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. 2017 Jan 18:6:e21592.
doi: 10.7554/eLife.21592.

Mitochondrial respiratory gene expression is suppressed in many cancers

Affiliations

Mitochondrial respiratory gene expression is suppressed in many cancers

Ed Reznik et al. Elife. .

Abstract

The fundamental metabolic decision of a cell, the balance between respiration and fermentation, rests in part on expression of the mitochondrial genome (mtDNA) and coordination with expression of the nuclear genome (nuDNA). Previously we described mtDNA copy number depletion across many solid tumor types (Reznik et al., 2016). Here, we use orthogonal RNA-sequencing data to quantify mtDNA expression (mtRNA), and report analogously lower expression of mtRNA in tumors (relative to normal tissue) across a majority of cancer types. Several cancers exhibit a trio of mutually consistent evidence suggesting a drop in respiratory activity: depletion of mtDNA copy number, decreases in mtRNA levels, and decreases in expression of nuDNA-encoded respiratory proteins. Intriguingly, a minority of cancer types exhibit a drop in mtDNA expression but an increase in nuDNA expression of respiratory proteins, with unknown implications for respiratory activity. Our results indicate suppression of respiratory gene expression across many cancer types.

Keywords: cancer; cancer biology; computational biology; human; metabolism; mitochondria; mtDNA; mtRNA; respiration; systems biology.

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Conflict of interest statement

The authors declare that no competing interests exist.

Figures

Figure 1.
Figure 1.. Summary of analysis.
(A) RNA-sequencing reads from the TCGA are aligned, and reads mapping to the mitochondrial genome are retained. (B) Changes in the expression of mtRNAs in tumors compared to adjacent-normal tissue are compared to analogous changes in mtDNA copy number. (C) Quantitative estimates of the correlation between mtDNA copy number and mtRNA are determined. (D) A comparison is made between the tumor vs. normal differential expression of OXPHOS subunits encoded in mtDNA and nuDNA. DOI: http://dx.doi.org/10.7554/eLife.21592.002
Figure 1—figure supplement 1.
Figure 1—figure supplement 1.. For each sample, the log10 ratio of the expression of the 13 mtRNAs (calculated using the sum of their TPM) to the expression of 175 mtDNA pseudogenes (calculated using the sum of their TPMs) was calculated.
Top and bottom of each box indicate the 75th and 25 percentile of this ratio. The whisker corresponds to a distance 1.5 times the interquartile range. In the vast majority of samples, bona fide expression from mtDNA is 2–3 orders of magnitude greater than the expression of mtDNA pseudogenes. DOI: http://dx.doi.org/10.7554/eLife.21592.003
Figure 1—figure supplement 2.
Figure 1—figure supplement 2.. Estimates of mtRNA abundance and differential expression from RSEM and featureCounts are in good agreement.
(A) Comparison of expression (in log2 normalized counts from limma voom) using featureCounts or RSEM. Each dot corresponds to a single mtRNA in a single cancer type.(B) Comparison of log-fold change estimates using featureCounts or RSEM. Each dot corresponds to the log2 ratio of expression in tumor compared to normal for a single mtRNA in a single cancer type. DOI: http://dx.doi.org/10.7554/eLife.21592.004
Figure 1—figure supplement 3.
Figure 1—figure supplement 3.. Comparison of mtRNA expression across different tissues.
The expression of MT-ND1 is depicted. Expression of other mtRNAs is similar. DOI: http://dx.doi.org/10.7554/eLife.21592.005
Figure 2.
Figure 2.. Differential expression of mitochondrial genes across cancer types.
Magnitude and statistical significance of differential expression evaluated by limma voom (see Materials and methods). The majority of mtRNAs are strongly down-regulated in several cancer types, including esophageal, breast, head and neck squamous, kidney clear cell, and liver cancers. One cancer type (kidney chromophobe), shows increases in the abundance of mtRNAs. All tumor types showing mtDNA copy number depletion in tumors relative to adjacent normal tissue (bottom annotation) show analogous depletion of mtRNAs. In contrast, mtDNA copy number changes in lung adenocarcinomas and kidney chromophobes are not reflected in differential expression of mtRNAs. DOI: http://dx.doi.org/10.7554/eLife.21592.006
Figure 3.
Figure 3.. Association of mtRNA expression levels with overall survival across cancer types.
Multiple-hypothesis-adjusted univariate p-values from Cox regression for each mtRNA are combined using Fisher’s method for each cancer type. Several cancer types show an association between high mtRNA expression and improved outcome (negative Cox regression coefficient). Dashed line indicates threshold for statistical significance. Representative Kaplan-Meier plots are shown for overall survival in ACC (B) and KICH (C), partitioning patients into high expression vs. low expression groups based on median expression of MT-ND4. DOI: http://dx.doi.org/10.7554/eLife.21592.007
Figure 4.
Figure 4.. Correlation between mtDNA copy number and mtRNA expression.
Relative mtDNA copy number was correlated against mtRNA expression (log2 normalized counts from limma voom). The expression of one gene, MT-ATP6, is depicted, although other mtDNA protein coding genes are similar. Lines indicate best fit linear trend between mtDNA copy number and log2 MT-ATP6 expression. Cancer types titled with an asterisk indicate a statistically significant correlation (Spearman, BH-adjusted p-value <0.05). Double asterisk indicates an especially strong correlation (BH-adjusted p-value <10−5). DOI: http://dx.doi.org/10.7554/eLife.21592.008
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. Correlation between mtDNA copy number and mtRNA expression is highly dependent on cancer type, as well as on mtRNA gene.
Radius of circle corresponds to BH-adjusted -log10 p-value, as assessed by Spearman correlation. Insignificant correlations are colored in grey. Correlations are calculated using all available (both tumor and adjacent-normal) samples. DOI: http://dx.doi.org/10.7554/eLife.21592.009
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. Correlation of RPPA with (A) mtDNA copy number (Spearman ρ 0.18, p-value 0.003) and (b) MT-CO2 RNA expression (Spearman ρ0.38, p-value <10−14) in KIRC.
Blue lines indicate best-fit linear trend. DOI: http://dx.doi.org/10.7554/eLife.21592.010
Figure 4—figure supplement 3.
Figure 4—figure supplement 3.. Correlation of mtDNA copy number and mtRNA expression in KICH.
Separate colors and trend lines correspond to tumor and normal samples. DOI: http://dx.doi.org/10.7554/eLife.21592.011
Figure 5.
Figure 5.. Comparison of differential expression (tumor vs. adjacent-normal tissue) of mtDNA-encoded OXPHOS subunits (mtOXPHOS) and nuclear-DNA-encoded OXPHOS subunits (nuOXPHOS).
(A) Differential expression scores for mtOXPHOS and nuOXPHOS across cancers. Red dashed boxes highlight cancer types with opposite trends in mtOXPHOS and nuOXPHOS differential expression. (B) Volcano plots highlighting differential expression of mtOXPHOS (red) and nuOXPHOS (blue) genes in BRCA and KIRC. DOI: http://dx.doi.org/10.7554/eLife.21592.012

References

    1. Ahn CS, Metallo CM. Mitochondria as biosynthetic factories for cancer proliferation. Cancer & Metabolism. 2015;3:1. doi: 10.1186/s40170-015-0128-2. - DOI - PMC - PubMed
    1. Birsoy K, Wang T, Chen WW, Freinkman E, Abu-Remaileh M, Sabatini DM. An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell. 2015;162:540–551. doi: 10.1016/j.cell.2015.07.016. - DOI - PMC - PubMed
    1. Brand MD, Nicholls DG. Assessing mitochondrial dysfunction in cells. Biochemical Journal. 2011;435:297–312. doi: 10.1042/BJ20110162. - DOI - PMC - PubMed
    1. Calvo SE, Mootha VK. The mitochondrial proteome and human disease. Annual Review of Genomics and Human Genetics. 2010;11:25–44. doi: 10.1146/annurev-genom-082509-141720. - DOI - PMC - PubMed
    1. Cancer Genome Atlas Research Network. Abeshouse A, Ahn J, Akbani R, Ally A, Amin S, Andry CD, Annala M, Aprikian A, Armenia J, Arora A, Auman JT, Balasundaram M, Balu S, Barbieri CE, Bauer T, Benz CC, Bergeron A, Beroukhim R, Berrios M, Bivol A, Bodenheimer T, Boice L, Bootwalla MS, Borges dos Reis R, Boutros PC, Bowen J, Bowlby R, Boyd J, Bradley RK, Breggia A, Brimo F, Bristow CA, Brooks D, Broom BM, Bryce AH, Bubley G, Burks E, Butterfield YS, Button M, Canes D, Carlotti CG, Carlsen R, Carmel M, Carroll PR, Carter SL, Cartun R, Carver BS, Chan JM, Chang MT, Chen Y, Cherniack AD, Chevalier S, Chin L, Cho J, Chu A, Chuah E, Chudamani S, Cibulskis K, Ciriello G, Clarke A, Cooperberg MR, Corcoran NM, Costello AJ, Cowan J, Crain D, Curley E, David K, Demchok JA, Demichelis F, Dhalla N, Dhir R, Doueik A, Drake B, Dvinge H, Dyakova N, Felau I, Ferguson ML, Frazer S, Freedland S, Fu Y, Gabriel SB, Gao J, Gardner J, Gastier-Foster JM, Gehlenborg N, Gerken M, Gerstein MB, Getz G, Godwin AK, Gopalan A, Graefen M, Graim K, Gribbin T, Guin R, Gupta M, Hadjipanayis A, Haider S, Hamel L, Hayes DN, Heiman DI, Hess J, Hoadley KA, Holbrook AH, Holt RA, Holway A, Hovens CM, Hoyle AP, Huang M, Hutter CM, Ittmann M, Iype L, Jefferys SR, Jones CD, Jones SJ, Juhl H, Kahles A, Kane CJ, Kasaian K, Kerger M, Khurana E, Kim J, Klein RJ, Kucherlapati R, Lacombe L, Ladanyi M, Lai PH, Laird PW, Lander ES, Latour M, Lawrence MS, Lau K, LeBien T, Lee D, Lee S, Lehmann KV, Leraas KM, Leshchiner I, Leung R, Libertino JA, Lichtenberg TM, Lin P, Linehan WM, Ling S, Lippman SM, Liu J, Liu W, Lochovsky L, Loda M, Logothetis C, Lolla L, Longacre T, Lu Y, Luo J, Ma Y, Mahadeshwar HS, Mallery D, Mariamidze A, Marra MA, Mayo M, McCall S, McKercher G, Meng S, Mes-Masson AM, Merino MJ, Meyerson M, Mieczkowski PA, Mills GB, Mills Shaw KR, Minner S, Moinzadeh A, Moore RA, Morris S, Morrison C, Mose LE, Mungall AJ, Murray BA, Myers JB, Naresh R, Nelson J, Nelson MA, Nelson PS, Newton Y, Noble MS, Noushmehr H, Nykter M, Pantazi A, Parfenov M, Park PJ, Parker JS, Paulauskis J, Penny R, Perou CM, Piché A, Pihl T, Pinto PA, Prandi D, Protopopov A, Ramirez NC, Rao A, Rathmell WK, Rätsch G, Ren X, Reuter VE, Reynolds SM, Rhie SK, Rieger-Christ K, Roach J, Robertson AG, Robinson B, Rubin MA, Saad F, Sadeghi S, Saksena G, Saller C, Salner A, Sanchez-Vega F, Sander C, Sandusky G, Sauter G, Sboner A, Scardino PT, Scarlata E, Schein JE, Schlomm T, Schmidt LS, Schultz N, Schumacher SE, Seidman J, Neder L, Seth S, Sharp A, Shelton C, Shelton T, Shen H, Shen R, Sherman M, Sheth M, Shi Y, Shih J, Shmulevich I, Simko J, Simon R, Simons JV, Sipahimalani P, Skelly T, Sofia HJ, Soloway MG, Song X, Sorcini A, Sougnez C, Stepa S, Stewart C, Stewart J, Stuart JM, Sullivan TB, Sun C, Sun H, Tam A, Tan D, Tang J, Tarnuzzer R, Tarvin K, Taylor BS, Teebagy P, Tenggara I, Têtu B, Tewari A, Thiessen N, Thompson T, Thorne LB, Tirapelli DP, Tomlins SA, Trevisan FA, Troncoso P, True LD, Tsourlakis MC, Tyekucheva S, Van Allen E, Van Den Berg DJ, Veluvolu U, Verhaak R, Vocke CD, Voet D, Wan Y, Wang Q, Wang W, Wang Z, Weinhold N, Weinstein JN, Weisenberger DJ, Wilkerson MD, Wise L, Witte J, Zhang J, Zhang J, Zhang W, Yang L, Zhang J. The molecular taxonomy of primary prostate cancer. Cell. 2015;163:1011–1025. doi: 10.1016/j.cell.2015.10.025. - DOI - PMC - PubMed

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