TAp73 promotes anabolism

Abstract

Metabolic adaptation has emerged as a hallmark of cancer and a promising therapeutic target, as rapidly proliferating cancer cells adapt their metabolism increasing nutrient uptake and reorganizing metabolic fluxes to support biosynthesis. The transcription factor p73 belongs to the p53-family and regulates tumorigenesis via its two N-terminal isoforms, with (TAp73) or without (ΔNp73) a transactivation domain. TAp73 acts as tumor suppressor, at least partially through induction of cell cycle arrest and apoptosis and through regulation of genomic stability. Here, we sought to investigate whether TAp73 also affects metabolic profiling of cancer cells. Using high throughput metabolomics, we unveil a thorough and unexpected role for TAp73 in promoting Warburg effect and cellular metabolism. TAp73-expressing cells show increased rate of glycolysis, higher amino acid uptake and increased levels and biosynthesis of acetyl-CoA. Moreover, we report an extensive TAp73-mediated upregulation of several anabolic pathways including polyamine and synthesis of membrane phospholipids. TAp73 expression also increases cellular methyl-donor S-adenosylmethionine (SAM), possibly influencing methylation and epigenetics, and promotes arginine metabolism, suggestive of a role in extracellular matrix (ECM) modeling. In summary, our data indicate that TAp73 regulates multiple metabolic pathways that impinge on numerous cellular functions, but that, overall, converge to sustain cell growth and proliferation.

TAp73 loss favors Smad-independent TGF-β signaling that drives EMT in pancreatic ductal adenocarcinoma

“…Previous studies reported the p53 homolog TAp73 as involved in cancer development through cell growth/death regulatory mechanisms and more recently in tumor angiogenesis 44 or metabolic adaptation. 45 Interestingly, although TAp73 has gained some importance within the cancer field, the significance of its altered expression in various cancers has not yet been clearly defined in terms of its clinical translation into cancer. 46 In parallel, several studies have confirmed the convergence of p53 and the TGF-β network, 47 two major cancer regulators driving multiple cancer-associated pathways.…”

TAp73 transcriptionally represses BNIP3 expression

“…BNIP3 expression is upregulated in lung carcinomas, and correlates with bad prognosis for patients with lung cancer. Therefore our current data, although still preliminary, might indicate TAp73/BNIP3 negative axis as a novel pathway for TAp73 tumor suppressor 100,101 BNIP3 upregulation as a consequence of TAp73 loss might therefore contribute to TAp73-dependent mitochondrial phenotype and be associated to the complex involvement of p73 [102][103][104] and the other family members in regulation of mitochondrial activity, [105][106][107][108] cell metabolism [109][110][111][112][113][114] and redox homeostasis. [115][116][117][118] However, currently it is still unclear whether the complex integration of all the p53 family members, in particular the truncated isoform of p73, DNp73, and the cancer-associated mutants of p53, impacts and affects the TAp73-dependent antagonism of BNIP3 expression and more generally of hypoxia response.…”

Application of metabolomics in sarcoma: From biomarkers to therapeutic targets

“…Through metabolomics analysis, 292 biochemical compounds were detected [44]. Among the 292 compounds, 55 and 42 significantly differential compounds were found in TAp73β-induced cells at time points 8h and 16h, respectively [44].…”

“…Through metabolomics analysis, 292 biochemical compounds were detected [44]. Among the 292 compounds, 55 and 42 significantly differential compounds were found in TAp73β-induced cells at time points 8h and 16h, respectively [44]. The TAp73-induced cells demonstrated significantly higher levels of glycolysis, amino acids and their metabolites, acetyl-CoA, polyamine, membrane phospholipids, methyl-donor S-adenosylmethionine (SAM) possibly influencing methylation and epigenetics, and arginine metabolism suggesting a role in extracellular matrix modeling [44].…”