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. 2019 Mar 26;20(6):1506.
doi: 10.3390/ijms20061506.

Cell Signaling Pathways in Mammary Carcinoma Induced in Rats with Low versus High Inherent Aerobic Capacity

Tymofiy Lutsiv  1 , John N McGinley  2 , Elizabeth S Neil  3 , Henry J Thompson  4
Affiliations

Cell Signaling Pathways in Mammary Carcinoma Induced in Rats with Low versus High Inherent Aerobic Capacity

Tymofiy Lutsiv et al. Int J Mol Sci. .

Abstract

An inverse association exists between physical activity and breast cancer incidence and outcomes. An objective indicator of an individual's recent physical activity exposure is aerobic capacity. We took advantage of the fact that there is an inherited as well as inducible component of aerobic capacity to show that experimentally induced mammary cancer is inversely related to inherent aerobic capacity (IAC). The objective of this study was to determine whether cell signaling pathways involved in the development of mammary cancer differed in rats with low inherent aerobic capacity (LIAC, n = 55) versus high inherent aerobic capacity (HIAC, n = 57). Cancer burden was 0.21 ± 0.16 g/rat in HIAC versus 1.14 ± 0.45 in LIAC, p < 0.001. Based on protein expression, cancer in LIAC animals was associated with upregulated glucose utilization, and protein and fatty acid synthesis. Signaling in cancers from HIAC rats was associated with energy sensing, fatty acid oxidation and cell cycle arrest. These findings support the thesis that pro-glycolytic, metabolic inflexibility in LIAC favors not only insulin resistance and obesity but also tumor development and growth. This provides an unappreciated framework for understanding how obesity and low aerobic fitness, hallmarks of physical inactivity, are associated with higher cancer risk and poorer prognosis.

Keywords: cell signaling; inherent aerobic capacity; mammary cancer.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Effect of inherent aerobic capacity on tumor burden. Total tumor mass per rat was computed and is displayed for rats with high (HIAC, red dot) or low (LIAC, blue dot) inherent aerobic running capacity. Cancer burden was 0.21 ± 0.16 g/rat, p < 0.001, in HIAC, which was significantly lower than in LIAC (1.14 ± 0.45, p < 0.001).
Figure 2
Figure 2
Effect of inherent aerobic capacity on tumor hormone receptor status. For all palpable mammary carcinomas, percent of cells staining positive for progesterone receptor (% PR) was determined. This data, plus the mass of the tumor determined gravimetrically at necropsy and the days post carcinogen (DPC) that the mass was initially detected by palpation were used to construct a bubble chart with the size of each bubble reflecting relative tumor mass.
Figure 3
Figure 3
Analysis of protein expression in the mammary carcinoma using a supervised clustering algorithm. Effects of high (HIAC) or low (LIAC) inherent aerobic running capacity on patterns of protein expression in the mammary carcinoma (n = 3/group) were assessed by multivariate discriminant analysis. Initially, inherent clustering patterns were determined by unsupervised analysis through principle component analysis (PCA) and complete separation of treatment groups was observed. (A) To determine contributing sources of variation, the scatter plot represents supervised analysis of the 2-class Orthogonal Projections to Latent Structures for Discriminant Analysis (OPLS-DA) model, which rotates the model plane to maximize separation due to class assignment. Complete separation of the two classes was observed. The circle on the graph is the 95% confidence ellipse for the OPLS-DA analysis; it was computed by the SIMCA software that was used for the analysis based on the Hotelling T2 statistic discriminant model. Samples lying outside the 95% confidence interval are considered outliers; (B) to determine the proteins responsible for class separation multivariate analysis was used to construct a biplot that identified influential proteins responsible for the separation between classes. The circles on the plot are graphed by SIMCA to assist with the visualization of the location of scores and loadings at the 0.5, 0.75 and 1.0 coordinates on the X and Y axes; (C) an S-plot was constructed by plotting modeled correlation in the first predictive principal component against modeled correlation from the first predictive component. Upper right and lower left regions of S-plots contain candidate proteins with both high reliability and high magnitude discriminatory proteins; (D) to determine the statistical reliability of the effects, variable importance plots were generated in which jack-knifed confidence intervals (JKCI) were created on the magnitude of covariance in the first component for the analytes assessed. Proteins with JKCI including 0 were considered not to account for separation.
Figure 3
Figure 3
Analysis of protein expression in the mammary carcinoma using a supervised clustering algorithm. Effects of high (HIAC) or low (LIAC) inherent aerobic running capacity on patterns of protein expression in the mammary carcinoma (n = 3/group) were assessed by multivariate discriminant analysis. Initially, inherent clustering patterns were determined by unsupervised analysis through principle component analysis (PCA) and complete separation of treatment groups was observed. (A) To determine contributing sources of variation, the scatter plot represents supervised analysis of the 2-class Orthogonal Projections to Latent Structures for Discriminant Analysis (OPLS-DA) model, which rotates the model plane to maximize separation due to class assignment. Complete separation of the two classes was observed. The circle on the graph is the 95% confidence ellipse for the OPLS-DA analysis; it was computed by the SIMCA software that was used for the analysis based on the Hotelling T2 statistic discriminant model. Samples lying outside the 95% confidence interval are considered outliers; (B) to determine the proteins responsible for class separation multivariate analysis was used to construct a biplot that identified influential proteins responsible for the separation between classes. The circles on the plot are graphed by SIMCA to assist with the visualization of the location of scores and loadings at the 0.5, 0.75 and 1.0 coordinates on the X and Y axes; (C) an S-plot was constructed by plotting modeled correlation in the first predictive principal component against modeled correlation from the first predictive component. Upper right and lower left regions of S-plots contain candidate proteins with both high reliability and high magnitude discriminatory proteins; (D) to determine the statistical reliability of the effects, variable importance plots were generated in which jack-knifed confidence intervals (JKCI) were created on the magnitude of covariance in the first component for the analytes assessed. Proteins with JKCI including 0 were considered not to account for separation.
Figure 4
Figure 4
Analysis of protein expression in the mammary gland using a supervised clustering algorithm. Effects of high (HIAC) or low (LIAC) inherent aerobic running on patterns of protein expression in the mammary gland (n = 7/group) were assessed by multivariate discriminant analysis. Initially, inherent clustering patterns were determined by unsupervised analysis through PCA and complete separation of treatment groups was observed. (A) To determine contributing sources of variation, the scatter plot represents supervised analysis of the 2-class OPLS-DA model, which rotates the model plane to maximize separation due to class assignment. Complete separation of the two classes was observed. The circle on the graph is the 95% confidence ellipse for the OPLS-DA analysis; it was computed by the SIMCA software that was used for the analysis based on the Hotelling T2 statistic discriminant model. Samples lying outside the 95% confidence interval are considered outliers; (B) to determine the proteins responsible for class separation multivariate analysis was used to construct a biplot that identified influential proteins responsible for the separation between classes. The circles on the plot are graphed by SIMCA to assist with the visualization of the location of scores and loadings at the 0.5, 0.75 and 1.0 coordinates on the X and Y axes; (C) an S-plot was constructed by plotting modeled correlation in the first predictive principal component against modeled correlation from the first predictive component. Upper right and lower left regions of S-plots contain candidate proteins with both high reliability and high magnitude discriminatory proteins; (D) to determine the statistical reliability of the effects, variable importance plots were generated in which JKCI were created on the magnitude of covariance in the first component for the analytes assessed. Proteins with JKCI including 0 were considered not to account for separation.
Figure 5
Figure 5
Metabolic and signaling pathway crosstalk in tissues of animals with high inherent aerobic capacity (HIAC) and animals with low inherent aerobic capacity (LIAC). (A) Greater fatty acid oxidation is upregulated by activity of AMPK and SIRT1 at the expense of glucose metabolism. Acetyl-CoA from TCA and β-oxidation activate PDK and impedes glycolysis from glucose oxidation by inhibiting PDH, whereas post TCA citrate inhibits HK and PFK attenuating glycolysis and undergoes ACLY-mediated conversion into acetyl-CoA. Further carboxylation of cytosolic acetyl-CoA into malonyl CoA is impeded by AMPK-induced inhibition of ACC. AMPK also downregulates lipogenesis (FASN, ACC) by inhibiting mTOR-SREBP-1 pathway and induces glucose uptake by upregulating GLUTs. SREBP-1 is also downregulated by SIRT1 which is in crosstalk with AMPK. In addition, inactive SRC, STAT3, AKT and PKA-CREB factors and corresponding signaling pathways are associated with HIAC tissues. Altogether, such metabolic and signaling pattern leads to cell cycle arrest (via p21 and p27), induction of apoptosis (via increase in BAX and decrease in BCL-2 levels), as well as increase in mitochondrial function (via PGC 1α); (B) glucose metabolism is upregulated in LIAC tissues at the expense of fatty acid oxidation leading to production of ROS and anabolic metabolism: AKT signaling induces glycolysis via GLUTs, HK, PFK; GLUTs are also induced by AKT-mTOR-mediated upregulation of HIF-1α and c-MYC factors; AKT-mTOR-SREBP1-stimulated ACLY and ACC promotes conversion of malonyl-CoA which inhibits CPT-1 and subsequent β oxidation of fatty acids. In turn, AKT-mTOR-SREBP1-stimulated FASN utilizes malonyl-CoA and increases lipogenesis. AKT is downstream target of SRC and FAK which can affect activity of one another. SRC also activates STAT3 and ERK1/2. cAMP, produced from stimulation of β2-AR stimulation, activates EPAC-1, which also activates ERK1/2. cAMP also activates PKA. Common downstream target of AKT, ERK1/2, and PKA is CREB. All of these as well as Notch signaling pathway induce angiogenesis (via VEGF), cell cycle progression (via stimulation of cyclin D and inhibition of p21 and p27) and downregulate apoptosis (by inducing BCL-2 and lowering BAX). Factors detected in our experimental analyses are depicted in orange, whereas those obtained from literature analyses are in gray.
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