bims-camemi Biomed news
on Mitochondrial metabolism in cancer
Issue of 2018‒09‒16
eight papers selected by
Christian Frezza
University of Cambridge, MRC Cancer Unit


  1. Mitochondrion. 2018 Sep 06. pii: S1567-7249(18)30114-4. [Epub ahead of print]
    Li D, Sun Y, Zhuang Q, Song Y, Wu B, Jia Z, Pan H, Zhou H, Hu S, Zhang B, Qiu Y, Dai Y, Chen S, Xu X, Zhu X, Lin A, Huang W, Liu Z, Yan Q.
      Hypertrophic cardiomyopathy (HCM), affecting approximately 1 in 500 in the general population, is the most prominent cause of sudden heart disease-related mortality in the young. Mitochondrial DNA (mtDNA) mutations are among the primary causes of HCM. We previously identified a novel m.2336T>C homoplasmic mutation in the mitochondrial 16S rRNA gene (MT-RNR2) in a Chinese maternally inherited HCM family. However, the molecular mechanisms by which m.2336T>C mutation contributes to HCM remain elusive. Here we generated transferring mitochondria cell lines (cybrids) with a constant nuclear background by transferring mitochondria from immortalized lymphoblastoid cell lines carrying the HCM-associated m.2336T>C mutation into human mtDNA-less (ρ°) cells. Functional assays showed a decreased stability for 16S rRNA and the steady-state levels of its binding proteins in the mutant cybrids. This mutation impaired the mitochondrial translation capacity and resulted in many mitochondrial dysfunctions, including elevation of ROS generation, reduction of ATP production and impairment of mitochondrial membrane potential. Moreover, the mutant cybrids had poor physiological status and decreased survival ability. These results confirm that the m.2336T>C mutation leads to mitochondrial dysfunction and strongly suggest that this mutation may play a role in the pathogenesis of HCM.
    Keywords:  Hypertrophic cardiomyopathy; M.2336T>C mutation; MT-RNR2; Mitochondrion; Transferring mitochondria cell lines
    DOI:  https://doi.org/10.1016/j.mito.2018.08.005
  2. Cell Syst. 2018 Aug 20. pii: S2405-4712(18)30316-8. [Epub ahead of print]
    Zelezniak A, Vowinckel J, Capuano F, Messner CB, Demichev V, Polowsky N, Mülleder M, Kamrad S, Klaus B, Keller MA, Ralser M.
      A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.
    Keywords:  enzyme abundance; genotype-phenotype problem; hierarchical regulation; high-throughput proteomics; machine learning; metabolic control analysis; metabolism; multi-omics
    DOI:  https://doi.org/10.1016/j.cels.2018.08.001
  3. Biochemistry (Mosc). 2018 Jun;83(6): 613-628
    Popov DV.
      A large body of experimental data have shown that aerobic exercise of different duration, intensity, and pattern affect molecular mechanisms regulating mitochondrial biogenesis in skeletal muscles. This review focuses on the effects of exercise duration and intensity on the molecular mechanisms of mitochondrial biogenesis regulation in skeletal muscles, namely PGC-1α-dependent signaling. Studies of the effects of acute exercise and exercise training showed that an increase in the duration of aerobic exercise from 30 to 90 min does not provide additional stimuli to activate signaling pathways regulating post-translational modification of peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) and expression of the PGC-1α gene (PPARGC1A). Conversely, exercise intensity substantially affects mitochondrial biogenesis due to the increase in the recruitment of type II muscle fibers with accompanying pronounced metabolic shift leading to the activation of signaling cascades and expression of genes regulating mitochondrial biogenesis. Therefore, intermittent exercise, which recruits type II muscle fibers, is more efficient in the activation of mitochondrial biogenesis than work-matched continuous exercise. In skeletal muscle adapted to aerobic training, intensity-dependent activation of mitochondrial biogenesis after acute exercise is associated primarily with the AMP-activated protein kinase/PGC-1α pathway, expression of PGC-1α-regulated genes, and expression of PPARGC1A from the alternative (distal) inducible promoter regulated by the cAMP response element-binding protein 1-related transcription factors and their coactivators. Elucidation of the effects of duration and intensity of aerobic exercise on the PGC-1α-dependent and -independent mechanisms of mitochondrial biogenesis is important for treatment of patients with various metabolic disorders, as well as for optimization of training in athletes.
    DOI:  https://doi.org/10.1134/S0006297918060019
  4. Methods Mol Biol. 2018 ;1842 265-281
    Tang C, Chen K, Bajic A, Choi WT, Baluya DL, Maletic-Savatic M.
      Over the past decade, advances in systems biology or 'omics techniques have enabled unprecedented insights into the biological processes that occur in cells, tissues, and on the organism level. One of these technologies is the metabolomics, which examines the whole content of the metabolites in a given sample. In a biological system, a stem cell for instance, there are thousands of single components, such as genes, RNA, proteins, and metabolites. These multiple molecular species interact with each other and these interactions may change over the life-time of a cell or in response to specific stimuli, adding to the complexity of the system. Using metabolomics, we can obtain an instantaneous snapshot of the biological status of a cell, tissue, or organism and gain insights on the pattern(s) of numerous analytes, both known and unknown, that result because of a given biological condition. Here, we outline the main methods to study the metabolism of stem cells, including a relatively recent technology of mass spectrometry imaging. Given the abundant and increasing interest in stem cell metabolism in both physiological and pathological conditions, we hope that this chapter will provide incentives for more research in these areas to ultimately reach wide network of applications in biomedical, pharmaceutical, and nutritional research and clinical medicine.
    Keywords:  Mass spectrometry; Mass spectrometry imaging; Metabolomics; Neural stem cells; Nuclear magnetic resonance; Stem cells
    DOI:  https://doi.org/10.1007/978-1-4939-8697-2_20
  5. Metabolism. 2018 Sep 06. pii: S0026-0495(18)30175-6. [Epub ahead of print]
    Franko A, Kovarova M, Feil S, Feil R, Wagner R, Heni M, Königsrainer A, Ruoß M, Nüssler AK, Weigert C, Häring HU, Lutz SZ, Peter A.
      BACKGROUND: The activation of hepatic stellate cells (HSCs) plays a crucial role in liver fibrosis, however the role of HSCs is less understood in hepatic insulin resistance. Since in the liver cGMP-dependent protein kinase I (cGKI) was detected in HSC but not in hepatocytes, and cGKI-deficient mice that express cGKI selectively in smooth muscle but not in other cell types (cGKI-SM mice) displayed hepatic insulin resistance, we hypothesized that cGKI modulates HSC activation and insulin sensitivity.MATERIALS AND METHODS: To study stellate cell activation in cGKI-SM mice, retinol storage and gene expression were studied. Moreover, in the human stellate cell line LX2, the consequences of cGKI-silencing on gene expression were investigated. Finally, cGKI expression was examined in human liver biopsies covering a wide range of liver fat content.
    RESULTS: Retinyl-ester concentrations in the liver of cGKI-SM mice were lower compared to wild-type animals, which was associated with disturbed expression of genes involved in retinol metabolism and inflammation. cGKI-silenced LX2 cells showed an mRNA expression profile of stellate cell activation, altered matrix degradation and activated chemokine expression. On the other hand, activation of LX2 cells suppressed cGKI expression. In accordance with this finding, in human liver biopsies, we observed a negative correlation between cGKI mRNA and liver fat content.
    CONCLUSIONS: These results suggest that the lack of cGKI possibly leads to stellate cell activation, which stimulates chemokine expression and activates inflammatory processes, which could disturb hepatic insulin sensitivity.
    Keywords:  Hepatic stellate cell; Inflammation; Insulin resistance; LX2 cell; Liver; cGKI
    DOI:  https://doi.org/10.1016/j.metabol.2018.09.001
  6. Eur J Med Chem. 2018 Sep 01. pii: S0223-5234(18)30773-6. [Epub ahead of print]157 1276-1291
    Granchi C.
      ATP citrate lyase (ACLY) is a cytosolic homotetrameric enzyme that catalyzes the conversion of citrate and coenzyme A (CoA) to acetyl-CoA and oxaloacetate, with the simultaneous hydrolysis of ATP to ADP and phosphate. Interestingly, ACLY is a strategic enzyme linking both the glycolytic and lipidic metabolism. In tumour cells characterized by an altered energetic metabolism, an increased glucose uptake and an accelerated glycolytic flux lead to an intensified production of mitochondrial citrate. Once transported to the cytosol, citrate is here converted by ACLY to acetyl-CoA, an essential biosynthetic precursor for fatty acid synthesis and mevalonate pathway. ACLY expression and activity proved to be aberrantly expressed in many types of tumours, and its pharmacological or genetic inhibition significantly inhibited cancer cell proliferation and induced apoptosis. Increasing evidences highlight the central role of ACLY, conferring a great therapeutic potential to this enzyme as a key target for the treatment of cancer. ACLY inhibitors, previously developed for metabolic disorders, have recently attracted interest as promising anti-cancer agents. After a brief introduction to the structure and the pathophysiological role of ACLY, this review article provides an overview of the main ACLY inhibitors reported in the literature.
    Keywords:  ATP citrate lyase; Cancer; Inhibitors; Lipid metabolism
    DOI:  https://doi.org/10.1016/j.ejmech.2018.09.001
  7. Biophys Chem. 2018 Aug 27. pii: S0301-4622(18)30223-0. [Epub ahead of print]242 15-21
    Nath S.
      A procedure is evolved to assess the maximum uncoupling activity of the classical unsubstituted phenolic uncouplers of mitochondrial oxidative phosphorylation (OX PHOS) 2,4-dinitrophenol and 2,6-dinitrophenol. The uncoupler concentrations, C, required for maximum uncoupling efficacy are found to be a strong function of the pH, and a linear relationship of pC with pH is obtained between pH 5 to pH 9. The slopes of the uncoupler concentrations in the aqueous and lipid phases as a function of pH have been estimated. It is shown that the experimental results can be derived from first principles by an enzyme kinetic model for uncoupling that is based on the same equations as formulated for the coupling of ion transport to ATP synthesis in a companion paper after imposition of the special conditions arising from the uncoupling process. The results reveal the catalysis of a reaction that involves both the anionic and protonated forms of the phenolic uncouplers in the vicinity of their binding sites in a non-aqueous region of the cristae membranes of mitochondria. The rate-limiting step in the overall process of uncoupling has been identified based on the uncoupling data. The data cannot be explained by a simple conduction of protons by uncouplers from one bulk aqueous phase to another as postulated by Mitchell's chemiosmotic theory. It is shown that Nath's two-ion theory of energy coupling/uncoupling in ATP synthase is consistent with the results. A molecular mechanism for uncoupling of ATP synthesis by the dinitrophenols is presented and the chief differences between coupling and uncoupling in ATP catalysis are summarized. The pharmacological consequences of our analysis of uncoupling are discussed, with particular reference to the mode of action of the anti-tuberculosis drug bedaquiline that specifically targets the c-subunit of the F1FO-ATP synthase and uncouples respiration from ATP synthesis in Mycobacterium tuberculosis. Hence the work is shown to be important both from the point of view of fundamental biology and is also pregnant with possibilities for practical pharmaceutical applications.
    Keywords:  ATP synthase; Bedaquiline; Cotransport; Dinitrophenol; Enzyme kinetic model; Enzymology; Mitchell's chemiosmotic theory; Mycobacterium tuberculosis; Nath's torsional mechanism of energy transduction and ATP synthesis; Nath's two-ion theory of biological energy coupling; Partition coefficients; Succinate anions; Uncouplers of oxidative phosphorylation; pH dependence of ATP synthesis/ATP hydrolysis
    DOI:  https://doi.org/10.1016/j.bpc.2018.08.006
  8. Curr Opin Biotechnol. 2018 Sep 05. pii: S0958-1669(18)30018-1. [Epub ahead of print]54 138-144
    Hirai MY, Shiraishi F.
      Plant metabolism is characterized by a wide diversity of metabolites, with systems far more complicated than those of microorganisms. Mathematical modeling is useful for understanding dynamic behaviors of plant metabolic systems for metabolic engineering. Time-series metabolome data has great potential for estimating kinetic model parameters to construct a genome-wide metabolic network model. However, data obtained by current metabolomics techniques does not meet the requirement for constructing accurate models. In this article, we highlight novel strategies and algorithms to handle the underlying difficulties and construct dynamic in vivo models for large-scale plant metabolic systems. The coarse but efficient modeling enables the prediction of unknown mechanisms regulating plant metabolism.
    DOI:  https://doi.org/10.1016/j.copbio.2018.08.005