The Roles of p53 in Mitochondrial Dynamics and Cancer Metabolism: The Pendulum between Survival and Death in Breast Cancer?

Moulder, David E; Hatoum, Diana; Tay, Enoch; Lin, Yiguang; McGowan, Eileen

doi:10.3390/cancers10060189

Cited by 63 publications

(53 citation statements)

References 181 publications

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“…We measured the expression of markers of beige fat following the treatment of primary mouse white adipocytes with media conditioned with plasma from study mice. 31 PPARα stimulation increases mitochondrial and peroxisomal fatty acid β-oxidation. These data suggest that VSG and hepatocyte p53 do not impact energy expenditure through a circulating factor; however, it is possible that an endocrine effect was not detected due to limitations of conditioned media experiments, such as factor stability, physiological concentrations, and temporal signaling that are present in vivo.…”

Section: Hepatocyte-specific P53 Ablation Promotes Compensatory Altmentioning

confidence: 99%

“…p53 regulates fatty acid oxidation by facilitating transport of fatty acids into the mitochondria and by enhancing β-oxidation of fatty acids in response to nutrient stress. 31 PPARα stimulation increases mitochondrial and peroxisomal fatty acid β-oxidation. 32 Pparα mRNA expression was increased in S-AL-KO mice when directly compared with S-AL-WT ( Figure 6D, P < .05), suggesting that in the absence of hepatocyte p53 there is a compensatory response in which Pparα expression is uncoupled from p53 regulation to increase mitochondrial fatty acid β-oxidation to reduce hepatic lipid levels.…”

Section: Hepatocyte-specific P53 Ablation Promotes Compensatory Altmentioning

confidence: 99%

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Hepatocyte p53 ablation induces metabolic dysregulation that is corrected by food restriction and vertical sleeve gastrectomy in mice

et al. 2019

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P53 has been implicated in the pathogenesis of obesity and diabetes; however, the mechanisms and tissue sites of action are incompletely defined. Therefore, we investigated the role of hepatocyte p53 in metabolic homeostasis using a hepatocytespecific p53 knockout mouse model. To gain further mechanistic insight, we studied mice under two complementary conditions of restricted weight gain: vertical sleeve gastrectomy (VSG) or food restriction. VSG or sham surgery was performed in highfat diet-fed male hepatocyte-specific p53 wild-type and knockout littermates. Shamoperated mice were fed ad libitum or food restricted to match their body weight to VSG-operated mice. Hepatocyte-specific p53 ablation in sham-operated ad libitumfed mice impaired glucose homeostasis, increased body weight, and decreased energy expenditure without changing food intake. The metabolic deficits induced by hepatocyte-specific p53 ablation were corrected, in part by food restriction, and completely by VSG. Unlike food restriction, VSG corrected the effect of hepatocyte p53 ablation to lower energy expenditure, resulting in a greater improvement in glucose homeostasis compared with food restricted mice. These data reveal an important new role for hepatocyte p53 in the regulation of energy expenditure and body weight and suggest that VSG can improve alterations in energetics associated with p53 dysregulation. K E Y W O R D Sbariatric surgery, body weight, energy expenditure, p53

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Section: Hepatocyte-specific P53 Ablation Promotes Compensatory Altmentioning

confidence: 99%

Section: Hepatocyte-specific P53 Ablation Promotes Compensatory Altmentioning

confidence: 99%

Hepatocyte p53 ablation induces metabolic dysregulation that is corrected by food restriction and vertical sleeve gastrectomy in mice

et al. 2019

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“…Furthermore, the observed overexpression of ERα ( Figure 1C) correlates well with the increased level of p53 ( Figure 6A), which is associated with inhibition of cellular growth [63]. Finally, while p53 can enhance metabolic activity by promoting OXPHOS [56], at the same time it inhibits cell cycle progression and causes a suppressive effect on growth of cancer cells.…”

Section: Discussionmentioning

confidence: 54%

“…The upregulation of p53 and the tumor-suppressive effect of Sirt3 was shown also in other types of cancers [54,55]. The p53 protein is a transcriptional regulator and tumor suppressor that activates target genes for cell cycle arrest, apoptosis, and DNA repair, and is stabilized within the nucleus upon DNA damage or oncogenic signals (reviewed by [56]). Furthermore, p53 balances mitochondrial respiration by inhibiting the glycolysis and promoting OXPHOS.…”

Section: Discussionmentioning

confidence: 99%

Sirt3 Exerts Its Tumor-Suppressive Role by Increasing p53 and Attenuating Response to Estrogen in MCF-7 Cells

et al. 2020

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Estrogen (E2) is a major risk factor for the initiation and progression of malignancy in estrogen receptor (ER) positive breast cancers, whereas sirtuin 3 (Sirt3), a major mitochondrial NAD+-dependent deacetylase, has the inhibitory effect on the tumorigenic properties of ER positive MCF-7 breast cancer cells. Since it is unclear if this effect is mediated through the estrogen receptor alpha (ERα) signaling pathway, in this study, we aimed to determine if the tumor-suppressive function of Sirt3 in MCF-7 cells interferes with their response to E2. Although we found that Sirt3 improves the antioxidative response and mitochondrial fitness of the MCF-7 cells, it also increases DNA damage along with p53, AIF, and ERα expression. Moreover, Sirt3 desensitizes cells to the proliferative effect of E2, affects p53 by disruption of the ERα–p53 interaction, and decreases proliferation, colony formation, and migration of the cells. Our observations indicate that these tumor-suppressive effects of Sirt3 could be reversed by E2 treatment only to a limited extent which is not sufficient to recover the tumorigenic properties of the MCF-7 cells. This study provides new and interesting insights with respect to the functional role of Sirt3 in the E2-dependent breast cancers.

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“…p53 is a tumor suppressor able to avoid cancer development through block of the cell cycle, programmed cell death, repair of damaged DNA and senescence. p53 is mutated in 20-30% of breast cancers and it is generally silenced by loss of upstream/downstream mechanisms 57 . Another interesting term is "cellular oxidant detoxification" which is consistent with the presence of oxidative stress in breast cancer cells 58 .…”

Section: Characterization Of Genes and Pathways Involved In Patients'mentioning

confidence: 99%

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Gliozzo

Perlasca

Mesiti

et al. 2020

Sci Rep

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Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification. Phenotype and outcome prediction using sets of selected biomarkers are well-established prediction tasks in the context of computational biology, including different prediction problems ranging from the response to a specific drug 1,2 , diagnosis and prognosis 3-5 , classification of cancer subtypes 6 , outcome and recurrence prediction 7-9 and other related prediction problems 10. State-of-the-art methods for these problems are largely based on inductive supervised models that use sets of selected biomarkers, usually represented as vectors, to predict the phenotype or outcome of interest (see, e.g. 11-13), without taking into account the relationships between individuals. Several works proposed "network-based" methods by constructing graphs of patients, in order to discover the underlying structure of the data (e.g. discovery of subtypes of diseases, clinical stratification of patients) 14-17. These methods mainly used unsupervised approaches and hence have been not specifically designed and are not appropriate for phenotype/outcome prediction problems. Recently a few works proposed semi-supervised "network-based" approaches for the prediction of the phenotype/outcome of patients, on the basis of their bio-molecular profiles (e.g. gene expression of genotypic profiles) 18,19 , including also methods able to integrate multiple sources of omics data 20 , and methods based on Supervised Random Walks 21 , specifically modified for the classification of tumors 22. In this work, we introduce a novel network-based method for modeling in the "patient space". In this context the nodes of the network represent patients through an n-dimensional set of biomarker values (e.g. a set of gene expression values), and edges represent similarities between the biomarkers of a pair of patients. Hence, this "patient-space" differs from the classical "biomarker-space", where nodes represent biomarkers and edges similarities between biomarkers and not between patients 23,24...

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The Roles of p53 in Mitochondrial Dynamics and Cancer Metabolism: The Pendulum between Survival and Death in Breast Cancer?

Cited by 63 publications

References 181 publications

Hepatocyte p53 ablation induces metabolic dysregulation that is corrected by food restriction and vertical sleeve gastrectomy in mice

Hepatocyte p53 ablation induces metabolic dysregulation that is corrected by food restriction and vertical sleeve gastrectomy in mice

Sirt3 Exerts Its Tumor-Suppressive Role by Increasing p53 and Attenuating Response to Estrogen in MCF-7 Cells

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

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