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Journal ArticleDOI: 10.1016/J.CMET.2020.12.003

The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure.

02 Mar 2021-Cell Metabolism (Elsevier)-Vol. 33, Iss: 3
Abstract: The metabolic rewiring of cardiomyocytes is a widely accepted hallmark of heart failure (HF). These metabolic changes include a decrease in mitochondrial pyruvate oxidation and an increased export of lactate. We identify the mitochondrial pyruvate carrier (MPC) and the cellular lactate exporter monocarboxylate transporter 4 (MCT4) as pivotal nodes in this metabolic axis. We observed that cardiac assist device-induced myocardial recovery in chronic HF patients was coincident with increased myocardial expression of the MPC. Moreover, the genetic ablation of the MPC in cultured cardiomyocytes and in adult murine hearts was sufficient to induce hypertrophy and HF. Conversely, MPC overexpression attenuated drug-induced hypertrophy in a cell-autonomous manner. We also introduced a novel, highly potent MCT4 inhibitor that mitigated hypertrophy in cultured cardiomyocytes and in mice. Together, we find that alteration of the pyruvate-lactate axis is a fundamental and early feature of cardiac hypertrophy and failure.

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Topics: Muscle hypertrophy (55%), Monocarboxylate transporter 4 (55%), Pyruvate decarboxylation (54%) ... show more
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21 results found


Journal ArticleDOI: 10.1161/CIRCRESAHA.121.318241
Gary D. Lopaschuk1, Qutuba G. Karwi1, Rong Tian2, Adam R. Wende3  +1 moreInstitutions (4)
Abstract: Alterations in cardiac energy metabolism contribute to the severity of heart failure. However, the energy metabolic changes that occur in heart failure are complex and are dependent not only on the severity and type of heart failure present but also on the co-existence of common comorbidities such as obesity and type 2 diabetes. The failing heart faces an energy deficit, primarily because of a decrease in mitochondrial oxidative capacity. This is partly compensated for by an increase in ATP production from glycolysis. The relative contribution of the different fuels for mitochondrial ATP production also changes, including a decrease in glucose and amino acid oxidation, and an increase in ketone oxidation. The oxidation of fatty acids by the heart increases or decreases, depending on the type of heart failure. For instance, in heart failure associated with diabetes and obesity, myocardial fatty acid oxidation increases, while in heart failure associated with hypertension or ischemia, myocardial fatty acid oxidation decreases. Combined, these energy metabolic changes result in the failing heart becoming less efficient (ie, a decrease in cardiac work/O2 consumed). The alterations in both glycolysis and mitochondrial oxidative metabolism in the failing heart are due to both transcriptional changes in key enzymes involved in these metabolic pathways, as well as alterations in NAD redox state (NAD+ and nicotinamide adenine dinucleotide levels) and metabolite signaling that contribute to posttranslational epigenetic changes in the control of expression of genes encoding energy metabolic enzymes. Alterations in the fate of glucose, beyond flux through glycolysis or glucose oxidation, also contribute to the pathology of heart failure. Of importance, pharmacological targeting of the energy metabolic pathways has emerged as a novel therapeutic approach to improving cardiac efficiency, decreasing the energy deficit and improving cardiac function in the failing heart.

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Topics: Diabetic cardiomyopathy (61%), Heart failure (59%), Beta oxidation (53%) ... show more

16 Citations


Open access
01 Aug 2016-
Abstract: Mitochondria house metabolic pathways that impact most aspects of cellular physiology. While metabolite profiling by mass spectrometry is widely applied at the whole-cell level, it is not routinely possible to measure the concentrations of small molecules in mammalian organelles. We describe a method for the rapid and specific isolation of mitochondria and use it in tandem with a database of predicted mitochondrial metabolites ("MITObolome") to measure the matrix concentrations of more than 100 metabolites across various states of respiratory chain (RC) function. Disruption of the RC reveals extensive compartmentalization of mitochondrial metabolism and signatures unique to the inhibition of each RC complex. Pyruvate enables the proliferation of RC-deficient cells but has surprisingly limited effects on matrix contents. Interestingly, despite failing to restore matrix NADH/NAD balance, pyruvate does increase aspartate, likely through the exchange of matrix glutamate for cytosolic aspartate. We demonstrate the value of mitochondrial metabolite profiling and describe a strategy applicable to other organelles.

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Topics: Matrix (mathematics) (61%)

9 Citations


Journal ArticleDOI: 10.1038/S41569-021-00569-6
Abstract: Peroxisome proliferator-activated receptor-α (PPARα), PPARδ and PPARγ are transcription factors that regulate gene expression following ligand activation. PPARα increases cellular fatty acid uptake, esterification and trafficking and regulates lipoprotein metabolism genes. PPARδ stimulates lipid and glucose utilization by increasing mitochondrial function and fatty acid desaturation pathways. By contrast, PPARγ promotes fatty acid uptake, triglyceride formation and storage in lipid droplets, thereby improving insulin sensitivity and glucose metabolism. PPARs also exert anti-atherogenic and anti-inflammatory effects on the vascular wall and immune cells. Clinically, PPARγ activation by glitazones and PPARα activation by fibrates improve insulin resistance and dyslipidaemia, respectively. PPARs are also physiological master switches in the heart, steering cardiac energy metabolism in cardiomyocytes, thereby affecting pathological heart failure and diabetic cardiomyopathy. Novel PPAR agonists in clinical development are providing new opportunities in the management of metabolic and cardiovascular diseases.

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Topics: Peroxisome proliferator-activated receptor (64%), PPAR agonist (63%), Lipid droplet (56%) ... show more

8 Citations


Journal ArticleDOI: 10.1016/J.TCB.2021.01.006
Aastha Garde1, David R. Sherwood1Institutions (1)
Abstract: Cell invasion through extracellular matrix (ECM) has pivotal roles in cell dispersal during development, immune cell trafficking, and cancer metastasis. Many elegant studies have revealed the specialized cellular protrusions, proteases, and distinct modes of migration invasive cells use to overcome ECM barriers. Less clear, however, is how invasive cells provide energy, specifically ATP, to power the energetically demanding membrane trafficking, F-actin polymerization, and actomyosin machinery that mediate break down, remodeling, and movement through ECMs. Here, we provide an overview of the challenges of examining ATP generation and delivery within invading cells and how recent studies using diverse invasion models, experimental approaches, and energy biosensors are revealing that energy metabolism is an integral component of cell invasive behavior that is dynamically tuned to overcome the ECM environment.

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Topics: Extracellular matrix (52%)

3 Citations


Open accessJournal ArticleDOI: 10.3390/CANCERS13071488
Ainhoa Ruiz-Iglesias1, Santos Mañes1Institutions (1)
24 Mar 2021-Cancers
Abstract: Pyruvate is a key molecule in the metabolic fate of mammalian cells; it is the crossroads from where metabolism proceeds either oxidatively or ends with the production of lactic acid. Pyruvate metabolism is regulated by many enzymes that together control carbon flux. Mitochondrial pyruvate carrier (MPC) is responsible for importing pyruvate from the cytosol to the mitochondrial matrix, where it is oxidatively phosphorylated to produce adenosine triphosphate (ATP) and to generate intermediates used in multiple biosynthetic pathways. MPC activity has an important role in glucose homeostasis, and its alteration is associated with diabetes, heart failure, and neurodegeneration. In cancer, however, controversy surrounds MPC function. In some cancers, MPC upregulation appears to be associated with a poor prognosis. However, most transformed cells undergo a switch from oxidative to glycolytic metabolism, the so-called Warburg effect, which, amongst other possibilities, is induced by MPC malfunction or downregulation. Consequently, impaired MPC function might induce tumors with strong proliferative, migratory, and invasive capabilities. Moreover, glycolytic cancer cells secrete lactate, acidifying the microenvironment, which in turn induces angiogenesis, immunosuppression, and the expansion of stromal cell populations supporting tumor growth. This review examines the latest findings regarding the tumorigenic processes affected by MPC.

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Topics: Warburg effect (58%), Glycolysis (56%), Mitochondrial matrix (54%) ... show more

3 Citations


References
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125 results found


Journal ArticleDOI: 10.1111/J.2517-6161.1995.TB02031.X
Abstract: SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.

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Topics: False discovery rate (72%), Per-comparison error rate (66%), False coverage rate (63%) ... show more

71,936 Citations


Open accessJournal Article
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

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33,540 Citations


Open accessJournal ArticleDOI: 10.1186/S13059-014-0550-8
05 Dec 2014-Genome Biology
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

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Topics: MRNA Sequencing (54%), Integrator complex (51%), Count data (50%) ... show more

29,675 Citations


Open accessPosted Content
02 Jan 2012-arXiv: Learning
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

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28,898 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTS635
01 Jan 2013-Bioinformatics
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Topics: MRNA Sequencing (57%)

20,172 Citations


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