit during the folate cycle. Then, serine is broken down into glycine, producing a 1C unit which combines with THF to form methylene-THF. Methionine from the diet can be used to produce SAM, which is subsequently used for methylation reactions and cellular antioxidant production. Different outputs from 1C metabolism also act as building blocks for the cellular biosynthesis of DNA, RNA, and protein.</p>
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<p>Regulation of redox homeostasis by glutathione (GSH). The enzyme glutathione peroxidase (GPx) uses glutathione (GSH) as a substrate to produce GSSG (oxidized glutathione) by utilizing the thiol (-SH) group of its cysteine residue to interact with reactive oxygen species (ROS) or electrophiles, whereas the enzyme glutathione reductase (GR) efficiently converts GSSG back to GSH with the help of NADPH, thereby preserving the antioxidant capacity of cells.</p>
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<p>Common transcription factors which are involved in 1C metabolism as well as cancer progression. This visual illustrates the enzymes involved in one-carbon metabolism and their regulating transcription factors, which also play a role in various stages of cancer progression. These transcription factors are organized into two main categories: serine synthesis, represented by different-colored star marks, and nucleotide synthesis, indicated by different-colored triangle marks. All the abbreviations are listed at the end of this article in the abbreviation section.</p>
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<p>Antiproliferative responses of damaged cells. This cartoon illustrates how damaged cells can become apoptotic, enter senescence, or continue replicating. If these antiproliferative responses are absent or fail, a cancerous lesion may be formed, further proliferating to form malignant cells.</p>
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<p>Schematic representation of the fate of cells undergoing senescence and apoptosis upon oncogenic insults. In response to various stressors, normal cells with pre-neoplastic lesions may undergo senescence or apoptosis with the final goal of removing the pre-neoplastic cells. However, in the absence of these antiproliferative responses, pre-neoplastic cells continue to grow and acquire additional oncogenic mutations. At this step, senescence can be reactivated, or it can progress toward malignant transformation.</p>
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25 pages, 2513 KiB  
Review
Mitochondrial Dysfunction and Metabolic Disturbances Induced by Viral Infections
by Sandra E. Pérez, Monika Gooz and Eduardo N. Maldonado
Cells 2024, 13(21), 1789; https://doi.org/10.3390/cells13211789 - 29 Oct 2024
Viewed by 806
Abstract
Viruses are intracellular parasites that utilize organelles, signaling pathways, and the bioenergetics machinery of the cell to replicate the genome and synthesize proteins to build up new viral particles. Mitochondria are key to supporting the virus life cycle by sustaining energy production, metabolism, [...] Read more.
Viruses are intracellular parasites that utilize organelles, signaling pathways, and the bioenergetics machinery of the cell to replicate the genome and synthesize proteins to build up new viral particles. Mitochondria are key to supporting the virus life cycle by sustaining energy production, metabolism, and synthesis of macromolecules. Mitochondria also contribute to the antiviral innate immune response. Here, we describe the different mechanisms involved in virus–mitochondria interactions. We analyze the effects of viral infections on the metabolism of glucose in the Warburg phenotype, glutamine, and fatty acids. We also describe how viruses directly regulate mitochondrial function through modulation of the activity of the electron transport chain, the generation of reactive oxygen species, the balance between fission and fusion, and the regulation of voltage-dependent anion channels. In addition, we discuss the evasion strategies used to avoid mitochondrial-associated mechanisms that inhibit viral replication. Overall, this review aims to provide a comprehensive view of how viruses modulate mitochondrial function to maintain their replicative capabilities. Full article
(This article belongs to the Section Mitochondria)
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<p>Schematics of mitochondrial metabolism. Oxidizable substrates, ADP and Pi, cross the outer mitochondrial membrane through VDACs. Acetyl-coenzyme A, generated from respiratory substrates, enters the TCA cycle, generating NADH and FADH<sub>2</sub>, which fuel the electron transport chain to support oxidative phosphorylation. The TCA cycle also produces metabolic intermediaries released to the cytosol for the synthesis of proteins and lipids. H<sup>+</sup> pumping by the respiratory chain across the inner mitochondrial membrane generates a ΔΨ and a proton motive force used by the F1F0-ATP synthase (complex V) to synthesize ATP. Mitochondrial ATP is exported from the matrix by the ANT and released to the cytosol through VDACs. The flow of electrons through complexes I, II, and III also generates ROS. AcCoA: Acetyl CoA; ANT: adenine nucleotide transporter; α-KG: alpha-ketoglutarate; IMM: inner mitochondrial membrane; OMM: outer mitochondrial membrane; Pi: inorganic phosphate; ROS: reactive oxygen species; VDACs: voltage-dependent anion channels; ΔΨ: mitochondrial membrane potential.</p>
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<p>Mitochondrial ROS production and the effects on virus infection. (<b>A</b>) Virus attachment triggers ROS generation. (<b>B</b>) ROS favors further virus binding to neighboring cells (bystander effect), which may lead to apoptotic cell death. (<b>C</b>) Direct interaction of viral proteins with mitochondrial components induces ROS production, leading to apoptotic cell death, alterations in lipids metabolism, activation of innate immunity, and the inflammatory response. DENV: <span class="html-italic">dengue virus</span>; LCMV: <span class="html-italic">lymphocytic choriomeningitis virus;</span> HIV: <span class="html-italic">human immunodeficiency virus;</span> KSHV: <span class="html-italic">Kaposi’s sarcoma-associated herpesvirus;</span> HCV: <span class="html-italic">hepatitis C virus</span>; HBV: <span class="html-italic">hepatitis B virus</span>; MDV: <span class="html-italic">Marek´s disease virus;</span> EBV: <span class="html-italic">Epstein–Barr virus;</span> RSV<span class="html-italic">: respiratory syncytial virus;</span> IAV<span class="html-italic">: influenza A virus;</span> mtDNA: mitochondrial DNA.</p>
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<p>ROS-mediated mechanisms favoring viral infections. ROS-induced conformational changes on cell receptors favor virus adsorption, trigger autophagy that leads to inflammasome inactivation, stimulate apoptotic cell death to allow virus release and spread, alter viral proteins that favor evasion of the immune response, and introduce mutations to the virus genome, increasing virulence.</p>
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<p>Major mitochondrial metabolic pathways and global mechanisms activated during viral infections. Virus replication is an energy demanding process. To cope with this energetic demand, viruses induce cellular metabolic reprogramming, which includes enhanced glycolysis (Warburg phenotype), glutaminolysis, fatty acids synthesis, and lipid oxidation. KSHV: <span class="html-italic">Kaposi´s sarcoma-associated herpesvirus;</span> HCMV: <span class="html-italic">human cytomegalovirus;</span> EBV<span class="html-italic">: Epstein–Barr virus;</span> NDV: <span class="html-italic">Newcastle disease virus;</span> MNV: <span class="html-italic">murine norovirus</span>; MDV: <span class="html-italic">Marek´s disease virus</span>; HBV: <span class="html-italic">hepatitis B virus</span>; HCV: <span class="html-italic">hepatitis C virus;</span> IAV<span class="html-italic">: influenza A virus;</span> WSSV<span class="html-italic">: white spot syndrome virus;</span> HIV<span class="html-italic">: human immunodeficiency virus.</span> GLUT<span class="html-italic">: glucose transporter;</span> AMPK: AMP-activated protein kinase; SAM complex: β-barrel-specific sorting and assembly machinery; mtROS: mitochondrial ROS.</p>
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<p>Metabolic switch in SARS-CoV2 infection. SARS-CoV2 infection follows a bimodal metabolic reprogramming. Initial SARS-CoV2 infection is characterized by mitochondrial ROS production that promotes hypoxia-inducible factor 1-alpha (HIF-1α) expression, lipolysis, and an increase in FA synthesis. Together with the induction of the Warburg effect, virus replication is enhanced, accompanied by a severe pro-inflammatory response (cytokine storm). During the second stage, glycolysis and oxygen consumption decrease, FA oxidation increases, and the mitochondria return to regular respiration and ATP production. It is a hypo-inflammatory stage, with decreased virus titers and immunotolerance [<a href="#B92-cells-13-01789" class="html-bibr">92</a>,<a href="#B133-cells-13-01789" class="html-bibr">133</a>].</p>
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14 pages, 3499 KiB  
Article
High Mitophagy and Low Glycolysis Predict Better Clinical Outcomes in Acute Myeloid Leukemias
by Amreen Salwa, Alessandra Ferraresi, Letizia Vallino, Chinmay Maheshwari, Riccardo Moia, Gianluca Gaidano and Ciro Isidoro
Int. J. Mol. Sci. 2024, 25(21), 11527; https://doi.org/10.3390/ijms252111527 - 27 Oct 2024
Viewed by 586
Abstract
Acute myeloid leukemia (AML) emerges as one of the most common and fatal leukemias. Treatment of the disease remains highly challenging owing to profound metabolic rewiring mechanisms that confer plasticity to AML cells, ultimately resulting in therapy resistance. Autophagy, a highly conserved lysosomal-driven [...] Read more.
Acute myeloid leukemia (AML) emerges as one of the most common and fatal leukemias. Treatment of the disease remains highly challenging owing to profound metabolic rewiring mechanisms that confer plasticity to AML cells, ultimately resulting in therapy resistance. Autophagy, a highly conserved lysosomal-driven catabolic process devoted to macromolecular turnover, displays a dichotomous role in AML by suppressing or promoting disease development and progression. Glycolytic metabolism represents a pivotal strategy for AML cells to sustain increasing energy needs related to uncontrolled growth during disease progression. In this study, we tested the hypothesis that a high glycolytic rate and low autophagy flux could represent an advantage for AML cell proliferation and thus be detrimental for patient’s prognosis, and vice versa. TCGA in silico analysis of the AML cohort shows that the high expression of MAP1LC3B (along with that of BECN1 and with low expression of p62/SQSTM1) and the high expression of BNIP3 (along with that of PRKN and of MAP1LC3B), which together are indicative of increased autophagy and mitophagy, correlate with better prognosis. On the other hand, the high expression of glycolytic markers HK2, PFKM, and PKM correlates with poor prognosis. Most importantly, the association of a low expression of glycolytic markers with a high expression of autophagy–mitophagy markers conferred the longest overall survival for AML patients. Transcriptomic analysis showed that this combined signature correlates with the downregulation of a subset of genes required for the differentiation of myeloid cells, lactate/pyruvate transporters, and cell cycle progression, in parallel with the upregulation of genes involved in autophagy/lysosomal trafficking and proteolysis, anti-tumor responses like beta-interferon production, and positive regulation of programmed cell death. Taken together, our data support the view that enhanced autophagy-mitophagy flux together with low glycolytic rate predisposes AML patients to a better clinical outcome, suggesting that autophagy inducers and glucose restrictors may hold potential as adjuvant therapeutics for improving AML management. Full article
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<p>Oncoprint reporting copy number variations and expression profile. The oncoprint shows the genetic alterations (upper part) and mRNA expression levels in the AML patient datasets (TCGA, OHSU, Nature 2018). [Note: mRNA expression profiles of the above genes are available only for 405 out of 622 AML patients].</p>
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<p>High expression of <span class="html-italic">HK2</span>, <span class="html-italic">PFKM</span>, and <span class="html-italic">PKM</span> is associated with poor prognosis in AML patients. (<b>A</b>,<b>C</b>,<b>E</b>) Box plots showing the distribution of <span class="html-italic">HK2</span> (<b>A</b>), <span class="html-italic">PFKM</span> (<b>C</b>), and <span class="html-italic">PKM</span>; (<b>E</b>) mRNA expression levels in AML patients (high vs. low). (<b>B</b>,<b>D</b>,<b>F</b>) Kaplan–Meier curves depicting the overall survival rate of AML patients, respectively, based on differential <span class="html-italic">HK2</span> (<b>B</b>), <span class="html-italic">PFKM</span> (<b>D</b>), and <span class="html-italic">PKM</span> (<b>F</b>) mRNA expression levels (low vs. high).</p>
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<p><span class="html-italic">PFKM</span> positively correlates with genes regulating cell cycle, myeloid cell proliferation, and glycolysis, whereas it negatively correlates with genes belonging to autophagy, proteolysis, and apoptotic cell death in AML patients. Bar graphs showing the Gene Ontology analysis reporting <span class="html-italic">PFKM</span>-positively (<b>A</b>) and negatively (<b>B</b>) correlated genes in AML patients, respectively.</p>
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<p>High <span class="html-italic">MAP1LC3B</span> expression, along with high <span class="html-italic">BECN1</span> and low <span class="html-italic">SQSTM1</span> expression is significantly associated with good prognosis in AML patients. (<b>A</b>) Box plot showing distribution of mRNA expression levels of <span class="html-italic">MAP1LC3B</span> (high vs. low). (<b>B</b>) Kaplan–Meier curve depicting overall survival rate of AML patients based on <span class="html-italic">MAP1LC3B</span> mRNA expression (low vs. high). (<b>C</b>,<b>D</b>) Kaplan–Meier curves representing overall survival status of AML patients stratified based on differential expression of <span class="html-italic">BECN1</span>/<span class="html-italic">MAP1LC3B</span> (<b>C</b>) and <span class="html-italic">MAP1LC3B</span>/<span class="html-italic">SQSTM1</span> (<b>D</b>) expression (high/high, high/low, low/high, and low/low groups, respectively).</p>
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<p>Enhanced expression of <span class="html-italic">BNIP3</span> together with high <span class="html-italic">PRKN</span> and high <span class="html-italic">MAP1LC3B</span> expression significantly correlates with longer overall survival in AML patients. (<b>A</b>,<b>B</b>) Kaplan–Meier plots representing overall survival status of AML patients stratified based on differential expression of <span class="html-italic">PRKN</span>/<span class="html-italic">BNIP3</span> (<b>A</b>) and <span class="html-italic">MAP1LC3B</span>/<span class="html-italic">BNIP3</span> (<b>B</b>) mRNA expression (high/high, high/low, low/high, and low/low groups, respectively).</p>
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<p>Low expression of glycolytic markers (<span class="html-italic">HK2</span>, <span class="html-italic">PKM</span>, and <span class="html-italic">PFKM</span>) with high <span class="html-italic">MAP1LC3B</span> or high <span class="html-italic">BNIP3</span> expression predicts longer overall survival in AML patients. Kaplan–Meier plots representing overall survival status of AML patients stratified based on differential expression of <span class="html-italic">HK2</span>/<span class="html-italic">MAP1LC3B</span> (<b>A</b>), <span class="html-italic">HK2</span>/<span class="html-italic">BNIP3</span> (<b>B</b>), <span class="html-italic">PFKM</span>/<span class="html-italic">MAP1LC3B</span> (<b>C</b>), <span class="html-italic">PFKM</span>/<span class="html-italic">BNIP3</span> (<b>D</b>), <span class="html-italic">PKM</span>/<span class="html-italic">MAP1LC3B</span> (<b>E</b>), and <span class="html-italic">PFKM</span>/<span class="html-italic">BNIP3</span> (<b>F</b>) mRNA expression (high/high, high/low, low/high, and low/low groups, respectively).</p>
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<p>Comparison of differentially expressed genes in two groups of patients stratified based on <span class="html-italic">PFKM</span>, <span class="html-italic">MAP1LC3B</span>, and <span class="html-italic">BNIP3</span> expression. Patients were in Group A (characterized by high <span class="html-italic">PFKM</span>/ low <span class="html-italic">MAP1LC3B</span>/low <span class="html-italic">BNIP3</span> expression) and Group B (characterized by low <span class="html-italic">PFKM</span>/high <span class="html-italic">MAP1LC3B</span>/high <span class="html-italic">BNIP3</span> expression). The heatmap includes the top five significant genes related to each biological process differentially correlated to <span class="html-italic">PFKM</span> expression.</p>
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18 pages, 2003 KiB  
Review
Dual Roles of microRNA-122 in Hepatocellular Carcinoma and Breast Cancer Progression and Metastasis: A Comprehensive Review
by Essam Al Ageeli
Curr. Issues Mol. Biol. 2024, 46(11), 11975-11992; https://doi.org/10.3390/cimb46110711 - 25 Oct 2024
Viewed by 792
Abstract
microRNA-122 (miR-122) plays crucial yet contrasting roles in hepatocellular carcinoma (HCC) and breast cancer (BC), two prevalent and aggressive malignancies. This review synthesizes current research on miR-122’s functions in these cancers, focusing on its potential as a diagnostic, prognostic, and therapeutic target. A [...] Read more.
microRNA-122 (miR-122) plays crucial yet contrasting roles in hepatocellular carcinoma (HCC) and breast cancer (BC), two prevalent and aggressive malignancies. This review synthesizes current research on miR-122’s functions in these cancers, focusing on its potential as a diagnostic, prognostic, and therapeutic target. A comprehensive literature search was conducted using PubMed, Web of Science, and Scopus databases. In HCC, miR-122 is downregulated in most cases, suppressing oncogenic pathways and reducing tumor growth and metastasis. Restoring miR-122 levels has shown promising therapeutic potential, increasing sensitivity to treatments like sorafenib. In contrast, in BC, miR-122 plays a pro-metastatic role, especially in triple-negative breast cancer (TNBC) and metastatic lesions. miR-122′s ability to influence key pathways, such as the Wnt/β-catenin and NF-κB pathways in HCC, and its role in enhancing the Warburg effect in BC underline its significance in cancer biology. miR-122, a key factor in breast cancer radioresistance, suppresses tumors in radiosensitive cells. Inhibiting miR-122 could reverse resistance and potentially overcome radiotherapy resistance. Given its context-dependent functions, miR-122 could serve as a potential therapeutic target, where restoring or inhibiting its expression may help in treating HCC and BC, respectively. The dual roles of miR-122 underscore its significance in cancer biology and its potential in precision medicine. Full article
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<p><span class="html-italic">Cyclin G1</span>, insulin-like growth factor 1 receptor (IGF-1R), <span class="html-italic">c-Myc</span>, <span class="html-italic">G9a</span>, and Toll-like receptor 4 (TLR4) are very important for hepatocarcinogenesis as they stop cell death and upregulate the pathways promoting cell growth. <span class="html-italic">Cyclin G1</span> and <span class="html-italic">IGF-1R</span> activate MDM2, leading to p53 degradation. <span class="html-italic">c-Myc</span> upregulates c-Myc-inducible lncRNA inactivating p53 (MILIP) expression and decreases p53 expression. <span class="html-italic">G9a</span> activates <span class="html-italic">Bcl-G</span>, an antiapoptotic protein. MicroRNA (miR)-122 negatively regulates these mediators by directly interacting with their mRNAs. <span class="html-italic">TLR4</span> orchestrates immune escape by activating regulatory T cells (T-regs) and suppressing CD<sup>8+</sup> activity in tumors.</p>
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<p>Pathways involved in HCC progression and metastasis.</p>
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<p>Effects of miR-122 on tumor cells: (<b>I</b>) miR-122 targets pyruvate kinase (PK) to decrease glucose uptake by downregulating glucose transporter 1 (GLUT1) expression and promoting pre-metastatic niche (PMN) formation; (<b>II</b>) miR-122 targets O-linked N-acetylglucosamine (OGT), increasing cytosolic calcium and promoting muscular atrophy via calpain-mediated degradation of muscular proteins; (<b>III</b>) at low PK activity, glycolysis does not generate many ATPs, thereby stopping membrane depolarization in pancreatic β-cells. Lack of membrane depolarization further prevents calcium entry into β-cells, leading to subnormal insulin secretion and hyperglycemia.</p>
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29 pages, 6547 KiB  
Article
Deciphering the Metabolic Basis and Molecular Circuitry of the Warburg Paradox in Lymphoma
by Dashnamoorthy Ravi, Athena Kritharis and Andrew M. Evens
Cancers 2024, 16(21), 3606; https://doi.org/10.3390/cancers16213606 - 25 Oct 2024
Viewed by 884
Abstract
Background/Objectives: Warburg’s metabolic paradox illustrates that malignant cells require both glucose and oxygen to survive, even after converting glucose into lactate. It remains unclear whether sparing glucose from oxidation intersects with TCA cycle continuity and if this confers any metabolic advantage in [...] Read more.
Background/Objectives: Warburg’s metabolic paradox illustrates that malignant cells require both glucose and oxygen to survive, even after converting glucose into lactate. It remains unclear whether sparing glucose from oxidation intersects with TCA cycle continuity and if this confers any metabolic advantage in proliferating cancers. This study seeks to understand the mechanistic basis of Warburg’s paradox and its overall implications for lymphomagenesis. Methods: Using metabolomics, we first examined the metabolomic profiles, glucose, and glutamine carbon labeling patterns in the metabolism during the cell cycle. We then investigated proliferation-specific metabolic features of malignant and nonmalignant cells. Finally, through bioinformatics and the identification of appropriate pharmacological targets, we established malignant-specific proliferative implications for the Warburg paradox associated with metabolic features in this study. Results: Our results indicate that pyruvate, lactate, and alanine levels surge during the S phase and are correlated with nucleotide synthesis. By using 13C1,2-Glucose and 13C6, 15N2-Glutamine isotope tracers, we observed that the transamination of pyruvate to alanine is elevated in lymphoma and coincides with the entry of glutamine carbon into the TCA cycle. Finally, by using fludarabine as a strong inhibitor of lymphoma, we demonstrate that disrupting the transamination of pyruvate to alanine correlates with the simultaneous suppression of glucose-derived nucleotide biosynthesis and glutamine carbon entry into the TCA cycle. Conclusions: We conclude that the transamination of pyruvate to alanine intersects with reduced glucose oxidation and maintains the TCA cycle as a critical metabolic feature of Warburg’s paradox and lymphomagenesis. Full article
(This article belongs to the Special Issue The Warburg Effect in Cancers)
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Graphical abstract

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<p><b>Metabolomic profiling of lymphoma cell cycle.</b> (<b>a</b>) A representative flow cytometric profile of Hoechst 33342-stained CA46 cells illustrating the gating strategy adapted for sorting cells according to their cell cycle phase for metabolomic profiling. (<b>b</b>) Heatmap of metabolic pools profiled from mass spectrometry analyses of CA46 cells sorted by phase of the cell cycle. The color gradient represents the absolute mean deviations between low and high levels of each metabolite’s pool sizes. Statistically significant differences from the comparison between G1 with S or G2 are denoted (*, ** with <span class="html-italic">p</span>-values of &lt;0.05, &lt;0.005, respectively (<b>c</b>) Identification of top significant metabolite features (<span class="html-italic">p</span> &lt; 0.001) by partial least squares—discriminant analysis and variable importance in projection scoring analysis. The colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group.</p>
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<p><b>Pyruvate and alanine transamination activity in cell cycle progression.</b> (<b>a</b>) Schematic diagram illustrates isotopomers arising from carbon and nitrogen exchanges in transaminase reactions and pyruvate metabolism. Bar graphs represent the pool sizes and isotope enrichment patterns from <sup>13</sup>C<sub>1,2</sub>-Glucose or <sup>13</sup>C<sub>5,</sub><sup>15</sup>N<sub>2</sub>-Glutamine tracers from 2 h labeling with flow sorted CA46 cells used in the identification of metabolic changes associated with cell cycle progression. (<b>b</b>) Bar graphs represent fractional labeling patterns detected in the transaminase metabolism from 12 h labeling with <sup>13</sup>C<sub>1,2</sub>-Glucose or <sup>13</sup>C<sub>5,</sub><sup>15</sup>N<sub>2</sub>-Glutamine tracers in CA46 cells.</p>
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<p><b>Comparative analysis of isotope enrichment patterns in glucose and nucleotide metabolism in lymphoblastoid cells and lymphoma cell lines.</b> (<b>a</b>) Bar graphs represent relative pool sizes of transaminase metabolites in LCL vs. lymphoma cells. The error bars represent the standard deviation from the mean of experimental triplicates. Statistically significant differences from the comparison between LCL with lymphoma cell lines (CA46 or SUDHL4) are denoted (**, ***, **** with <span class="html-italic">p</span>-values of &lt;0.005, &lt;0.0005, and &lt;0.0001, respectively), by 2-way ANOVA, as differences in the metabolite pool sizes between normal versus lymphoma cells. (<b>b</b>,<b>c</b>) Bar graphs represent mean fractional isotope enrichment patterns in glycolysis, citric acid cycle, transaminase, pentose phosphate, and nucleotide metabolism in LCL, CA46, and SUDHL4 with (<b>b</b>) <sup>13</sup>C<sub>1,2</sub>-Glucose or (<b>c</b>) <sup>13</sup>C<sub>5,</sub><sup>15</sup>N<sub>2</sub>-Glutamine isotope tracers. Each bar represents the mean of experimental triplicates.</p>
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<p><b>Modeling the molecular metabolic circuitry of lymphoma.</b> (<b>a</b>) This diagram illustrates the metabolic intersections linking pyruvate with lactate, transaminase, the TCA cycle, and the associated solute carrier transporters. As described in the Methods Section, these metabolic components were used to model the molecular metabolic circuitry of the Warburg effect. (<b>b</b>) The boxplots illustrate the mRNA expression of metabolic genes and their putative regulatory factors in lymphoma tumors (<span class="html-italic">n</span> = 481) and cell lines (<span class="html-italic">n</span> = 10). Whiskers represent standard deviations from the normalized mean of Log<sub>2</sub> expression values for each experimental dataset. (<b>c</b>) Molecular-metabolic network rendering using Cytoscape shows metabolic genes with putative regulators as interactors (<b>top</b>), further filtered by putative regulators with four or more interactions with metabolic genes (<b>bottom</b>).</p>
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<p><b>Perturbation of molecular metabolic network with pharmacological inhibitors.</b> (<b>a</b>) The heatmap from metabolomic profiling shows the effect of pharmacological inhibitors on targeting Warburg metabolism regulators in LCL, CA46, and SUDHL4 cells. Color gradient indicates the absolute mean deviation between pool sizes for each metabolite. (<b>b</b>) Plots from the principal component analysis of LCL, CA46, and SUDHL4 demonstrate overall behaviors of each experimental set from treatment with pharmacological inhibitors intended to target the Warburg metabolism. (<b>c</b>) Partial least squares-based analysis (<b>left</b>) identifies the top significant metabolite features by discriminant analysis and variable importance (<b>right</b>) and metabolites aligned with changes in glucose metabolism with fludarabine based pharmacological inhibition of Warburg regulators by Spearman rank correlation analysis with <span class="html-italic">p</span>-values &lt; 0.005 (<b>right</b>).</p>
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<p><b>Fludarabine decreases <sup>13</sup>C incorporation from glucose into nucleotides.</b> (<b>a</b>) Dose–response curves from fludarabine lymphoma and LCL cells, with log molar concentration (x-axis) and percent cell viability (y-axis), determined at 72 h by CellTitre glo assay. (<b>b</b>) The line graph represents the mean of log<sub>2</sub> fold-changes in the pool sizes of corresponding metabolites in LCL, and lymphoma cells, treated with fludarabine. (<b>c</b>) Schematics represent isotope enrichment pattern for ribose phosphate and synthesis of nucleotides derived from glucose through oxidative pentose phosphate pathway (Ox-PPP). Bar graphs represent mean fractional <sup>13</sup>C enrichment patterns in nucleotides of control and fludarabine-treated LCL, CA46, and SUDHL4 cells. (<b>d</b>) Bar graphs represent percentages of average relative carbon contributions from <sup>13</sup>C enriched nucleotides of control and fludarabine-treated LCL, CA46 and SUDHL4 cells. The error bars represent the standard deviation from the mean of all nucleotides. (<b>e</b>) Distribution plot represents relative carbon contributions from <sup>13</sup>C enrichments in lactate, citrate, alanine, and ribulose-5-phosphate (R5P) in control and fludarabine-treated LCL, CA46, and SUDHL4 cells. (<b>f</b>) Bar graphs represent pool size changes in the metabolites with fludarabine treatment in LCL, CA46, and SUDHL4 cells. (<b>g</b>) Diagrammatic summary of the metabolic fate of glucose metabolism with fludarabine treatment in the lymphoma cells. The error bars represent the standard deviation from the mean of experimental triplicates. Statistically significant differences from the comparison between control and fludarabine are denoted by * with <span class="html-italic">p</span>-values of &lt;0.05 by student <span class="html-italic">t</span> Test.</p>
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<p><b>Fludarabine decreases</b><sup>13</sup>C <b>incorporation from glutamine into TCA cycle in lymphoma.</b> (<b>a</b>) Schematics illustrate patterns of isotopomers expected from <sup>13</sup>C enriched TCA cycle metabolites derived from <sup>13</sup>C<sub>5</sub>,<sup>15</sup>N2-Glutamine. (<b>b</b>,<b>c</b>). The distribution plot represents <sup>13</sup>C enriched isotopomers representing TCA cycle metabolites in CA46 cells and the bar graphs show <sup>13</sup>C fractional enrichment changes in TCA cycle intermediates with fludarabine treatment in LCL, CA46 and SUDHL4 cells. (<b>d</b>) The bar graphs represent the mean changes in the pool sizes of TCA cycle intermediates and associated metabolites with fludarabine treatment in LCL, CA46 and SUDHL4 cells. (<b>e</b>) Relative carbon contribution from glucose and glutamine from <sup>13</sup>C enriched TCA cycle intermediates, and the effect of fludarabine in LCL, CA46, and SUDHL4 cells are represented as a distribution plot. (<b>f</b>) An overview of metabolic pool size changes in lymphoma cells treated with fludarabine is summarized in this illustration based on <a href="#cancers-16-03606-f006" class="html-fig">Figure 6</a>f and <a href="#cancers-16-03606-f007" class="html-fig">Figure 7</a>d, with pool size decreases (in blue) and increases (in red). The error bars in this figure represent the standard deviation from the mean of experimental triplicates. Statistically significant differences from the comparison between control and fludarabine are denoted by * with <span class="html-italic">p</span>-values of &lt;0.05 by student <span class="html-italic">t</span> Test.</p>
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<p><b>Warburg paradoxical metabolic features are elevated in lymphoma.</b> (<b>a</b>,<b>b</b>). Heatmaps from metabolomic profiling comparing (<b>a</b>) primary lymphocyte (LCL) with lymphoma cell lines (CA46 and SUDHL4) and (<b>b</b>) normal lymph node and lymphoma tumors show upregulation in the pool sizes of metabolites associated with glucose metabolism, lactate, alanine and nucleotides in lymphoma. Color gradient indicates the absolute mean deviation between pool sizes for each metabolite. (<b>c</b>) Western blot analysis comparing lymphoma tumor and normal lymph nodes show that Jun (denoted as ***, <span class="html-italic">p</span> &lt; 0.005) and STAT1 (denoted as **, <span class="html-italic">p</span> &lt; 0.05) expressions (normalized by β-actin) represented as violin plots, are significantly upregulated in the tumors. Original uncropped blots are presented in <a href="#app1-cancers-16-03606" class="html-app">Supplementary Figure S3</a>.</p>
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<p>(<b>a</b>) Diagrammatic summary illustrating the results from isotopic tracer experiments (highlighting the predicted metabolic sequence of steps, outlined in the results), providing an integrated overview of proliferative metabolic functions in lymphoma. (<b>b</b>) Diagrammatic summary of physiological metabolic regulations in proliferation, highlighting the disruption of these regulations by the Warburg phenomenon in malignancy.</p>
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22 pages, 1524 KiB  
Review
Insights into Metabolic Reprogramming in Tumor Evolution and Therapy
by Ching-Feng Chiu, Jonathan Jaime G. Guerrero, Ric Ryan H. Regalado, Ma. Joy B. Zamora, Jiayan Zhou, Kin Israel Notarte, Yu-Wei Lu, Paolo C. Encarnacion, Cidne Danielle D. Carles, Edrian M. Octavo, Dan Christopher I. Limbaroc, Charupong Saengboonmee and Shih-Yi Huang
Cancers 2024, 16(20), 3513; https://doi.org/10.3390/cancers16203513 - 17 Oct 2024
Viewed by 889
Abstract
Background: Cancer remains a global health challenge, characterized not just by uncontrolled cell proliferation but also by the complex metabolic reprogramming that underlies its development and progression. Objectives: This review delves into the intricate relationship between cancer and its metabolic alterations, drawing an [...] Read more.
Background: Cancer remains a global health challenge, characterized not just by uncontrolled cell proliferation but also by the complex metabolic reprogramming that underlies its development and progression. Objectives: This review delves into the intricate relationship between cancer and its metabolic alterations, drawing an innovative comparison with the cosmological concepts of dark matter and dark energy to highlight the pivotal yet often overlooked role of metabolic reprogramming in tumor evolution. Methods: It scrutinizes the Warburg effect and other metabolic adaptations, such as shifts in lipid synthesis, amino acid turnover, and mitochondrial function, driven by mutations in key regulatory genes. Results: This review emphasizes the significance of targeting these metabolic pathways for therapeutic intervention, outlining the potential to disrupt cancer’s energy supply and signaling mechanisms. It calls for an interdisciplinary research approach to fully understand and exploit the intricacies of cancer metabolism, pointing toward metabolic reprogramming as a promising frontier for developing more effective cancer treatments. Conclusion: By equating cancer’s metabolic complexity with the enigmatic nature of dark matter and energy, this review underscores the critical need for innovative strategies in oncology, highlighting the importance of unveiling and targeting the “dark energy” within cancer cells to revolutionize future therapy and research. Full article
(This article belongs to the Section Methods and Technologies Development)
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<p>A comparison of the major metabolic pathways in (<b>A</b>) normal and (<b>B</b>) cancer cells. In normal cells (<b>A</b>), glucose enters via GLUT transporters, fueling glycolysis and predominantly generating ATP through oxidative phosphorylation (OXPHOS) in the mitochondria. Fatty acid oxidation and glutaminolysis also contribute to ATP production and lipid synthesis. Pathways with relatively low activity, such as lactate production, are indicated by dashed lines. In contrast, cancer cells (<b>B</b>) demonstrate increased glucose uptake via upregulated GLUT transporters, resulting in enhanced glycolysis and the Warburg effect, where pyruvate is converted to lactate even in the presence of oxygen. This metabolic reprogramming supports rapid ATP production and proliferation. Despite the dominance of the Warburg effect, minimal TCA cycle activity and OXPHOS are retained, as indicated by the dashed lines. Abbreviations: glucose transporter type 1 (GLUT1); Monocarboxylate Transporter (MCT); adenosine triphosphate (ATP); mammalian target of rapamycin (mTOR); α-Ketoglutarate (α-KG); branched-chain amino acids (BCAAs); Solute Carrier Family 7 Member 5 (SLC7A5); Solute Carrier Family 1 Member 5 (SLC1A5); Fatty Acid Transport Protein (FATP); Plasma Membrane Fatty Acid-Binding Protein (FABPpm).</p>
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<p>An overview of the major metabolic pathways at work within cancer cells. Cell survival, growth, and proliferation require glucose to generate ATP, lipids, and amino acids through glycolysis, alongside other downstream reactions and pathways, including the pentose phosphate pathway, glutaminolysis, lipid synthesis, and branched-chain amino acid (BCAA) metabolism. The Warburg effect, characterized by increased glucose uptake and lactate production despite adequate oxygen, highlights metabolic reprogramming, supporting rapid tumor growth and survival even under oxidative conditions. The mTOR signaling pathway regulates cell growth, proliferation, survival, and cytoskeletal organization in response to insulin, growth factors, and other metabolic and cellular cues. Additionally, p53 plays an important role in promoting ATP production, facilitating citric acid cycle (also referred to as the TCA cycle or Krebs cycle) and glutamate synthesis, while regulating glycolysis and lipid synthesis. Dysregulation of mTOR signaling and p53 has been implicated in numerous diseases, including cancer and metabolic disorders. Moreover, the metabolic processes of cancer cells operate in distinct ways depending on the availability of nutrients. In situations where nutrients are abundant (nutrient-replete conditions), there is a focus on nucleotide production, lipid generation, and the utilization of glutamine. Conversely, under nutrient-deprived conditions, cancer cells favor fatty acid oxidation, acetate breakdown, the utilization of BCAAs, and glutaminolysis related to macropinocytosis and autophagy. Understanding the metabolic adaptations of cancer cells to diverse nutrient environmental conditions is vital for developing targeted therapies to combat disease progression. Abbreviations: Pentose phosphate pathway. glucose-6-phosphate dehydrogenase (G6PD); Ribulose 5-phosphate (Ribulose-5P); Xylulose 5-phosphate (Xylulose-5P); Ribose 5-phosphate (Ribose-5P); Glyceraldehyde 3-phosphate (G3P); Sedoheptulose 7-phosphate (sedoheptulose-7P); Transaldolase (TALDO); Erythrose 4-phosphate (Erythrose-4P); Fructose 6-phosphate (Fructose 6-p). Glycolysis. glucose-6-phosphate dehydrogenase (G6PD); Fructose 6-phosphate (Fructose 6-p); Fructose 1,6-biphosphate (Fructose 1,6-biP); Fructose 2,6-biphosphate (Fructose 2,6-biP); Glyceraldehyde 3-phosphate (GA3P); Dihydroxyacetone phosphate (DHAP); Glyceraldehyde-3-phosphate dehydrogenase (GAPDH); Phosphoglycerate mutase (PGAM); Pyruvate kinase M2 (PKM2); Lactate dehydrogenase (LDH). mTOR pathway. Phosphatidylinositol-3 kinase (PI3K); Protein kinase B (AKT); Rat sarcoma (Ras); Rapidly Accelerated Fibrosarcoma (Raf); Mitogen-Activated Protein Kinase (MEK); Extracellular Signal-Regulated Kinase (ERK); p90 Ribosomal S6 Kinase (RSK); Tuberous Sclerosis Complex1/2 (TSC1/2); Ras Homolog Enriched in Brain (Rheb); Guanosine Triphosphate (GTP); Ras-related GTP binding A/B (Rag A/B); Ras-related GTP binding C/D (Rag C/D); Guanosine Diphosphate (GDP); mammalian target of rapamycin (mTOR); Hypoxia-Inducible Factor 1 (HIF-1). branched-chain amino acid (BCAA). α-ketoglutarate (α-KG); glutamine (Gln); glutamate (Glu); Branched-chain Aminotransferases (BCAT); 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA); α-ketoisocaproate (KIC); branched-chain amino acid Aminotransferase (BCAT). Glutaminolysis. glutaminase (GLS); Glutamate Dehydrogenase (GLUD); Sodium-Dependent Neutral Amino Acid Transporter (SLC1A5). Lipid Synthesis. 3-hydroxyl3-methyl-glutaryl-coenzyme A reductase (HMG-CoA); Acetyl-CoA Carboxylase (ACACA); fatty acid synthase (FASN); 3-hydroxy-3-methylglutaryl-CoA Reductase (HMGCR); Farnesyl Pyrophosphate (FPP); Stearoyl-Coa Desaturase (SCD); Monounsaturated Fatty Acid (MUFA); Polyunsaturated Fatty Acid (PUFA).</p>
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15 pages, 8118 KiB  
Article
Highland Barley Alleviates High-Fat Diet-Induced Obesity and Liver Injury Through the IRS2/PI3K/AKT Signaling Pathway in Rats
by Xiaodong Shi, Wei Song, Boyue Jiang, Jie Ma, Wanyang Li, Mingyao Sun, Hongyuan Cui and Wei Chen
Nutrients 2024, 16(20), 3518; https://doi.org/10.3390/nu16203518 - 17 Oct 2024
Viewed by 860
Abstract
Objectives: Highland barley (HB) consumption offers numerous health benefits; however, its impact on glycolipid metabolism abnormalities induced by a high-fat diet remains unclear. Consequently, this study aimed to investigate the therapeutic effects and underlying molecular mechanisms of HB in the context of obesity; [...] Read more.
Objectives: Highland barley (HB) consumption offers numerous health benefits; however, its impact on glycolipid metabolism abnormalities induced by a high-fat diet remains unclear. Consequently, this study aimed to investigate the therapeutic effects and underlying molecular mechanisms of HB in the context of obesity; Methods: Rats were fed either a high-fat diet (HFD) to induce obesity or a standard diet (SD) for six weeks. The rats in the HFD group were randomly assigned into five groups: HFD+HFD, HFD+SD, and low (30%), medium (45%), and high (60%) doses of the HB diet for an additional ten weeks. Analyses of serum lipid profiles, liver histology, transcriptomes, and untargeted metabolomes were conducted; Results: HB intake resulted in decreased weight gain, reduced feed intake, lower serum triglyceride and cholesterol levels, and diminished hepatic lipid accumulation. It also improved insulin and fasting blood glucose levels, and antioxidant capacity in the HFD-fed rats. Transcriptome analysis revealed that HB supplementation significantly suppressed the HFD-induced increase in the expression of Angptl8, Apof, CYP7A1, GDF15, Marveld1, and Nr0b2. Furthermore, HB supplementation reversed the HFD-induced decrease in Pex11a expression. Untargeted metabolome analysis indicated that HB primarily influenced the pentose phosphate pathway, the Warburg effect, and tryptophan metabolism. Additionally, integrated transcriptome and metabolome analyses demonstrated that the treatments affected the expression of genes associated with glycolipid metabolism, specifically ABCG8, CYP2C12, CYP2C24, CYP7A1, and IRS2. Western blotting confirmed that HB supplementation impacted the IRS2/PI3K/AKT signaling pathway; Conclusions: HB alleviates HFD-induced obesity and liver injury in an obese rat model possibly through the IRS2/PI3K/Akt signaling pathway. Full article
(This article belongs to the Section Nutrition and Metabolism)
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<p>Animal study design. HFD+HFD group (maintained on D12492 diet throughout), HFD+SD group (administered D12492 diet for 6 weeks, subsequently transitioned to a standard diet), HFD+LHB group (administered D12492 diet for 6 weeks, then shifted to a 30% highland barley diet), HFD+MHB group (administered D12492 diet for 6 weeks, then switched to a 45% highland barley diet), HFD+HHB group (administered D12492 diet for 6 weeks, followed by a switch to a 60% highland barley diet), SD+SD group (consistently fed with a standard diet.).</p>
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<p>Effect of highland barley (HB) on body weight (<b>A</b>), feed intake (<b>B</b>), serum total triglyceride (TG) (<b>C</b>), total cholesterol (TC) (<b>D</b>), low-density lipoprotein cholesterol (LDL-C) (<b>E</b>), high-density lipoprotein cholesterol (HDL-C) (<b>F</b>), insulin (<b>G</b>), fasting blood glucose (FBG) (<b>H</b>), malonaldehyde (MDA) (<b>I</b>), superoxide dismutase (SOD) (<b>J</b>), total antioxidant capacity (TAC) (<b>K</b>), leptin (LEP) (<b>L</b>), adiponectin (ADP) (<b>M</b>), and liver tissues (<b>N</b>) in obese rats. Representative liver tissue samples were stained with Oil Red O. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Scatter plot of PCA for differentially expressed genes (DEGs) in the hepatic tissues of rats across the HFD+SD, HFD+HFD, and HFD+HHB groups (<b>A</b>). Venn plot showing overlapping DEGs in the HFD+HHB vs. HFD+HFD and HFD+HFD vs. HFD+SD (<b>B</b>), HFD+HHB vs. HFD+SD and HFD+HFD vs. HFD+SD (<b>C</b>), HFD+HHB vs. HFD+HFD and HFD+HHB vs. HFD+SD groups (<b>D</b>). Transcriptomic analysis of DEGs (<b>E</b>). Scatter plot of PCA for metabolites in the hepatic tissues of rats across the HFD+SD, HFD+HFD, and HFD+HHB groups (<b>F</b>). Heatmap of identified metabolites in the HFD+SD, HFD+HFD, and HFD+HHB groups (<b>G</b>). ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effect of HB on relative mRNA expression of ABCG8, CYP2C12, CYP2C24, CYP7A1, and IRS2 (<b>A</b>). Effect of HB on the expression of proteins in the IRS2/PI3K/AKT signaling pathway (<b>B</b>). ** <span class="html-italic">p</span> &lt; 0.01.</p>
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28 pages, 3458 KiB  
Review
Decoding Cancer through Silencing the Mitochondrial Gatekeeper VDAC1
by Tasleem Arif, Anna Shteinfer-Kuzmine and Varda Shoshan-Barmatz
Biomolecules 2024, 14(10), 1304; https://doi.org/10.3390/biom14101304 - 15 Oct 2024
Viewed by 942
Abstract
Mitochondria serve as central hubs for regulating numerous cellular processes that include metabolism, apoptosis, cell cycle progression, proliferation, differentiation, epigenetics, immune signaling, and aging. The voltage-dependent anion channel 1 (VDAC1) functions as a crucial mitochondrial gatekeeper, controlling the flow of ions, such as [...] Read more.
Mitochondria serve as central hubs for regulating numerous cellular processes that include metabolism, apoptosis, cell cycle progression, proliferation, differentiation, epigenetics, immune signaling, and aging. The voltage-dependent anion channel 1 (VDAC1) functions as a crucial mitochondrial gatekeeper, controlling the flow of ions, such as Ca2+, nucleotides, and metabolites across the outer mitochondrial membrane, and is also integral to mitochondria-mediated apoptosis. VDAC1 functions in regulating ATP production, Ca2+ homeostasis, and apoptosis, which are essential for maintaining mitochondrial function and overall cellular health. Most cancer cells undergo metabolic reprogramming, often referred to as the “Warburg effect”, supplying tumors with energy and precursors for the biosynthesis of nucleic acids, phospholipids, fatty acids, cholesterol, and porphyrins. Given its multifunctional nature and overexpression in many cancers, VDAC1 presents an attractive target for therapeutic intervention. Our research has demonstrated that silencing VDAC1 expression using specific siRNA in various tumor types leads to a metabolic rewiring of the malignant cancer phenotype. This results in a reversal of oncogenic properties that include reduced tumor growth, invasiveness, stemness, epithelial–mesenchymal transition. Additionally, VDAC1 depletion alters the tumor microenvironment by reducing angiogenesis and modifying the expression of extracellular matrix- and structure-related genes, such as collagens and glycoproteins. Furthermore, VDAC1 depletion affects several epigenetic-related enzymes and substrates, including the acetylation-related enzymes SIRT1, SIRT6, and HDAC2, which in turn modify the acetylation and methylation profiles of histone 3 and histone 4. These epigenetic changes can explain the altered expression levels of approximately 4000 genes that are associated with reversing cancer cells oncogenic properties. Given VDAC1’s critical role in regulating metabolic and energy processes, targeting it offers a promising strategy for anti-cancer therapy. We also highlight the role of VDAC1 expression in various disease pathologies, including cardiovascular, neurodegenerative, and viral and bacterial infections, as explored through siRNA targeting VDAC1. Thus, this review underscores the potential of targeting VDAC1 as a strategy for addressing high-energy-demand cancers. By thoroughly understanding VDAC1’s diverse roles in metabolism, energy regulation, mitochondrial functions, and other cellular processes, silencing VDAC1 emerges as a novel and strategic approach to combat cancer. Full article
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<p>VDAC1 structure and its multi-functional protein involved in Ca<sup>2+</sup> and metabolite transport, energy production, apoptosis, inflammation, and immune responses. (<b>A</b>) VDAC1 structure (PDB ID: 3EMN). VDAC1 structure; (<b>a</b>,<b>b</b>) top view, (<b>c</b>) side view and (<b>d</b>) side view with N-terminal α-helix exposed to the channel surface. (<b>B</b>) VDAC1, as a multifunctional channel, plays a crucial role in mediating mitochondrial interactions with the endoplasmic reticulum (ER) and cytosol. Its functions include: (<b>a</b>) ER–Mitochondria contacts: VDAC1 contributes to both the structural and functional contacts between the ER and mitochondria. It facilitates the transfer of Ca<sup>2+</sup> released by inositol 1,4,5-trisphosphate receptors (IP3R) in the ER to the intermembrane space (IMS). This Ca<sup>2+</sup> is then transported into the mitochondrial matrix by the Ca<sup>2+</sup> uniporter (MCU) complex, where it regulates energy production through the activation of tricarboxylic acid cycle (TCA) enzymes such as pyruvate dehydrogenase (PDH), isocitrate dehydrogenase (ICDH), and α-ketoglutarate dehydrogenase (α-KGDH), as well as the electron transport chain (ETC) and ATP synthase (FoF1). (<b>b</b>) Metabolite transport: VDAC1 mediates the transport of metabolites up to 5 kDa between the mitochondria and the cytosol. (<b>c</b>) Energy production regulation: VDAC1 regulates energy production by facilitating the transport of ATP/ADP, NAD+/NADH, and acyl-CoA between the cytosol and IMS. It also binds to hexokinase (HK), channeling mitochondrially produced ATP directly to HK, which in turn regulates glycolysis. (<b>d</b>) Ions and Ca<sup>2+</sup> homeostasis: VDAC1 controls the passage of ions and Ca<sup>2+</sup> between the cytosol and IMS according to their concentration gradients, thus, maintaining Ca<sup>2+</sup> homeostasis. (<b>e</b>) VDAC1 plays a crucial role in the transport of cholesterol, as it is a component of the multi-protein complex known as the transduceosome, which is responsible for cholesterol transport. VDAC1 is involved in the transport of long-chain fatty acids into the mitochondria through the carnitine shuttle, as part of the Acyl-CoA synthetase (ACSL1/5)/VDAC1/Carnitine Palmitoyl-Transferase 1A (CPT1a) complex located in the OMM. Long-chain fatty acids are activated to form acyl-CoA by ACSL1/5, which is then transferred across the OMM by VDAC1 into the IMS, where CPT1a converts the acyl-CoA into acyl-carnitine. This acyl-carnitine is subsequently transported across the inner mitochondrial membrane (IMM) via carnitine-acylcarnitine translocase (CACT) and is converted back into acyl-CoA by CPT2. Finally, the regenerated acyl-CoA can undergo β-oxidation in the mitochondrial matrix to produce energy. (<b>f</b>) Anti-viral immunity: VDAC1 interacts with the mitochondrial antiviral-signaling protein (MAVS) to enable antiviral signaling. (<b>g</b>) mtDNA releases inflammasome activation: VDAC1 oligomers facilitate the release of mitochondrial DNA (mtDNA), triggering the assembly of the inflammasome complex, including NLRP3, ASC, and caspase-1. This leads to the activation of NLRP3-dependent caspase-1 and conversion of pro-inflammatory factors such as pro-IL-1β to IL-1β. (<b>h</b>) Apoptosis: Upon apoptotic stimuli and stress conditions, VDAC1 is oligomerized, forming a hydrophilic protein-conducting channel that mediates the release of apoptogenic proteins, such as cytochrome c (Cyto c) and apoptosis-inducing factor (AIF), from the IMS to the cytosol, leading to apoptosis. Bcl-2 can interfere with the release of the pro-apoptotic proteins.</p>
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<p>VDAC1 is overexpressed in different tumors. Cancer tissue arrays (Biomax), including sections from healthy and tumor tissues, subjected to immunohistochemistry (IHC) staining using anti-VDAC1 antibodies and representative tissue sections from cervical, lung, thyroid, bladder and melanoma (<b>A</b>), glioblastoma (<b>B</b>) and mesothelioma (<b>C</b>) cancers. Percentages of sections stained at the intensity indicated are depicted.</p>
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<p>Overview of the effects of mitochondrial VDAC1 depletion on tumor properties: metabolic reprogramming and reversal of oncogenic properties. In cancer cells, overexpression of VDAC1 helps maintain energy and metabolic balance. Silencing VDAC1 disrupts this balance, leading to metabolic reprogramming that reduces energy production and metabolite generation critical for tumor growth and survival. This metabolic shift triggers changes in gene expression related to transcription factors (TFs), epigenetics, signaling pathways, the tumor microenvironment (TME), and stem cells. As a result, VDAC1 depletion impairs cell proliferation, remodels the TME, decreases angiogenesis, and reduces tumor-associated macrophages (TAMs). It also inhibits epithelial-to-mesenchymal transition (EMT), which is associated with increased cell migration and cancer progression and lowers the presence of cancer stem cells (CSCs) that are typically resistant to conventional therapies, while promoting the differentiation of cancer cells.</p>
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12 pages, 2190 KiB  
Article
In Vitro Methylene Blue and Carboplatin Combination Triggers Ovarian Cancer Cells Death
by Jorgelindo da Veiga Moreira, Laurent Schwartz and Mario Jolicoeur
Int. J. Mol. Sci. 2024, 25(20), 11005; https://doi.org/10.3390/ijms252011005 - 13 Oct 2024
Viewed by 885
Abstract
Ovarian cancer presents a dire prognosis and high mortality rates, necessitating the exploration of alternative therapeutic avenues, particularly in the face of platinum-based chemotherapy resistance. Conventional treatments often overlook the metabolic implications of cancer, but recent research has highlighted the pivotal role of [...] Read more.
Ovarian cancer presents a dire prognosis and high mortality rates, necessitating the exploration of alternative therapeutic avenues, particularly in the face of platinum-based chemotherapy resistance. Conventional treatments often overlook the metabolic implications of cancer, but recent research has highlighted the pivotal role of mitochondria in cancer pathogenesis and drug resistance. This study delves into the metabolic landscape of ovarian cancer treatment, focusing on modulating mitochondrial activity using methylene blue (MB). Investigating two epithelial ovarian cancer (EOC) cell lines, OV1369-R2 and OV1946, exhibiting disparate responses to carboplatin, we sought to identify metabolic nodes, especially those linked to mitochondrial dysfunction, contributing to chemo-resistance. Utilizing ARPE-19, a normal retinal epithelial cell line, as a control model, our study reveals MB’s distinct cellular uptake, with ARPE-19 absorbing 5 to 7 times more MB than OV1946 and OV1369-R2. Treatment with 50 µM MB (MB-50) effectively curtailed the proliferation of both ovarian cancer cell lines. Furthermore, MB-50 exhibited the ability to quell glutaminolysis and the Warburg effect in cancer cell cultures. Regarding mitochondrial energetics, MB-50 spurred oxygen consumption, disrupted glycolytic pathways, and induced ATP depletion in the chemo-sensitive OV1946 cell line. These findings highlight the potential of long-term MB exposure as a strategy to improve the chemotherapeutic response in ovarian cancer cells. The ability of MB to stimulate oxygen consumption and enhance mitochondrial activity positions it as a promising candidate for ovarian cancer therapy, shedding light on the metabolic pressures exerted on mitochondria and their modulation by MB, thus contributing to a deeper understanding of mitochondrial dysregulation and the metabolic underpinnings of cancer cell proliferation. Full article
(This article belongs to the Special Issue Targeted Therapies and Molecular Methods in Cancer, 2nd Edition)
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<p>(<b>A</b>) Differential intake of methylene blue (MB) by ARPE-19, OV1369-R2, and OV1946 cell lines. ARPE-19 exhibited higher MB intake compared to the cancerous lines, with approximately 5-fold and 7-fold greater accumulation than OV1369-R2 and OV1946, respectively. (<b>B</b>) Modulation of free ATP levels in response to MB treatment. ARPE-19 cells showed an increase in ATP content upon MB treatment, while both OV1369-R2 and OV1946 displayed an MB-inhibitory effect on ATP levels. The adjusted <span class="html-italic">p</span>-value ≤ 0.05 were considered significant and the notations of * (<span class="html-italic">p</span> ≤ 0.05), and *** (<span class="html-italic">p</span> ≤ 0.001) were used.</p>
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<p>Sensitivity of cancer cells to methylene blue and carboplatin treatment. (<b>A</b>) Cells were incubated for 24 h under different conditions: 50 µM methylene blue (MB-50), 50 µM carboplatin (CPN-50), and the combination of MB-50 and CPN-50. The “CONTROL” condition included the solvent of MB and CPN, all in OSE culture medium. The combination of MB-50 and CPN-50 did not show a significant effect compared to MB-50 alone. Notably, OV1946 showed sensitivity to CPN-50, whereas ARPE-19 and OV1369-R2, known to be resistant to carboplatin treatment, did not. (<b>B</b>) Induction of apoptosis was observed to a limited extent under 24 h treatment conditions for all three cell lines. The apoptotic cell population was slightly higher in the intermediate chemo-sensitive cell line OV1946. The adjusted <span class="html-italic">p</span>-value ≤ 0.05 were considered significant and the notations of * (<span class="html-italic">p</span> ≤ 0.05), and *** (<span class="html-italic">p</span> ≤ 0.001) were used.</p>
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<p>Glutaminolysis and Warburg effect in ovarian cancer cells. (<b>A</b>–<b>C</b>) The glycolytic metabolic profile of cells was assessed under various treatment conditions, and the concentrations of glucose, lactate, glutamine, glutamate, and phosphate in the culture medium were measured after a 24 h treatment period. (<b>D</b>,<b>E</b>) Methylene blue treatment exerts inhibitory effects on both the Warburg effect and glutaminolysis in ARPE-19 and the chemo-sensitive OV1946 cell line. Specifically, MB significantly reduces the Warburg effect, as indicated by decreased lactate production and glucose consumption. Additionally, MB suppresses glutaminolysis, leading to reduced glutamate production and glutamine consumption. These metabolic alterations are consistent with the observed inhibition of cell proliferation. In contrast, the chemo-resistant OV1369-R2 cell line exhibited an opposing response to MB treatment. Here, MB slightly enhanced the Warburg effect and upregulated glutaminolysis. Notably, the combination of MB with carboplatin (CPN-50) did not significantly augment the Warburg effect. The opposite effect was observed on glutaminolysis, with a significant effect of drug combination (<span class="html-italic">p</span> ≤ 0.01). The adjusted <span class="html-italic">p</span>-value ≤ 0.05 were considered significant and the notations of ** (<span class="html-italic">p</span> ≤ 0.01), and *** (<span class="html-italic">p</span> ≤ 0.001) were used.</p>
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<p>Methylene blue and carboplatin combination trigger the apoptotic pathway in ovarian cancer cells. Cells were treated for 24 h with 50 µM methylene blue (MB-50), 50 µM carboplatin (CPN-50), or a combination of MB-50 and CPN-50. After treatment, cells were rinsed and incubated for an additional 24 h in normal OSE culture medium. The “CONTROL” condition contained the solvents used for methylene blue and carboplatin in OSE culture medium. This illustrates the impact of these treatments on cell viability (<b>A</b>) and apoptotic cell populations (<b>B</b>), demonstrating that the combination of MB-50 and CPN-50 significantly reduces cancer cell viability (<b>A</b>) and increases apoptosis (<b>B</b>) compared to individual treatments and controls. The adjusted <span class="html-italic">p</span>-value ≤ 0.05 were considered significant and the notations of * (<span class="html-italic">p</span> ≤ 0.05), ** (<span class="html-italic">p</span> ≤ 0.01), and *** (<span class="html-italic">p</span> ≤ 0.001) were used.</p>
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<p>Oxygen consumption rate (OCR) in ARPE-19, OV1369-R2, and OV1946 cell lines. (<b>A</b>) ARPE-19 cells showed a marked increase in OCR upon treatment with 50 µM methylene blue (MB-50) and further enhancement when combined with 50 µM carboplatin (CPN-50). (<b>B</b>) OV1369-R2 cells exhibited a significant rise in OCR following MB-50 treatment, with an even greater increase when combined with CPN-50, indicating enhanced sensitivity to the combined treatment. (<b>C</b>) In OV1946 cells, MB-50 treatment resulted in a moderate increase in OCR compared to OV1369-R2 cells. However, the combination of MB-50 and CPN-50 led to a higher OCR than MB-50 alone.</p>
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24 pages, 2349 KiB  
Article
Intratumoral Heterogeneity and Metabolic Cross-Feeding in a Three-Dimensional Breast Cancer Culture: An In Silico Perspective
by Jorge E. Arellano-Villavicencio, Aarón Vázquez-Jiménez, Juan José Oropeza-Valdez, Cristian Padron-Manrique, Heriberto Prado-García, Armando R. Tovar and Osbaldo Resendis-Antonio
Int. J. Mol. Sci. 2024, 25(20), 10894; https://doi.org/10.3390/ijms252010894 - 10 Oct 2024
Viewed by 769
Abstract
Today, the intratumoral composition is a relevant factor associated with the progression and aggression of cancer. Although it suggests a metabolic interdependence among the subpopulations inside the tumor, a detailed map of how this interdependence contributes to the malignant phenotype is still lacking. [...] Read more.
Today, the intratumoral composition is a relevant factor associated with the progression and aggression of cancer. Although it suggests a metabolic interdependence among the subpopulations inside the tumor, a detailed map of how this interdependence contributes to the malignant phenotype is still lacking. To address this issue, we developed a systems biology approach integrating single-cell RNASeq and genome-scale metabolic reconstruction to map the metabolic cross-feeding among the subpopulations previously identified in the spheroids of MCF7 breast cancer. By calibrating our model with expression profiles and the experimental growth rate, we concluded that the reverse Warburg effect emerges as a mechanism to optimize community growth. Furthermore, through an in silico analysis, we identified lactate, alpha-ketoglutarate, and some amino acids as key metabolites whose disponibility alters the growth rate of the spheroid. Altogether, this work provides a strategy for assessing how space and intratumoral heterogeneity influence the metabolic robustness of cancer, issues suggesting that computational strategies should move toward the design of optimized treatments. Full article
(This article belongs to the Special Issue Deciphering the Dynamics: Exploring Tumor Evolution in Cancer)
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<p>Workflow for community modeling from expression data. (<b>a</b>) Generation of genome-scale metabolic reconstructions. The analysis process started with obtaining scRNA-seq expression data from MCF-7 spheroids. Subsequently, the metabolic reconstructions were created by implementing the CORDA algorithm, assuming the presence of the metabolic enzymes based on their expression levels and using RECON 2.2 as a base template. The three cellular profiles previously described by Muciño et al. were generated, and, finally, three GEMs corresponding to each subpopulation were obtained. To ensure the quality of the reconstructions, they underwent an analysis using MEMOTE software version 0.17.0. (<b>b</b>) Community modeling and metabolic coupling. Part of the process involved gathering data on the community, including the abundance of each cellular subpopulation in the spheroid during the two days of analysis, the type of culture medium in which they grew, and a specific GEM for each phenotype. The community was constructed using the MICOM tool, which was used to optimize growth. As the final result, a report detailing the metabolic fluxes of each reaction in the individual reconstructions, as well as their interaction with the extracellular environment and population and individual growth, was obtained.</p>
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<p>Spatial distribution simulation with oxygen gradients. (<b>a</b>) Graphical representation of metabolite exchange between each cell group in the community modeling. In the Venn diagram, the number of metabolites exchanged between and shared by the three groups collectively is shown in the central part, and the number of metabolites that they secreted to the extracellular medium is shown in the individual sections. (<b>b</b>) Proposed oxygen gradients for a three-dimensional model. The six combinations for the three subpopulations consider that each stratum change goes from its normoxia approach to levels close to hypoxia. (<b>c</b>,<b>d</b>) Community growth rates. The community rate measurement for each subpopulation shows that combination E on day 6 is the highest, and combination A on day 19 is the one suggested by the simulation. In both graphs, the Y-axis shows the community growth in flow units, and the X-axis shows the groups of combinations of possible cellular organization scenarios.</p>
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<p>Metabolite exchange dynamics and Intracellular metabolism. (<b>a</b>) The secretion and consumption behaviors of the subpopulations. The consumption (blue) and secretion (red) behaviors of each subpopulation in the community were characterized by selecting the top 20 reactions with the highest fluxes on their respective days. Cells are identified by 6 or 19, along with their phenotype: I for invasive, P for proliferative, and R for reservoir. (<b>b</b>,<b>c</b>) Cross-feeding among subpopulations: The simulation suggests an exchange of metabolites absent from the initial growth medium. EX_ or exchange reactions with the highest fluxes in each subpopulation were filtered based on their direction (secretion or consumption). The left side of the panels shows the metabolite origin, the center shows its identifier (representing the extracellular medium), and the right side indicates its final destination—consumed by a remaining subpopulation. Colors represent the subpopulations: green for proliferative, blue for reservoir, and red for invasive. Although the same analysis was used, the two days of growth are shown with differing behaviors.</p>
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<p>Enzymes with metabolic activity. The metabolic pathways were grouped according to the VMH database (see <a href="#sec4-ijms-25-10894" class="html-sec">Section 4</a>), in which each reaction is assigned a subsystem such as (<b>a</b>) glycolytic activity or glycolysis, (<b>b</b>) galactose metabolism, (<b>c</b>) tricarboxylic acid pathway or Krebs cycle, and (<b>d</b>) glutamate metabolism. Only reactions with activity in at least one of the three subpopulations were considered for visualization. To represent the directionality, a color identifier was used: the reddish color corresponds to the directionality of the reaction in its classical pathway described in the database, and blue corresponds to a reversal of the directionality of the reaction when observed. The blanks do not exclude the presence of enzymes in the reconstruction; they only reflect whether there was activity. The units of the FBA modeling correspond to the metabolic biotransformation rate, mmol/cell/hour, reflected in the size of the spheres for each reaction.</p>
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<p>Community response to oxygen depletion and KO in silico. From right to left, the gradient from normoxia to hypoxia is represented, with oxygen fluxes decreasing. Each of the 16 marked points reflects the behavior of the subpopulations in terms of the consumption or production of a metabolite. Values above zero represent secretion, and those below zero represent consumption. Red lines correspond to invading cells, green lines to reservoirs, and blue lines to proliferative cells. Each point on the X-axis represents a different community simulation or scenario. As this is a deterministic scenario, we do not have any statistics since there were no variations in the repetitions of the simulations. To visualize the behavior of the community with the disposition of the elements of the medium, we selected metabolites that could be used as an energy source on different study days: (<b>a</b>) glutamine, (<b>c</b>) lactate, and (<b>e</b>) alpha-ketoglutarate on day six, and (<b>b</b>) glutamine, (<b>d</b>) lactate, and (<b>f</b>) alpha-ketoglutarate on day 19. (<b>g</b>) Essential metabolites in silico KO. The essentiality analysis generated a list of 17 metabolites whose absence in the medium proved lethal to the community’s growth. The state of the community, such as normoxia (upper part) or hypoxia (lower part), and a midpoint between the two, was recorded according to the growth day (red: day 6; blue: day 19).</p>
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<p>Metabolic interconnection between subpopulations (cross-feeding). A schematic representation of the interaction between the three cell subpopulations of the MCF-7 spheroid. Day 6 interaction is presented, where metabolism modeling suggested a connection between invasive (reddish) and proliferating (green) cells as metabolic sustenance. The primary exchange metabolites are lactate and AKG, which could be used as energy sources by the action of lactate dehydrogenase (LDH) and alpha-ketoglutarate dehydrogenase (AKGD). The lactate released into the extracellular medium (M) could induce lactic acidosis; this inhibits glycolytic activity (bibliographic information), which supports the cells’ use of alternative carbon sources. However, the third subpopulation (blue) or the reservoir shares high energy metabolites such as ATP, which, in addition to being energetic, could have secondary functions such as promoting the epithelial–mesenchymal transition (EMT) via the HIF1a pathway. This is similar to the effect of fumarate in regulating the inhibition of the HIF1a inhibitor prolyl hydroxylated (PHD). Both metabolites theoretically increase HIF1a expression (red dates) and promote transformation from a proliferating to an invasive spheroid, as observed via day 19 abundance.</p>
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19 pages, 4778 KiB  
Article
Development of a Competitive Nutrient-Based T-Cell Immunotherapy Designed to Block the Adaptive Warburg Effect in Acute Myeloid Leukemia
by Huynh Cao, Jeffrey Xiao, David J. Baylink, Vinh Nguyen, Nathan Shim, Jae Lee, Dave J. R. Mallari, Samiksha Wasnik, Saied Mirshahidi, Chien-Shing Chen, Hisham Abdel-Azim, Mark E. Reeves and Yi Xu
Biomedicines 2024, 12(10), 2250; https://doi.org/10.3390/biomedicines12102250 - 3 Oct 2024
Viewed by 1068
Abstract
Background: T-cell-based adoptive cell therapies have emerged at the forefront of cancer immunotherapies; however, failed long-term survival and inevitable exhaustion of transplanted T lymphocytes in vivo limits clinical efficacy. Leukemia blasts possess enhanced glycolysis (Warburg effect), exploiting their microenvironment to deprive nutrients (e.g., [...] Read more.
Background: T-cell-based adoptive cell therapies have emerged at the forefront of cancer immunotherapies; however, failed long-term survival and inevitable exhaustion of transplanted T lymphocytes in vivo limits clinical efficacy. Leukemia blasts possess enhanced glycolysis (Warburg effect), exploiting their microenvironment to deprive nutrients (e.g., glucose) from T cells, leading to T-cell dysfunction and leukemia progression. Methods: Thus, we explored whether genetic reprogramming of T-cell metabolism could improve their survival and empower T cells with a competitive glucose-uptake advantage against blasts and inhibit their uncontrolled proliferation. Results: Here, we discovered that high-glucose concentration reduced the T-cell expression of glucose transporter GLUT1 (SLC2A1) and TFAM (mitochondrion transcription factor A), an essential transcriptional regulator of mitochondrial biogenesis, leading to their impaired expansion ex vivo. To overcome the glucose-induced genetic deficiency in metabolism, we engineered T cells with lentiviral overexpression of SLC2A1 and/or TFAM transgene. Multi-omics analyses revealed that metabolic reprogramming promoted T-cell proliferation by increasing IL-2 release and reducing exhaustion. Moreover, the engineered T cells competitively deprived glucose from allogenic blasts and lessened leukemia burden in vitro. Conclusions: Our findings propose a novel T-cell immunotherapy that utilizes a dual strategy of starving blasts and cytotoxicity for preventing uncontrolled leukemia proliferation. Full article
(This article belongs to the Section Immunology and Immunotherapy)
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<p>High glucose significantly reduced the expression of GLUT1 and TFAM in T cells. (<b>A</b>) Gene expression of <span class="html-italic">TFAM</span>, a mitochondrial transcription factor was analyzed by qPCR. Data of mRNA expressions show the fold change (normalized to <span class="html-italic">β-actin</span>) of the gene encoding TFAM in T cells from AML patient samples, which were pretreated with different doses of glucose; (<b>B</b>) representative FC histograms show the expression of GLUT1 and Ki67 in Jurkat cells which were pretreated with 25 mM glucose for 48 h (red line plot) or persistent supplementation of 25 mM glucose for 4 weeks in vitro (blue line plot); the filled grey line and pink line plots represent Jurkat without treatment and IgG-fluorescent control; (<b>C</b>) representative FC histograms show the expression of TFAM in Jurkat cells which were pretreated with 25 mM glucose for 48 h (red line plot) or persistent supplementation of 25 mM glucose for 4 weeks in vitro (blue line plot); the filled grey line and pink line plots represent Jurkat without treatment and IgG-fluorescent control; Right panel: qPCR data of mRNA expressions show the fold change (normalized to <span class="html-italic">β-actin</span>) of the gene encoding <span class="html-italic">TFAM</span> in Jurkat which were pretreated with or without different time points of 25 mM glucose; where applicable, data are means ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">n</span> = 3.</p>
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<p>Generation of transgenic T-cell lines overexpressing transgenes of <span class="html-italic">SLC2A1 (GLUT1)</span> and/or <span class="html-italic">TFAM</span> in vitro. (<b>A</b>) Schematic diagram of lentiviral expression construct containing human <span class="html-italic">GLUT1</span> or <span class="html-italic">TFAM</span> open-reading frames and <span class="html-italic">GFP</span> or <span class="html-italic">Cherry</span> reporters, with promoters EF1a and IRES2, respectively; (<b>B</b>) a representative FC plot shows GFP and/or Cherry expression in GLUT1-T, TFAM-T, and GLUT1/TFAM-T cells (FACS-sorted, see Materials and Methods); (<b>C</b>) overexpressed gene expression of <span class="html-italic">GLUT1</span> and <span class="html-italic">TFAM</span> was analyzed by qPCR. Data of mRNA expressions show the fold change (normalized to <span class="html-italic">β-actin</span>) of genes encoding GLUT1 and TFAM in new T-cell lines; (<b>D</b>) representative FC histograms show GLUT1 and TFAM expression in GLUT1-T (red line plots) versus vector control (GFP-T cells, the filled grey line) and IgG-fluorescent control (pink line plots); Where applicable, data are means ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.005, <span class="html-italic">n</span> = 3.</p>
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<p>Phenotypic characterizations of metabolism-engineered T-cell lines in vitro. (<b>A</b>) A representative FC histogram (left panel) shows the expression of Ki67 in new transgenic T-cell lines, including GLUT1-T (green line plot), TFAM-T (orange line plot), GLUT1/TFAM-T (blue line plot), versus GFP-T cells (the filled grey line), and IgG-fluorescent control (pink line plot); cumulative data (right panel) of the mean fluorescence intensity (MFI) levels of Ki67 expression in the new T-cell lines; (<b>B</b>) qPCR analysis of the gene expression of different cytokines and biomarkers for cell divisions, inhibitory signals, and exhaustion. Data of mRNA expressions show the fold change (normalized to <span class="html-italic">β-actin</span>) of genes encoding <span class="html-italic">IL-2, CDK1</span>, and <span class="html-italic">NUR77</span> in new T-cell lines; (<b>C</b>) image of blot films, with reference spots (blue arrows), interferon-gamma (IFN-γ, IFNG) (green circle), and interleukin-2 (IL-2) (red circle), developed for proteomic analyses of cell-free supernatants from new T-cell lines, including GLUT1-T and TFAM-T versus GFP-T cells; where applicable, data are means ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">n</span> = 3.</p>
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<p>GLUT1-T or GLUT1/TFAM-T cells performed anti-leukemia effects through advanced glucose-uptake capability in vitro. (<b>A</b>) Left panel: Representative FC histograms show the 2-NBDG uptake in viable MV4-11 co-cultured with GLUT1-T (red line plots) and GLUT1/TFAM-T (blue line plots) versus vector control (GFP-T cells, green line) and viable MV4-11 alone (the filled grey line plots); a visual diagram of the transwell culture (inset) with MV4-11 cells (black) and engineered Jurkat T cells (blue); Right panel: mean fluorescence intensity (MFI) levels of 2-NBDG uptake in different experimental groups; (<b>B</b>) gene expression of CYCLIN B1 (<span class="html-italic">CCNB1</span> gene), a biomarker for mitosis in the cell cycle was analyzed by qPCR. Data of mRNA expressions show the fold change (normalized to <span class="html-italic">β-actin</span>) of CYCLIN B1 gene in MV4-11 alone or MV4-11 co-cultured with different engineered T-cell lines in transwell; (<b>C</b>) representative FC plots show the expression of an Annexin V/PI apoptosis and necrosis assay in transwell co-cultures, including GLUT1-T co-culturing with MV4-11 versus GFP-T co-culturing with MV4-11 and MV4-11 only in vitro; Right panel: cumulative FC percentage data of Annexin V+ cells in the different experimental groups; (<b>D</b>) representative FC plots show the expression of CD33, a biomarker for primary AML blasts and CD3 in different cytotoxic co-cultures, including GLUT1-engineered primary T cells (GLUT1-T) co-culturing with primary CD33+ blasts versus GFP-T cells (vector control) co-culturing with CD33+ blasts, CD33+ blasts only and GLUT1-T only in vitro; Right panel: cumulative FC percentage data of viable CD33+ cells in different experimental groups; where applicable, data are means ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span>&lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.005, <span class="html-italic">n</span> = 3.</p>
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<p>A schematic diagram illustrating the strategy of developing a nutrient-competency-based and metabolism-enhanced T-cell therapy with dual cytotoxicity for AML. (<b>A</b>) To escape current therapies, blasts utilize the Warburg effect (increased glycolysis) to support their uncontrolled proliferation and initiate AML relapse. (<b>B</b>) In current adoptive T-cell therapies, T cells might not be able to compete in glucose uptake against blasts, which are known to suppress T cells by inhibitory signals (e.g., PD-L1) and to deprive glucose from T cells. (<b>C</b>) When the Warburg effect is inhibited in blasts, cancer cells cannot survive as well due to the decreased glycolysis and ATP production. (<b>D</b>) Consequently, to improve T-cell therapy for AML, we metabolically reprogrammed T cells by the overexpression of <span class="html-italic">GLUT1</span> and <span class="html-italic">TFAM</span> transgenes. <span class="html-italic">GLUT1</span> overexpression significantly enhanced T-cells’ competitive glucose-uptake capability against blasts and deprived glucose from blasts to reduce the Warburg effect, potentially leading to the increased blast death. Moreover, <span class="html-italic">GLUT1</span> and/or <span class="html-italic">TFAM</span> overexpression can enhance the production and release of IL-2 to promote T-cell survival and proliferation while reducing <span class="html-italic">NUR77</span>, a transcription initiator of T-cell exhaustion. In summary, our preliminary data suggest that genetically reprogramming T cells with enhanced <span class="html-italic">GLUT1</span>/or <span class="html-italic">TFAM</span> might be a novel approach to develop a durable and effective T-cell therapy to treat AML.</p>
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15 pages, 1319 KiB  
Review
How Does Cancer Occur? How Should It Be Treated? Treatment from the Perspective of Alkalization Therapy Based on Science-Based Medicine
by Reo Hamaguchi, Masahide Isowa, Ryoko Narui, Hiromasa Morikawa, Toshihiro Okamoto and Hiromi Wada
Biomedicines 2024, 12(10), 2197; https://doi.org/10.3390/biomedicines12102197 - 26 Sep 2024
Viewed by 722
Abstract
This review article investigates the relationship between mitochondrial dysfunction and cancer progression, emphasizing the metabolic shifts that promote tumor growth. Mitochondria are crucial for cellular energy production, but they also play a significant role in cancer progression by promoting glycolysis even under oxygen-rich [...] Read more.
This review article investigates the relationship between mitochondrial dysfunction and cancer progression, emphasizing the metabolic shifts that promote tumor growth. Mitochondria are crucial for cellular energy production, but they also play a significant role in cancer progression by promoting glycolysis even under oxygen-rich conditions, a phenomenon known as the Warburg effect. This metabolic reprogramming enables cancer cells to maintain an alkaline internal pH and an acidic external environment, which are critical for their proliferation and survival in hypoxic conditions. The article also explores the acidic tumor microenvironment (TME), a consequence of intensive glycolytic activity and proton production by cancer cells. This acidic milieu enhances the invasiveness and metastatic potential of cancer cells and contributes to increased resistance to chemotherapy. Alkalization therapy, which involves neutralizing this acidity through dietary modifications and the administration of alkalizing agents such as sodium bicarbonate, is highlighted as an effective strategy to counteract these adverse conditions and impede cancer progression. Integrating insights from science-based medicine, the review evaluates the effectiveness of alkalization therapy across various cancer types through clinical assessments. Science-based medicine, which utilizes inductive reasoning from observed clinical outcomes, lends support to the hypothesis of metabolic reprogramming in cancer treatment. By addressing both metabolic and environmental disruptions, this review suggests that considering cancer as primarily a metabolic disorder could lead to more targeted and effective treatment strategies, potentially improving outcomes for patients with advanced-stage cancers. Full article
(This article belongs to the Section Cancer Biology and Oncology)
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<p>The exchange of entropy between the outside and the inside (Reproduced by permission of “Science”. For further details see Ref. [<a href="#B45-biomedicines-12-02197" class="html-bibr">45</a>]). The diagram shows the entropy change (dS), divided into entropy transferred across the system boundary (deS) and entropy generated within the system (diS). While diS is typically non-negative as per the Second Law of Thermodynamics, dissipative structures can exhibit reductions in dS, demonstrating negative entropy phenomena.</p>
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<p>Association between overall survival and urine pH in stage 4 pancreatic cancer patients [Cited from [<a href="#B83-biomedicines-12-02197" class="html-bibr">83</a>]]. Kaplan–Meier curves show the OS for three groups based on initial urine pH: Group 1 (pH ≥ 7.5, n = 15) with a median OS of 29.9 months, Group 2 (7.5 &gt; pH ≥ 6.5, n = 42) with a median OS of 15.2 months, and Group 3 (pH &lt; 6.5, n = 41) with a median OS of 8.0 months.</p>
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<p>Overall survival comparison in small cell lung cancer patients [Cited from [<a href="#B78-biomedicines-12-02197" class="html-bibr">78</a>]]. Kaplan–Meier curves show the OS from diagnosis for small cell lung cancer patients. The intervention group, treated with chemotherapy, alkalization therapy, and vitamin C (n = 12), shows a median OS of 44.2 months, compared to 17.7 months for the control group, which received chemotherapy only (n = 15).</p>
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<p>Association between overall survival and urine pH in hepatocellular carcinoma patients [Cited from [<a href="#B79-biomedicines-12-02197" class="html-bibr">79</a>]]. Kaplan–Meier curves compare the OS from the start of alkalization therapy in hepatocellular carcinoma patients categorized by mean urine pH levels. Patients with a mean urine pH ≥ 7.0 (n = 12) showed a median OS that was not reached, significantly longer than the 15.4 months median OS for those with a mean urine pH &lt; 7.0 (n = 17).</p>
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10 pages, 3002 KiB  
Commentary
IDH2 Inhibitors Gain a Wildcard Status in the Cancer Therapeutics Competition
by Roberto Piva, Nariman Gharari, Maria Labrador and Sylvie Mader
Cancers 2024, 16(19), 3280; https://doi.org/10.3390/cancers16193280 - 26 Sep 2024
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Abstract
The metabolic reprogramming characteristic of cancer cells, including the Warburg effect, has long been recognized as a hallmark of malignancy. This commentary explores three recent investigations focusing on the role of wild-type IDH2 in cancer and immune cell function. The first publication identifies [...] Read more.
The metabolic reprogramming characteristic of cancer cells, including the Warburg effect, has long been recognized as a hallmark of malignancy. This commentary explores three recent investigations focusing on the role of wild-type IDH2 in cancer and immune cell function. The first publication identifies wild-type IDH2 as a crucial factor in the survival of triple-negative breast cancer (TNBC) cells, with its inhibition leading to disrupted energy metabolism, reduced tumor growth, and enhanced apoptosis. The second analysis examines the role of IDH2 in CD8+ T cells, revealing that its inhibition promotes the differentiation of memory T cells, thereby enhancing the efficacy of cell-based immunotherapies like CAR T cells. A third investigation supports these findings, demonstrating that IDH2 inhibition in CAR T cells reduces exhaustion, enhances memory T cell formation, and improves anti-tumor efficacy. Collectively, these reports highlight wild-type IDH2 as a promising therapeutic target, with potential applications as a two-edged sword in both cancer treatment and immunotherapy. The development of specific wild-type IDH2 inhibitors could offer new avenues for therapy, particularly in tumors reliant on IDH2 activity as well as in enhancing the effectiveness of CAR T cell therapies. Full article
(This article belongs to the Special Issue Insights from the Editorial Board Member)
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Figure 1

Figure 1
<p>Distribution of <span class="html-italic">IDH2</span> mRNA levels in breast cancer subtypes. Distribution of <span class="html-italic">IDH2</span> expression levels across different breast cancer subtypes in a breast invasive carcinoma dataset from The Cancer Genome Atlas [<a href="#B26-cancers-16-03280" class="html-bibr">26</a>] analyzed using the PAM50 classifier as previously described [<a href="#B27-cancers-16-03280" class="html-bibr">27</a>]. The <span class="html-italic">x</span>-axis represents the breast cancer subtypes, including luminal A, luminal B, HER2-enriched, basal, and normal-like. The <span class="html-italic">y</span>-axis indicates the expression levels of <span class="html-italic">IDH2</span>, measured in RPKM (Reads Per Kilobase of transcript per Million mapped reads). Each box plot displays the median, interquartile range, whiskers and outliers of <span class="html-italic">IDH2</span> expression within each subtype.</p>
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<p>Impact of IDH2 inhibition on the metabolic pathways in Triple-Negative Breast Cancer (TNBC). In TNBC, IDH2 mainly controls the reductive tricarboxylic acid (TCA) cycle, which is slowed down by inhibition of IDH2 (IDH2i) (lighter shade red arrows), leading to the accumulation of alpha-ketoglutarate (<span class="html-italic">α</span>-KG). This accumulation promotes the degradation of hypoxia-inducible factor 1 (HIF1), thereby impeding glycolysis, lipid synthesis, and ATP production. These metabolic disruptions collectively result in reduced tumor cell proliferation, inhibition of epithelial–mesenchymal transition (EMT), and decreased metastasis. Oxalacetate (OAA), phosphoserine aminotransferase 1 (PSAT1), prolyl hydroxylase (PHD), and E3 ubiquitin ligase von Hippel–Lindau (VHL). (Created using BioRender.com accessed on 21 August 2024).</p>
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<p>Impact of IDH2 inhibition on T cell metabolism and differentiation. IDH2 inhibition (IDH2i) in T cells disrupts both the oxidative and reductive TCA cycles (lighter shade red and black arrows). In response, T cells increase glutamine uptake, fatty acid oxidation (FAO), and the pentose phosphate pathway (PPP). These metabolic adaptations support lipid synthesis, reduce reactive oxygen species (ROS) levels, and induce epigenetic reprogramming, leading to the differentiation of T cells into memory cells. This metabolic strategy presents a potential approach to enhance the efficacy of CAR T cell therapies. Oxalacetate (OAA), malate (Mal), fumarate (Fum), alpha-ketoglutarate (<span class="html-italic">α</span>-KG), selectin L (SELL), transcription factor-7 (TCF7), and lysine demethylase 5A (KDM5A). (Created by BioRender.com accessed on 21 August 2024).</p>
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