mTOR Inhibition Restores Amino Acid Balance in Cells Dependent on Catabolism of Extracellular Protein

Abstract

Scavenging of extracellular protein via macropinocytosis is an alternative to monomeric amino acid uptake. In pancreatic cancer, macropinocytosis is driven by oncogenic Ras signaling and contributes substantially to amino acid supply. While Ras signaling promotes scavenging, mTOR signaling suppresses it. Here, we present an integrated experimental-computational method that enables quantitative comparison of protein scavenging rates across cell lines and conditions. Using it, we find that, independently of mTORC1, amino acid scarcity induces protein scavenging and that under such conditions the impact of mTOR signaling on protein scavenging rate is minimal. Nevertheless, mTOR inhibition promotes growth of cells reliant on eating extracellular protein. This growth enhancement depends on mTORC1’s canonical function in controlling translation rate: mTOR inhibition slows translation, thereby matching protein synthesis to the limited amino acid supply. Thus, paradoxically, in amino acid-poor conditions the pro-anabolic effects of mTORC1 are functionally opposed to growth.

eTOC Blurb

Catabolism of extracellular protein enables tumor cells to grow in amino acid-poor conditions. Nofal et al. show that inhibition of mTORC1 promotes growth in these conditions in large part by reducing protein synthesis to preserve limited amino acid pools.

INTRODUCTION

Amino acids are required substrates for protein synthesis and thus cell growth. While some organisms can synthesize all proteinogenic amino acids from primitive carbon and nitrogen sources, mammals cannot. For this reason, mammalian cells have been thought to strictly depend on the availability of amino acid monomers in their extracellular environment to support growth. Recently, it was shown that Ras signaling stimulates an alternative route of amino acid acquisition whereby cells take up extracellular protein via macropinocytosis and catabolize it in lysosomes to yield free amino acids. This process enables K-Ras-mutant pancreatic ductal adenocarcinoma (PDAC) cells to survive and proliferate despite amino acid scarcity (Commisso et al., 2013; Davidson et al., 2016).

The mechanistic target of rapamycin complex 1 (mTORC1) is a master growth regulator that promotes anabolism (Laplante and Sabatini, 2012). In the presence of amino acids, mTORC1 is recruited to the cytoplasmic surface of lysosomes, where it can be activated by growth factor signaling (Sancak et al., 2010). Upon activation, it phosphorylates multiple targets, which collectively stimulate amino acid uptake and protein synthesis (Ma and Blenis, 2009) while suppressing autophagy (Kamada et al., 2000). Amino acid depletion renders mTORC1 inactive, and protein synthesis rates decline as a result. Meanwhile, cells engage in autophagy – i.e. they catabolize pre-existing intracellular protein, yielding amino acids necessary to prevent starvation. These amino acids reactivate mTORC1, attenuating autophagy and restoring protein synthesis (Yu et al., 2010). The implications of mTORC1 reactivation in the context of prolonged starvation remain poorly understood.

Recently, Palm et al. showed that mTORC1 activity inhibits the growth of cancer cells fed the major serum protein, albumin, in place of free essential amino acids. Torin1, an ATP-competitive mTOR inhibitor, promoted growth in such conditions. The authors reasoned that in addition to blocking autophagy, mTORC1 suppresses the catabolism of extracellular protein (Palm et al., 2015). This claim was supported by an assay for extracellular protein degradation which uses a fluorescently labeled form of bovine serum albumin (BSA) known as DQ-BSA, whose fluorogenic component is initially hidden – many self-quenching BODIPY molecules are conjugated to each albumin molecule – and only “de-quenches” once this albumin has been degraded (Reis et al., 1998). Indeed, Torin1 increases protein scavenging as measured with DQ-BSA. While an elegant tool for visualizing serum protein catabolism, DQ-BSA fluorescence does not provide an absolute measure of protein catabolic rate.

To this end, we previously reported a method that, using stable isotope tracers, distinguishes amino acids derived from the catabolism of extracellular protein from amino acids imported as monomers. Cells are pre-incubated for multiple generations in medium containing uniformly ¹³C-labeled amino acids (¹³C-AA medium), such that intracellular amino acids and cellular protein become almost completely labeled. Cells are then switched to ¹³C-AA medium supplemented with physiologic levels of unlabeled BSA. At this point, when cells scavenge and degrade the unlabeled albumin, they release unlabeled amino acids into otherwise labeled amino acid pools. High amounts of unlabeled amino acids produced by these cells reflect fast serum protein uptake and catabolism (Kamphorst et al., 2015).

In murine pancreatic cancer cells grown in physiological nutrient conditions, we found that almost half of both intracellular and extracellular amino acid pools were derived from BSA (i.e. unlabeled), demonstrating that protein catabolism can be a major contributor to amino acid pools in pancreatic cancer (Kamphorst et al., 2015). The measured unlabeled fractions, however, depend not only on the rate of serum protein catabolism, but also on the number of cells present in the experiment and on their rate of protein synthesis. Thus, differences in amino acid labeling patterns between cell lines or growth conditions do not reliably reflect differences in protein scavenging rates.

Here, we present an integrated experimental-computational method that enables reliable and quantitative comparison of protein scavenging rates across cell lines and conditions. We then apply this method to investigate the mechanism by which mTOR inhibition enhances the growth of cells fed by protein scavenging. We find that, independently of mTORC1, amino acid scarcity strongly turns on protein scavenging, and that under such conditions the impact of Torin1 on protein scavenging rate is small. Inhibition of mTOR by Torin1 promotes growth of protein-scavenging cells instead by decreasing their translation rate and thereby matching protein synthesis to the limited amino acid supply.

RESULTS

Isotope-Tracer Method Measures Amino Acid Release Due to Extracellular Protein Catabolism

Our general strategy for measuring extracellular protein catabolic rate involves pre-labeling cells with ¹³C-AA medium and then feeding them a mixture of ¹³C-AA medium and unlabeled albumin. Protein scavenging is then the only source of unlabeled amino acids, and the rate of appearance of such amino acids can be used to calculate the protein scavenging rate. The challenge is making such measurements in a manner that accurately reflects per cell protein scavenging rates.

To this end, as cells grew in ¹³C-AA medium and unlabeled albumin, we took serial time point measurements of intracellular amino acid labeling, extracellular amino acid labeling, and total cell volume (Figure 1A). We further developed a simple model of amino acid metabolism, which includes the following reactions for production and consumption of intracellular amino acid monomers: (i) import and export from the cell via amino acid transporters, (ii) incorporation into protein, (iii) catabolism of extracellular protein, and (iv) catabolism of intracellular protein (i.e. via autophagy) (Figure 1B) (Shlomi et al., 2014; Zhao et al., 2007). This model applies exclusively to essential amino acids, which are not synthesized, and it assumes that catabolism of essential amino acid monomers is negligible. Using this model, the dynamic cell growth data and the extracellular amino acid labeling data (both unlabeled fractions and absolute amounts) are sufficient to uniquely determine the per cell rate of protein scavenging. Because intracellular amino acid pools mix rapidly with extracellular pools (Supplementary Figure 1), data from intracellular amino acids is not required, making this method relatively facile (see Methods).

Figure 1.

When implementing this method, the rate of production of each essential amino acid from albumin can be measured independently. Thus, as a first test of the method, we assessed whether the measurements for different amino acids were in agreement, focusing on five amino acids that we can easily and accurately measure (Figure 1C, D). We anticipated that the release rates of different amino acids would reflect their relative abundances in BSA. Indeed, this was the case: there are 59 lysines and only 17 histidines in BSA, and the measured rate for lysine exceeded that for histidine by approximately 59:17 fold, with the other amino acids intermediate between these two (Figure 1D). Dividing the release rate of each amino acid by the number of times that amino acid appears in BSA yields the protein scavenging rate in units of moles albumin per cell per unit time (Figure 1E).

As validation of this method, we sought to confirm the effect of constitutive Ras activation on extracellular protein catabolism. To do so, we compared the protein scavenging rate of immortalized baby mouse kidney cells (iBMK) with or without constitutively active Ras or Akt alleles. While Akt activation did not induce any change, constitutive Ras signaling roughly doubled the rate of extracellular protein catabolism, consistent with the long-standing observation that Ras induces macropinocytosis (Figure 1F) (Bar-Sagi and Feramisco, 1986).

As further validation, we examined the protein scavenging rate of cells before and after extended growth in conditions that select for accelerated protein scavenging. For these experiments, we used KRPC cells, which were originally isolated from spontaneously arising, K-Ras-driven murine pancreatic tumors that resemble human PDAC (Lito et al., 2014). These cells were grown in leucine-free medium supplemented with 5% BSA (Kamphorst et al., 2015). Initial growth was slow, but after prolonged culture (100 generations), the resulting adapted population (KRPC^A cells) doubled approximately every 24 hours despite the absence of free leucine (Figure 2A). Using the isotope-tracer method, we found that KRPC^A cells have roughly 5-fold higher rates of extracellular protein catabolism (Figure 2B). Thus, we provide a quantitative method for assessment of the rate of albumin catabolism by protein-eating cells.

Figure 2.

Impact of Intracellular Protein Catabolism on Scavenging Measurements

An important element of the isotope-tracer method is the extended pre-labeling in ¹³C-AA medium. Extended pre-labeling ensures that autophagy and other modes of intracellular protein degradation do not generate unlabeled amino acids and thereby do not confound measurements of extracellular protein catabolism. To examine the relative magnitudes of intracellular protein degradation and extracellular protein scavenging, we conducted analogous experiments with only 1 h pre-labeling, which is insufficient to substantially label intracellular protein. These experiments were conducted in murine embryonic fibroblasts harboring an oncogenic K-Ras^G12D allele (K-Ras^G12D MEFs) and KRPC^A cells. In K-Ras^G12D MEFs, the pre-labeling duration did not significantly impact the production rates of unlabeled amino acids, suggesting that extracellular protein scavenging predominates over intracellular protein degradation. In contrast, in KRPC^A cells, we observed a two-fold increase in unlabeled amino acid production with the brief pre-labeling, indicating similar magnitudes of extracellular and intracellular protein catabolism (Supplementary Figure 2). To confirm that the measurements of extracellular protein scavenging do not reflect autophagy in the murine pancreatic cancer cells, we used a well-established KPC cells line harboring inducible shRNA against the essential autophagy gene Atg5. With the extended pre-labeling that results in selective measurement of extracellular protein degradation, knockdown of Atg5 did not significantly impact the measured scavenging rate (Supplementary Figure 3), validating the selectivity of this isotope-tracer approach for extracellular protein scavenging.

Excessive mTOR Inhibition Slows Growth on Extracellular Protein

Recent evidence suggests that Ras-activated cells, even without adaptation, can grow robustly on extracellular protein if mTORC1 activity is suppressed (Palm et al., 2015). We wondered if KRPC^A cells achieve high levels of protein scavenging in amino acid-rich medium by suppressing mTORC1 signaling. We observed a modest decrease in mTORC1 signaling in adapted KRPC cells, as measured by the phosphorylation of S6 kinase 1, ribosomal protein S6, and 4E-BP1 (Figure 2C).

Given that mTOR inhibition has been shown to promote protein scavenging and that mTORC1 remains at least partially active in the adapted KRPC cells, which have high basal scavenging rates, we tested the impact of the ATP-competitive mTOR inhibitor Torin1 on KRPC cell growth. These experiments were conducted for a range of Torin1 doses (100–2000 nM) in both parental KRPC and KRPC^A cells, in amino acid-replete, leucine-free, arginine-free, and glutamine-free medium, all supplemented with 5% BSA (Figure 3). Among these amino acid-deficient conditions, we anticipated that leucine deprivation would be most easily overcome by albumin scavenging, as leucine is the most abundant amino acid in albumin. In contrast, we anticipated that glutamine deprivation would be hardest to overcome, as glutamine is not particularly abundant in albumin but required by cells in unusually large amounts. We expected arginine deprivation to be intermediate. Deprivation of other amino acids was not examined.

Figure 3.

As expected, in amino acid-replete conditions, mTOR inhibition slowed the growth of both parental and adapted KRPC cells. Importantly, however, cells doubled in 24 hours despite high doses of Torin1, indicating that these cells are capable of considerable growth even when mTOR signaling is pharmacologically inhibited. In leucine-deficient conditions, parental cells grew faster in the presence of Torin1, but optimal growth was achieved in the middle of the Torin1 dose range, indicating that some mTOR signaling is beneficial. Strikingly, in KRPC^A cells, only the lowest dose of Torin1 promoted growth; higher doses slowed it. While parental cells struggled to grow without arginine or glutamine, KRPC^A cells were able to grow without these amino acids, with optimal growth occurring in the middle of the Torin1 dose range. Collectively, these findings show that for cells fed by protein scavenging, mTOR inhibition has both growth-promoting and growth-suppressing effects. The relative strengths of these effects seem to depend on the protein scavenging rate of the treated cells and the inherent difficulty of overcoming the amino acid deprivation. In more deprived states (e.g. parental cells, glutamine-free medium), mTOR signaling inhibits growth. Conversely, in more favorable states (e.g. adapted cells, leucine-free medium), mTOR signaling promotes growth.

Amino Acid-Deficiency Induces Protein Scavenging Flux Independently of mTOR

To further investigate the effects of mTORC1 inhibition, we measured, using the above isotope-tracer approach, the effect of Torin1 on protein scavenging flux. In both KRPC^A cells and K-Ras^G12D MEFs cultured in amino acid-replete medium, Torin1 increased protein scavenging in dose-dependent fashion (Figure 4A). The largest increase we observed was less than 2-fold, however, whereas Palm et al. reported that in K-Ras^G12D MEFs, Torin1 induced a ~10-fold increase in DQ-BSA fluorescence and a ~5-fold increase in growth in leucine-free medium.

Figure 4.

We next asked if the effect of Torin1 on protein scavenging rates depends on amino acid availability. We measured the rates of extracellular protein catabolism in the same three amino acid drop-out media as above in the presence or absence of high-dose Torin1 (1000 nM). Amino acid deprivation increased protein catabolism at least as much as high-dose Torin1 (Figures 4B). Interestingly, the degree to which scavenging was induced aligned with the severity of amino acid starvation. For example, in K-Ras^G12D MEFs, leucine deprivation, the least severe, increased scavenging by 60%; glutamine deprivation, the most severe, by 220%. One might expect that a reduction in mTORC1 activity upon amino acid deprivation accounts for these increases. However, mTORC1 activity persists in these cells (Figure 4C). Thus, amino acid deprivation turns on scavenging independently of mTOR.

We were initially puzzled that mTORC1 was active in amino acid-deficient conditions. Others have demonstrated, however, that in cells deprived of amino acids for long periods of time, mTORC1 signaling is re-activated once protein catabolic programs begin to take effect (Palm et al., 2015; Yu et al., 2010). Indeed, we observed that when K-Ras^G12D MEFs were switched to media lacking all amino acids, phosphorylation of S6 Kinase 1 immediately declined, but eventually returned, although phosphorylation of another key substrate of mTORC1, 4E-BP1, was maintained throughout the time course. Notably, removal of leucine alone resulted in no initial decline in the phosphorylation of either mTORC1 substrate (Supplementary Figure 4). Thus, at least over 24 h, leucine-, arginine-, and glutamine-deprived cells maintain mTORC1 activity. In fact, at 24 h, glutamine-deprived cells displayed increased mTORC1 signaling, potentially because glutamine deprivation resulted in accumulation of essential amino acids within the cell (Supplementary Figure 5).

Given this persistent mTORC1 activity, we studied the impact of mTOR inhibition on protein scavenging in amino acid-deprived cells. In leucine-free medium, Torin1 increased extracellular protein catabolism by only 14% in KRPC^A cells and by 7% in K-Ras^G12D MEFs. While these enhancements in scavenging flux may contribute to the pro-growth effects of mTOR inhibition in scavenging cells, they are not quantitatively commensurate with the substantial enhancements in cell growth observed upon mTOR inhibition. Thus, the growth-promoting effects of Torin1 in cells reliant on protein scavenging extend beyond enhancing protein catabolism.

mTOR Inhibition Induces Punctate DQ-BSA Fluorescence

We were struck by the difference in magnitude of the effect of Torin1 on protein scavenging flux measured here via isotope tracing (less than 2x) versus previously via DQ-BSA fluorescence (roughly 10x). To address this discrepancy, we repeated the DQ-BSA fluorescence experiment which produced this result, using identical cells and conditions to Palm et al. Specifically, DQ-BSA was added concurrently with 250 nM Torin1 to K-Ras^G12D MEFs grown in serum-free DMEM, and cells were imaged after 6 h (Palm et al., 2015). As expected, Torin1 induced a visible increase in DQ-BSA fluorescence (Figure 5A) and a corresponding rightward shift in the histogram of pixel fluorescence intensity (Figure 5B). Thus, we confirmed that mTOR inhibition induces an increase in DQ-BSA fluorescence.

Figure 5.

We next sought to quantify this induction. When we included all measurable fluorescence in our calculation, we found that Torin1 increases DQ-BSA fluorescence per cell by less than two-fold, in line with our isotope tracing results (Figure 5C). Some fluorescence, however, is inevitably noise. To minimize noise, standard methods for quantification of fluorescence ignore lower intensity signals, only using pixels that exceed an arbitrarily chosen fluorescence intensity threshold. We found that the choice of fluorescence intensity threshold greatly affected the apparent magnitude of the Torin1 effect: low thresholds produced effects less than 2-fold, while high thresholds produced effects greater than 5-fold (Figure 5D). To explore this phenomenon further, we divided the distribution of pixel intensities into five ranges and calculated the sum of the intensities within each range. This revealed that mTOR inhibition dramatically increases only very high-intensity fluorescence, which accounts for a minor portion of the total fluorescent signal while having a modest effect on overall fluorescence (Figure 5E). The relative contributions of each range of pixel intensities are apparent in discretized images, which enable simultaneous visualization of all ranges of green fluorescence (Figure 5F and Supplementary Figure 6).

Comparing our results to those of Palm et al. (Palm et al., 2015), we note no major differences in the raw data: analysis of our data using a high fluorescence intensity threshold yields images and quantitative results comparable to Palm et al. We believe, however, that lower thresholds are more accurate, as they encompass a substantially greater amount of total fluorescent signal and give quantitative results in line with the our isotope-tracer data. In essence, the isotope-tracer data, which was not available to Palm et al., inform the proper choice of threshold for quantitation of the fluorescence data.

Focusing specifically on the high-intensity fluorescence which was strongly induced by Torin1, we observed this fluorescence in discrete punctae that overlap with lysosomal staining (Figure 5A). One possible explanation for this strong increase in lysosomal DQ-BSA signal is that mTORC1 may simultaneously inhibit protein scavenging and promote egress of scavenged material from the lysosome. In summary, the combined isotope tracing and fluorescence results show that mTOR, while profoundly impacting lysosomal DQ-BSA fluorescence accumulation, has a modest overall impact on protein scavenging rate.

mTOR Inhibition Restores Amino Acid Balance and Prevents Cell Death in Amino Acid-Deprived Cells

How does mTOR inhibition promote growth of amino acid-deprived cells on extracellular protein if not by directly increasing their rate of extracellular protein catabolism? mTORC1 is well-known for its role in regulating protein synthesis, phosphorylating multiple proteins which collectively activate 5′ cap-dependent translation (Ma and Blenis, 2009). We reasoned that reduction of protein synthesis rates upon Torin1 treatment might prevent cells deprived of free extracellular amino acids from promoting translation to the point of cellular stress.

As others have shown and we have confirmed, mTORC1 activity persists in amino acid-deprived cells fed extracellular protein, perhaps because scavenged protein feeds directly into the lysosomal amino acid pool that is sensed by mTORC1 (Palm et al., 2015). Correspondingly, protein synthesis rates are not limited by low mTORC1 activity in these cells. However, protein scavenging cannot support the high protein synthesis rates of cells growing in copious free monomeric amino acids. GCN2, which senses amino acid depletion by binding uncharged tRNAs (Berlanga et al., 1999; Dong et al., 2000; Wek et al., 1995), attenuates global translation independently of mTORC1, while inducing specific translation of genes involved in maintaining amino acid homeostasis, including the transcription factor ATF4 (Harding et al., 2000). ATF4 induces expression of proteins that collectively promote cell survival during amino acid deprivation by up-regulating amino acid uptake and enhancing protein folding capacity. Nevertheless, this cellular response to amino acid starvation (known as the Integrated Stress Response) fails to prevent apoptosis in cells which are chronically unable to translate mRNAs into properly folded proteins. Moreover, some proteins induced by the Integrated Stress Response, such as CHOP, promote cell death if amino acid starvation remains unresolved (Han et al., 2013; Zinszner et al., 1998).

To test our hypothesis that mTOR inhibition promotes growth on extracellular protein by reducing translation rates and thereby preventing severe amino acid starvation, we measured the expression of Integrated Stress Response proteins. In K-Ras^G12D MEFs deprived of leucine, we observed a strong induction of both ATF4 and CHOP, regardless of whether cells were supplemented with 5% BSA. This induction was suppressed in Torin1-treated cells, suggesting that mTOR inhibition in amino acid-deprived cells restores amino acid homeostasis (Figure 6A). We next sought to measure cell death directly, to confirm that mTORC1 activity results in apoptosis. After 48 hours in leucine-free media, close to 50% of cells grown in leucine-free medium were either apoptotic or dead. mTOR inhibition prevented cell death. This prevention did not depend on the presence of added BSA. Thus, the effects of mTORC1 on the survival of amino acid-deprived cells do not directly depend on the uptake or catabolism of extracellular protein (Figure 6B).

Figure 6.

To verify that mTOR inhibition prevents cell death by suppressing protein synthesis, we tested the effect of direct inhibition of translation on the viability of leucine-deprived cells. Low doses of harringtonin, which inhibits translation initiation, prevented apoptosis and cell death to a similar extent as mTOR inhibition (Figure 6C). We were not, however, able to stimulate cell growth in leucine-free, BSA-supplemented medium with harringtonin (i.e. to replicate the pro-growth effects of Torin1), suggesting that the growth-promoting effects of mTOR inhibition go beyond non-specific translation inhibition. This is consistent with the idea that mTOR inhibition coordinately suppresses translation of a specific subset of genes and increases protein catabolism. Collectively, these results show that, in cells reliant on protein scavenging, excessive translation can result in lethal amino acid depletion.

While K-Ras^G12D MEFs require exogenous translation inhibition to maintain amino acid balance when using extracellular protein in place of free leucine, KRPC^A cells have adapted to such conditions and can grow robustly without pharmacological mTOR inhibition. We hypothesized that these cells rely on other translational regulators to properly tune protein synthesis rates to limited amino acid availability. We reasoned that GCN2, which slows translation upon amino acid depletion, might play such a role. Using CRISPR-Cas9 technology, we generated KRPC^A cell lines deficient in GCN2 activity, as measured by lack of ATF4 induction in leucine-free medium (Supplementary Figure 7A). We tested the ability of GCN2-deficient KRPC^A cells to grow in leucine-free medium supplemented with 5% BSA. Remarkably, these GCN2-deficient cells almost completely lost the ability to grow using extracellular protein in place of free leucine (Supplementary Figure 7B–C). If this defect is due to excessive translation, it should be rescued by mTOR inhibition. Indeed, GCN2-deficient KRPC^A cells, much like parental KRPC cells, benefited from high-dose Torin1 treatment (Supplementary Figure 7D). Thus, the ability to turn down translation rates to match limited amino acid availability is essential for growth via protein scavenging.

DISCUSSION

All cells require amino acids for cell growth. Classically, mammals maintain a steady concentration of circulating amino acids, which individual cells import as needed. If perfusion is impaired, however, cells may struggle to support growth requirements using only amino acids from the environment. This appears to be the case in pancreatic tumors, which are marked by dense fibrosis and poor perfusion (Mahadevan and Von Hoff, 2007; Provenzano et al., 2012). Pancreatic tumor cells, driven by K-Ras mutations, mitigate the shortage of monomeric amino acids in their immediate environment by taking up and catabolizing extracellular protein. This process enables these cells to survive and proliferate despite amino acid deprivation (Commisso et al., 2013; Davidson et al., 2016; Kamphorst et al., 2015).

Recently, mTOR inhibition was shown to promote growth of amino acid-deprived cells on extracellular protein. To explain this, Palm et al. proposed that mTORC1 activity represses extracellular protein catabolism and that mTOR inhibition alleviates this repression (Palm et al., 2015). In the present study, we demonstrate that mTORC1 signaling does not prevent extracellular protein catabolism in amino acid-deprived cells. Rather, these cells simultaneously maintain mTOR activity and increase protein eating. Nevertheless, cells relying on extracellular protein for amino acids cannot support the high rates of translation that are possible in amino acid-replete conditions. Because mTORC1 remains active, these cells are prone to death by over-translation. Thus, mTOR inhibition enhances growth on extracellular protein in part by restricting translation and restoring amino acid balance (Figure 7).

Figure 7.

We also show that cells deprived of free leucine or glutamine increase extracellular protein catabolism while maintaining mTORC1 signaling. This implicates an mTOR-independent signaling pathway as an activator of this process during amino acid deprivation. The other ubiquitous amino acid sensing pathway involves GCN2, which, upon binding to uncharged tRNA, phosphorylates and inhibits translation initiation factor eIF2α (Berlanga et al., 1999; Dong et al., 2000; Wek et al., 1995). While inhibiting translation of most mRNAs, phosphorylation of eIF2α promotes translation of ATF4 and other transcription factors which induce genes involved in adaptation to amino acid starvation, including amino acyl-tRNA synthetases, amino acid transporters, and protein folding chaperones (Han et al., 2013; Harding et al., 2000). Other proteins expressed upon eIF2α phosphorylation are involved in diverse cellular processes such as expansion of the endoplasmic reticulum, which houses a substantial fraction of nascent peptides (Han et al., 2013). It is tempting to speculate that these proteins might also include unknown activators of protein scavenging.

This study highlights the inability of cancer cells fed extracellular protein to optimally adjust levels of mTORC1 signaling to match amino acid availability. These cells maintain mTORC1 activity even when free leucine or glutamine is absent from the extracellular environment. mTORC1 signaling is insensitive to such amino acid scarcities in part because multiple amino acids are activators of mTORC1, but even when no free amino acids are present, mTORC1 signaling, initially suppressed, can be re-activated by protein catabolism via either autophagy (Yu et al., 2010) or catabolism of extracellular protein (Palm et al., 2015). Thus, while mTORC1 can sense acute amino acid starvation, it is insufficient to balance biosynthesis and catabolism in response to chronic amino acid deprivation in cells with constitutive growth factor signaling. In accordance with this, proline starvation was recently shown to result in mTORC1 hyperactivation, unresolved ER stress, and decreased tumorigenesis of multiple cancer cell lines (Sahu et al., 2016). In a different context, dysregulated mTORC1 renders cells dependent on an exogenous supply of unsaturated fatty acids (whose production requires oxygen) in hypoxia (Young et al., 2013). Thus, excessive mTORC1 signaling can push cells into fatal stress when biosynthetic substrates are limiting.

These findings have implications for mTOR inhibition in cancer therapy. While mTOR inhibitors have shown anti-tumor activity in certain cancers, they have unexpectedly had limited efficacy in most cases. In assessing the therapeutic potential of these agents, the deleterious activity of mTORC1 in cells deprived of amino acids may have been overlooked. We find that moderate mTOR inhibition protects these cells from cell death by restricting translation. Moreover, if these cells catabolize extracellular protein, mTOR inhibition facilitates robust growth. The pro-survival effects of mTOR inhibition on amino acid-deprived cells may explain the minimal clinical activity of mTOR inhibitors on pancreatic tumors (Javle et al., 2010; Wolpin et al., 2009), which are glutamine-poor (Kamphorst et al., 2015). In accordance with this idea, Palm et al. showed that inhibition of mTORC1 enhances the growth of pancreatic tumors in a murine PDAC model. Specifically, rapamycin decreased the fraction of proliferating cells in outer, vascularized regions of these tumors, but increased the proliferation of cells in interior, hypovascularized regions (Palm et al., 2015). The present work suggests that mTOR inhibition promotes the growth of these cells not only by promoting protein scavenging, but also by reducing biosynthetic demands. As a result, cells enduring prolonged nutrient shortages can stably assimilate biosynthetic substrates for anabolism (e.g. by degradation of extracellular protein) while simultaneously avoiding the lethal cellular stresses associated with starvation. More generally, many chemotherapeutics target upregulated biosynthesis in cancer cells. Our results emphasize the importance of finding new ways to amplify cellular stresses associated with excessive biosynthesis, rather than focusing solely on slowing these biosynthetic processes down. Indeed, in tumors poorly supplied with nutrients, slowing anabolism can paradoxically promote growth.

STAR METHODS

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
phospho-T389 p70 S6 Kinase	Cell Signaling	Cat# 9205
p70 S6 Kinase	Cell Signaling	Cat# 2708
phospho-S240/244 S6	Cell Signaling	Cat# 5364
S6	Cell Signaling	Cat# 2217
phospho-T37/46 4E-BP1	Cell Signaling	Cat# 2855
4E-BP1	Cell Signaling	Cat# 9452
ATF-4	Cell Signaling	Cat# 11815
CHOP	Cell Signaling	Cat# 5554
GCN2	Cell Signaling	Cat# 3302
β-Actin-HRP Conjugate	Cell Signaling	Cat# 5125
Vinculin-HRP Conjugate	Cell Signaling	Cat# 18799
Chemicals, Peptides, and Recombinant Proteins
DMEM w/low glucose, w/o Amino acids, pyruvate	US Biologicals	Cat# D9800-13
U-13C6, U-15N3 Histidine:HCl:H2O	Cambridge Isotopes Laboratories (CIL)	Cat# CNLM-758
U-13C6, U-15N2 Lysine:2HCl	CIL	Cat# CNLM-291
U-13C9, 15N Phenylalanine	CIL	Cat# CNLM-575
U-13C4, 15N Threonine	CIL	Cat# CNLM-587
U-13C5, 15N Valine	CIL	Cat# CNLM-442
Bovine Serum Albumin, lyophilized, BioReagent	Sigma-Aldrich	Cat# A9418
DQ Green BSA	Life Technologies	Cat# D12050
LysoTracker Red DND-99	Life Technologies	Cat# L7528
Hoechst 33342	Life Technologies	Cat# H3570
16% aqueous paraformaldehyde	EMS	Cat# 15710
SlowFade Antifade Gold Mounting Medium (with TES buffer)	Thermo Scientific	Cat# S36936
Critical Commercial Assays
CellEvent Caspase-3/7 Green Detection Reagent	Life Technologies	Cat# C10740
Deposited Data
Raw Imaging Data	This paper	http://dx.doi.org/10.17632/4zb6z95zz4.1
Raw Western Blot Data	This paper	http://dx.doi.org/10.17632/4zb6z95zz4.1
Experimental Models: Cell Lines
K-RasG12D MEFs	Craig Thompson Lab, MSKCC	N/A
iBMK-parental, iBMK-Akt, and iBMK-Ras cells	Eileen White Lab, CINJ	N/A
KRPC cells	Scott Lowe Lab, MSKCC	N/A
Oligonucleotides
sgEif2ak4_1 (forward): CACCgGGCTACCCACAGAGAAATGG	IDT	N/A
sgEif2ak4_1 (reverse): AAACCCATTTCTCTGTGGGTAGCCc	IDT	N/A
sgEif2ak4_2 (forward): CACCgAGGCTCAGGAGAAGCAGCAG	IDT	N/A
sgEif2ak4_2 (reverse): AAACCTGCTGCTTCTCCTGAGCCTc	IDT	N/A
Recombinant DNA
lentiCRISPR v2	Sanjana et al., 2014	Addgene Plasmid #52961
Other
Nunc Lab Tek II Chamber Slides	Thermo Scientific	Cat# 12-565-8
Cover slips (#1.5)	Thermo Scientific	Cat# 12-544-G

CONTACT FOR REAGENTS AND RESOURCE SHARING

Further information and requests for resources and reagents should be directed to and will be fulfilled by Josh Rabinowitz (joshr@princeton.edu).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Cell lines and culture

All cell lines used in this study are listed in the Key Resources Table. All cells were propagated in DMEM with 25 mM glucose and 4 mM glutamine and without pyruvate (Mediatech). DMEM was supplemented with 10% FBS (HyClone) and 25 IU/mL penicillin and 25 mg/mL streptomycin (MP Biomedicals), unless specified otherwise.

Knockout cell lines

Oligonucleotides targeting murine Gcn2 (also known as Eif2ak4) were cloned in lentiCRISPR v2 (Addgene #52961) (Sanjana et al., 2014). Virus was produced in HEK293FT cells, and KRPC^A cells were infected. Infected cells were selected in puromycin, and clonal knockout cell lines were produced by isolation of single cells from this infected population. Oligonucleotide sequences are listed in the Key Resources Table.

METHOD DETAILS

Custom Media Preparation

Custom media were prepared using DMEM powder containing all DMEM salts and vitamins, low glucose, and no amino acids or pyruvate (US Biologicals). Glucose was added to a final concentration of 25 mM glucose, and sodium bicarbonate to a final concentration of 3.7 g/L. Pyruvate was not added to any media. To facilitate custom media preparation, concentrated (20–100X) amino acid stock solutions were prepared and stored at 4°C. Such solutions were used to add all amino acids except glutamine (unstable) and tyrosine (insoluble), which were added directly in powder form. ¹³C-AA medium, with or without supplemented BSA, contained uniformly ¹³C-labeled histidine, lysine, phenylalanine, threonine, and valine; all other amino acids were unlabeled.

In ¹³C-AA medium not supplemented with BSA and in all amino acid-deficient media, amino acid concentrations were identical to standard DMEM (glutamine: 4 mM; isoleucine, leucine, lysine, threonine, and valine: 0.8 mM; arginine, glycine, serine, phenylalanine: 0.4 mM; cystine, histidine, methionine, and tyrosine: 0.2 mM; and tryptophan: 0.078 mM). For ¹³C-AA medium supplemented with BSA, ¹³C-labeled amino acids were added at reduced concentrations to facilitate amino acid uptake measurements (lysine, threonine, and valine: 0.32 mM; phenylalanine: 0.16 mM; and histidine: 0.08 mM). All BSA-supplemented media contained 5% w/v BSA. All custom media was adjusted to pH 7.2 immediately before sterile filtration and was additionally supplemented with 5% dialyzed FBS.

Stable isotope-labeled amino acids (including U-¹³C₆ L-Lysine:2HCl, U-¹³C₉ L-Phenylalanine, U-¹³C₄ L-Threonine, U-¹³C₅ L-Valine, and U-¹³C₆ L-Histidine) were from Cambridge Isotope Laboratories. All other components were standard tissue culture-grade reagents (Sigma). Tissue culture-grade BSA, which was not delipidated, was from Sigma.

Isotope-Tracer Experiments

Cells were grown for five doublings in ¹³C-AA medium, as described above. After five doublings, cells were seeded at low cell density in 60 mm tissue culture dishes and switched to 2 mL of ¹³C-AA medium supplemented with 5% BSA. After 16 h and 24 h (and, where indicated, additional time points), medium amino acids and total cellular volume were measured as below. Absolute concentrations were determined by comparison of peak intensities in samples of interest and samples from fresh medium, in which amino acid concentrations are known.

Metabolite Extraction and LC-MS Analysis

For analysis of intracellular amino acids, medium was aspirated and plates were rinsed three times with room temperature PBS. Metabolism was quenched and amino acids extracted in ice-cold 80:20 methanol:water extraction solution. Plates were scraped and cell extracts were transferred to eppendorf tubes, which were vortexed and centrifuged at 16,100 g for 5 minutes. The resulting supernatant was dried under nitrogen flow and resuspended in HPLC-grade water. 40 microliters of the resulting solution was added to 160 uL HPLC-grade methanol in a new tube. 10 uL triethylamine and 2 uL benzyl chloroformate were added sequentially, and the resulting mixture was vortexed and incubated at room temperature for 30 minutes to derivatize and thereby enhance measurement sensitivity of amino acids.

For analysis of amino acids in culture medium, 50 microliters of medium was directly added to 200 microliters of HPLC-grade methanol. This mixture was vortexed then centrifuged at 16,100 g for 5 minutes. 200 uL supernatant was transferred to a new tube. 10 uL triethylamine and 2 uL benzyl chloroformate were added sequentially, and the resulting mixture was vortexed and incubated at room temperature for 30 minutes.

After derivatization, samples were diluted such that amino acids fell within the linear range of a triple quadrupole mass spectrometer (TSQ Quantum Discovery Max; Thermo Scientific), operating in negative multiple reaction monitoring mode, coupled to C18 high-performance reversed-phase ion pair liquid chromatography (Lu et al., 2008; Lu et al., 2006). Data were analyzed using open-source software (Melamud et al., 2010).

Extracellular Protein Catabolism Rate Computation

To derive an expression for the rate of amino acid release due to serum protein catabolism, we start with the following basic relationship: any cellular reaction rate (in units of moles per unit time per unit cell volume) is equal to the total amount of product being produced by this reaction in all cells (in units of moles per unit time) divided by the total volume of all cells. In this case, for a given amino acid, the rate of amino acid release due to serum protein catabolism is equal to the amount of amino acid being released by all cells divided by total cell volume. Recalling that amino acids generated by extracellular protein scavenging are unlabeled:

$$V_{AA\mathit{release}}=\frac{{\mathit{dAA}}^0/dt}{\mathit{Vol}(t)}$$ (1)

After integrating this equation with respect to time, the rate of amino acid release is equal to the total amount of amino acid released by all cells over the course of the experiment divided by the time-integral of total cellular volume:

$$V_{AA\mathit{release}}=\frac{A{A}^0(T)}{{\int }_0^T\mathit{Vol}(t)dt}$$ (2)

Unlabeled amino acids released by extracellular protein catabolism can meet one of three fates: they can end up as (i) intracellular amino acid monomers, (ii) amino acid monomers in the medium, or (iii) amino acids which have been incorporated into cellular protein. (We assume catabolism of essential amino acid monomers is negligible.) Thus:

$$V_{AA\mathit{release}}=\frac{A{A}_{\mathit{intra}}^0(T)+A{A}_{\mathit{extra}}^0(T)+A{A}_{\mathit{prot}}^0(T)}{{\int }_0^T\mathit{Vol}(t)dt}$$ (3)

Because the aggregate volume of cells is very small relative to the volume of the medium in each dish, the first term in the numerator of Eq. (3) is negligible. The second term in the numerator, which represents the molar amount of unlabeled amino acids in the medium at the end of the experiment, is directly measurable. The amount of unlabeled amino acids in protein at the end of the experiment was determined indirectly, assuming recycling of cellular protein is negligible (Figure S2):

$$A{A}_{\mathit{prot}}^0(T)=V_{\mathit{synth}}{\int }_0^T\frac{A{A}_{\mathit{cyto}}^0}{A{A}_{\mathit{cyto}}^{\mathit{total}}}(t)\times \mathit{Vol}(t)dt$$ (4)

We assume metabolic steady state to derive a simple expression for V_synth:

$$V_{\mathit{synth}}=V_{in}-V_{\mathit{out}}+V_{AA\mathit{release}}$$ (5)

Substituting Eqs. (4) and (5) into Eq. (3) gives us an expression for the rate of amino acid release:

$$V_{AA\mathit{release}}=\frac{A{A}_{\mathit{extra}}^0(T)+(V_{in}-V_{\mathit{out}}+V_{AA\mathit{release}}){\int }_0^T\frac{A{A}_{\mathit{cyto}}^0}{A{A}_{\mathit{cyto}}^{\mathit{total}}}(t)\times \mathit{Vol}(t)dt}{{\int }_0^T\mathit{Vol}(t)dt}$$ (6)

Finally, we solve for the rate of amino acid release due to extracellular protein catabolism:

$$V_{AA\mathit{release}}=\frac{A{A}_{\mathit{extra}}^0(T)+(V_{in}-V_{\mathit{out}}){\int }_0^T\frac{A{A}_{\mathit{cyto}}^0}{A{A}_{\mathit{cyto}}^{\mathit{total}}}(t)\times \mathit{Vol}(t)dt}{{\int }_0^T\mathit{Vol}(t)dt-{\int }_0^T\frac{A{A}_{\mathit{cyto}}^0}{A{A}_{\mathit{cyto}}^{\mathit{total}}}(t)\times \mathit{Vol}(t)dt}$$ (7)

Finally, dividing each amino acid release rate by the number of times that amino acid appears in BSA yields protein scavenging flux estimates:

$$V_{\mathit{serum}\mathit{protein}\mathit{catabolism}}=\frac{V_{AA\mathit{release}}}{{\alpha }_{AA}}$$ (8)

To demonstrate how to compute the rate of amino acid release from extracellular protein catabolism and corresponding protein scavenging flux, we provide an example in which we compute the rate of lysine release in K-Ras^G12D MEFs growing in amino acid-replete medium, using data shown in Figure 1C–E. K-Ras^G12D MEFs pre-grown for five doublings in ¹³C-AA medium were switched to 2 mL of ¹³C-AA medium supplemented with 5% BSA. The first term in the numerator of Eq. (7) is directly measurable: after 24 h, we measured 22,700 pmol unlabeled lysine in the medium:

$$A{A}_{\mathit{extra}}^0(T=24h)=22,700\text{pmol}$$ (9)

The second term in the numerator is equal to the net uptake rate multiplied by the time-integral of the product of the instantaneous unlabeled amino acid fraction and the instantaneous total cell volume. Net uptake rate can be measured by tracking amino acid abundance in the medium over time and normalizing to total cell volume. In this example, we found that net lysine uptake was 2,900 pmol/μL cell/hr:

$$V_{in}-V_{\mathit{out}}=2,900\text{pmol}/\mathrm{\mu }\mathrm{L}\text{cell}/\text{hr}$$ (10)

To calculate the product of the instantaneous total cell volume and the instantaneous cytosolic unlabeled amino acid fraction integrated with respect to time, we first fit the dynamic amino acid labeling data and the dynamic total cell volume data, separately, to exponential functions of the following form:

$$f(t)=A{e}^{kt}$$ (11)

After fitting, the equations describing the unlabeled fraction of lysine in the medium over time (in hours) and the total cell volume (in μL) over time (in hours) are the following:

$$\frac{A{A}_{\mathit{cyto}}^0}{A{A}_{\mathit{cyto}}^{\mathit{total}}}(t)=0.014e^{(0.0756)t}$$ (12)

$$\mathit{Vol}(t)=2.718e^{(0.0278)t}$$ (13)

Multiplication of Eq. (12) by Eq. (13) gives a single exponential function under the integral in Eq. (7). Integration with respect to time (from 0 h to 24 h) yields 4.19 μL cell ×hr.

$${\int }_0^T\frac{A{A}_{\mathit{cyto}}^0}{A{A}_{\mathit{cyto}}^{\mathit{total}}}(t)\times \mathit{Vol}(t)dt={\int }_0^{24}0.040e^{(0.0756)t+(0.0278)t}dt=4.19\mathrm{\mu }\mathrm{L}\text{cell}\times \text{hr}$$ (14)

The first term in the denominator is equal to the time-integral of total cellular volume. For this, we can use the fitted exponential function describing cellular growth from the previous step. We found that this integral was equal to 92.3 μL cell ×hr.

$${\int }_0^T\mathit{Vol}(t)dt={\int }_0^{24}{e}^{(1.0+(0.028)t)}dt=92.3\mathrm{\mu }\mathrm{L}\text{cell}\times \text{hr}$$ (15)

The second term in the denominator, which also appears in the numerator and was calculated above, is equal to 4.19 μL cell ×hr. Plugging in Eqs. (9)–(15) into Eq. (7):

$$V_{AA\mathit{release}}=\frac{22,700+(2,930\times 4.19=12,300)}{92.3-4.19}=397\text{pmol}/\mathrm{\mu }\mathrm{L}\text{cell}/\text{hr}$$ (16)

Thus, the release rate of lysine from extracellular protein catabolism is 397 pmol/μL cell/hr. To compute the corresponding protein scavenging flux, we divide this number by the number of lysines per BSA molecule (59), as per Eq. (8), to yield the following protein scavenging flux: 6.73 pmol/uL cell/hr:

$$V_{\mathit{serum}\mathit{protein}\mathit{catabolism}}=\frac{V_{AA\mathit{release}}}{{\alpha }_{AA}}=\frac{397}{59}=6.73\text{pmol}/\text{uL}\text{cell}/\text{hr}$$ (17)

As a final note, the equation for the rate of amino acid release due to extracellular protein catabolism includes a term containing the unlabeled amino acid fraction in the cytoplasm over time. For this, we can either use the intracellular unlabeled fraction, which contains cellular compartments other than the cytoplasm, or the extracellular unlabeled fraction. We observed that intracellular amino acid pools rapidly exchange with amino acids in the medium: when we switched cells growing in standard unlabeled medium to ¹³C-AA medium, intracellular amino acid pools became predominantly labeled (>90%) in roughly 10 minutes (Figure S1). Given this rapid exchange, we use extracellular amino acid labeling to represent cytosolic labeling in our calculations. This has the benefit of requiring only extracellular, not intracellular, amino acid measurements.

Proliferation Assays

For absolute measurements of proliferation (i.e. using cell volume, cell number), 500K (parental KRPC) or 200K (adapted KRPC) cells were seeded in standard 60 mm tissue culture dishes in DMEM supplemented with 5% FBS. After 24 h, cells were washed once with PBS and switched to amino acid-deficient medium supplemented with 5% (w/v) BSA. Cell number was measured using a Countess Automated Cell Counter (Invitrogen), and total cell volume was measured using Packed Cell Volume tubes (Techno Plastic Products).

For relative measurements of proliferation (i.e. using absorbance of resorufin), 60K (parental KRPC) or 20K (adapted KRPC) cells were seeded in standard 24-well tissue culture plates in DMEM supplemented with 5% FBS. After 24 h, cells were washed once with PBS and switched to amino acid-deficient medium supplemented with 5% (w/v) BSA. After the indicated time in culture, cells were washed twice with PBS, and standard DMEM supplemented with 10% FBS and 0.1 mg/mL resazurin, but without additional BSA, was added. After 2 h, absorbance was measured.

Western Blotting

Cells were washed 3x with PBS, then lysed with ice-cold RIPA buffer (Cell Signaling) with cOmplete protease inhibitor and PhosSTOP phosphatase inhibitor cocktails (Roche). Soluble lysate fractions were isolated by centrifugation at 16,100 g for 10 min. Relative protein content was estimated using total cellular volume as a surrogate, and equal amounts of protein per sample were analyzed by SDS-PAGE and Western Blotting.

Fluorescence Microscopy

5,000 cells were seeded in DMEM supplemented with 10% FBS in each well of a fibronectin-coated 8-well Chamber Slide (Nunc Inc). After 48 h, cells were washed once with serum-free DMEM and switched to medium containing 1 mg/mL DQ Green BSA, 50 nM LysoTracker Red, and 0.5 ug/mL Hoechst. After 6 h, cells were washed three times with PBS and fixed in 4% paraformaldehyde for 15 minutes. After three more washes to remove fixative, the polystyrene chamber was removed, mounting medium was applied, and a coverslip was mounted. The mounting medium was allowed to set overnight, and samples were imaged on a Nikon A1 Confocal Microscope, with imaging parameters set such that no pixels were saturated. Images were analyzed in Matlab.

Cell Viability Measurements

Cell viability was assayed by flow cytometric detection of caspase activity. After 48 h in the specified condition, medium from each sample was collected. Cells were washed once with room temperature PBS, which was added to the collected medium. Cells were then detached with trypsin and added to the collected medium and saline. The resulting cell suspension was centrifuged for 5 min at 2,000 g, and the pellet was resuspended in DMEM supplemented with 2% FBS, containing CellEvent Caspase-3/7 Green Detection Reagent (Invitrogen). After 2 h at 37°, SYTOX AADvanced Dead Cell Stain was added, and samples were analyzed by fluorescence-activated cell sorting using a BD LSRII Multi-Laser Analyzer.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical Analysis

For proliferation, fluorescence microscopy, and apoptosis experiments, p-values were calculated using a two-tailed unpaired t-test; for relative protein scavenging rates, a two-tailed paired t-test. 95% confidence intervals were calculated as the standard error of the mean multiplied by 1.96.

DATA AND SOFTWARE AVAILABILITY

Raw data have been deposited to Mendeley Data with the following DOI: 10.17632/4zb6z95zz4.1.

HIGHLIGHTS.

• Protein scavenging by macropinocytosis can be quantified using isotope tracers

• Amino acid deprivation induces protein scavenging despite persistent mTORC1 activity

• In amino acid-starved cells, mTOR inhibition only modestly increases protein scavenging

• Partial mTOR inhibition enhances growth by restoring amino acid balance