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Increased mTOR activity and metabolic efficiency in mouse and human cells containing the African-centric tumor-predisposing p53 variant Pro47Ser

  1. Keerthana Gnanapradeepan
  2. Julia I-Ju Leu
  3. Subhasree Basu
  4. Thibaut Barnoud
  5. Madeline Good
  6. Joyce V Lee
  7. William J Quinn
  8. Che-Pei Kung
  9. Rexford Ahima
  10. Joseph A Baur
  11. Kathryn E Wellen
  12. Qin Liu
  13. Zachary T Schug
  14. Donna L George
  15. Maureen E Murphy  Is a corresponding author
  1. Program in Molecular and Cellular Oncogenesis, The Wistar Institute, United States
  2. Graduate Group in Biochemistry and Molecular Biophysics, Perelman School of Medicine, University of Pennsylvania, United States
  3. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, United States
  4. Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, United States
  5. Department of Physiology and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, United States
  6. Washington University in St. Louis, United States
  7. Division of Endocrinology, Diabetes & Metabolism, Johns Hopkins University School of Medicine, United States
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Cite this article as: eLife 2020;9:e55994 doi: 10.7554/eLife.55994

Abstract

The Pro47Ser variant of p53 (S47) exists in African-descent populations and is associated with increased cancer risk in humans and mice. Due to impaired repression of the cystine importer Slc7a11, S47 cells show increased glutathione (GSH) accumulation compared to cells with wild -type p53. We show that mice containing the S47 variant display increased mTOR activity and oxidative metabolism, as well as larger size, improved metabolic efficiency, and signs of superior fitness. Mechanistically, we show that mTOR and its positive regulator Rheb display increased association in S47 cells; this is due to an altered redox state of GAPDH in S47 cells that inhibits its ability to bind and sequester Rheb. Compounds that decrease glutathione normalize GAPDH-Rheb complexes and mTOR activity in S47 cells. This study reveals a novel layer of regulation of mTOR by p53, and raises the possibility that this variant may have been selected for in early Africa.

Introduction

The p53 tumor suppressor protein serves as a master regulator of the cellular response to intrinsic and extrinsic stress. Mutations in the TP53 gene occur in more than 50% of human cancers, and this gene is well known as the most frequently mutated gene in cancer (Hollstein et al., 1991). p53 works to suppress uncontrolled cellular growth and proliferation through various pathways including apoptosis, senescence, cell cycle arrest, and ferroptosis (Stockwell et al., 2017; Vousden and Prives, 2009). More recently, a role for p53 in the control of metabolism has emerged. The metabolic functions of p53 include the regulation of mitochondrial function, autophagy, cellular redox state, and the control of lipid and carbohydrate metabolism; for review see Berkers et al., 2013; Gnanapradeepan et al., 2018.

As an integral part of its control of metabolism, p53 negatively regulates the activity of mTOR (mammalian target of rapamycin), which is a master regulator of metabolism in the cell. mTOR is a serine-threonine protein kinase that is stimulated by mitogenic signals, and phosphorylates downstream targets that in turn regulate protein synthesis and cell growth (Ben-Sahra and Manning, 2017). mTOR exists in two distinct signaling complexes: mTORC1 is primarily responsible for cell growth and protein synthesis, while mTORC2 plays roles in growth factor signaling, cytoskeletal control, and cell spreading (Liu and Sabatini, 2020). Not surprisingly, mTOR activity is frequently upregulated in a diverse range of cancers. p53 negatively regulates the mTOR pathway in part through transactivation of the target genes PTEN, TSC2, PRKAB1 and SESN1/SESN2 (Budanov and Karin, 2008; Feng et al., 2005). The regulation of mTOR by p53 is believed to couple the control of genome integrity with the decision to proliferate (Hasty et al., 2013).

TP53 harbors several functionally impactful genetic variants or single-nucleotide polymorphisms (SNPs) (Basu et al., 2018; Jennis et al., 2016; Kung et al., 2016). A naturally occurring SNP in TP53 exists at codon 47, encoding serine instead of a proline (Pro47Ser, rs1800371, G/A). This variant exists predominantly in African-descent populations, and occurs in roughly 1% of African Americans and 6% of Africans from sub-Saharan Africa (Murphy et al., 2017). The S47 variant is associated with increased risk for pre-menopausal breast cancer in African American women (Murphy et al., 2017). In a mouse model, the S47 mouse develops markedly increased incidence of spontaneous cancer, particularly hepatocellular carcinoma (Jennis et al., 2016). This variant is likewise defective in the regulation of the small subset of p53 target genes that play roles in ferroptosis sensitivity, including the cystine importer SLC7A11. As a result, increased levels of cysteine and glutathione (GSH) accumulate in cells from S47 humans and mice (Jennis et al., 2016; Leu et al., 2019). More recently, we showed that the ferroptotic defect in S47 mice leads to iron accumulation in their livers, spleens, and macrophages. We also showed that the S47 variant is positively associated with markers of iron overload in African Americans (Singh et al., 2020).

An emerging paradigm in the cancer literature is that tumor-predisposing genetic variants may paradoxically provide selection benefit to individuals, thus potentially explaining the frequency of these damaging alleles in the population. As an example, women carrying tumor-predisposing mutations in the BRCA1 gene tend to be physically larger and show increased fertility (Smith et al., 2012). Here-in we show that mice carrying a knock-in S47 allele in a pure C57Bl/6 background show increased size, lean content (muscle), and metabolic efficiency, relative to littermate mice with WT p53. We report that mouse and human S47 cells show a significant increase in mTOR activity, due in part to increased mTOR-Rheb binding in S47 cells. We propose that these attributes may have led to a positive selection for this variant in sub-Saharan Africa. Our studies shed further light on the intricate regulation that exists between p53, mTOR activity, and metabolic output, in this case mediated by GSH and the control of cellular redox state.

Results

Higher basal mTOR activity in cells containing the S47 variant

We previously showed that human lymphoblastoid cells (LCLs) that are homozygous for the S47 variant of p53 are impaired for the transcriptional regulation of less than a dozen p53 target genes, compared to cells from individuals from the same family containing WT p53 (Jennis et al., 2016). We noted that several of these genes encode proteins that play roles in the negative regulation of mTOR (Budanov and Karin, 2008; Feng et al., 2007). We confirmed via qRT-PCR that S47 LCLs show modestly decreased expression of the p53 target genes SESN1 and PTEN, and decreased transactivation of PRKAB1, relative to WT LCLs following cisplatin treatment (Figure 1—figure supplement 1A and B). These findings prompted us to assess basal mTOR activity in WT and S47 LCLs, and in MEFs from WT and S47 mice. To corroborate our findings, we also analyzed tissues from humanized p53 knock-in (Hupki) mice carrying WT and S47 alleles on a pure C57Bl/6 background, which we previously generated and characterized (Jennis et al., 2016). Western blot analysis of WT and S47 LCLs, along with multiple clones of WT and S47 MEFs, revealed increased p-S6K1 (Thr389) in S47 cells (Figure 1A). Following normalization to total S6K1, this increase ranged between two- and threefold (Figure 1B). We next compared mTOR activity in age- and sex-matched pairs of lung and muscle tissue from WT and S47 mice; we did this because mTOR activity is influenced by age and gender, and there is increased mTOR activity in female and older mice (Baar et al., 2016). We found increased levels of p-S6K1 (T389) and p-mTOR (Ser2448) in S47 lung and skeletal muscle, relative to WT tissues (Figure 1C and D). Immunohistochemical analysis of tissues from multiple age- and sex-matched WT and S47 mice confirmed these findings (Figure 1E). Interestingly, increased mTOR activity was not seen in all tissues of the S47 mouse (Figure 1—figure supplement 1C), and lung and skeletal muscle were the most consistently different between WT and S47. We also did not detect significant differences in p-AKT (Ser473) in WT and S47 lung tissue, suggesting that mTORC1 and not mTORC2 is likely responsible for the observed differences in mTOR activity (Figure 1—figure supplement 1D).

Figure 1 with 1 supplement see all
Increased markers of mTOR activity in S47 cells and tissues.

(A) Western blot analyses reveal higher phospho-S6K1 expression in S47 LCLs and S47 MEFs; the latter were obtained from two separate embryos per genotype. (B) Densitometry quantification of phospho-S6K1 protein expression in WT and S47 MEFs from four independent experiments; all values normalized to total S6K1. Error bars represent standard error, (*) p value < 0.05. (C) Whole cell lysates were extracted from three WT and three S47 mouse lungs and analyzed by western blot for the proteins indicated. Pairs 1 and 3 are lungs isolated from male mice, pair 2 is lungs isolated from female mice. Densitometry quantification of phospho-S6K1 and phospho-mTOR was performed and normalized to total S6K1 and total mTOR protein expression, respectively. (D) Whole cell lysates were extracted from WT and S47 mouse skeletal muscle and analyzed as described above. Densitometry quantification of phospho-S6, phospho-S6K1, phospho-mTOR was performed and normalized to total S6, total S6K1 and total mTOR protein expression, respectively. (E) Immunohistochemical analysis of hematoxylin and eosin (H and E), phospho-mTOR and phospho-S6K1 in WT and S47 mouse lung and skeletal tissue. Data are representative of n = 4 mice per genotype. Scale bar represents 100 μm. (F) WT and S47 MEFs were grown in glucose-free media for 16 hr, then in media containing 4.5 g/L glucose. Samples were collected at indicated time points and analyzed by western blot for p-S6K1, total S6K1, and GAPDH. (G) WT and S47 MEFs were cultured in media containing 0.1% FBS for 16 hr, followed by media containing 10% serum and samples were collected at indicated time points. Cell lysates were extracted from samples and subjected to western blot analysis for the proteins indicated.

We next sought to test the kinetics of mTOR activation in WT and S47 cells by subjecting early passage WT and S47 MEFs to nutrient deprivation, followed by monitoring of mTOR activation markers after nutrient restoration using antisera to p-S6K1 and p-mTOR. Glucose deprivation experiments revealed consistent albeit modest increases in p-S6K1 following glucose refeed in S47 MEFs, compared to WT (Figure 1F). Serum deprivation experiments revealed more pronounced results. For serum deprivation, we subjected three independent cultures each of WT and S47 MEFs to 0.1% serum for 16 hr, followed by 10% serum, after which total and phospho -S6K1 and -mTOR were monitored in a time course. S47 cells consistently showed increased induction of markers of mTOR activation after serum re-feed compared to WT cells (Figure 1G). We next performed amino acid deprivation experiments; these likewise showed increased response in S47 cells (Figure 1—figure supplement 1E). Combined densitometry results from all forms of nutrient deprivation revealed an approximately two- to threefold increase in p-S6K1 in S47 cells following nutrient restoration at 30 or 60 min (Figure 1—figure supplement 1F).

Given that mTOR plays a role in autophagy inhibition (Jung et al., 2010; White et al., 2011), we wondered whether basal autophagy or autophagic flux might be decreased in S47 cells. We were unable to see any differences in the steady state levels of LC3B or the autophagy adaptor protein p62SQSTM1 in WT and S47 MEFs or tissues, either at steady state (Figure 1—figure supplement 1G) or following HBSS treatment to induce autophagy (Figure 1—figure supplement 1H). Likewise, we failed to see differences in autophagic flux (conversion of LC3-I to LC3-II when the lysosome is inhibited (Figure 1—figure supplement 1I) between WT and S47 cells, or in cell viability after HBSS treatment (Figure 1—figure supplement 1J). Therefore, while markers of mTOR activity are clearly increased in S47 cells and tissues, this does not appear to be accompanied by alterations of basal or induced autophagy.

Enhanced mitochondrial function and glycolysis in S47 cells

To determine the functional consequences of the increased markers of mTOR activity in S47 cells, we used a Seahorse BioAnalyzer to assess the oxygen consumption rate (OCR), as well as basal and compensatory glycolytic rate in WT and S47 MEFs and LCLs. Seahorse analyses revealed that S47 LCLs show increased OCR under stressed conditions compared to WT (Figure 2A). These analyses also revealed that human S47 LCLs and mouse S47 MEFs show increased basal and compensatory glycolysis, compared to WT cells (Figure 2B and C). We next assessed glucose and glutamine consumption using a Yellow Springs Instrument (YSI) Analyzer. These analyses revealed that S47 LCLs and MEFs show significantly increased consumption of glucose and glutamine, along with increased production of lactate and glutamate, compared to WT cells (Figure 2D and E); again, multiple independent MEF lines were analyzed. Interestingly, LCLs from individuals heterozygous for the S47 variant (S47/WT), and MEFs from S47/WT mice, showed values typically intermediate between homozygous WT and S47 cells (Figure 2D and E). We next performed metabolic flux analyses in WT and S47 cells using 13C-labeled glucose. Analysis of 13C6-glucose tracing in WT and S47 MEFs provided evidence for a higher contribution of glucose carbon into the TCA cycle in S47 cells compared to WT cells, as evidenced by increased labeling of citrate, malate, aspartate, and glutamate in S47 MEFs (Figure 2—figure supplement 1A–D). We reasoned that one possibility for the increased metabolism in S47 cells might be due to increased mitochondrial content, which is regulated by mTOR (Morita et al., 2013). However, MitoTracker analyses and western blotting for mitochondrial proteins revealed no obvious increase in mitochondrial content in S47 cells (Figure 2—figure supplement 1E and F). We find no evidence that S47 LCLs and MEFs proliferate more quickly than WT cells (Jennis et al., 2016); this raises the possibility that this increased nutrient consumption may be used for biomass instead of proliferation.

Figure 2 with 1 supplement see all
Increased metabolism in S47 cells compared to WT cells.

(A) Oxygen consumption rates (OCR) in WT and S47 LCLs were assessed using the Seahorse XF Mito Stress Test. OCR was measured first in basal conditions, and following injection of oligomycin, FCCP and rotenone/antimycin. The bar graph depicts maximal OCR after FCCP injection at ~40 min timepoint; data are representative of three independent experiments performed with at least six technical replicates, presented as mean ± SD. (B–C) Basal and compensatory glycolysis in WT and S47 LCLs (B) and MEFs (C) were assessed using the Seahorse Glycolytic Rate Assay. Basal glycolysis is first measured, followed by treatment of cells with rotenone/antimycin and 2-deoxy-D-glucose (2-DG). The bar graph depicts basal glycolysis at ~1 min timepoint and compensatory glycolysis after antimycin/rotenone injection at ~22 min timepoint; data are representative of three independent experiments performed with at least 10 technical replicates. Bar graphs are presented as mean ± SD. (D–E) Consumption of glucose and glutamine from media and production of lactate and glutamate were analyzed from homozygous WT, heterozygous WT/S47 and homozygous S47 human LCLs (D) and primary MEFs (E) using a YSI-7100 Bioanalyzer. Means and SEM are shown (n = 5).

Because mTOR is known to regulate mitochondrial function (Morita et al., 2013; Schieke et al., 2006; Ye et al., 2012), we next assessed the impact of mTOR inhibitors on mitochondrial function in WT and S47 cells. Seahorse analysis of WT and S47 LCLs revealed that S47 cells are less susceptible to inhibition of oxygen consumption rate and maximal respiration by the mTOR inhibitors rapamycin (Figure 3A and B) and Torin1 (Figure 3C and D). This finding was not due to altered efficacy of each inhibitor, as evidenced by similar decreases in p-mTOR and p-S6 in WT and S47 cells following treatment with rapamycin (Figure 3—figure supplement 1A) and Torin1 (Figure 3E), and by the finding that very high concentrations of Torin1 were able to inhibit oxygen consumption equally well in both WT and S47 cells (Figure 3—figure supplement 1B). These data support the possibility of an S47 dependent, but mTOR-independent, effect on OCR as well.

Figure 3 with 1 supplement see all
S47 mitochondria show decreased sensitivity to mTOR inhibition.

(A) Oxygen consumption rate (OCR) as measured by the Seahorse XF Mito Stress Test in WT and S47 LCLs treated with 200 nM of rapamycin for 24 hr. (B) Bar graph depicts maximal OCR after FCCP injection at ~40 min timepoint; fold changes between rapamycin treated and untreated samples are shown. Data are representative of two independent experiments performed with at least 10 technical replicates. (C) OCR as measured by the Seahorse XF Mito Stress Test in WT and S47 LCLs treated with 100 nM of Torin1 for 24 hr. (D) Bar graph depicts maximal OCR after FCCP injection at ~40 min timepoint; fold changes between Torin1 treated and untreated samples are shown. Data are representative of two independent experiments performed with at least eight technical replicates. (E) WT and S47 LCLs were treated with 100 nM of Torin1, harvested at indicated time points after treatment and analyzed for the shown mTOR markers via western blot.

Increased mTOR activity in S47 is due to increased mTOR-Rheb interaction

We next sought to identify the mechanism underlying increased mTOR activity in S47 cells. Unfortunately, although we identified decreased mRNA levels of some mTOR regulators in S47 cells, we found no evidence for significant differences at the protein level of any p53-regulated mTOR regulators in steady state MEFs (Figure 4—figure supplement 1A) or following treatment with Nutlin to induce p53 (Figure 4—figure supplement 1B). Therefore, we turned to a key regulator of mTOR activity, the small GTPase Rheb, which binds and activates mTOR (Long et al., 2005). We monitored the mTOR-Rheb association in WT and S47 MEFs using the technique of proximity ligation assay (PLA), which quantitatively detects protein-protein interactions. PLA experiments revealed that there were consistently increased mTOR-Rheb complexes in S47 cells, compared to WT; this was true in multiple replicates, in multiple MEF clones, and using single antibody controls that showed no signal (Figure 4A). Quantification of multiple experiments revealed an approximately two-fold increase in mTOR-Rheb complexes in S47 cells compared to WT (Figure 4B), which is consistent with all of our analyses of mTOR activity.

Figure 4 with 1 supplement see all
Increased mTOR-Rheb binding in S47 cells is due to decreased GAPDH-Rheb binding.

(A–B) An in situ proximity ligation assay (PLA) was performed in WT and S47 MEFs. Each red dot represents an interaction between mTOR-Rheb or GAPDH-Rheb as indicated; scale bar represents 50 μm. The samples were counterstained with DAPI to detect nuclei. Cells stained in the absence of one primary antibody were used as a negative control. (B) Quantification of the mTOR-Rheb interactions, measured as the average number of PLA signals per nuclei. Data were quantified by counting the number of cells in five random fields per experimental condition. (***) p-value<0.001, Student’s t-test. (C) Lysates extracted from WT and S47 skeletal tissue were immunoprecipitated with anti-Rheb. The amount of co-precipitating mTOR and GAPDH, as well as immunoprecipitated Rheb, were assessed by western blot. Whole cell lysate (WCL) is shown on the right. (D) Quantification of the amount of GAPDH bound to Rheb, divided by total Rheb pulled down, in WT and S47 skeletal tissue, n = 3 independent experiments, (*) p-value<0.05.

One regulator of the mTOR-Rheb interaction is the cytosolic enzyme GAPDH. This enzyme binds to Rheb and sequesters it from mTOR in cultured cells, in a manner that is regulated by glucose levels (Lee et al., 2009). First, we confirmed that the interaction between Rheb and GAPDH is detectable in the skeletal muscle of mice using IP-western, and moreover that this interaction is regulated by glucose (Figure 4—figure supplement 1C). Next, we performed immunoprecipitation (IP)-western of Rheb in skeletal muscle extracts from WT and S47 mice; we found that there was increased mTOR, and significantly decreased GAPDH, in Rheb IPs from S47 skeletal muscle compared to WT (Figure 4C). The combined data from three independent IPs of Rheb in WT and S47 skeletal muscle revealed an approximately twofold decrease in the amount of GAPDH co-precipitating with Rheb in S47 skeletal muscle compared to WT (p<0.05, Figure 4D). In contrast, there were no differences in the levels of mTOR, Rheb and GAPDH in these extracts (Figure 4C – WCL, Figure 4—figure supplement 1D). Consistent with our IP-western findings, PLA analyses corroborated that the GAPDH-Rheb association is markedly decreased in S47 MEFs relative to WT MEFs (Figure 4A). Confocal microscopy analyses revealed no significant differences in the cellular localization of Rheb at the lysosome, as assessed by LAMP1 localization, nor were there any differences in TSC2 localization at the lysosome in WT and S47 MEFs (Figure 4—figure supplement 1E). The combined data support the premise that the increased mTOR activity in S47 cells may be due to increased Rheb-mTOR association, caused by a decreased association of Rheb with GAPDH. We sought to test this hypothesis, and identify the underlying mechanism.

GAPDH is a multi-functional enzyme that is known to be sensitive to redox status (Brandes et al., 2009; Chernorizov et al., 2010). We hypothesized that the increased glutathione (GSH) levels in S47 cells (Leu et al., 2019) might alter the redox state of GAPDH and impact its ability to bind to Rheb. We first verified that lung tissue and skeletal muscle from S47 mice possess increased GSH compared to WT tissues, as assessed by an increased ratio of reduced versus oxidized glutathione (GSH:GSSG) (Figure 5A). We then validated that the GSH alkylating agent diethylmaleate (DEM) could successfully decrease the level of GSH, and the GSH/GSSG ratio, in these cells (Figure 5A); these findings are consistent with previously published findings by our group (Leu et al., 2019). Notably, DEM treatment of immortalized S47 MEFs (iMEFs) caused a dramatic decrease in markers of mTOR activity (p-mTOR and p-S6K1), showing that modulation of GSH, even for as little as five hours, can impact mTOR activity (Figure 5B). Moreover, we found that the impact of DEM on mTOR activity was enhanced by the addition of glutamate, which decreases cystine import through system Xc(-), leading to decreased level of GSH (Figure 5B).

Figure 5 with 1 supplement see all
Increased glutathione drives decreased GAPDH-Rheb binding in S47 cells.

(A) WT and S47 lung (left) and skeletal muscle (center) were assessed for GSH/GSSG ratio (mean ± SD, n = 3). WT and S47 immortalized MEFs (iMEFs), either untreated or treated with 50 μM DEM for 5 hr, were analyzed for GSH/GSSG ratio (mean ± SD, n = 4). (B) WT and S47 iMEFs were untreated or treated with 50 μM of DEM or 50 μM of DEM + 0.5 mM glutamate for 5 hr and protein lysates were analyzed by western blot for indicated mTOR markers. (C) Whole cell lysates were extracted from WT and S47 mouse lung (left) and skeletal (center) tissue. Proteins were cross-linked with BMH, resolved by SDS/PAGE, and detected by western blotting with a GAPDH specific antibody (Top). Untreated protein lysates were analyzed by western blot analysis for total GAPDH (Bottom). WT and S47 iMEFs were treated with 50 μM of DEM for 5 hr and protein lysates were analyzed as described (right). (D) WT cells were treated with PBS or 3 mM GSH for 24 hr. S47 cells were treated with PBS for 24 hr, PBS for 5 hr, 100 μM BSO for 24 hr, or 50 μM DEM for 5 hr. IP of the lysates with anti-Rheb followed by western analysis for associated GAPDH and Rheb (top panel). The same lysates were analyzed by western blotting for GAPDH and Rheb (bottom panel). (E–F) Proximity ligation analysis (PLA) was performed in WT and S47 MEFs treated with 50 μM of DEM for 5 hr and analyzed as described in Figure 4A–B. Scale bar is 30 μm.

We next sought to test the hypothesis that the redox state of GAPDH was altered in WT and S47 cells. Toward this end, we employed cross-linking experiments using the cysteinyl cross-linking agent bismaleimidohexane (BMH), which cross-links cysteine residues within 13 Å by covalently conjugating free (reduced) sulfhydryl groups (Green et al., 2001). We treated freshly isolated lung and skeletal muscle lysates from WT and S47 mice, and from immortalized WT and S47 MEFs (iMEFs), with BMH. Cysteinyl-crosslinked proteins were resolved on SDS-PAGE gels and compared to untreated extracts. Notably, we found consistent differences in GAPDH cross-linking patterns in S47 samples compared to WT, as evidenced by altered mobility of GAPDH on SDS-PAGE of BMH-treated samples (Figure 5C). Moreover, the altered mobility of GAPDH in S47 cells could be reversed following glutathione depletion by DEM treatment (Figure 5C) or by the compound BSO (buthionine sulfoximine; Figure 5—figure supplement 1A) which inhibits GSH biosynthesis. Treatment with the compound erastin, which inhibits the system Xc(-) cystine transporter and decreases GSH, also led to altered mobility of GAPDH in S47 and WT cells (Figure 5—figure supplement 1B). We next tested the impact of modulating GSH on the interaction between Rheb and GAPDH in WT and S47 cells using both IP-western and PLA. By IP-western we found that supplementation of culture media with exogenous GSH decreased the Rheb-GAPDH interaction in WT cells; conversely, depleting free GSH using either BSO or DEM increased the Rheb-GAPDH interaction in S47 cells (Figure 5D). These findings were corroborated using PLA, which revealed that depleting GSH using either DEM or BSO completely restores GAPDH-Rheb complex formation and mTOR-Rheb complex formation in S47 cells, to levels equivalent to WT cells (Figure 5E and F; Figure 5—figure supplement 1C). The combined data support the conclusion that the increased GSH pool in S47 cells affects the status of reactive cysteines in GAPDH, and the ability of this protein to bind and sequester Rheb, thereby leading to increased Rheb-mTOR interaction and increased mTOR activity in S47 cells.

Enhanced metabolic efficiency of S47 mice

mTOR is known to regulate body mass and muscle regeneration (Laplante and Sabatini, 2012; Yoon, 2017). We therefore next assessed body weight and fat/lean content in age-matched male mice of WT and S47 genotypes. We also tracked body weight with age of multiple male and female sibling littermate mice of WT/WT, WT/S47, and S47/S47 genotypes in our colony. S47 mice showed significantly increased weight with time, compared to WT/WT and WT/S47 sibling littermates (Figure 6—figure supplement 1A). Body composition analysis using nuclear magnetic resonance revealed that S47 mice had significantly increased fat and lean content, compared to WT mice (Figure 6A; Figure 6—figure supplement 1B). We next analyzed the metabolic activities of WT and S47 mice using a comprehensive lab animal monitoring system (CLAMS) over the course of 48 hr. In this analysis, S47 mice showed comparable locomotor activity to WT mice but reduced food intake, oxygen consumption, and heat production (Figure 6B). These CLAMS data suggested that S47 mice might possess enhanced metabolic efficiency compared to WT mice, and prompted us to assess the response of WT and S47 mice to exercise challenge.

Figure 6 with 1 supplement see all
Increased size and improved metabolic efficiency in S47 mice.

(A) Nuclear magnetic resonance (NMR) studies revealed S47 mice have increased body weight, increased fat content and increased lean content, n = 7 WT mice, n = 8 S47 mice. (***) p-value<0.001, (****) p-value<0.0001. Bar graphs are presented as mean ± SD. (B) Changes in metabolic parameters for WT mice (blue) and S47 mice (red) were determined by using the Comprehensive Lab Animal Monitoring System for 48 hr. Parameters assessed includes oxygen consumption, carbon dioxide production, respiratory exchange rate, energy expenditure, total food intake, and locomotor activity. The data are representative of five 6-week old male mice per genotype and are normalized to total body weight. (C) WT and S47 mice (n = 6–7) were subjected to a treadmill study of increasing intensity over time. Oxygen consumption (VO2) is normalized to body mass. (D) Mean basal VO2 in WT and S47 mice. (E) VO2 range in WT and S47 mice determined by subtracting the mean basal VO2 from the VO2 max, obtained during the most strenuous point of exercise at the tail end of the treadmill study. (F) Lactate dehydrogenase (LDH) levels measured in the serum of WT and S47 mice obtained before and after the treadmill study. (*) p-value<0.05, Student’s t-test.

We subjected WT and S47 mice to treadmill exercise with increasing intensity over time. For this analysis, we studied eight age-matched male mice of each genotype during a 50 min forced exercise at increasing speed and slope. During this time course, oxygen consumption and serum metabolites were quantified. Consistent with our CLAMs experiment, S47 mice started with lower basal VO2 and exhibited generally lower VO2 for the work being performed; however, as they approached the final, most strenuous point of the exercise, the VO2 values in WT and S47 converged, so the VO2 range for S47 mice was significantly greater than WT mice (Figure 6C–E). Analysis of serum metabolites and proteins before and after exercise revealed decreased lactate dehydrogenase (LDH) levels in the sera of S47 mice, which is indicative of decreased muscle damage in S47 mice compared to WT (Figure 6F). Additionally, we found that Ki-67 staining in S47 skeletal muscle was consistently increased relative to WT muscle, suggesting an enhanced ability for S47 muscle to recover (Figure 6—figure supplement 1C). In these tissues, we found no differences in p53 level or markers of mitochondrial content (Figure 6—figure supplement 1D and E), nor were there other differences in other serum metabolites between WT and S47 mice (Figure 6—figure supplement 1F). These exercise data, like the CLAMS data, point to increased metabolic efficiency in S47 mice relative to WT mice. To address this further, we analyzed a small cohort of WT and S47 mice on a continuous strenuous treadmill run. Although the numbers are small, we found that three out of four WT mice failed to complete a 60 min strenuous run, while three out of four S47 successfully completed this run (Figure 6—figure supplement 1G).

Discussion

In this study, we report that cells and mice with the S47 variant of p53 have increased mTOR activity and evidence for increased metabolic efficiency. The animals also display increased mass and signs of superior fitness. Our data support the premise that the enhanced mTOR activity is due, at least in part, to the higher levels of GSH in S47 cells and tissues. The increased GSH results in impaired ability of the redox sensitive protein GAPDH to bind to Rheb. This leads to greater mTOR-Rheb binding, resulting in increased mTOR activity in S47 cells and tissues. These data indicate that, along with pH (Walton et al., 2018), cellular redox status can also regulate mTOR activity, in a manner controlled by p53. We show that oxidative metabolism in S47 cells is less sensitive to mTOR inhibitors, thus tying these two phenotypes together; this is not surprising, as a link between mTOR and a number of cellular metabolic processes is well known (Morita et al., 2013; Schieke et al., 2006).

We see evidence for increased mTOR activity only in certain tissues of the S47 mouse, so the metabolic impact of this genetic variant appears to be influenced by tissue type and cellular environment. At present, we do not know if this tissue specificity is due to differences in GSH level, or to altered mTOR-Rheb or GAPDH-Rheb interactions in different tissues, or to other parameters. We also see evidence for some unexpected findings regarding the increase in mTOR activity in S47 cells: given that mTOR negatively regulates autophagy (Jung et al., 2010), we expected to see differences in steady state autophagy or autophagic flux in WT and S47 cells, but we found no evidence for this. This finding may be due to the rather complex relationship between mTOR and autophagy (Jung et al., 2010; White et al., 2011), and/or that other signaling pathways regulate autophagy aside from mTOR, including the PI3K pathway, GTPases, and calcium (Yang et al., 2005).

The increased lean content in S47 mice likely contributes to the increased fitness observed in these mice. Human studies have shown that mTOR activation is crucial for human muscle protein synthesis (Dickinson and Rasmussen, 2011). Treatment with the well-studied mTOR inhibitor rapamycin blocks the effects of amino acid ingestion on mTOR activity and leads to decreased protein synthesis in human skeletal muscle (Dickinson and Rasmussen, 2011; Drummond et al., 2009). Additionally, mTOR signaling driven through IGF-1 plays a key role in promoting muscle hypertrophy (Coleman et al., 1995; Musarò et al., 2001; Vandenburgh et al., 1991). One caveat of this study, however, is that we do not directly demonstrate that the increased mTOR activity in S47 mice is causing their increased lean content or superior performance on treadmill assays. Transient treatment with mTOR inhibitors elicits highly complex and often contrasting effects on energy expenditure and treadmill performance, likely due to the existence of feedback loops and the effect of inhibitors on multiple organ systems in the mouse. As just two examples: rapamycin has shown contrasting effects on energy expenditure in animals, depending on how long mice are treated (Fang et al., 2013); similarly, treatment of mice with rapamycin has shown limited impact on treadmill endurance, despite causing decreased expression of genes involved in mitochondrial biogenesis and oxidative phosphorylation in the muscle (Ye et al., 2013). Possibly, the most consistent findings in the literature reflect the general consensus that mTORC1 is involved in mechanisms that drive increased muscle mass (Goodman, 2019) and that heightened mTOR activity leads to enhanced muscle recovery after exercise (Song et al., 2017; Yoon, 2017). It remains to be tested if these are the pathways affected in S47 mice.

We hypothesize that the more efficient metabolism and enhanced fitness provided by the S47 variant may have once provided carriers with a bio-energetic advantage in Sub-Saharan western Africa, where this variant is most common. For example, those carrying the S47 SNP may have possessed superior athletic prowess and/or ability to withstand famine (see model, Figure 7). This metabolic advantage may explain the high frequency of this genetic variant in sub-Saharan Africa, despite the fact that it predisposes individuals to cancer later in life. Another positive selection for this variant in Africa may include an improved ability to withstand malaria infection: we recently reported that the S47 variant alters the immune micro-environment in mice and confers improved response to the malaria toxin hemozoin (Singh et al., 2020).

Proposed model.

(A) Proposed model of how S47 contributes to increased metabolism. The elevated levels of GSH alter the redox state of the S47 cell, in turn affecting GAPDH conformation and impairing GAPDH-Rheb binding. This results in increased mTOR-Rheb binding, leading to increased mTOR activity and resulting an overall increase in metabolism in S47 mice, as seen by increased fat and lean content. (B) Broad impact of S47 variant: although providing an adaptive advantage to individuals residing in sub-Saharan Africa at one point in time, it now predisposes modern humans with this SNP to cancer.

Our findings provide further support for the growing premise that some tumor suppressor genetic variants may provide evolutionary selection benefit (Vicens and Posada, 2018). For example, women who carry the BRCA1/2 mutation exhibit increased size and enhanced fertility when compared to controls (Smith et al., 2012). Similarly, people with Li Fraumeni syndrome who inherit germline mutations in TP53, as well as mice with tumor-derived germline mutations in Tp53, demonstrate increased fitness endurance (Wang et al., 2013); however, this is due to increased mitochondrial content, which we do not see in S47 cells. A common genetic variant in TP53 at codon 72, encoding proline at amino acid 72, confers increased longevity while conversely causing increased cancer risk (Zhao et al., 2018). In contrast, the arginine 72 variant of p53 induces increased expression of LIF, which improves fecundity (Kang et al., 2009). The take home message from all these studies is that the diverse roles of tumor suppressor proteins like p53 in metabolism, fertility, and fitness may allow for positive selection for certain variants, even at the expense of increased cancer risk. In mice, this increased cancer risk occurs quite late in life, well past reproductive selection (12–18 months). More needs to be done to analyze cancer risk in S47 humans. A more comprehensive understanding of the function of tumor suppressor genetic variants, including the S47 SNP, will enable improved understanding of cancer risk, along with superior personalized medicine approaches, with the ultimate goal of improving clinical outcomes and survival of people who carry this variant.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Strain, strain background (M. musculus)C57Bl/6; P47/S47 HupkiJennis et al., 2016PMID:27034505
Cell lines (M. musculus)WT/S47 Primary MEFsJennis et al., 2016PMID:27034505
Cell lines (Homo-sapiens)WT/S47 LCLsCoriell InstituteGM18870
GM18871
GM18872
PMID:27034505
AntibodyAnti-phospho-mTOR (Ser2448) (Rabbit polyclonal)Cell Signaling TechnologiesCat# 2971
RRID:AB_330970
WB (1:1000) IHC (1:100)
AntibodyAnti-mTOR (7C10) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 2983
RRID:AB_2105622
WB (1:1000)
PLA (1:500)
AntibodyAnti-phospho-S6K1 (Thr389) (Rabbit polyclonal)Cell Signaling TechnologiesCat# 9205
RRID:AB_330944
WB (1:1000)
AntibodyAnti-phospho-S6K1 (Thr421) (Rabbit polyclonal)Thermo Fisher ScientificCat# PA5-37733
RRID:AB_2554341
IHC (1:100)
AntibodyAnti-S6K1 (Rabbit polyclonal)Cell Signaling TechnologiesCat# 9202
RRID:AB_331676
WB (1:1000)
AntibodyAnti-GAPDH (14C10) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 2118
RRID:AB_561053
WB (1:10,000)
PLA (1:1000)
AntibodyAnti-TFAM (Rabbit polyclonal)AbcamCat# ab131607
RRID:AB_11154693
WB (1:2000)
AntibodyAnti-MTCO1 (Mouse monoclonal)AbcamCat# ab14705
RRID:AB_2084810
WB (1:2000)
AntibodyAnti-SDHA (Rabbit polyclonal)Cell Signaling TechnologiesCat# 5839
RRID:AB_10707493
WB (1:1000)
AntibodyAnti-Tom20 (F10) (Mouse monoclonal)Santa Cruz BiotechnologyCat# sc-17764
RRID:AB_628381
WB (1:100)
AntibodyAnti-phospho-Akt (Ser473) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 4060
RRID:AB_2315049
WB (1:1000)
AntibodyAnti-p62 (Rabbit polyclonal)Cell Signaling TechnologiesCat# 5114
RRID:AB_10624872
WB (1:1000)
AntibodyAnti-LC3B (D11) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 3868
RRID:AB_2137707
WB (1:1000)
AntibodyAnti-HSP90 (C45G5) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 4877S
RRID:AB_2233307
WB (1:1000)
AntibodyAnti-Rheb (E1G1R) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 13879
RRID:AB_2721022
WB (1:1000)
IF (1:800)
AntibodyAnti-TSC2 (D93F12) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 4308
RRID:AB_10547134
WB (1:1000)
IF (1:100)
AntibodyAnti-Akt (Rabbit
polyclonal)
Cell Signaling TechnologiesCat# 9272
RRID:AB_329827
WB (1:1000)
AntibodyAnti-Deptor (Rabbit polyclonal)Novus BiologicalsCat# NBP1-49674
RRID:AB_10011798
WB (1:1000)
AntibodyAnti-phospho-AMPKα (Thr172) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 2535
RRID:AB_331250
WB (1:1000)
AntibodyAnti-Ki67 (D3B5) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 12202
RRID:AB_2620142
IHC (1:400)
AntibodyAnti-phospho-S6 (Ser235/236) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 4856
RRID:AB_2181037
WB (1:1000)
AntibodyAnti-S6 (54D2) (Mouse monoclonal)Cell Signaling TechnologiesCat# 2317
RRID:AB_2238583
WB (1:1000)
AntibodyAnti-Rheb (B-12) (Mouse monoclonal)Santa Cruz BiotechnologyCat# sc-271509
RRID:AB_10659102
PLA (1:50)
IP (1:20)
AntibodyAnti-phospho-4EBP1 (Ser65) (Rabbit polyclonal)Cell Signaling TechnologiesCat# 9451
RRID:AB_330947
WB (1:1000)
AntibodyAnti-4EBP1 (53H11) (Rabbit monoclonal)Cell Signaling TechnologiesCat# 9644
RRID:AB_2097841
WB (1:1000)
AntibodyAnti-p53
(1C12) (Mouse monoclonal)
Cell Signaling TechnologiesCat# 2524
RRID:AB_331743
WB (1:1000)
AntibodyAnti-Sco2 (Rabbit polyclonal)LS BioCat# LS-C349015WB (1:500)
AntibodyAnti-LAMP1(H4A3) (Mouse monoclonal)Santa Cruz BiotechnologyCat# sc-20011
RRID:AB_626853
IF (1:50)
AntibodyAlexa Fluor 594 AffiniPure Goat Anti-Rabbit IgG (H+L)Jackson ImmunoResearch LaboratoriesCat #111-585-144
RRID:AB_2307325
IF (1:100)
AntibodyAlexa Fluor 488 AffiniPure Goat Anti-Mouse IgG (H+L)Jackson ImmunoResearch LaboratoriesCat #115-545-062
RRID:AB_2338845
IF (1:100)
AntibodyPeroxidase AffiniPure F(ab’)2 Goat Anti-Mouse IgG (H+L)Jackson ImmunoResearch LaboratoriesCat #115-036-062
RRID:AB_2307346
WB (1:10,000)
AntibodyPeroxidase AffiniPure F(ab’)2 Goat Anti-Rabbit IgG (H+L)Jackson ImmunoResearch LaboratoriesCat #111-036-003
RRID:AB_2337942
WB (1:10,000)
AntibodyPeroxidase AffiniPure F(ab’)2 Donkey Anti-Rabbit IgG (H+L)Jackson ImmunoResearch LaboratoriesCat #711-036-152
RRID:AB_2340590
WB (1:10,000)
Commercial assay or kitSeahorse XF Cell Mito Stress KitAgilentPart Number: 1037015–100
Commercial assay or kitSeahorse XF Glycolytic Rate AssayAgilentPart Number: 103344–100
Commercial assay or kitGSH/GSSG Glo AssayPromegaCat# V6611
Commercial assay or kitPLA Duolink In Situ Starter KitSigma AldrichDUO92101
Chemical compound, drugRapamycinSigma AldrichR0395; CAS 53123-88-9
Chemical compound, drugTorin1Cayman ChemicalItem No. 10997; CAS 1222998-36-8
Chemical compound, drugBMH (bismaleimidohexane)Thermo Fisher ScientificCat# 22330; CAS 4856-87-5
Chemical compound, drugDEM (diethylmaleate)Sigma AldrichD97703; CAS 141-05-9
Chemical compound, drugBSO (buthionine sulfoximine)Cayman ChemicalItem no. 14484; CAS 83730-53-4
Chemical compound, drugErastinCayman ChemicalItem no. 17754; CAS 571203-78-6
Chemical compound, drugGSH (L-Glutathione)Sigma AldrichCat# G6013, CAS 70-18-8
Chemical compound, drugL-MethionineSigma AldrichCat# M9625,
CAS 63-68-3
Chemical compound, drugL-GlutamineThermo Fisher ScientificCat# 25030081
Chemical compound, drugL-CystineSigma AldrichCat# C6727
Software, algorithmImageJNIHRRID:SCR_003070
Software, algorithmGraphPad PrismGraphPadRRID:SCR_002798
OtherPyruvate-free DMEMThermo Fisher ScientificCat# 21013024
OtherGlucose-free DMEMThermo Fisher ScientificCat# 11966025
OtherEBSSThermo Fisher ScientificCat# 24010043
OtherMEM VitaminThermo Fisher ScientificCat# 11120052
OtherMEM Amino AcidsThermo Fisher ScientificCat# 1130051
OtherHBSSThermo Fisher ScientificCat# 14025092
OtherTrypan BlueThermo Fisher ScientificCat# 15250061
OtherProtein G AgaroseThermo Fisher ScientificCat# 15920010
OtherMitoTracker GreenThermo Fisher ScientificCat# M7514

Mammalian cell culture

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All cell lines have been confirmed of identity using STR profiling; most were obtained by the Coriell Institute. All were confirmed to be free of mycoplasma prior to each experiment. WT and S47 MEFs were generated and maintained as previously described (Jennis et al., 2016). Human WT LCLs (Catalog ID GM18870) and S47 LCLs (Catalog ID GM18871) were obtained from the Coriell Institute (Camden, New Jersey) and maintained as previously described (Jennis et al., 2016). MEF cultured cells were grown in DMEM (Corning Cellgro) supplemented with 10% fetal bovine serum (HyClone, GE Healthcare Life Sciences) and 1% penicillin/streptomycin (Corning Cellgro). Human LCLs were grown in RPMI (Corning Cellgro) supplemented with 15% heat inactivated fetal bovine serum (HyClone, GE Healthcare Life Sciences) and 1% penicillin/streptomycin (Corning Cellgro). Cells were grown in a 5% CO2 humidified incubator at 37°C. For serum starvation experiments, cells were starved in DMEM containing 0.1% FBS for 16 hr. Following starvation, DMEM containing 10% FBS was re-introduced and cells were harvested at 0 min, 10 min, 30 min, 1 hr, 2 hr, and 8 hr after this point. For glucose starvation experiments, cells were starved in glucose-free DMEM (Thermo Fisher Scientific 11966025) for 16 hr. Following starvation, DMEM containing 4.5 g/L glucose was re-introduced and cells were harvested at 0 min, 10 min, 30 min, 1 hr, 2 hr, and 5 hr after this point. For amino acid starvation experiments, cells were starved for 4 hr in EBSS (Thermo Fisher Scientific 24010043) containing 25 mM glucose, 0.5 mM Glutamine, 1X MEM Vitamin (Thermo Fisher Scientific 11120052), 0.2% FBS, 25 mM HEPES, 1X Penicillin/Streptomycin. Following starvation, the same media recipe now containing 1X MEM Amino Acids (Thermo Fisher 1130051) was re-introduced and cells were harvested at 0 min, 10 min, 30 min, 1 hr, 2 hr, and 5 hr after this point. For HBSS experiments, cells were washed once with PBS (Corning 21–031-CV) and then incubated with HBSS (Thermo Fisher Scientific 14025092) for 0, 2, or 6 hr. Viability was assessed using Trypan Blue (Thermo Fisher Scientific 15250061).

Western blot

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For western blot analyses, 50–100 µg of protein was resolved over SDS-PAGE gels using 10% NuPAGE Bis-Tris precast gels (Life Technologies) and were then transferred onto polyvinylidene difluoride membranes (IPVH00010, pore size: 0.45 mm; Millipore Sigma). Membranes were blocked for 1 hr in 5% bovine albumin serum (Sigma Aldrich, A9647). The following antibodies were used for western blot analyses: phospho-mTOR 1:1000 (Cell Signaling, 2971), mTOR 1:1000 (7C10, Cell Signaling, 2983), phospho-p70S6K1 1:1000 (Cell Signaling, 9205), p70S6K1 1:1000 (Cell Signaling, 9202), GAPDH 1:10,000 (14C10, Cell Signaling, 2118), TFAM 1:2000 (Abcam, ab131607), MTCO1 1:2000 (Abcam, ab14705), SDHA 1:1000 (Cell Signaling, 5839), Tom20 1:100 (F-10, Santa Cruz, sc17764), phospho-Akt (D9E, Cell Signaling, 4060), p62 1:1000 (Cell Signaling, 5114), LC3B 1:1000 (D11, Cell Signaling, 3868), HSP90 1:1000 (Cell Signaling, 4877S), Rheb 1:1000 (E1G1R, Cell Signaling, 13879), TSC2 1:1000 (D93F12, Cell Signaling, 4308), Akt 1:1000 (Cell Signaling, 9272), Deptor 1:1000 (Novus Bio, NBP1-49674SS), phospho-AMPKα (Cell Signaling, 2535). Rabbit or mouse secondary antibodies conjugated to horseradish peroxidase were used at a 1:10,000 dilution (Jackson Immunochemicals), followed by a 5-min treatment with ECL (Amersham, RPN2232). Protein levels were detected using autoradiography and densitometry analysis of protein content was conducted using ImageJ software (NIH, Rockville, MD).

Immunohistochemistry

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Tissues were harvested and fixed in formalin overnight at 4°C, followed by a wash with 1X PBS and were then placed in 70% ethanol prior to paraffin embedding. The Wistar Institute Histotechnology Facility performed the tissue embedding and sectioning. For the immunohistochemistry (IHC) studies, paraffin embedded tissue sections were de-paraffinized in xylene (Fisher, X5-SK4) and re-hydrated in ethanol (100%–95%-85–75%) followed by distilled water. Samples underwent antigen retrieval by steaming slides in 10 mM Citrate Buffer (pH 6). Endogenous peroxidase activity was quenched with 3% hydrogen peroxide and slides were incubated in blocking buffer (Vector Laboratories, S-2012) for 1 hr. The slides were incubated with phospho-p70S6K1 (1:100, ThermoFisher Scientific, PA5-37733) or phospho-mTOR (1:100, Cell Signaling, 2971) primary antibody overnight at 4°C. The following day, slides were washed with PBS and incubated with HRP-conjugated secondary antibody for 30 min. Antibody complexes were detected using DAB chromogen (D5637). Light counterstaining was done with hematoxylin. Slides were imaged using the Nikon 80i upright microscope and at least four fields were taken per section.

Co-immunoprecipitation

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Following overnight seeding of WT and S47 immortalized MEFs, the cells were washed with 1X DPBS and the cell culture medium was replaced with 1% FBS DMEM medium [pyruvate-free DMEM (Thermo Fisher Scientific #21013024) supplemented with 1% FBS, 1% penicillin/streptomycin, 0.1 mM L-Methionine, 0.5 mM L-Glutamine, and 0.033 mM L-Cystine]. WT cells were treated with PBS or 3 mM GSH for 24 hr; while the S47 cells were treated with PBS for 5 hr or 24 hr, 100 μM BSO for 24 hr, or 50 μM DEM for 5 hr. Cells were harvested and centrifuged at 500 x g for 10 min at 4°C. The cell pellets were resuspended in CHAPS Lysis Buffer (1X DPBS with 0.3% CHAPS and freshly added protease inhibitors) at 4°C. Cell disruption was performed by passing the cells through a 23-gauge needle attached to a 1 ml syringe. The skeletal muscles were homogenized using the Qiagen Tissue Lyser II. Total cellular homogenates were rotated/nutated at 4°C for 30 min, and spun at 11,000 x g for 20 min at 4°C. Protein extracts (3 mg per reaction) were incubated with the Rheb antibody (Santa Cruz, sc-271509) overnight at 4°C. The Rheb-immunocomplexes were captured using recombinant protein G agarose (Thermo Fisher Scientific, 15920010) at 4°C for 2 hr. Resins were washed three times using the CHAPS Lysis Buffer. Equal volumes of 2x Laemmli Sample Buffer were added to each reaction, samples were heated for 10 min at 100°C. The Rheb-associated proteins were analyzed by western blotting, using GAPDH (Cell Signaling, 2118), Rheb (Cell Signaling, 13879), and mTOR (7C10, Cell Signaling, 2983) antibodies.

Mitochondrial metabolism and mTOR inhibition assays

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The oxygen consumption rate (OCR) and glycolytic rate were determined using the Seahorse XF MitoStress Assay and the Seahorse XF Glycolytic Rate Assay, respectively, according to the manufacturer’s protocol. Cells were plated one day prior to the assay, LCLs at 100,000 cells/well and MEFs at 60,000 cells/well. LCLs were treated with 200 nM rapamycin, 100 nM Torin1 or 1 μM Torin1 for 24 hr prior to running the MitoStress Assay. To assess differences in mTOR inhibition, WT and S47 LCLs were treated with 200 nM rapamycin or 100 nM Torin1 for 0 min, 10 min, 30 min, 1 hr, 2 hr, or 6 hr and subsequently cells were harvested for western blot analysis. To determine mitochondrial content, WT and S47 MEFs were incubated with 500 nM of MitoTracker Green (ThermoFisher Scientific, M7514) for 1 hr at 37°C. Cells were then spun down, washed once with PBS, spun down and resuspended in PBS. The FACSCelesta (BD Biosciences) was used to detect fluorescence and at least 10,000 events were measured per sample.

Metabolite measurements

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Media was collected after 24 hr after plating LCLs or MEFs, and the YSI-71000 Bioanalyzer was used to determine glucose, glutamine, lactate and glutamate levels as previously described (Londoño Gentile et al., 2013). For the metabolic flux studies, cells were incubated in uniformly labeled 13C-glucose (25 mM) as indicated in the figure legends. For intracellular extracts, after incubation, the culture medium was aspirated and cells were washed once in ice-cold PBS. Metabolites were extracted by adding a solution of methanol/acetonitrile/water (5:3:2) to the well. Plates were incubated at 4°C for 5 min on a rocker and then the extraction solution was collected. The metabolite extract was cleared by centrifuging at 15,000 x g for 10 min at 4°C. Supernatants were transferred to LC-MS silanized glass vials with PTFE caps and either run immediately on the LC-MS or stored at −80°C. LC-MS analysis was performed on a Q Exactive Hybrid Quadrupole-Orbitrap HF-X MS (ThermoFisher Scientific) equipped with a HESI II probe and coupled to a Vanquish Horizon UHPLC system (ThermoFisher Scientific). 0.002 ml of sample is injected and separated by HILIC chromatography on a ZIC-pHILIC 2.1 mm. Samples were separated by ammonium carbonate, 0.1% ammonium hydroxide, pH 9.2, and mobile phase B is acetonitrile. The LC was run at a flow rate of 0.2 ml/min and the gradient used was as follows: 0 min, 85% B; 2 min, 85% B; 17 min, 20% B; 17.1 min, 85% B; and 26 min, 85% B. The column was maintained at 45°C and the mobile phase was also pre-heated at 45°C before flowing into the column. The relevant MS parameters were as listed: sheath gas, 40; auxiliary gas, 10; sweep gas, 1; auxiliary gas heater temperature, 350°C; spray voltage, 3.5 kV for the positive mode and 3.2 kV for the negative mode. Capillary temperature was set at 325°C, and funnel RF level at 40. Samples were analyzed in full MS scan with polarity switching at scan range 65 to 975 m/z; 120,000 resolution; automated gain control (AGC) target of 1E6; and maximum injection time (max IT) of 100 milliseconds. Identification and quantitation of metabolites was performed using an annotated compound library and TraceFinder 4.1 software. The ‘M+X’ nomenclature refers to the isotopologue for that given metabolite. Isotopologues are chemically identical metabolites that differ only in their number of carbon-13 atoms. For instance, ‘M+two citrate’ means that two of the six carbons in citrate are carbon-13 while the other four are carbon-12. ‘M+four citrate’ means that four of the six carbons in citrate are carbon-13 while the other two are carbon-12. Isotopologue fractional labeling was corrected for carbon-13 natural abundance.

GSH/GSSG abundance and BMH crosslinking

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Relative GSH/GSSG abundance was measured using the GSH/GSSG-Glo Assay (Promega catalog #V6611), according to the manufacturer’s instruction. Immortalized WT and S47 MEFs were generated and maintained as previously described (Jennis et al., 2016; Leu et al., 2019). For BMH crosslinking studies, the WT and S47 cells were cultured in 1% FBS DMEM medium and treated with PBS or 50 μM diethyl maleate (DEM, ThermoFisher Scientific AC114440010) for 5 hr; PBS or 100 μM BSO (Cayman Chemical item #14484) for 24 hr; or DMSO or 2 μM Erastin (Cayman Chemical item #17754) for 24 hr. Proteins were extracted from cultured cells or mouse tissue (skeletal muscle, lungs) using 1X DPBS (Thermo Fisher Scientific 14190144) supplemented with 0.5% IGEPAL CA-630, 1 mM PMSF, 6 μg/ml aprotinin, and 6 μg/ml leupeptin at 4 °C. The tissues were homogenized using the Wheaton Overhead Stirrer. Total cellular homogenates were pulse sonicated using the Branson digital sonifier set at 39% amplitude. Total protein extracts (100 μg per reaction) were incubated with or without 1 mM BMH (Thermo Fisher Scientific 22330) for 30 min at 30°C. The samples were quenched with an equal volume of 2x Laemmli Sample Buffer (BioRad 1610737) supplemented with 5% β-Mercaptoethanol (BioRad 1610710) and heated for 10 min at 100°C. The protein samples were size fractionated on Novex 4–20% Tris-Glycine Mini Gels (Thermo Fisher Scientific XP04200BOX) at room temperature and subsequently transferred overnight onto Immuno-Blot PVDF membranes (BioRad 1620177) at 4°C. The membranes were blocked with 3% nonfat dry milk (BioRad 1706404) in 1X PBST for 30 min at room temperature and incubated with the GAPDH antibody (Cell Signaling Technology 2118) overnight with rotation/nutation at 4°C. After washing the blots in 1X PBST, the membranes were incubated with Donkey anti-Rabbit (Jackson ImmunoResearch 711-036-152) for 2 hr at room temperature. Membrane-immobilized protein detection used ECL western blotting detection reagents (GE Healthcare RPN2106; Millipore Sigma GERPN2106).

Proximity ligation assay

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Cells were grown on Lab-Tek II eight-well chamber slides, and were either untreated, treated with 50 μM diethyl maleate (DEM, ThermoFisher Scientific AC114440010) for 5 hr or treated with 10 μM of buthionine sulfoximine for 24 hr (BSO, Cayman Chemicals, 83730-53-4) and fixed with 4% paraformaldehyde (Electron Microscopy Sciences, 15710). Protein-protein interactions were assessed using the PLA Duolink in situ starter kit (Sigma Aldrich, DUO92101) following the manufacturer’s protocol. The following primary antibodies were used: Rheb 1:50 (B-12, Santa Cruz, sc271509), mTOR 1:500 (7C10, Cell Signaling, 2983), GAPDH 1:1000 (14C10, Cell Signaling, 2118). ImageJ software (NIH, Rockville, MD) was used to quantify PLA signals.

Immunofluorescence staining

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Cells were fixed in 4% paraformaldehyde for 10 min, followed by 3 PBS washes and then permeabilization with 0.25% Triton X-100 for 10 min. Cells were washed 3x with PBS, blocked for 1 hr in a PBS solution containing 1% bovine serum albumin and 5% normal goat serum (Jackson Immunoresearch 005-000-121). Cells were incubated overnight at 4°C with the following primary antibodies diluted in blocking buffer: Rheb 1:800 (Cell Signaling Technologies, #13879), TSC2 1:100 (Cell Signaling Technologies, #4308), LAMP1 1:50 (Santa Cruz, sc-20011). Cells were washed with PBS and incubated with the following secondary antibodies at 37°C for 45 min: Alexa Fluor 594 AffiniPure Goat Anti-Rabbit IgG (Jackson Immunoresearch 111-585-144) and Alexa Fluor 488 AffiniPure Goat Anti-Mouse IgG (Jackson Immunoresearch 115-545-062). The cells were mounted with media containing DAPI and images were captured using the Leica TSC SP5 microscope.

Body composition and metabolic cage studies

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WT and S47 mice in a pure C57Bl/6 background are previously described (Jennis et al., 2016). All mouse studies were performed in accordance with the guidelines in the Guide for the Care and Use of Laboratory Animals of the NIH and all protocols were approved by the Wistar Institute Institutional Animal Care and Use Committee (IACUC). Mice were fed an ad libitum diet and were housed in plastic cages with a 12 hr/12 hr light cycle at 22°C unless otherwise stated. Fat and lean content were measured in live male mice at 6 weeks of age using nuclear magnetic resonance (NMR) with the Minispec LF90 (Bruker Biospin, Billerica, MA). Indirect calorimetry was conducted to assess metabolic capabilities in mice (Oxyman/Comprehensive Laboratory Animal Monitoring System (CLAMS); Columbus Instruments). Data for the WT mice was previously published in supplemental data of Kung et al., 2016. Six-week-old mice were single caged, provided with water and food ad libitum and allowed to acclimate to the cages for 2 days. Oxygen consumption (VO2) and carbon dioxide production (VCO2) were recorded for 48 hr using an air flow of 600 ml/min and temperature of 22°C. Respiratory exchange ratio (RER) is calculated as VCO2/VO2 and heat (kcal/h) is calculated by 3.815 + 1.232*(RER). Photodetectors were used to measure physical activity (Optovarimex System; Columbus Instruments).

Treadmill and serum metabolite studies

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Mice were allowed to acclimate to the metabolic treadmill (Columbus Instruments) for 5 min before beginning their runs. The treadmill was then set to 5 m/min and speed increased by 5 m/min every 2 min until 20 m/min was reached. Upon reaching 20 m/min, the incline was increased by 5° every 2 min until reaching a maximum of 25 degrees. Mice were allowed to run at this maximum speed and incline until exhaustion, defined by the mice spending 10 continuous seconds on the shock grid. Lactate (Nova Biomedical) and glucose (One Touch) measurements were taken using test strips just prior to treadmill entry and immediately after exhaustion using handheld meters. Tail blood was also taken prior to treadmill entry and immediately after exhaustion and metabolites measured using the Vettest serum analyzer (Idexx Laboratories).

Statistical analysis

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Unless otherwise stated, all experiments were performed in triplicate. The two-tailed unpaired Student t-test was performed. All in vitro data are reported as the mean ± SD unless stated otherwise, and in vivo are reported as the mean ± SE. Statistical analyses were performed using GraphPad Prism, p-values are as follows: (*) p-value<0.05, (**) p-value<0.01, (***) p-value<0.001, (****) p-value<0.0001. For the CLAMs and mouse exercise data, the Wilcoxon rank-sum test was used to compare the differences between S47 and WT mice.

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Decision letter

  1. Päivi M Ojala
    Senior Editor; University of Helsinki, Finland
  2. Lydia WS Finley
    Reviewing Editor; Memorial Sloan Kettering Cancer Center, United States
  3. Gina DeNicola
    Reviewer; H Lee Moffitt Cancer Center and Research Institute, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This work highlights a novel mechanism whereby the p53 tumor suppressor protein regulates mTOR activity, via the control of cellular redox state. Being based on using both mouse and human tissues and cells containing an African-centric variant of p53, the work has direct implications for understanding cancer disparities in African-descent populations. This work suggests that enhanced mTOR activity and metabolic efficiency may have been selected for in early Africa, making this variant one of a growing number of cancer-predisposing variants.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Increased mTOR activity and metabolic efficiency in mice and human cells containing the tumor-prone p53 variant Pro47Ser" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Gina DeNicola (Reviewer #2).

This manuscript was reviewed by three experts in the field. While all three reviewers agreed that the findings were of considerable potential interest, all reviewers agreed that the manuscript in its current form did not reach the level of mechanistic depth required for publication in eLife. The reviewer consensus was that considerable additional experiments would be required and that these added experiments could not realistically be done in two months.

Specifically, the reviewers noted two major areas which would require additional experimentation. First, the link between the S47 variant of p53, GSH levels and mTOR activity required additional experiments and orthogonal approaches to validate the effect of p53 P47S on GSH levels and to connect altered GSH to mTORC1 activation. Second, further experiments are required to assess the degree to which the reported metabolic phenotypes arise as a consequence of increased mTORC1 activity. Given the length of time required to perform the recommended experiments, we are returning the manuscript and reviews to you so that they may guide you as you seek publication elsewhere. Should you wish to resubmit to eLife at a future date, we would be happy to reconsider a resubmission with increased mechanistic detail.

Reviewer #1:

In this manuscript, the authors present a metabolic characterization of cells and mice expressing the S47 variant of the tumor suppressor p53. S47 lines have increased basal mTOR activity, increased nutrient consumption and increased capacity for oxygen consumption. Mechanistically, the increase in mTOR activity is linked to alterations in GSH levels. in vivo, mice expressing S47 are bigger but do not consume more food or exhibit more activity. Nevertheless, these mice demonstrate increased capacity to respond to an exercise challenge. While the manuscript reports several interesting observations, how the various phenotypes relate to each other is unclear. While the link between p53, redox stress, and mTOR activity via alterations in GAPDH levels is a very interesting new mode of metabolic control by p53, several important experiments are required to strengthen this link.

Essential revisions:

1) The link between GSH levels and mTOR activity requires further confirmation. What do manipulations to intracellular GSH do to mTOR activity in S47 cells? Is increasing GSH, for example adding eGSH, sufficient to activate mTOR in WT cells? Is activation of mTOR downstream of altered cysteine regulation by the S47 variant? Would adding cell-permeable reduced cysteine in the form of N-acetylcysteine restore mTOR activity back to normal levels? Additionally, orthogonal validation of the changes in Rheb interactions following GSH manipulation, for example by co-IP, would further strengthen this point.

2) To what extent whole-body metabolic phenotypes are related to changes in mTOR activity is not addressed. In some cases, the phenotypes are not consistent with genetic manipulation of S6K, which increases organismal oxygen consumption and exercise capacity. There are many potential reasons why mice might perform differently in response to an exercise challenge: for example, altered muscle fiber type composition, among others. Furthermore, how the in vitro metabolic assays, showing increased nutrient consumption and increased oxygen consumption, relate to the in vivo phenotypes is not clear.

Reviewer #2:

In their manuscript "Increased mTOR activity and metabolic efficiency in mice and human cells containing the tumor predisposing p53 variant Pro47Ser", Gnanapradeepan et al., describe an association between this variant and increased mTOR activity, oxidative mitochondrial metabolism, and metabolic efficiency. This finding is interesting and may serve to explain how this variant, found in sub-Saharan African populations, may confer fitness at the organismal level while concomitantly increasing cancer risk. Mechanistically, they propose that increased glutathione content in P47S cells promotes the association between mTOR and Rheb via an alteration in the redox state of GAPDH. However, this mechanism needs further development.

Essential revisions:

1) DEM is a non-specific alkylator of thiols, including those found in proteins. It is therefore not surprising that DEM would influence the mobility in BMH-treated samples. The authors should repeat this experiment with BSO. Further, as the authors have proposed SLC7A11 as the mediation of redox/GSH modulator, does erastin treatment produce the same effect?

2) The authors have not demonstrated a causal role for the altered redox state of GAPDH in the association between mTOR and Rheb. Can exogenous GSH (ester) and/or SLC7A11 overexpression influence mTOR signaling in WT cells? Can a GAPDH mutant that is insensitive to oxidation (C152S) influence the mTOR-Rheb interaction?

3) The authors do not show evidence that cysteine and GSH in are higher in S47 cells. Rather, they reference their previous study. When using compounds like DEM and BSO, evidence for efficacy in their current experiments is necessary. Further, GSH levels in mouse tissues should be included to support the link between mTOR signaling and GSH. Do only the mouse tissues with altered mTOR signaling have altered GSH?

Reviewer #3:

Gnanapradeepan et al., set out to dissect the metabolic outputs of a p53 variant (S47), present in African-descent populations and associated with increased cancer risk. Their previous work on the S47 variant affecting several negative regulators of mTORC1 signaling prompted them to examine mTORC1 signaling. Using both cell culture and their previously-reported S47 mouse model, the authors observe enhanced phosphorylation of mTORC1 substrate p-S6K1 in S47 cells and tissues, but no detectable changes in the phosphorylation of 4EBP1, another key substrate of mTORC1. Mechanistically, they attribute the enhanced mTORC1 activity to an increased mTOR-Rheb interaction, at the expense of Rheb-GAPDH interaction.

The authors then set out to determine the functional consequence of mTORC1 activity in metabolism and find several interesting metabolic phenotypes associated with the S47 variant, including increased glycolytic rate and OCR, enhanced muscle and body mass, and improved metabolic efficiency.

While some of the findings are interesting, it is unclear whether the changes in mTORC1 activity are connected to the observed metabolic phenotypes. Surprisingly, the authors have overlooked the well-known primary modes of regulation of mTORC1 activity (e.g., mTOR/Rheb localization at the lysosome, TSC phosphorylation, and AMPK signaling), but instead focus on the obscure GAPDH-Rheb-mTOR interaction, the contribution of which to mTORC1 activation is unclear and only reported under low-glucose conditions. More work is necessary to substantiate the authors' conclusions.

Essential revisions:

1) The metabolic phenotypes that the authors associate with mTORC1 signaling are based on correlative observations. The authors should attempt to rescue some of the observed phenotypes in Figure 2D, 2E, and Figure 6 using both allosteric (Rapamycin) and catalytic (Torin-1) inhibitors of mTORC1.

2) Figure 3: What are the authors' thoughts on Rapamycin failing to decrease OCR in S47 cells to the same extent as in WT cells? Is this due to partial inhibition of mTORC1 activity in these cells? Are the FKBP12 levels similar in these cells? Is ECAR dependent on mTORC1? The authors should also use a catalytic inhibitor of mTORC1 (e.g., Torin-1) and compare its effect to the effect of Rapamycin. It would be necessary to determine inhibition of mTORC1 signaling by these inhibitors on multiple substrates of mTORC1 (at least, S6K1 and 4EBP1) to be able to draw conclusions on this.

3) Figure 5: More work is needed to substantiate the mechanistic conclusions on increased mTORC1 activity in S47 cells because of an enhanced mTORC1-Rheb association and reduced GAPDH-Rheb. Using Proximity Ligation Assay (PLA) assays, the authors suggest that the Rheb-GAPDH association is dependent on the redox status of GAPDH, and in a reduced state as in S47, the GAPDH-Rheb interaction would decrease, allowing for more Rheb to associate with mTOR. While this is an interesting hypothesis, the authors should first validate the reagents used in their studies and employ orthogonal methods to verify these findings. The antibodies used for the PLA should be validated using knockdown experiments followed by PLA. Complementary experiments such as immunoprecipitation assays should be used to test the mTOR-Rheb and GAPDH-Rheb localization interaction changes in WT vs. S47 contexts.

4) Since the primary mode of activation of mTORC1 is at the lysosomes, the authors should test whether mTORC1, Rheb and TSC2 localization on the lysosomes is affected or not in the S47 cells. No conclusions regarding overactivation of mTORC1 in S47 cells can be made without this crucial bit of data.

5) Figure 2F, G and Figure 2—figure supplement 1A, B: The authors conclude a higher flux of glucose carbon into the TCA cycle in S47 cells. Why do the authors think that these minute changes (the differences appear to be less than 5%) are biologically significant? Can the authors observe significant changes in glycolytic intermediates in the 13C6-Glucose tracing experiment? Could the changes in OCR be due to an effect on NAD/NADH instead?

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for choosing to send your work entitled "Increased mTOR activity and metabolic efficiency in mice and human cells containing the tumor-prone p53 variant Pro47Ser" for consideration at eLife. Your letter of appeal has been considered by a Senior Editor along with a Reviewing Editor and the original manuscript reviewers, and we are prepared to consider a revised submission with no guarantees of acceptance.

All three reviewers agreed that the initial submission required extensive additional data to support the connection between p53 S47 and the GSH-GAPDH-Rheb-mTORC1 axis. Additionally, the reviewers believed while the in vivo data were quite interesting for the potential to provide an evolutionary explanation for the persistence of the p53 S47 polymorphism, these findings lacked mechanistic connection to the mTORC1 phenotypes reported in vitro.

All reviewers have now reviewed your outline for revision experiments and agree that these proposed experiments will likely go a long way to strengthening the link between p53 S47, GSH, and mTORC1 activity in vitro. However, because the GAPDH-Rheb-mTORC1 relationship is not a well-established mechanism for mTORC1 activation in the field (to date, there is only one report supporting this mechanism, which is proposed to occur under glucose deprived conditions), the authors will need to perform experiments both to address this hypothesis and rule out other, more canonical explanations for regulation of mTORC1 activity. One reviewer emphasized that experiments testing the effects of mTORC1 activation in response to amino acids, insulin, or glucose in WT vs S47 cells are needed to clarify the mechanism of regulation of mTORC1 (related to Figure 1F).

While all reviewers appreciated that in vivo experiments probing the relevance of mTORC1 activity to the in vivo phenotypes are challenging, in the absence of such experiments the in vivo metabolic phenotypes cannot be directly linked to mTORC1 activation. One reviewer suggested that ex vivo experiments could be used to address this point. For example, the authors could perform a Seahorse assay on muscle fibers, for which protocols from Agilent are available, and/or PLA/western blots after modulation of glutathione. Lacking such experiments, the animal phenotypes remain correlative and disconnected from the cell culture experiments, and the authors would need to revise the manuscript to ensure that the conclusions drawn from the in vitro data were separated from presentation of the in vivo data.

We will look forward to receiving a revised article and a file describing the changes made in response to the decision and review comments for further consideration. Of course, given the current circumstances, we do not expect a revised manuscript within two months. Instead we give you as much time as you need to submit a revised submission. Please be aware that the revision requirements outlined above, along with your response to these comments, will be published should your article be accepted for publication, subject to author approval. In this event you acknowledge and agree that the these will be published under the terms of the Creative Commons Attribution license.

https://doi.org/10.7554/eLife.55994.sa1

Author response

[Editors’ note: The authors appealed the original decision. What follows is the authors’ response to the first round of review.]

Reviewer #1:

Essential revisions:

1) The link between GSH levels and mTOR activity requires further confirmation. What do manipulations to intracellular GSH do to mTOR activity in S47 cells? Is increasing GSH, for example adding eGSH, sufficient to activate mTOR in WT cells? Is activation of mTOR downstream of altered cysteine regulation by the S47 variant? Would adding cell-permeable reduced cysteine in the form of N-acetylcysteine restore mTOR activity back to normal levels? Additionally, orthogonal validation of the changes in Rheb interactions following GSH manipulation, for example by co-IP, would further strengthen this point.

We thank the reviewer for these suggestions. We have extensively revised the figures to address these concerns. Specifically, we now show:

a) GSH levels clearly regulate mTOR activity. Specifically, five hour treatment of cells with the GSH alkylator DEM, and more dramatically the combination of DEM with glutamate (inhibits cystine import), causes a significant decrease in mTOR activity in S47 cells (new Figure 5B).

b) Moreover, by IP-western we now show: exogenous GSH decreases the Rheb-GAPDH complex in WT cells, while conversely, either DEM or BSO (the latter decreases GSH biosynthesis) increases the Rheb-GAPDH complex in S47 cells. These treatments do not affect the steady state level of Rheb or GAPDH (New Figure 5D). We hope the reviewer agrees that these new data strengthen our conclusions.

2) To what extent whole-body metabolic phenotypes are related to changes in mTOR activity is not addressed. In some cases, the phenotypes are not consistent with genetic manipulation of S6K, which increases organismal oxygen consumption and exercise capacity. There are many potential reasons why mice might perform differently in response to an exercise challenge: for example, altered muscle fiber type composition, among others. Furthermore, how the in vitro metabolic assays, showing increased nutrient consumption and increased oxygen consumption, relate to the in vivo phenotypes is not clear.

As discussed and agreed upon in the previously approved plan of attack, we have added a paragraph in the Discussion section that addresses this topic:

“One caveat of this study, however, is that we do not directly demonstrate that the increased mTOR activity in S47 mice is causing their increased lean content or superior performance on treadmill assays. Transient treatment with mTOR inhibitors elicits highly complex and often contrasting effects on energy expenditure and treadmill performance, likely due to the existence of feedback loops and the effect of inhibitors on multiple organ systems in the mouse. As just two examples: rapamycin has shown contrasting effects on energy expenditure in animals, depending on how long mice are treated (Fang et al., 2031); similarly, treatment of mice with rapamycin has shown limited impact on treadmill endurance, despite causing decreased expression of genes involved in mitochondrial biogenesis and oxidative phosphorylation in the muscle (Ye et al., 2013). Possibly the most consistent findings in the literature reflect the general consensus that mTORC1 is involved in mechanisms that drive increased muscle mass (Goodman, 2019) and that heightened mTOR activity leads to enhanced muscle recovery after exercise (Song er al., 2017; Yoon, 2017). It remains to be tested if these are the pathways affected in S47 mice.”

Reviewer #2:

Essential revisions:

1) DEM is a non-specific alkylator of thiols, including those found in proteins. It is therefore not surprising that DEM would influence the mobility in BMH-treated samples. The authors should repeat this experiment with BSO. Further, as the authors have proposed SLC7A11 as the mediation of redox/GSH modulator, does erastin treatment produce the same effect?

These are excellent suggestions, and we have done them. We now show that pre-treatment of cells with BSO for 24 hours also causes the mobility of BMH-cross-linked GAPDH in S47 cells to ‘restore’ to the mobility in WT cells (new Figure 5—figure supplement 1A). Likewise, we show that Erastin treatment causes GAPDH mobility in WT and S47 cells to shift toward identical mobility (new Figure 5—figure supplement 1B).

2) The authors have not demonstrated a causal role for the altered redox state of GAPDH in the association between mTOR and Rheb. Can exogenous GSH (ester) and/or SLC7A11 overexpression influence mTOR signaling in WT cells? Can a GAPDH mutant that is insensitive to oxidation (C152S) influence the mTOR-Rheb interaction?

This has been done. As indicated above (see similar comment 1 from reviewer 1), we now show that DEM (inactivates GSH), or a combination of DEM and glutamate (inhibits cystine import from the SLC7A11 transporter) markedly inhibits mTOR activity in S47 cells (new Figure 5B). We also show by IP-western that exogenous GSH decreases the Rheb-GAPDH complex in WT cells, while conversely, either DEM or BSO (the latter decreases GSH biosynthesis) increases the Rheb-GAPDH complex in S47 cells, while these treatments do not affect the steady state level of Rheb or GAPDH. (New Figure 5D). We thank the reviewer for these suggestions.

3) The authors do not show evidence that cysteine and GSH in are higher in S47 cells. Rather, they reference their previous study. When using compounds like DEM and BSO, evidence for efficacy in their current experiments is necessary. Further, GSH levels in mouse tissues should be included to support the link between mTOR signaling and GSH. Do only the mouse tissues with altered mTOR signaling have altered GSH?

These are excellent points, and this has been done. In new Figure 5A, we now show increased GSH:GSSG ratio in lung and skeletal muscle of S47 mice, compared to WT, and in S47 MEFs compared to WT. Further, in this figure, we show that the DEM successfully decreases these GSH:GSSG ratios in WT and S47 MEFs (new Figure 5A). With regard to the question “Do only the mouse tissues with altered mTOR signaling have altered GSH?”, we too wonder about this point and while our data in liver suggest this may be the case, we think it is premature to come to that conclusion. Therefore we state in the Discussion section that this is an open question: “We see evidence for increased mTOR activity only in certain tissues of the S47 mouse, so the metabolic impact of this genetic variant appears to be influenced by tissue type and cellular environment. At present we do not know if this tissue specificity is due to differences in GSH level, or to altered mTOR-Rheb or GAPDH-Rheb interactions in different tissues, or to other parameters.”

Reviewer #3:

[…] While some of the findings are interesting, it is unclear whether the changes in mTORC1 activity are connected to the observed metabolic phenotypes. Surprisingly, the authors have overlooked the well-known primary modes of regulation of mTORC1 activity (e.g., mTOR/Rheb localization at the lysosome, TSC phosphorylation, and AMPK signaling), but instead focus on the obscure GAPDH-Rheb-mTOR interaction, the contribution of which to mTORC1 activation is unclear and only reported under low-glucose conditions. More work is necessary to substantiate the authors' conclusions.

We agree with the reviewer that the Rheb-mTOR-GAPDH axis was an unusual place to end up, but we now make clearer in the text that we turned to this pathway only when all of our efforts analyzing other conventional mTOR regulators did not yield differences. We now show in supplemental data some of our cumulative data that the WT/S47 variation does not appear to impact p-AMPK or TSC2 levels or Rheb localization at the lysosome (Figure 4—figure supplement 1A, B and E). We have also clarified the text to this regard.

Importantly, we now also confirm in mouse skeletal muscle the major findings of Lee et al., 2009 in Supplemental figure 4- figure 1C, where we show that the Rheb-GAPDH interaction exists in skeletal muscle, and moreover that this interaction is regulated by glucose deprivation (as shown in cultured cells in Lee et al., 2009). We thank the reviewer for this suggestion, as we feel that addressing it has strengthened our findings.

Essential revisions:

1) The metabolic phenotypes that the authors associate with mTORC1 signaling are based on correlative observations. The authors should attempt to rescue some of the observed phenotypes in Figure 2D, 2E, and Figure 6 using both allosteric (Rapamycin) and catalytic (Torin-1) inhibitors of mTORC1.

This is an excellent suggestion. As requested, we have now performed the Seahorse experiments with Torin1 (new Figure 3C and D), and found that these data corroborate our findings with rapamycin. With regard to exercise studies, as described above in reviewer 1 point 1, we now clearly state:

“One caveat of this study, however, is that we do not directly demonstrate that the increased mTOR activity in S47 mice is causing their increased lean content or superior performance on treadmill assays. […] It remains to be tested if these are the pathways affected in S47 mice.”

2) Figure 3: What are the authors' thoughts on Rapamycin failing to decrease OCR in S47 cells to the same extent as in WT cells? Is this due to partial inhibition of mTORC1 activity in these cells? Are the FKBP12 levels similar in these cells? Is ECAR dependent on mTORC1? The authors should also use a catalytic inhibitor of mTORC1 (e.g., Torin-1) and compare its effect to the effect of Rapamycin. It would be necessary to determine inhibition of mTORC1 signaling by these inhibitors on multiple substrates of mTORC1 (at least, S6K1 and 4EBP1) to be able to draw conclusions on this.

As requested, we have used Torin1 and found similar effects to rapamycin; also as suggested by this Reviewer, we have performed a time course analysis on WT and S47 LCLs treated with Torin1 and rapamycin, and show that these agents appear comparable in their ability to inhibit markers of mTOR activity in WT and S47 cells (new Figure 3E; new Figure 3—figure supplement1A). We also show that very high concentrations of Torin1 (1 uM) are capable of fully inhibiting OCR in both WT and S47 LCLs (new Figure 3—figure supplement 1B). The combined data suggest that it is the sensitivity of OCR to mTOR inhibition, and not the ability of the drugs to inhibit mTOR, that is distinct between WT and S47 cells. We propose that there may be mTOR-independent effects of S47 on OCR.

3) Figure 5: More work is needed to substantiate the mechanistic conclusions on increased mTORC1 activity in S47 cells because of an enhanced mTORC1-Rheb association and reduced GAPDH-Rheb. Using Proximity Ligation Assay (PLA) assays, the authors suggest that the Rheb-GAPDH association is dependent on the redox status of GAPDH, and in a reduced state as in S47, the GAPDH-Rheb interaction would decrease, allowing for more Rheb to associate with mTOR. While this is an interesting hypothesis, the authors should first validate the reagents used in their studies and employ orthogonal methods to verify these findings. The antibodies used for the PLA should be validated using knockdown experiments followed by PLA. Complementary experiments such as immunoprecipitation assays should be used to test the mTOR-Rheb and GAPDH-Rheb localization interaction changes in WT vs. S47 contexts.

These excellent points are very similar to reviewer 1 Essential revision #1, and we have taken them very seriously. These corroborative approaches have been done. Specifically:

a) We show that GSH impacts mTOR activity, as five hour treatment of cells with the GSH alkylator DEM, and more dramatically the combination of DEM with glutamate (inhibits cystine import), causes a significant decrease in mTOR activity in S47 cells (new Figure 5B).

b) Moreover, by IP-western we now show: exogenous GSH decreases the Rheb-GAPDH complex in WT cells, while conversely, either DEM or BSO (the latter decreases GSH biosynthesis) increases the Rheb-GAPDH complex in S47 cells, as shown by IP-western. These treatments do not affect the steady state level of Rheb or GAPDH. (New Figure 5D). We hope the reviewer agrees that these new data strengthen our conclusions, and again we are grateful for the suggestions.

4) Since the primary mode of activation of mTORC1 is at the lysosomes, the authors should test whether mTORC1, Rheb and TSC2 localization on the lysosomes is affected or not in the S47 cells. No conclusions regarding overactivation of mTORC1 in S47 cells can be made without this crucial bit of data.

This has been done. We now show co-IF experiments for LAMP1-Rheb and Rheb-TSC2 and do not see differences between WT and S47 cells (see new Figure 4—figure supplement 1E).

5) Figure 2F, G and Figure 2—figure supplement 1A, B: The authors conclude a higher flux of glucose carbon into the TCA cycle in S47 cells. Why do the authors think that these minute changes (the differences appear to be less than 5%) are biologically significant? Can the authors observe significant changes in glycolytic intermediates in the 13C6-Glucose tracing experiment? Could the changes in OCR be due to an effect on NAD/NADH instead?

The metabolomics findings were modest, as they were performed after a 15 minute incubation with 13C-glucose. Unfortunately, due to quarantine our colleague was unable to repeat these findings using a longer timepoint in this experiment; therefore, we have moved these findings to Supplemental data. Similarly, we regret that we were unable due to time constraints to assess NAD/NADH levels and agree with this reviewer that this remains an interesting question to address.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

All reviewers have now reviewed your outline for revision experiments and agree that these proposed experiments will likely go a long way to strengthening the link between p53 S47, GSH, and mTORC1 activity in vitro. […] In this event you acknowledge and agree that the these will be published under the terms of the Creative Commons Attribution license.

We have addressed all of the concerns from the previous review, and the result is a greatly strengthened manuscript.

In sum, our work highlights a novel mechanism whereby the p53 tumor suppressor protein regulates mTOR activity, via the control of cellular redox state. Notably, this work uses as its foundation mouse and human tissues and cells containing an African-centric variant of p53, so the work has direct implications for understanding cancer disparities in African-descent populations. This work suggests that enhanced mTOR activity and metabolic efficiency may have been selected for in early Africa, making this variant one of a growing number of cancer-predisposing variants that possesses other activities that may be under positive selection. All of the authors have seen and approve this work.

https://doi.org/10.7554/eLife.55994.sa2

Article and author information

Author details

  1. Keerthana Gnanapradeepan

    1. Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, United States
    2. Graduate Group in Biochemistry and Molecular Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8984-9742
  2. Julia I-Ju Leu

    Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Subhasree Basu

    Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Thibaut Barnoud

    Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5588-6281
  5. Madeline Good

    Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  6. Joyce V Lee

    Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  7. William J Quinn

    Department of Physiology and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Che-Pei Kung

    Washington University in St. Louis, St Louis, United States
    Contribution
    Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1150-4998
  9. Rexford Ahima

    Division of Endocrinology, Diabetes & Metabolism, Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Data curation, Funding acquisition, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Joseph A Baur

    Department of Physiology and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Data curation, Funding acquisition, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8262-6549
  11. Kathryn E Wellen

    Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Data curation, Funding acquisition, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2281-0042
  12. Qin Liu

    Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, United States
    Contribution
    Formal analysis, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  13. Zachary T Schug

    Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, United States
    Contribution
    Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  14. Donna L George

    Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Funding acquisition, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  15. Maureen E Murphy

    Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Writing - review and editing
    For correspondence
    mmurphy@wistar.org
    Competing interests
    Senior Editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7644-7296

Funding

National Cancer Institute (R01 CA102184)

  • Maureen E Murphy

National Cancer Institute (R01 CA139319)

  • Donna L George
  • Maureen E Murphy

National Cancer Institute (CA238611)

  • Maureen E Murphy

National Cancer Institute (P01 CA114046)

  • Donna L George
  • Maureen E Murphy

National Cancer Institute (T32 CA009171)

  • Thibaut Barnoud

National Cancer Institute (F32 CA220972)

  • Thibaut Barnoud

National Cancer Institute (K99 CA241367)

  • Thibaut Barnoud

National Cancer Institute (R01 CA174761)

  • Kathryn E Wellen

National Institute on Aging (R01 AG043483)

  • Joseph A Baur

National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK098656)

  • Joseph A Baur

National Institute of Diabetes and Digestive and Kidney Diseases (P30 DK19525)

  • Joseph A Baur

National Cancer Institute (DP2-CA249951)

  • Zachary T Schug

Bloomberg Family Foundation

  • Rexford Ahima

National Cancer Institute (Cancer Center Support Grant (CCSG) P30 CA010815)

  • Maureen E Murphy

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by R01 CA102184 (MEM), R01 CA139319 (DLG and MEM), R01 CA238611 (MEM), P01 CA114046 (DLG and MEM), T32 CA009071, F32 CA220972 and K99 CA241367 (TB), R01 CA174761 (KEW), R01 AG043483 and DK098656 (JAB) and the Rodent Metabolic Phenotyping Core (P30-DK19525), and DP2-CA249951 (ZTS). RSA is partly supported by a Bloomberg Distinguished Professorship. The authors acknowledge the Histotechnology, Laboratory Animal and Imaging facilities at the Wistar Institute. The authors are grateful to Allie Lipshutz and Lindsey Schweitzer for expert technical help, and Matthew Jennis for the mouse weight data.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols of the University of Pennsylvania (protocol 804474) and The Wistar Institute (protocol 201283).

Senior Editor

  1. Päivi M Ojala, University of Helsinki, Finland

Reviewing Editor

  1. Lydia WS Finley, Memorial Sloan Kettering Cancer Center, United States

Reviewer

  1. Gina DeNicola, H Lee Moffitt Cancer Center and Research Institute, United States

Publication history

  1. Received: February 13, 2020
  2. Accepted: October 28, 2020
  3. Version of Record published: November 10, 2020 (version 1)

Copyright

© 2020, Gnanapradeepan et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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