Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Sep 13;23(18):10587.
doi: 10.3390/ijms231810587.

Metabolic Adaptation as Potential Target in Papillary Renal Cell Carcinomas Based on Their In Situ Metabolic Characteristics

Ildikó Krencz  1 , Enikő Vetlényi  1 , Titanilla Dankó  1 , Gábor Petővári  1 , Dorottya Moldvai  1 , Dániel Sztankovics  1 , Regina Raffay  1 , Katalin Mészáros  2 , Endre Sebestyén  1 , Gyula Végső  3 , Judit Pápay  1 , Anna Sebestyén  1
Affiliations

Metabolic Adaptation as Potential Target in Papillary Renal Cell Carcinomas Based on Their In Situ Metabolic Characteristics

Ildikó Krencz et al. Int J Mol Sci. .

Abstract

Metabolic characteristics of kidney cancers have mainly been obtained from the most frequent clear cell renal cell carcinoma (CCRCC) studies. Moreover, the bioenergetic perturbances that affect metabolic adaptation possibilities of papillary renal cell carcinoma (PRCC) have not yet been detailed. Therefore, our study aimed to analyze the in situ metabolic features of PRCC vs. CCRCC tissues and compared the metabolic characteristics of PRCC, CCRCC, and normal tubular epithelial cell lines. The protein and mRNA expressions of the molecular elements in mammalian target of rapamycin (mTOR) and additional metabolic pathways were analyzed in human PRCC cases compared to CCRCC. The metabolic protein expression pattern, metabolite content, mTOR, and metabolic inhibitor sensitivity of renal carcinoma cell lines were also studied and compared with tubular epithelial cells, as "normal" control. We observed higher protein expressions of the "alternative bioenergetic pathway" elements, in correlation with the possible higher glutamine and acetate consumption in PRCC cells instead of higher glycolytic and mTOR activity in CCRCCs. Increased expression of certain metabolic pathway markers correlates with the detected differences in metabolite ratios, as well. The lower lactate/pyruvate, lactate/malate, and higher pyruvate/citrate intracellular metabolite ratios in PRCC compared to CCRCC cell lines suggest that ACHN (PRCC) have lower Warburg glycolytic capacity, less pronounced pyruvate to lactate producing activity and shifted OXPHOS phenotype. However, both studied renal carcinoma cell lines showed higher mTOR activity than tubular epithelial cells cultured in vitro, the metabolite ratio, the enzyme expression profiles, and the higher mitochondrial content also suggest increased importance of mitochondrial functions, including mitochondrial OXPHOS in PRCCs. Additionally, PRCC cells showed significant mTOR inhibitor sensitivity and the used metabolic inhibitors increased the effect of rapamycin in combined treatments. Our study revealed in situ metabolic differences in mTOR and metabolic protein expression patterns of human PRCC and CCRCC tissues as well as in cell lines. These underline the importance in the development of specific new treatment strategies, new mTOR inhibitors, and other anti-metabolic drug combinations in PRCC therapy.

Keywords: in situ expression and in vitro studies; mTOR; metabolism; papillary renal cell carcinoma.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Immunohistochemical analysis of mTOR and metabolic pathway markers in papillary (PRCC) and clear cell renal cell carcinomas (CCRCC). The expression of p-mTOR (marker for mTOR kinase activity), p-S6 (mTORC1 activity marker), Rictor (marker of the amount of mTORC2), GLUT1, HXK2, PFKP (glycolysis markers), G6PD (pentose phosphate pathway marker), GLS (glutaminolysis marker), ACSS2 (marker for acetate consumption), CPT1A (fatty acid β-oxidation marker), ATPB (OXPHOS marker), COX IV, and TOM20 (mitochondrial markers) was analyzed in normal kidney, PRCCs, and CCRCCs. The detected characteristic differences in enzyme expression profiles between PRCCs and CCRCCs were highlighted by red frame. Immunohistochemistry (DAB chromogen–brown) and hematoxylin counterstaining were used. Scale bars indicate 50 μm.
Figure 2
Figure 2
Heat map visualization of the scored in situ expression analyses of mTOR and metabolic pathway markers in papillary (PRCC) and clear cell renal cell carcinomas (CCRCC). Immunohistochemical expression of the markers was visualized on a heat map in each case. Blue color indicates low expression, whereas red color indicates high expression. Missing values are shown in grey. The pT stage and nuclear grade are also available.
Figure 3
Figure 3
Expression and correlation of the mTOR and metabolic pathway markers in papillary (PRCC) and clear cell renal cell carcinomas (CCRCC). (A) Correlation between the protein expression of GLUT1, HXK2, PFKP (glycolysis markers), G6PD (pentose phosphate pathway marker), GLS (glutaminolysis marker), ACSS2 (marker for acetate consumption), CPT1A (fatty acid β-oxidation marker), ATPB (OXPHOS marker), p-mTOR, p-S6, and Rictor (mTOR markers) in papillary (lower, red triangle) and in clear cell (upper, blue triangle) RCCs based on our IHC analysis. Green color indicates a negative, yellow color labels a positive correlation. Strong correlations (Spearman’s R > 0.4 and p < 0.05) are marked with a thick outline. (B) The mRNA expression of metabolic pathway markers in papillary and clear cell RCCs according to the TCGA KIRP (PRCC) and KIRC (CCRCC) datasets. SLC2A1 and ATP5B are mRNAs coding for GLUT1 and ATPB, respectively. Two-sided p values were considered statistically significant below 0.05 (Mann–Whitney U test). (C) Correlation between the mRNA expression of metabolic pathway markers in papillary (lower, red triangle) and clear cell (upper, blue triangle) RCCs according to the TCGA datasets. Green color indicates a negative, yellow color labels a positive correlation. Strong correlations (Spearman’s R > 0.4 and p < 0.05) are marked with a thick outline.
Figure 4
Figure 4
WES analysis of mTOR activity, metabolic enzyme expressions, and in situ mitochondrial stainings of papillary (ACHN) and clear cell (786-O) renal cell carcinoma cell lines. (A) WES analysis of mTOR pathway and metabolic markers in papillary (ACHN), clear cell renal cell carcinoma (786-O), and normal tubular epithelial (HK-2) cell lines. β-actin was used as loading control. GLS levels were analyzed by a dual-specificity antibody showing both the KGA (60 kDa) and GAC (55 kDa) splice variants. Predicted protein sizes are provided in Table 2. (B) Higher staining intensity with Mitotracker, anti-ATPB, anti-COX IV in ACHN (PRCC) than 786-O (CCRCC) cell lines suggest pronounced mitochondrial OXPHOS function in PRCC cells; however, TOM20 stainings showed no differences (magnification 63×).
Figure 5
Figure 5
LC–MS analyses of metabolic features and inhibitor sensitivity of papillary (ACHN) and clear cell renal cell carcinoma (786-O) cell lines. (A) LC–MS analysis of the intracellular metabolite concentrations in papillary (ACHN), clear cell (786-O) RCC and normal tubular epithelial (HK-2) cell lines. Lactate/pyruvate, pyruvate/citrate, and lactate/malate ratios were used to assess the glycolytic capacity and the activity of the TCA cycle of the cell lines (CIT–citrate, iCIT–isocitrate, α-KG–alpha-ketoglutarate, s-CoA–succinyl-CoA, SUC–succinate, FUM–fumarate, MAL–malate, OA–oxaloacetate), whereas lactate/glutamate ratio was used to evaluate the glutamine utilization. (B) The mTORC1 (rapamycin–rapa) and metabolic inhibitors (ACSS2i–inhibitor of acetate utilization, BPTES–glutaminolysis inhibitor, metformin–metf–AMPK-inhibitor, doxycycline–doxy–antibiotics with mitochondrial inhibitory effect) sensitivity of papillary (ACHN) and clear cell (786-O) RCC cell lines were evaluated as monotherapy or in combination. Regarding the 786-O cells, the treatments were performed both in RPMI-1640 (2000 mg/L glucose) medium under generally applied and optimized conditions and in DMEM high glucose (4500 mg/L)–to compare the role of maintaining parameters with similar glucose concentration levels as ACHN and HK-2 cells. * p values below 0.05, ** p values below 0.01 (one-way ANOVA with Tukey’s post hoc test), and at least 20% decrease in proliferation were considered biologically relevant. Synergistic treatment interactions were labeled with S (based on combination index calculation).
Figure 6
Figure 6
Simplified scheme of the metabolic pathways analyzed in papillary (PRCC) and clear cell renal cell carcinoma (CCRCC). Red and blue arrows on left and right side of black ones indicate the characteristic rewiring of metabolic pathways in PRCCs and in CCRCCs, respectively. High expressions of certain enzymes suggest balanced nutrient utilization, intensive glutaminolysis (GLS), acetate utilization (ACSS2), fatty acid oxidation (CPT1A), and oxidative phosphorylation (ATPB) were detected in PRCCs (thick red arrows), while high expressions of glycolytic enzymes (GLUT1, HXK2, PFKP) could correlate with higher glycolytic capacity in CCRCCs (thick blue arrows). Created with BioRender.com.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Escudier B., Porta C., Schmidinger M., Rioux-Leclercq N., Bex A., Khoo V., Grünwald V., Gillessen S., Horwich A. Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Ann. Oncol. 2019;30:706–720. doi: 10.1093/annonc/mdz056. - DOI - PubMed
    1. Siegel R.L., Miller K.D., Jemal A. Cancer statistics, 2020. CA Cancer J. Clin. 2020;70:7–30. doi: 10.3322/caac.21590. - DOI - PubMed
    1. Linehan W.M., Schmidt L.S., Crooks D.R., Wei D., Srinivasan R., Lang M., Ricketts C.J. The Metabolic Basis of Kidney Cancer. Cancer Discov. 2019;9:1006–1021. doi: 10.1158/2159-8290.CD-18-1354. - DOI - PubMed
    1. Weiss R.H. Metabolomics and Metabolic Reprogramming in Kidney Cancer. Semin. Nephrol. 2018;38:175–182. doi: 10.1016/j.semnephrol.2018.01.006. - DOI - PMC - PubMed
    1. Tong W.H., Sourbier C., Kovtunovych G., Jeong S.Y., Vira M., Ghosh M., Romero V.V., Sougrat R., Vaulont S., Viollet B., et al. The glycolytic shift in fumarate-hydratase-deficient kidney cancer lowers AMPK levels, increases anabolic propensities and lowers cellular iron levels. Cancer Cell. 2011;20:315–327. doi: 10.1016/j.ccr.2011.07.018. - DOI - PMC - PubMed