DOCSLIB.ORG

Supplementary Materials: ©

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supplementary Materials: © S1 of S29 Supplementary Materials Leucine-Rich Diet Modulates the Metabolomic and Proteomic Profile of Skeletal Muscle during Cancer Cachexia Bread Cruz, André Oliveira, Lais Rosa Viana, Leisa Lopes-Aguiar, Rafael Canevarolo, Maiara Caroline Colombera, Rafael Rossi Valentim, Fernanda Garcia-Fóssa, Lizandra Maia de Sousa, Bianca Gazieri Castelucci, Sílvio Roberto Consonni, Daniel Martins-de-Souza, Marcelo Bispo de Jesus, Steven Thomas Russell and Maria Cristina Cintra Gomes-Mardondes S2 of S29 Figure S1. Detailed information about western blot in Figure 6. S3 of S29 Table S1. Total skeletal muscle metabolic profile identified in rats euthanatized at 7th day of the experiment. C 7 L 7 W 7 WL 7 Metabolite Mean ± SD Mean ± SD Mean ± SD Mean ± SD p value (mM) (mM) (mM) (mM) 2-Hydroxyisobutyrate 0.0307 ± 0.0157 0.0271 ± 0.0109 0.0196 ± 0.0056 0.0339 ± 0.0145 0.432 5,6-Dihydrothymine 0.3135 ± 0.1855 0.3072 ± 0.2264 0.3447 ± 0.1242 0.2894 ± 0.0772 0.970 ADP 0.3619 ± 0.2529 0.5366 ± 0.3415 0.1881 ± 0.1327 0.3090 ± 0.1025 0.240 AMP 0.0593 ± 0.0260 0.0462 ± 0.0193 0.0587 ± 0.0265 0.0628 ± 0.0309 0.817 ATP 0.0433 ± 0.0317 0.0304 ± 0.0103 0.0319 ± 0.0118 0.0388 ± 0.0164 0.765 Acetate 1.0418 ± 0.7943 0.2557 ± 0.1462 0.7260 ± 0.5012 0.5849 ± 0.2952 0.216 Alanine 4.0665 ± 2.1213 3.9517 ± 1.2937 3.7843 ± 1.0968 4.3584 ± 1.0101 0.952 Anserine 4.0762 ± 5.7168 0.0966 ± 0.0122 0.0955 ± 0.0681 0.1022 ± 0.0791 0.178 Ascorbate 1.1292 ± 0.7133 1.5219 ± 0.5562 1.6517 ± 0.4228 1.6328 ± 0.5660 0.560 Carnosine 3.3706 ± 1.9132 2.9143 ± 0.5788 2.4496 ± 1.6897 3.5031 ± 2.4611 0.836 Creatine 44.3915 ± 19.5185 30.4124 ± 5.5437 36.9566 ± 9.5283 34.8272 ± 9.6078 0.462 Creatine phosphate 0.4060 ± 0.1809 0.0904 ± 0.0836 0.4079 ± 0.2796 0.6346 ± 0.5495 0.182 Creatinine 0.5812 ± 0.2931 0.4002 ± 0.0759 0.6065 ± 0.1290 0.4207 ± 0.2669 0.420 Ethanol 0.1999 ± 0.1922 0.1603 ± 0.0927 0.0958 ± 0.0411 0.0914 ± 0.0360 0.466 Formate 0.3833 ± 0.2495 0.2202 ± 0.0569 0.4161 ± 0.1918 0.3781 ± 0.1277 0.410 Fumarate 0.1686 ± 0.0919 0.1763 ± 0.0880 0.1598 ± 0.0521 0.1588 ± 0.0251 0.982 Glucose 2.8980 ± 2.4123 1.5286 ± 0.5475 2.5253 ± 0.4909 2.1417 ± 0.7593 0.523 Glucose-1-phosphate 0.2911 ± 0.1491 0.1216 ± 0.0142 0.2489 ± 0.0820 0.2842 ± 0.1099 0.118 Glucose-6-phosphate 3.0885 ± 1.7516 0.8264 ± 0.1496 2.9016 ± 1.0911 3.2799 ± 1.7332 0.081 Glutamate 1.2576 ± 0.6533 2.0700 ± 0.9594 1.5909 ± 0.5283 1.6377 ± 0.7913 0.523 Glutamine 3.2098 ± 1.8013 3.7628 ± 3.1131 3.3612 ± 1.1906 4.2098 ± 1.8377 0.907 Glycerol 0.9306 ± 0.7770 1.5961 ± 0.8273 2.7112 ± 0.4269 1.5330 ± 0.3722 0.013 Glycine 3.3228 ± 2.0279 1.1678 ± 0.1664 2.2952 ± 0.6924 1.6309 ± 0.6017 0.085 IMP 4.7320 ± 2.2176 3.1616 ± 0.6800 4.5099 ± 0.9254 4.5916 ± 1.4109 0.411 Inosine 0.1618 ± 0.0821 0.1620 ± 0.1305 0.1372 ± 0.0291 0.1995 ± 0.0861 0.804 Lactate 64.2890 ± 31.6685 43.4124 ± 4.0930 57.0051 ± 14.3153 50.4049 ± 16.5041 0.491 Leucine 0.2314 ± 0.1297 0.3456 ± 0.0348 0.1758 ± 0.0518 0.3927 ± 0.0477 0.006 Lysine 0.8365 ± 0.5052 0.8012 ± 0.5443 0.6754 ± 0.2552 0.5683 ± 0.4182 0.822 Malonate 0.1085 ± 0.0670 0.0818 ± 0.0268 0.0843 ± 0.0556 0.1547 ± 0.0875 0.370 Methionine 0.1148 ± 0.0384 0.0938 ± 0.0234 0.0652 ± 0.0375 0.3812 ± 0.1771 0.001 NAD+ 0.3261 ± 0.1806 0.2433 ± 0.0597 0.2079 ± 0.0523 0.1730 ± 0.0675 0.245 S4 of S29 Niacinamide 0.3693 ± 0.1992 0.2871 ± 0.1149 0.4194 ± 0.1105 0.4489 ± 0.0667 0.368 O-Acetylcarnitine 0.6142 ± 0.3278 0.4876 ± 0.1737 0.4467 ± 0.2726 0.2858 ± 0.2527 0.400 Pyruvate 0.0890 ± 0.0535 0.0463 ± 0.0112 0.1286 ± 0.1470 0.0897 ± 0.0292 0.564 Sarcosine 0.0774 ± 0.0369 0.0834 ± 0.0144 0.0791 ± 0.0285 0.0848 ± 0.0185 0.974 Succinate 0.6369 ± 0.4278 0.8098 ± 0.4044 0.4344 ± 0.1393 0.4443 ± 0.3017 0.381 Taurine 28.0479 ± 13.0051 29.4458 ± 12.7660 28.1842 ± 8.8871 28.6319 ± 5.0354 0.997 Threonine 0.5777 ± 0.5406 0.4275 ± 0.0660 0.5565 ± 0.2623 0.5504 ± 0.4774 0.944 Tyrosine 0.2074 ± 0.0976 0.1731 ± 0.0261 0.1977 ± 0.0691 0.1964 ± 0.0273 0.884 Valine 0.3104 ± 0.1555 0.2724 ± 0.0697 0.2293 ± 0.0654 0.2113 ± 0.0326 0.460 β-Alanine 0.2016 ± 0.1044 0.1870 ± 0.0788 0.1388 ± 0.0260 0.1974 ± 0.0831 0.663 Methylhistidine 0.0375 ± 0.0208 0.0972 ± 0.0545 0.0969 ± 0.0523 0.0790 ± 0.0790 0.417 Rats were distributed into control (C7); fed a leucine-rich diet (L7); Walker tumour-bearing (W7) and Walker tumour-bearing fed a leucine-rich diet (WL7) euthanatized at 7th day of the experiment. Data were expressed as mean ± standard deviation (SD) and analysed by one-way ANOVA (comparison among C7, L7, W7 and WL7). Bold p values represented a significant difference. Table S2. Total skeletal muscle metabolic profile identified in rats euthanatized at 14th day of the experiment. C 14 L 14 W 14 WL 14 Metabolite Mean ± SD Mean ± SD Mean ± SD Mean ± SD p Value (mM) (mM) (mM) (mM) 2-Hydroxyisobutyrate 0.0307 ± 0.0157 0.0472 ± 0.0024 0.0262 ± 0.0195 0.0227 ± 0.0062 0.085 5,6-Dihydrothymine 0.3137 ± 0.1859 0.3320 ± 0.0144 0.4500 ± 0.0418 0.2813 ± 0.0424 0.134 ADP 0.3620 ± 0.2529 0.5429 ± 0.0333 0.3265 ± 0.1107 0.3626 ± 0.0679 0.189 AMP 0.0594 ± 0.0260 0.0638 ± 0.0110 0.0769 ± 0.0205 0.1004 ± 0.0629 0.407 ATP 0.0434 ± 0.0317 0.0463 ± 0.0116 0.0668 ± 0.0479 0.0377 ± 0.0091 0.551 Acetate 1.0423 ± 0.7945 0.3235 ± 0.1469 1.3057 ± 0.6831 0.8249 ± 0.3115 0.131 Alanine 4.0684 ± 2.1228 4.8933 ± 0.0625 4.3933 ± 0.2019 5.1546 ± 0.8497 0.557 Anserine 4.0806 ± 5.7268 0.0666 ± 0.0266 0.2080 ± 0.2463 0.1746 ± 0.1120 0.186 Ascorbate 1.1294 ± 0.7132 0.8371 ± 0.5842 1.7339 ± 0.3261 1.4115 ± 0.2142 0.124 Carnosine 3.3725 ± 1.9157 3.3095 ± 0.0018 4.8081 ± 0.6785 4.0164 ± 0.5616 0.209 Creatine 44.4119 ± 19.5488 37.4204 ± 3.2481 52.2857 ± 4.3232 41.0840 ± 1.4348 0.252 Creatine phosphate 0.4060 ± 0.1809 0.1811 ± 0.0260 0.3042 ± 0.2813 0.3250 ± 0.3551 0.639 Creatinine 0.5815 ± 0.2935 0.3702 ± 0.0135 0.7157 ± 0.0496 0.6153 ± 0.0885 0.050 Ethanol 0.2000 ± 0.1926 0.1275 ± 0.0460 0.1310 ± 0.0701 0.0847 ± 0.0901 0.571 Formate 0.3835 ± 0.2498 0.2895 ± 0.0204 0.6032 ± 0.1839 0.4575 ± 0.1420 0.119 S5 of S29 Fumarate 0.1687 ± 0.0920 0.1693 ± 0.0030 0.1781 ± 0.0343 0.1850 ± 0.0449 0.968 Glucose 2.9004 ± 2.4169 1.4456 ± 0.4042 1.8538 ± 0.9147 2.1494 ± 0.4338 0.489 Glucose-1-phosphate 0.2913 ± 0.1494 0.1388 ± 0.0063 0.1739 ± 0.0867 0.2644 ± 0.0315 0.091 Glucose-6-phosphate 3.0903 ± 1.7551 0.8283 ± 0.1405 2.3611 ± 1.2505 2.9234 ± 0.1588 0.045 Glutamate 1.2582 ± 0.6540 1.4199 ± 0.0916 1.2259 ± 0.3348 1.8763 ± 0.7381 0.315 Glutamine 3.2109 ± 1.8015 2.8937 ± 0.7215 2.9475 ± 0.4088 4.0777 ± 1.7318 0.574 Glycerol 0.9307 ± 0.7770 0.5856 ± 0.0662 0.8393 ± 0.1139 0.9789 ± 0.1251 0.532 Glycine 3.3249 ± 2.0323 1.9920 ± 0.4937 2.8609 ± 0.7782 2.6599 ± 0.3377 0.442 IMP 4.7342 ± 2.2210 4.3544 ± 0.9823 5.0563 ± 1.0573 4.5896 ± 0.4451 0.900 Inosine 0.1618 ± 0.0822 0.1242 ± 0.0413 0.1344 ± 0.0317 0.1412 ± 0.0585 0.815 Lactate 64.3203 ± 31.7140 48.9957 ± 4.8405 56.4696 ± 15.4306 58.3740 ± 7.6167 0.701 Leucine 0.2315 ± 0.1297 0.3741 ± 0.0872 0.2730 ± 0.0640 0.2537 ± 0.0376 0.151 Lysine 0.8368 ± 0.5054 0.6026 ± 0.0321 1.0468 ± 0.1850 1.1623 ± 0.3119 0.109 Malonate 0.1085 ± 0.0671 0.1213 ± 0.0430 0.1834 ± 0.1049 0.0693 ± 0.0272 0.170 Methionine 0.1149 ± 0.0385 0.0816 ± 0.0070 0.1511 ± 0.2442 0.0708 ± 0.0320 0.799 NAD+ 0.3262 ± 0.1807 0.3890 ± 0.0027 0.3826 ± 0.1050 0.2764 ± 0.0094 0.417 Niacinamide 0.3695 ± 0.1994 0.3197 ± 0.0269 0.4358 ± 0.0983 0.4016 ± 0.0670 0.562 O-Acetylcarnitine 0.6144 ± 0.3278 0.6521 ± 0.0268 0.4632 ± 0.3096 0.3073 ± 0.2766 0.287 Pyruvate 0.0890 ± 0.0536 0.0560 ± 0.0048 0.1210 ± 0.0587 0.0874 ± 0.0232 0.233 Sarcosine 0.0775 ± 0.0369 0.1084 ± 0.0029 0.1130 ± 0.0197 0.0867 ± 0.0215 0.155 Succinate 0.6371 ± 0.4278 0.7984 ± 0.0609 0.5683 ± 0.1306 0.5916 ± 0.1139 0.521 Taurine 28.0577 ± 13.0109 32.3942 ± 0.5020 31.6355 ± 3.6816 31.4140 ± 6.3537 0.849 Threonine 0.5778 ± 0.5406 0.4557 ± 0.1088 0.5248 ± 0.2253 0.7597 ± 0.3794 0.662 Tyrosine 0.2075 ± 0.0977 0.1779 ± 0.0130 0.2023 ± 0.0587 0.2047 ± 0.0201 0.881 Valine 0.3105 ± 0.1557 0.2315 ± 0.0021 0.2912 ± 0.0573 0.2440 ± 0.0444 0.534 β-Alanine 0.2017 ± 0.1045 0.1986 ± 0.0051 0.1666 ± 0.0331 0.1948 ± 0.0447 0.827 Methylhistidine 0.0375 ± 0.0207 0.1526 ± 0.0975 0.0780 ± 0.0749 0.1799 ± 0.1699 0.251 Rats were distributed into control (C14); fed a leucine-rich diet (L14); Walker tumour-bearing (W14) and Walker tumour-bearing fed a leucine-rich diet (WL14) euthanatized at 14th day of the experiment.
Load more

Recommended publications
  • Diapositiva 1
    ns 10 A Unmethylated C Methylated ) 8 Normal Normal Tissue Cervix Esophagus 4 cervical esophageal a.u. * *** * *** ( 6 TargetID MAPINFO Position Ca-Ski HeLa SW756 tissue COLO-680N KYSE-180 OACM 5.1C tissue cg04255070 22851591 TSS1500 0.896 0.070 0.082 0.060 0.613 0.622 0.075 0.020 2 (log2) 4 cg01593340 22851587 TSS1500 0.826 0.055 0.051 0.110 0.518 0.518 0.034 0.040 cg16113692 22851502 TSS200 0.964 0.073 0.072 0.030 0.625 0.785 0.018 0.010 2 0 cg26918510 22851422 TSS200 0.985 0.019 0.000 0.000 0.711 0.961 0.010 0.060 expression cg26821579 22851416 TSS200 0.988 0.083 0.058 0.000 0.387 0.955 0.021 0.050 TRMT12 mRNA Expression mRNA TRMT12 0 cg01129966 22851401 TSS200 0.933 0.039 0.077 0.020 0.815 0.963 0.078 0.010 -2 cg25421615 22851383 TSS200 0.958 0.033 0.006 0.000 0.447 0.971 0.037 0.020 mRNA cg17953636 22851321 1stExon 0.816 0.092 0.099 0.080 0.889 0.750 0.136 0.030 -4 Methylated SVIP Unmethylated SVIP relative expression (a.u.) expression relative SVIP D Cervix Esophagus HNC Meth ) HNC Unmeth Cervix Meth 3.0 *** Cervix Unmeth *** Esophagus Meth a.u. ( 2.5 CaCaEsophagus-Ski-Ski Unmeth COLO-680N Haematological Meth TSS B TSS Haematological Unmeth 2.0 -244 bp +104 bp 244 bp +104 bp 1.5 ** * expression -SVIP 1.0 HeLa KYSE-180 HeLa TSS TSS 0.5 -ACTB 244 bp +104 bp - 244 bp +104 bp mRNA 0.0 HeLa SW756 Ca-Ski SW756 SW756 OACM 5.1C TSS KYSE-180 TSS COLO-680NOACM 5.1 C - 244 bp +104 bp 244 bp +104 bp E ) 3.0 * Ca-Ski COLO-680N WT TSS TSS 2.0 a.u.
    [Show full text]
  • Altered Expression and Function of Mitochondrial Я-Oxidation Enzymes
    0031-3998/01/5001-0083 PEDIATRIC RESEARCH Vol. 50, No. 1, 2001 Copyright © 2001 International Pediatric Research Foundation, Inc. Printed in U.S.A. Altered Expression and Function of Mitochondrial ␤-Oxidation Enzymes in Juvenile Intrauterine-Growth-Retarded Rat Skeletal Muscle ROBERT H. LANE, DAVID E. KELLEY, VLADIMIR H. RITOV, ANNA E. TSIRKA, AND ELISA M. GRUETZMACHER Department of Pediatrics, UCLA School of Medicine, Mattel Children’s Hospital at UCLA, Los Angeles, California 90095, U.S.A. [R.H.L.]; and Departments of Internal Medicine [D.E.K., V.H.R.] and Pediatrics [R.H.L., A.E.T., E.M.G.], University of Pittsburgh School of Medicine, Magee-Womens Research Institute, Pittsburgh, Pennsylvania 15213, U.S.A. ABSTRACT Uteroplacental insufficiency and subsequent intrauterine creased in IUGR skeletal muscle mitochondria, and isocitrate growth retardation (IUGR) affects postnatal metabolism. In ju- dehydrogenase activity was unchanged. Interestingly, skeletal venile rats, IUGR alters skeletal muscle mitochondrial gene muscle triglycerides were significantly increased in IUGR skel- expression and reduces mitochondrial NADϩ/NADH ratios, both etal muscle. We conclude that uteroplacental insufficiency alters of which affect ␤-oxidation flux. We therefore hypothesized that IUGR skeletal muscle mitochondrial lipid metabolism, and we gene expression and function of mitochondrial ␤-oxidation en- speculate that the changes observed in this study play a role in zymes would be altered in juvenile IUGR skeletal muscle. To test the long-term morbidity associated with IUGR. (Pediatr Res 50: this hypothesis, mRNA levels of five key mitochondrial enzymes 83–90, 2001) (carnitine palmitoyltransferase I, trifunctional protein of ␤-oxi- dation, uncoupling protein-3, isocitrate dehydrogenase, and mi- Abbreviations tochondrial malate dehydrogenase) and intramuscular triglycer- CPTI, carnitine palmitoyltransferase I ides were quantified in 21-d-old (preweaning) IUGR and control IUGR, intrauterine growth retardation rat skeletal muscle.
    [Show full text]
  • Upregulation of Peroxisome Proliferator-Activated Receptor-Α And
    Upregulation of peroxisome proliferator-activated receptor-α and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer Chih-Yang Wang, Ying-Jui Chao, Yi-Ling Chen, Tzu-Wen Wang, Nam Nhut Phan, Hui-Ping Hsu, Yan-Shen Shan, Ming-Derg Lai 1 Supplementary Table 1. Demographics and clinical outcomes of five patients with ampullary cancer Time of Tumor Time to Age Differentia survival/ Sex Staging size Morphology Recurrence recurrence Condition (years) tion expired (cm) (months) (months) T2N0, 51 F 211 Polypoid Unknown No -- Survived 193 stage Ib T2N0, 2.41.5 58 F Mixed Good Yes 14 Expired 17 stage Ib 0.6 T3N0, 4.53.5 68 M Polypoid Good No -- Survived 162 stage IIA 1.2 T3N0, 66 M 110.8 Ulcerative Good Yes 64 Expired 227 stage IIA T3N0, 60 M 21.81 Mixed Moderate Yes 5.6 Expired 16.7 stage IIA 2 Supplementary Table 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of an ampullary cancer microarray using the Database for Annotation, Visualization and Integrated Discovery (DAVID). This table contains only pathways with p values that ranged 0.0001~0.05. KEGG Pathway p value Genes Pentose and 1.50E-04 UGT1A6, CRYL1, UGT1A8, AKR1B1, UGT2B11, UGT2A3, glucuronate UGT2B10, UGT2B7, XYLB interconversions Drug metabolism 1.63E-04 CYP3A4, XDH, UGT1A6, CYP3A5, CES2, CYP3A7, UGT1A8, NAT2, UGT2B11, DPYD, UGT2A3, UGT2B10, UGT2B7 Maturity-onset 2.43E-04 HNF1A, HNF4A, SLC2A2, PKLR, NEUROD1, HNF4G, diabetes of the PDX1, NR5A2, NKX2-2 young Starch and sucrose 6.03E-04 GBA3, UGT1A6, G6PC, UGT1A8, ENPP3, MGAM, SI, metabolism
    [Show full text]
  • Deficiency of Skeletal Muscle Succinate Dehydrogenase and Aconitase
    Deficiency of skeletal muscle succinate dehydrogenase and aconitase. Pathophysiology of exercise in a novel human muscle oxidative defect. R G Haller, … , K Ayyad, C G Blomqvist J Clin Invest. 1991;88(4):1197-1206. https://doi.org/10.1172/JCI115422. Research Article We evaluated a 22-yr-old Swedish man with lifelong exercise intolerance marked by premature exertional muscle fatigue, dyspnea, and cardiac palpitations with superimposed episodes lasting days to weeks of increased muscle fatigability and weakness associated with painful muscle swelling and pigmenturia. Cycle exercise testing revealed low maximal oxygen uptake (12 ml/min per kg; healthy sedentary men = 39 +/- 5) with exaggerated increases in venous lactate and pyruvate in relation to oxygen uptake (VO2) but low lactate/pyruvate ratios in maximal exercise. The severe oxidative limitation was characterized by impaired muscle oxygen extraction indicated by subnormal systemic arteriovenous oxygen difference (a- v O2 diff) in maximal exercise (patient = 4.0 ml/dl, normal men = 16.7 +/- 2.1) despite normal oxygen carrying capacity and Hgb-O2 P50. In contrast maximal oxygen delivery (cardiac output, Q) was high compared to sedentary healthy men (Qmax, patient = 303 ml/min per kg, normal men 238 +/- 36) and the slope of increase in Q relative to VO2 (i.e., delta Q/delta VO2) from rest to exercise was exaggerated (delta Q/delta VO2, patient = 29, normal men = 4.7 +/- 0.6) indicating uncoupling of the normal approximately 1:1 relationship between oxygen delivery and utilization in dynamic exercise. Studies of isolated skeletal muscle mitochondria in our patient revealed markedly impaired succinate oxidation with normal glutamate oxidation implying a metabolic defect at […] Find the latest version: https://jci.me/115422/pdf Deficiency of Skeletal Muscle Succinate Dehydrogenase and Aconitase Pathophysiology of Exercise in a Novel Human Muscle Oxidative Defect Ronald G.
    [Show full text]
  • Role of De Novo Cholesterol Synthesis Enzymes in Cancer Jie Yang1,2, Lihua Wang1,2, Renbing Jia1,2
    Journal of Cancer 2020, Vol. 11 1761 Ivyspring International Publisher Journal of Cancer 2020; 11(7): 1761-1767. doi: 10.7150/jca.38598 Review Role of de novo cholesterol synthesis enzymes in cancer Jie Yang1,2, Lihua Wang1,2, Renbing Jia1,2 1. Department of Ophthalmology, Ninth People’s Hospital of Shanghai, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2. Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China. Corresponding authors: Renbing Jia, [email protected] and Lihua Wang, [email protected]. Department of Ophthalmology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, No. 639 Zhi Zao Ju Road, Shanghai 200011, China © The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. Received: 2019.07.21; Accepted: 2019.11.30; Published: 2020.01.17 Abstract Despite extensive research in the cancer field, cancer remains one of the most prevalent diseases. There is an urgent need to identify specific targets that are safe and effective for the treatment of cancer. In recent years, cancer metabolism has come into the spotlight in cancer research. Lipid metabolism, especially cholesterol metabolism, plays a critical role in membrane synthesis as well as lipid signaling in cancer. This review focuses on the contribution of the de novo cholesterol synthesis pathway to tumorigenesis, cancer progression and metastasis. In conclusion, cholesterol metabolism could be an effective target for novel anticancer treatment. Key words: metabolic reprogramming, de novo cholesterol synthesis, cancer progress Introduction Over the past few decades, numerous published that cholesterol plays a critical role in cancer studies have focused on cancer cell metabolism and progression15-19.
    [Show full text]
  • RESEARCH COMMUNICATION HADHA Is a Potential Predictor Of
    HADHA is a Potential Predictor of the Response to Platinum-based Chemotherapy RESEARCH COMMUNICATION HADHA is a Potential Predictor of Response to Platinum-based Chemotherapy for Lung Cancer Taihei Kageyama1, Ryo Nagashio1, 2, Shinichiro Ryuge 3, Toshihide Matsumoto1,5, Akira Iyoda4, Yukitoshi Satoh4, Noriyuki Masuda3, Shi-Xu Jiang5, Makoto Saegusa5, Yuichi Sato1, 2* Abstract To identify a cisplatin resistance predictor to reduce or prevent unnecessary side effects, we firstly established four cisplatin-resistant sub-lines and compared their protein profiles with cisplatin-sensitive parent lung cancer cell lines using two-dimensional gel electrophoresis. Between the cisplatin-resistant and -sensitive cells, a total of 359 protein spots were differently expressed (>1.5 fold), and 217 proteins (83.0%) were identified. We focused on a mitochondrial protein, hydroxyl-coenzyme A dehydrogenase/3-ketoacyl-coenzyme A thiolase/enoyl-coenzyme A hydratase alpha subunit (HADHA), which was increased in all cisplatin-resistant cells. Furthermore, pre- treated biopsy specimens taken from patients who showed resistance to platinum-based treatment showed a significantly higher positive rate for HADHA in all cases (p=0.00367), including non-small cell lung carcinomas (p=0.002), small-cell lung carcinomas (p=0.038), and adenocarcinomas (p=0.008). These results suggest that the expression of HADHA may be a useful marker to predict resistance to platinum-based chemotherapy in patients with lung cancer. Keywords: Cisplatin - HADHA - lung cancer - two-dimensional gel electrophoresis Asian Pacific J Cancer Prev, 12, 3457-3463 Introduction cisplatin resistance rose due to a decrease of blood flow in the tumor and increased DNA repair (Stewart, 2007), Lung cancer is the leading cause of cancer-related the mechanisms underlying cisplatin resistance have not death in the world, and the five-year overall survival rate yet been clarified, and an effective cisplatin resistance is still below 16% (Jemal et al., 2009).
    [Show full text]
  • (LCHAD) Deficiency / Mitochondrial Trifunctional Protein (MTF) Deficiency
    Long chain acyl-CoA dehydrogenase (LCHAD) deficiency / Mitochondrial trifunctional protein (MTF) deficiency Contact details Introduction Regional Genetics Service Long chain acyl-CoA dehydrogenase (LCHAD) deficiency / mitochondrial trifunctional Levels 4-6, Barclay House protein (MTF) deficiency is an autosomal recessive disorder of mitochondrial beta- 37 Queen Square oxidation of fatty acids. The mitochondrial trifunctional protein is composed of 4 alpha London, WC1N 3BH and 4 beta subunits, which are encoded by the HADHA and HADHB genes, respectively. It is characterized by early-onset cardiomyopathy, hypoglycemia, T +44 (0) 20 7762 6888 neuropathy, and pigmentary retinopathy, and sudden death. There is also an infantile F +44 (0) 20 7813 8578 onset form with a hepatic Reye-like syndrome, and a late-adolescent onset form with primarily a skeletal myopathy. Tandem mass spectrometry of organic acids in urine, Samples required and carnitines in blood spots, allows the diagnosis to be unequivocally determined. An 5ml venous blood in plastic EDTA additional clinical complication can occur in the pregnant mothers of affected fetuses; bottles (>1ml from neonates) they may experience maternal acute fatty liver of pregnancy (AFLP) syndrome or Prenatal testing must be arranged hypertension/haemolysis, elevated liver enzymes and low platelets (HELLP) in advance, through a Clinical syndrome. Genetics department if possible. The genes encoding the HADHA and HADHB subunits are located on chromosome Amniotic fluid or CV samples 2p23.3. The pathogenic
    [Show full text]
  • Lipid Metabolic Reprogramming: Role in Melanoma Progression and Therapeutic Perspectives
    cancers Review Lipid metabolic Reprogramming: Role in Melanoma Progression and Therapeutic Perspectives 1, 1, 1 2 1 Laurence Pellerin y, Lorry Carrié y , Carine Dufau , Laurence Nieto , Bruno Ségui , 1,3 1, , 1, , Thierry Levade , Joëlle Riond * z and Nathalie Andrieu-Abadie * z 1 Centre de Recherches en Cancérologie de Toulouse, Equipe Labellisée Fondation ARC, Université Fédérale de Toulouse Midi-Pyrénées, Université Toulouse III Paul-Sabatier, Inserm 1037, 2 avenue Hubert Curien, tgrCS 53717, 31037 Toulouse CEDEX 1, France; [email protected] (L.P.); [email protected] (L.C.); [email protected] (C.D.); [email protected] (B.S.); [email protected] (T.L.) 2 Institut de Pharmacologie et de Biologie Structurale, CNRS, Université Toulouse III Paul-Sabatier, UMR 5089, 205 Route de Narbonne, 31400 Toulouse, France; [email protected] 3 Laboratoire de Biochimie Métabolique, CHU Toulouse, 31059 Toulouse, France * Correspondence: [email protected] (J.R.); [email protected] (N.A.-A.); Tel.: +33-582-7416-20 (J.R.) These authors contributed equally to this work. y These authors jointly supervised this work. z Received: 15 September 2020; Accepted: 23 October 2020; Published: 27 October 2020 Simple Summary: Melanoma is a devastating skin cancer characterized by an impressive metabolic plasticity. Melanoma cells are able to adapt to the tumor microenvironment by using a variety of fuels that contribute to tumor growth and progression. In this review, the authors summarize the contribution of the lipid metabolic network in melanoma plasticity and aggressiveness, with a particular attention to specific lipid classes such as glycerophospholipids, sphingolipids, sterols and eicosanoids.
    [Show full text]
  • Supplementary Materials
    Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented.
    [Show full text]
  • Additional File 1
    Additional file 1. The Primer information of DEGs for q-PCR validation Gene Sequence of primer(5'→3') Tm(℃） Length(bp) F: TGTTTGCTCTAAGCCTGGTTG NewGene_126260 59 113 R: CGGTCGCTAAGGGGAAGTT F: CTCAACAAAGCCGTCTGGG NewGene_41572 61 96 R: TGGGGAATCTTCATCCTCATT F: AGGAGCCCAAAACCGAAGA MME 63.3 184 R: GCTGACCAAGAAGTACCGTATGT F: AAAGCCCTTCAGTCAGCACG NewGene_70974 63.3 141 R: CCAGTCACAAGCAGCAAACC F: GCACAAGGCAGTCATGTTGC FAM43A 63.3 117 R: CGTTTAAATTCCGCCAGAGC F: ACGGCAGCCCAAATACCCT LOC108177184 63.3 147 R: GCCTTGACATCCACAATGAACA F: ATGTTTGTGATGGGCGTGAA GAPDH 58 94 R: GGAGGCAGGGATGATGTTCT F: GCTGACCTGCTGGATTAT HPRT1 58 135 R: ATCTCCACCGATTACTTT Additional file 2. Summary and quality assessment of RNA-Seq data Raw Clean Raw Clean Effective Q20 Q30 GC Sample Reads Reads Bases(Gb) Bases(Gb) Rate(%) (%) (%) (%) MR-1 128008342 125607312 19.2 18.84 98.12 97.46 93.74 50.78 MR-2 125804790 122452284 18.87 18.37 97.34 97.45 93.65 50.23 TR-1 128011648 124452604 19.2 18.67 97.22 97.33 93.35 51.65 TR-2 127085532 123882470 19.06 18.58 97.48 97.45 93.66 49.5 TR-3 127994130 124635486 19.2 18.7 97.38 97.3 93.3 51.18 YR-1 128014314 125504116 19.2 18.83 98.04 97.56 93.95 49.98 YR-2 127974678 124512778 19.2 18.68 97.29 97.42 93.57 50.04 YR-3 123173408 120110494 18.48 18.02 97.51 97.28 93.32 49.55 Note: Sample represents the name of the sample. Raw Reads represents the original sequence data. Clean reads represents the filtered sequenced data. Raw bases represents the number of raw sequence data multiplied by the length of the paired-end reads.
    [Show full text]
  • Aconitase: to Be Or Not to Be Inside Plant Glyoxysomes, That Is the Question
    biology Review Aconitase: To Be or not to Be Inside Plant Glyoxysomes, That Is the Question Luigi De Bellis 1,* , Andrea Luvisi 1 and Amedeo Alpi 2 1 Department of Biological and Environmental Sciences and Technologies, University of Salento, Via Prov. le Monteroni, I-73100 Lecce, Italy; [email protected] 2 Approaching Research Educational Activities (A.R.E.A.) Foundation, I-56126 Pisa, Italy; [email protected] * Correspondence: [email protected] Received: 10 June 2020; Accepted: 10 July 2020; Published: 12 July 2020 Abstract: After the discovery in 1967 of plant glyoxysomes, aconitase, one the five enzymes involved in the glyoxylate cycle, was thought to be present in the organelles, and although this was found not to be the case around 25 years ago, it is still suggested in some textbooks and recent scientific articles. Genetic research (including the study of mutants and transcriptomic analysis) is becoming increasingly important in plant biology, so metabolic pathways must be presented correctly to avoid misinterpretation and the dissemination of bad science. The focus of our study is therefore aconitase, from its first localization inside the glyoxysomes to its relocation. We also examine data concerning the role of the enzyme malate dehydrogenase in the glyoxylate cycle and data of the expression of aconitase genes in Arabidopsis and other selected higher plants. We then propose a new model concerning the interaction between glyoxysomes, mitochondria and cytosol in cotyledons or endosperm during the germination of oil-rich seeds. Keywords: aconitase; malate dehydrogenase; glyoxylate cycle; glyoxysomes; peroxisomes; β-oxidation; gluconeogenesis 1. Introduction Glyoxysomes are specialized types of plant peroxisomes containing glyoxylate cycle enzymes, which participate in the conversion of lipids to sugar during the early stages of germination in oilseeds.
    [Show full text]
  • And NADPH-Driven Reactions of Mitochondrial Isocitrate
    www.nature.com/scientificreports OPEN SIRT3 and GCN5L regulation of NADP+- and NADPH-driven reactions of mitochondrial isocitrate dehydrogenase IDH2 Katarína Smolková 1 ✉ , Jitka Špačková1, Klára Gotvaldová1, Aleš Dvořák1,4, Alena Křenková2, Martin Hubálek2, Blanka Holendová 1, Libor Vítek3 & Petr Ježek1 Wild type mitochondrial isocitrate dehydrogenase (IDH2) was previously reported to produce oncometabolite 2-hydroxyglutarate (2HG). Besides, mitochondrial deacetylase SIRT3 has been shown to regulate the oxidative function of IDH2. However, regulation of 2HG formation by SIRT3- mediated deacetylation was not investigated yet. We aimed to study mitochondrial IDH2 function in response to acetylation and deacetylation, and focus specifcally on 2HG production by IDH2. We used acetylation surrogate mutant of IDH2 K413Q and assayed enzyme kinetics of oxidative decarboxylation of isocitrate, 2HG production by the enzyme, and 2HG production in cells. The purifed IDH2 K413Q exhibited lower oxidative reaction rates than IDH2 WT. 2HG production by IDH2 K413Q was largely diminished at the enzymatic and cellular level, and knockdown of SIRT3 also inhibited 2HG production by IDH2. Contrary, the expression of putative mitochondrial acetylase GCN5L likely does not target IDH2. Using mass spectroscopy, we further identifed lysine residues within IDH2, which are the substrates of SIRT3. In summary, we demonstrate that 2HG levels arise from non-mutant IDH2 reductive function and decrease with increasing acetylation level. The newly identifed lysine residues might apply in regulation of IDH2 function in response to metabolic perturbations occurring in cancer cells, such as glucose-free conditions. Lysine acylation is a reversible post-translational modification representing one of the primary regulatory mechanisms in mitochondria, typically inhibition of protein function, and includes acetylation, malonylation, succinylation, glutarylation, etc.1.
    [Show full text]