9 research outputs found

    Hypoxia Triggers Major Metabolic Changes in AML Cells without Altering Indomethacin-Induced TCA Cycle Deregulation

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    Our previous studies have shown that the nonsteroidal anti-inflammatory drug indomethacin exhibits antileukemic activity <i>in vitro</i> and can inhibit the aldo-keto reductase AKR1C3, which we identified as a novel target in acute myeloid leukemia. However, the antileukemic actions of indomethacin are likely to be complex and extend beyond inhibition of either AKR1C3 or cycloxygenases. To further understand the antileukemic activity of indomethacin we have used untargeted nuclear magnetic resonance-based metabolic analysis to characterize the responses of KG1a and K562 cell lines in both normal culture conditions and in hypoxia, which better represents the tumor environment <i>in vivo</i>. Hypoxia induced dramatic metabolic changes in untreated KG1a and K562, including adaptation of both phospholipid and glycolytic metabolism. Despite these changes, both cell lines sustained relatively unaltered mitochondrial respiration. The administration of indomethacin induced similar metabolic responses regardless of the oxygen level in the environment. Notable exceptions included metabolites associated with <i>de novo</i> fatty acid synthesis and choline phospholipid metabolism. Collectively, these results suggest that leukemia cells have the inherent ability to tolerate changes in oxygen tension while maintaining an unaltered mitochondrial respiration. However, the administration of indomethacin significantly increased oxidative stress in both KG1a and K562, inducing mitochondrial dysfunction, regardless of the oxygenation conditions. These findings emphasize the particular pertinence of the tricarboxylic acid cycle to the survival of cancer cells and may explain why some antileukemic drugs have been discovered and developed successfully despite the use of culture conditions that do not reflect the hypoxic environment of cancer cells <i>in vivo</i>

    Patients characteristics.

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    <p>Footnotes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056422#pone-0056422-t001" target="_blank">table 1</a>. Serum reference ranges:- beta2 microglobulin 0.5‚Äď4.0 mg/l; serum creatinine 45‚Äď110 ¬Ķmol/l (0.5 to 1.2 mg/dl). Paraprotein types detected by immunofixation of serum and urine: GLO IgG lambda, no flc in urine; GKU IgG kappa, flc in urine; NS non secretory, no Ig detected in blood or urine; KUO kappa flc only detected in urine but not serum; AKU IgA kappa, kappa flc in urine; ALO IgA lambda, no flc in urine; KUS kappa flc only detected in serum and urine; ALU IgA lambda, flc in urine; DLU IgD lambda, flc in urine.</p

    Partial Least Squares Discriminant Analysis of NMR spectra acquired on blood serum samples.

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    <p>Scores (<b>A</b> and <b>B</b>) and weights (on LV1; <b>C</b> and <b>D</b>) plots obtained from OSC-PLS-DA performed on the NMR spectra of 37 and 42 blood serum samples for the comparison of groups B versus C1 (<b>A</b> and <b>C</b>) and B versus C2 (<b>B</b> and <b>D</b>). Group B (solid green, 27 samples): patients after chemotherapy; group C1 (solid blue, 10 samples): sustained remission; group C2 (empty blue, 15 samples): in relapse after chemotherapy. Cho: choline; Suc: succinate; Pyr: pyruvate; Ace: acetate; 2HiB: 2-hydroxyisobutyrate; Car: carnitine; AcCar: acetylcarnitine.</p

    Partial Least Squares Discriminant Analysis of NMR spectra acquired on blood serum samples.

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    <p>Scores (<b>A</b>) and weights (on LV1, <b>B</b>, and LV2, <b>C</b>) plots obtained from OSC-PLS-DA performed on the NMR spectra of 44 blood serum samples. Group A (solid red, 19 samples): patients at diagnosis; group C1 (solid blue, 10 samples): sustained remission and group C2 (empty blue, 15 samples): in relapse after chemotherapy. Cho: choline; Cre: creatinine; Pyr: pyruvate; Ala: alanine; 2HiB: 2-hydroxyisobutyrate; Lac: lactate; 2HB: 2-hydroxybutyrate. H-xan: hypoxantine; Glu: glutamate; Gln: glutamine; Ace: acetate.</p

    Proton NMR spectra and Partial Least Squares Discriminant Analysis of blood serum samples.

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    <p>Representative sections of proton NMR spectra at the diagnosis (red), remission (green) and prolonged remission (blue) of one multiple myeloma patient. (<b>A</b>) up-field region (0.75‚Äď4.25 ppm), (<b>B</b>) down-field region (5.1‚Äď8.9 ppm), 20 times increased intensity compared to (A). (<b>C</b>) Scores plot obtained from OSC-PLS-DA performed on the NMR spectra of 71 blood serum samples. Group A (solid red, 19 samples): patients at diagnosis; group B (solid green, 27 samples): patients after chemotherapy; group C1 (solid blue, 10 samples): sustained remission and group C2 (empty blue, 15 samples): in relapse after chemotherapy.</p

    Proton NMR-Based Metabolite Analyses of Archived Serial Paired Serum and Urine Samples from Myeloma Patients at Different Stages of Disease Activity Identifies Acetylcarnitine as a Novel Marker of Active Disease

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    <div><p>Background</p><p>Biomarker identification is becoming increasingly important for the development of personalized or stratified therapies. Metabolomics yields biomarkers indicative of phenotype that can be used to characterize transitions between health and disease, disease progression and therapeutic responses. The desire to reproducibly detect ever greater numbers of metabolites at ever diminishing levels has naturally nurtured advances in best practice for sample procurement, storage and analysis. Reciprocally, since many of the available extensive clinical archives were established prior to the metabolomics era and were not processed in such an ‚Äėideal‚Äô fashion, considerable scepticism has arisen as to their value for metabolomic analysis. Here we have challenged that paradigm.</p> <p>Methods</p><p>We performed proton nuclear magnetic resonance spectroscopy-based metabolomics on blood serum and urine samples from 32 patients representative of a total cohort of 1970 multiple myeloma patients entered into the United Kingdom Medical Research Council Myeloma IX trial.</p> <p>Findings</p><p>Using serial paired blood and urine samples we detected metabolite profiles that associated with diagnosis, post-treatment remission and disease progression. These studies identified carnitine and acetylcarnitine as novel potential biomarkers of active disease both at diagnosis and relapse and as a mediator of disease associated pathologies.</p> <p>Conclusions</p><p>These findings show that samples conventionally processed and archived can provide useful metabolomic information that has important implications for understanding the biology of myeloma, discovering new therapies and identifying biomarkers potentially useful in deciding the choice and application of therapy.</p> </div

    Partial Least Squares Discriminant Analysis of NMR spectra acquired on blood serum samples.

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    <p>Scores (<b>A</b>) and weights (on LV1; <b>B</b>) plots obtained from OSC-PLS-DA performed on the NMR spectra of 46 blood serum samples. Group A (solid red, 19 samples): patients at diagnosis; Group B (solid green, 27 samples): patients after chemotherapy. Lac: lactate; Cho: choline; Cre: creatinine; Suc: succinate; Ace: acetate; Ala: alanine; 3HB: 3-hydroxybutyrate; 2HB: 2-hydroxybutyrate.</p

    Partial Least Squares Discriminant Analysis of NMR spectra acquired on blood serum samples.

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    <p>Scores (<b>A</b>) and weights (on LV1; <b>B</b>) plots obtained from OSC-PLS-DA performed on the NMR spectra of 52 blood serum samples. Group B (solid green, 27 samples): patients after chemotherapy; group C1 (solid blue, 10 samples): sustained remission; group C2 (empty blue, 15 samples): in relapse after chemotherapy. Lac: lactate; Gly: glycine; Tau: taurine; Cho: choline; Suc: succinate; Pyr: pyruvate; Ace: acetate; 2HiB: 2-hydroxyisobutyrate.</p

    Blood serum concentration of acetylcarnitine and carnitine.

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    <p>Blood serum concentration of acetylcarnitine (AcCAR) and carnitine (CAR) in MM patients at diagnosis, in remission and after relapse of active disease following chemotherapy. Data shown as mean ¬Ī s.e.m. Statistical significance calculated according to Kruskal-Wallis one-way ANOVA (p<0.05).</p
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