Targeting the Mitochondrial Pyruvate Carrier for Neuroprotection

Tang, Bor Luen

doi:10.3390/brainsci9090238

Cited by 13 publications

(12 citation statements)

References 128 publications

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“…Parkinson disease Tang et al [194] Chen et al [195] Polycystic kidney disease Podrini et al [196] Millet-Boureima et al [197] Diabetic kidney disease Morita and Kawaski et al [198] Promsan and Lungkaphin et al [199 The altered glucose metabolism, known as the Warburg effect, was initially discovered in cancer cells [222] but within the last two decades, it has also been found to occur in non-neoplastic, diseased cells as well. In the case of tumors, reversing the Warburg effect is considered a potential target for tumor therapy and several synthetic molecules have been developed for this purpose.…”

Section: References Indicating Melatonin Inhibits These Cell Typesmentioning

confidence: 99%

Anti-Warburg Effect of Melatonin: A Proposed Mechanism to Explain its Inhibition of Multiple Diseases

Reíter

Sharma

Rosales-Corral

2021

IJMS

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Glucose is an essential nutrient for every cell but its metabolic fate depends on cellular phenotype. Normally, the product of cytosolic glycolysis, pyruvate, is transported into mitochondria and irreversibly converted to acetyl coenzyme A by pyruvate dehydrogenase complex (PDC). In some pathological cells, however, pyruvate transport into the mitochondria is blocked due to the inhibition of PDC by pyruvate dehydrogenase kinase. This altered metabolism is referred to as aerobic glycolysis (Warburg effect) and is common in solid tumors and in other pathological cells. Switching from mitochondrial oxidative phosphorylation to aerobic glycolysis provides diseased cells with advantages because of the rapid production of ATP and the activation of pentose phosphate pathway (PPP) which provides nucleotides required for elevated cellular metabolism. Molecules, called glycolytics, inhibit aerobic glycolysis and convert cells to a healthier phenotype. Glycolytics often function by inhibiting hypoxia-inducible factor-1α leading to PDC disinhibition allowing for intramitochondrial conversion of pyruvate into acetyl coenzyme A. Melatonin is a glycolytic which converts diseased cells to the healthier phenotype. Herein we propose that melatonin’s function as a glycolytic explains its actions in inhibiting a variety of diseases. Thus, the common denominator is melatonin’s action in switching the metabolic phenotype of cells.

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Section: References Indicating Melatonin Inhibits These Cell Typesmentioning

confidence: 99%

Anti-Warburg Effect of Melatonin: A Proposed Mechanism to Explain its Inhibition of Multiple Diseases

Reíter

Sharma

Rosales-Corral

2021

IJMS

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“…Regarding anion transport, to properly regulate the mitochondrial β-oxidation, Hr96 seems to also coordinate the transcription of carboxylic acid transport targets such glutamate carrier (GC1), mitochondrial pyruvate carrier ( mpc1 ), and Cln3 , the Batten disease-associated gene involved in arginine transport and mitochondrial β-oxidation support (Dawson et al, 1996 ; Chan et al, 2009 ). Among those, MPC1 has an important role in mitochondrial function since it is found in the inner mitochondrial membrane, and mutant D. melanogaster for mpc1 display impaired pyruvate metabolism, leading to a shortage of intermediates necessary for the tricarboxylic acid cycle, ultimately reducing ATP production (Bricker et al, 2012 ; Tang, 2019 ; Rossi et al, 2020 ). These findings are in line with the most recent research on Hr96 functionality that points toward its relevance in the regulation of sterol trafficking, housing, and consumption (Sieber and Thummel, 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study

et al. 2021

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Gene Regulatory Networks (GRNs) allow the study of regulation of gene expression of whole genomes. Among the most relevant advantages of using networks to depict this key process, there is the visual representation of large amounts of information and the application of graph theory to generate new knowledge. Nonetheless, despite the many uses of GRNs, it is still difficult and expensive to assign Transcription Factors (TFs) to the regulation of specific genes. ChIP-Seq allows the determination of TF Binding Sites (TFBSs) over whole genomes, but it is still an expensive technique that can only be applied one TF at a time and requires replicates to reduce its noise. Once TFBSs are determined, the assignment of each TF and its binding sites to the regulation of specific genes is not trivial, and it is often performed by carrying out site-specific experiments that are unfeasible to perform in all possible binding sites. Here, we addressed these relevant issues with a two-step methodology using Drosophila melanogaster as a case study. First, our protocol starts by gathering all transcription factor binding sites (TFBSs) determined with ChIP-Seq experiments available at ENCODE and FlyBase. Then each TFBS is used to assign TFs to the regulation of likely target genes based on the TFBS proximity to the transcription start site of all genes. In the final step, to try to select the most likely regulatory TF from those previously assigned to each gene, we employ GENIE3, a random forest-based method, and more than 9,000 RNA-seq experiments from D. melanogaster. Following, we employed known TF protein-protein interactions to estimate the feasibility of regulatory events in our filtered networks. Finally, we show how known interactions between co-regulatory TFs of each gene increase after the second step of our approach, and thus, the consistency of the TF-gene assignment. Also, we employed our methodology to create a network centered on the Drosophila melanogaster gene Hr96 to demonstrate the role of this transcription factor on mitochondrial gene regulation.

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“…Thiazolidinediones, a widely used class of insulin sensitizers, were shown to bind to and inhibit the MPC [25]. The findings in liver, muscle and neurons have demonstrated that inhibition of MPC activities could be contextually beneficial in diseased conditions [34]. However, Feng et al demonstrated that there was no different MPC expression in high glucose-treated podocytes compared to that in the control group, inhibition of MPC2 in podocytes led to pyruvate accumulation, mitochondrial dysfunction and cell apoptosis [35].…”

Section: Discussionmentioning

confidence: 99%

Mitochondrial pyruvate carrier: a potential target for diabetic nephropathy

Zhu

Wan

et al. 2020

BMC Nephrol

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Background: Mitochondrial dysfunction contributes to the pathogenesis of diabetic nephropathy (DN). Mitochondrial pyruvate carrier 1 (MPC1) and mitochondrial pyruvate carrier 2 (MPC2) play a bottleneck role in the transport of pyruvate into mitochondrial across the mitochondrial inner membrane. A previous study showed that increasing mitochondrial pyruvate carrier content might ameliorate diabetic kidney disease in db/db mice. However, the expression status of MPC1 and MPC2 in patients with DN is unclear. Methods: Patients with primary glomerulonephropathy (PGN, n = 30), PGN with diabetes mellitus (PGN-DM, n = 30) and diabetic nephropathy (DN, n = 30) were included. MPC1 and MPC2 protein levels were examined by immunohistochemistry. The expression of MPC in different groups was evaluated by the Kruskal-Wallis test. Spearman's rank correlation was performed for correlation analysis between MPC levels and clinical factors. Results: Both MPC1 and MPC2 were localized in renal tubules. Levels of MPC1 and MPC2 were lower in DN patients than in PGN patients and in PGN patients with DM, whereas there were no differences in MPC1 and MPC2 levels among DN stage II to stage IV. Moreover, both MPC1 and MPC2 levels were significantly correlated with serum creatinine, BUN and eGFR in patients with DN, whereas no analogous trend was observed in nondiabetic kidney disease. Conclusions: Our study indicated that MPC localized in renal tubules, which were significantly decreased in DN. MPC was associated with clinical features, especially those representing renal functions.

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Targeting the Mitochondrial Pyruvate Carrier for Neuroprotection

Cited by 13 publications

References 128 publications

Anti-Warburg Effect of Melatonin: A Proposed Mechanism to Explain its Inhibition of Multiple Diseases

Anti-Warburg Effect of Melatonin: A Proposed Mechanism to Explain its Inhibition of Multiple Diseases

Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study

Mitochondrial pyruvate carrier: a potential target for diabetic nephropathy

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