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Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism

  1. Hongyun Zhao
  2. Lifeng Yang
  3. Joelle Baddour
  4. Abhinav Achreja
  5. Vincent Bernard
  6. Tyler Moss
  7. Juan C Marini
  8. Thavisha Tudawe
  9. Elena G Seviour
  10. F Anthony San Lucas
  11. Hector Alvarez
  12. Sonal Gupta
  13. Sourindra N Maiti
  14. Laurence Cooper
  15. Donna Peehl
  16. Prahlad T Ram
  17. Anirban Maitra
  18. Deepak Nagrath Is a corresponding author
  1. Rice University, United States
  2. University of Texas MD Anderson Cancer Center, United States
  3. University of Texas, MD Anderson, United States
  4. Baylor College of Medicine, United States
  5. Stanford University, United States
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Cite as: eLife 2016;5:e10250 doi: 10.7554/eLife.10250

Abstract

Cancer-associated fibroblasts (CAFs) are a major cellular component of tumor microenvironment in most solid cancers. Altered cellular metabolism is a hallmark of cancer, and much of the published literature has focused on neoplastic cell-autonomous processes for these adaptations. We demonstrate that exosomes secreted by patient-derived CAFs can strikingly reprogram the metabolic machinery following their uptake by cancer cells. We find that CAF-derived exosomes (CDEs) inhibit mitochondrial oxidative phosphorylation, thereby increasing glycolysis and glutamine-dependent reductive carboxylation in cancer cells. Through 13C-labeled isotope labeling experiments we elucidate that exosomes supply amino acids to nutrient-deprived cancer cells in a mechanism similar to macropinocytosis, albeit without the previously described dependence on oncogenic-Kras signaling. Using intra-exosomal metabolomics, we provide compelling evidence that CDEs contain intact metabolites, including amino acids, lipids, and TCA-cycle intermediates that are avidly utilized by cancer cells for central carbon metabolism and promoting tumor growth under nutrient deprivation or nutrient stressed conditions.

https://doi.org/10.7554/eLife.10250.001

eLife digest

Cancer cells behave differently from healthy cells in many ways. Healthy cells rely on structures called mitochondria to provide them with energy via a process that requires oxygen. However cancer cells don’t rely on this process, and instead release energy by breaking down sugars outside of the mitochondria. This may explain why cancer cells are able to thrive even when little oxygen is available.

Cancer cells also interact with neighboring cells called fibroblasts, which are a major part of a tumor’s microenvironment, and recruit them into the tumors. The fibroblasts communicate with cancer cells, in part, by releasing chemical messengers packaged into tiny bubble-like structures called exosomes. Recent studies have suggested that these exosomes may help cancer cells to thrive, but there are many questions remaining about how they might do this.

Now, Zhao et al. show that the fibroblasts smuggle essential nutrients to cancer cells via the exosomes and disable oxygen-based energy production in cancer cells. First, exosomes released by cancer-associated fibroblasts from people with prostate cancer were collected and marked with a green dye. Next, the green-labeled exosomes were mixed with prostate cancer cells, and shown to be absorbed by the cells. Oxygen-based energy release was dramatically reduced in the exosome-absorbing cells, and sugar-based energy release increased.

Next, Zhao et al examined the contents of the exosomes, and found that they contain the building blocks of proteins, fats, and other important molecules. Next, the experiments revealed that both prostate cancer and pancreatic cancer cells deprived of nutrients can use these smuggled resources to continue to grow. Importantly, this process did not involve the protein Kras, which previous studies had show helps cancer cells absorb nutrients. These findings suggest that preventing exosomes from smuggling resources to starving cancer cells might be an effective strategy to treat cancers.

https://doi.org/10.7554/eLife.10250.002

Introduction

The understanding of interaction mechanisms between cancer cells and the tumor microenvironment (TME) is crucial for developing therapies that can arrest tumor progression and metastasis. Recent studies have identified the TME as a key player in regulating cancer cell growth (Whiteside, 2008). Although the TME is comprised of a variety of cell types including cancer-associated fibroblasts cells (CAFs), immune cells, and angiogenic elements, CAFs are the major constituent of the TME in many cancers (Whiteside, 2008; Allinen et al., 2004; Feig et al., 2012). Accumulating evidence suggests that paracrine signals from cancer cells can both recruit and activate CAFs within the TME, and contribute to their activation (Whiteside, 2008; Liao et al., 2009; Orimo et al., 2005; Chung et al., 2006). Although CAFs have been associated with tumor growth, progression, and metastasis through intercellular communications with cancer cells; little is known about their role in inducing metabolic reprogramming in cancer cells.

Studies have shown that extracellular vesicles known as exosomes can facilitate crosstalk between cancer and stromal cells in the TME. Exosomes have emerged as a vital communication mechanism between different cell types in the TME. Exosomes carry information from one cell to another and reprogram the recipient cells (Gangoda et al., 2015), and recent findings report that exosomes harbor the potential to regulate proliferation, survival and immune effector status in recipient cells. Exosomes range between 30–100 nm in diameter, have a bilayered membrane (Johnstone et al., 1987) and express surface marker such as CD63 (Christianson et al., 2013). Recent studies indicate that they contain proteins, nucleic acids and miRNAs (Ekström et al., 2012Costa-Silva et al., 2015; Simons and Raposo, 2009). Most of the current studies are focused on cancer cell secreted exosomes; and little is known about CAF-derived exosomes (CDEs) and their metabolic influence on cancer cells. Although it has been shown that CAFs can induce metabolic reprogramming in cancer cells (Brauer et al., 2013), the contribution of CDEs in this phenomenon, if any, has not been elucidated.

Here, we report a novel regulation of cancer cell metabolism in prostate and pancreatic cancers mediated by CDEs. Our results demonstrate that patient-derived CDEs reprogram cancer cell metabolism through disabling mitochondrial oxidative metabolism and providing de novo 'off the shelf' metabolites through exosomal cargo. Specifically, we find that inhibition of mitochondrial oxidative phosphorylation by CDEs is associated with a compensatory increase in glycolysis. Interestingly, the inhibition of electron transport chain by CDEs significantly increased glutamine’s reductive carboxylation for biosynthesis in cancer cells. Further, we demonstrate through isotope tracing and intra-exosomal metabolomic experiments that exosomes act as a source of metabolite cargo carrying lactate, acetate, amino acids, TCA cycle intermediates, and lipids; and these metabolites are utilized by recipient cancer cells for proliferation, precursor metabolites and replenishing levels of TCA cycle metabolites. Notably, we demonstrate in wild-type and activated Kras-expressing pancreatic cancer cells that the metabolite cargo delivery mechanism by exosomes is similar to macropinocytosis, albeit without the previously described dependence on oncogenic Kras signaling (Commisso et al., 2013). Our results reveal a novel metabolism-centric regulatory role of TME-secreted exosomes in cancers and we uncover the underlying mode of action of this regulation. These findings can lead to novel therapeutics targeting communication between cancer cells and their microenvironment.

Results

CDEs are internalized by prostate cancer cells

To illustrate that CAFs secrete exosomes, and that cancer cells internalize these exosomes, we first isolated exosomes from conditioned media obtained from patient-derived prostate CAFs. The particle size analysis of isolated exosomes showed particles with size distribution from 30 to 100 nm (Figure 1A), which is consistent with previous observations (Xiao et al., 2014). Since exosomes are below the size range to allow direct detection by flow cytometry, we confirmed exosomes’ expression of CD63, a surface antigen marker, through flow analysis of Dynabeads conjugated with anti-CD63 antibody (Figure 1B). To examine if CDEs are taken up by prostate cancer cells (PC3), we pre-labeled CDEs with PKH green dye and added them to PC3 cells for 3h and analyzed their internalization by cancer cells. As indicated by shift in the peaks, CDEs are indeed taken up by cancer cells (Figure 1C). Examination by fluorescence microscopy also confirmed the uptake of PKH red labeled exosomes by PC3 cells, evidenced through colocalization of red fluorescence and DAPI (Figure 1D). Furthermore, we estimated the saturable concentration of CDEs taken up by cancer cells (Figure 1E). Hence, in subsequent experiments we used 200 μg/ml of CDEs as the working concentration (Zhu et al., 2012).

Exosomes secreted by CAF-derived from prostate cancer patients are internalized by prostate cancer cells.

(A) Size analysis of stromal exosomes. Three samples of exosomes derived from prostate cancer patient CAFs were analyzed with the Zetaview instrument. The profiles indicate that the size distribution of exosomes is within the range of 30-100 nm. For exosomes isolation, conditioned medium was obtained from CAFs cultured with exosomes-depleted FBS. (B) Flow analysis of CAF exosomes bound to Dynabeads conjugated with anti-CD63 antibody (anti-CD63) or an irrelevant control antibody (anti-Rabbit IgG antibody, Rb IgG). The graph and table show that these microvesicles express CD63, an exosome surface antigen biomarker. (C) Flow cytometry analysis shows uptake of CAF exosomes by prostate cancer cells. Prostate cancer cells were incubated with PKH67-labeled stromal exosomes for 3 hr. Freshly prepared exosomes were used in this and subsequent experiments. Exosome-depleted serum was used for cell culture. (D) Representative fluorescence image shows CAFs exosomes were uptaken by prostate cancer cells. Prostate cancer cells were incubated with PKH26-labeled CAFs exosomes for 3 hr. Blue, cell nuclei; Red, PKH-Exo. (E) Flow cytometry analysis shows saturable uptake curve of CAFs secreted exosomes in prostate cancer cells. (n=4).

https://doi.org/10.7554/eLife.10250.003

CDEs downregulate mitochondrial function of prostate cancer cells

Since CAFs have been shown to regulate cancer cell growth (Liao et al., 2009), we first examined influence of CDEs on cancer cell proliferation. We isolated exosomes from the conditioned media of CAFs derived from a prostate cancer patient and cultured prostate cancer cells in the presence of freshly isolated exosomes. CDEs enhanced proliferation of PC3 cells with increasing exosomes concentration (Figure 2A). To determine whether CDEs induce metabolic rewiring in cancer cells, we cultured PC3 cells in CDEs for 24 hr and measured the oxygen consumption rate (OCR) with increasing amounts of exosomes. Surprisingly, we observed that basal oxidative phosphorylation (OXPHOS, indicated by OCR) was significantly inhibited with increasing concentration of CDEs added to PC3 cells (Figure 2B). To ascertain whether the inhibition of mitochondrial respiration of cancer cells is specific to CDEs and to prove similar behavior is not exhibited with exosomes derived from other cells, we isolated exosomes from prostate cancer cell line (PC3), human fibroblasts (IMR-90), and also used blank media for isolation method control (Figure 2—figure supplement 1). As seen in the figure, exosomes from control conditions were ineffective in modulating cancer cells’ OCR. To expand our observations, we next isolated exosomes from three independent prostate cancer patient CAFs and cultured four prostate cancer cell lines (PC3, DU145, 22RV1 and E006AA) in presence and absence of the exosomes (Figure 2C). Remarkably, exogenous addition of CDEs reduced OCR in all prostate cancer cell lines. To confirm if this reduction of OCR in cancer cells was indeed because of uptake of exosomes, we added the endocytosis inhibitor Cytochalasin D (CytoD) in PC3 culture media along with CDEs. CytoD has been shown to inhibit exosome uptake in various cell systems (Casella et al., 1981; Feng et al., 2010). Notably, CytoD could partially rescue this reduction of OCR in PC3 cells, thus confirming the CAF exosomes mediated reduction of OCR in cancer cells (Figure 2D).

Figure 2 with 2 supplements see all
CDEs increase proliferation of prostate cancer cells but significantly downregulate their mitochondrial function.

(A) Effect of CAFs-derived exosomes on viability of prostate cancer cells, 48h culture period (PC3) (n≥9). (B) Prostate cancer cells show reduced basal mitochondrial oxygen consumption rate (OCR) when cultured with range of concentrations of CDEs for 24 hr. Basal OCR is a measure of OXPHOS activity. The OCR was normalized with protein content inside cells. PC3 cells were cultured with patient-1 derived CAFs’ exosomes (n≥9). (C) Basal OCR was measured for PC3, DU145, 22RV1, E006AA prostate cancer cell lines cultured with patient derived CDEs and control conditions. Six patient-derived CAFs were used for exosomes isolation. (n≥9). (D) OCR of prostate cancer cells were measured after 24 hr culture with and without CDEs. Cytochalasin D (CytoD), an inhibitor of exosomes uptake through actin depolymerization, rescues reduced OCR in prostate cancer cells when cultured with CAFs exosomes. CytoD disturbs actin filament inside cells, thus inhibit phagocytosis. CytoD concentration of 1.5 μg/ml was used. (n≥5). (E) Maximal and reserve mitochondrial capacities were measured using FCCP and antimycin. Maximal OCR is maximal capacity of mitochondrial OCR. (n≥9). (F) Role of CAFs secreted exosomes in regulating mitochondrial membrane potential (MMP) of prostate cancer cells. MMP is an important indicator of mitochondrial functions. (n≥5). (G) Reduced OXPHOS genes expression in cancer cells cultured with exogenous CDES. (H) qPCR results show that mitochondrial OXPHOS genes of prostate cancer cells were downregulated when cultured with CDEs. (n=3). (I) Most abundant miRNAs targeting OXPHOS genes were abundant in CAFs exosomes. (n=4). (J) miRNAs in CAFs exosomes targeting specific OXPHOS genes. Nanostring was used to measure miRNA expression levels in stromal exosomes. (n=4). (K) OCR of PC3 were measured after transfection of targeted miRNAs together into cells. (n=5). miRNAs were transfected into cells according to the manufacturer’s protocol (Lipofectamine 2000 Transfection Reagent, Thermofisher). Cells were seeded in 6-well plate for 24 hr. Transfection was performed followed by incubation for 48 hr. Cells were then reseeded onto Seahorse plates for OCR measurements after the cells were attached. Data information: data in (A), (B), (C), (F), (H) are expressed as mean ± SD, data in (D), (E), (K) are expressed as mean ± SEM;*p<0.05, **p<0.01, ***p<0.001. Figure 2—figure supplement 12.

https://doi.org/10.7554/eLife.10250.004

To conclusively associate CDEs induced metabolic reprogramming with metabolic content of exosomes, we verified if synthetic liposomes (DOPC/CHOL liposomes labeled with DiO, size 85-110 nm; DOPC: 1,2-dioleoyl-sn-glycero-3-phosphocholine, CHOL: cholesterol) with a size distribution similar to exosomes could similarly modulate cancer cells. Our data suggests that liposomes did not alter cell proliferation, OCR and ECAR in both prostate (PC3) and pancreatic cancer cells (MiaPaCa-2 and BxPC3) (Figure 2—figure supplement 2). These results implicate metabolic content of exosomes towards observed changes in CDEs-induced increase in cancer cell proliferation, mitochondrial dysfunction and increased glycolysis.

The mitochondrial respiratory capacity inhibition in prostate cancer cells by prostate CAF-exosomes was further confirmed by measuring maximal and reserve mitochondrial capacity using oligomycin, the protonophoric uncoupler FCCP, and the electron transport inhibitor rotenone. Both maximal and reserve mitochondrial capacity of cancer cells were significantly reduced in presence of CDEs (Figure 2E). These results suggest that CAFs downregulate mitochondrial OXPHOS in cancer cells and CDEs play a key role in this reprogramming. To further examine the effect of these exosomes on mitochondrial activity, we measured the mitochondrial membrane potential of PC3 cells with and without exosomes. As seen in Figure 2F, exogenous addition of CDEs significantly reduced mitochondrial membrane potential within cancer cells.

To unravel the mechanism behind OCR reduction in cancer cells by CDEs; we performed microarray and q-PCR analysis to estimate the changes in mitochondrial gene expression levels of PC3 cells with and without exogenous CDEs (Figures 2G,H). Microarray data revealed that transcript levels for OXPHOS related ATP synthase complex genes were downregulated in cells cultured with CDEs. Gene Set Enrichment Analysis (GSEA) on microarray data corroborates these observations by estimating a negative enrichment score (p-value <0.05) for the entire set of 109 OXPHOS-related genes in cancer cells cultured in presence of CDEs relative to control (Figure 2G). Furthermore, we found that both cytochrome B (CYTB) and cytochrome C oxidase I (COXI), which are components of Complexes III and IV of the electron transport chain, respectively, have lower transcript expression levels when PC3 cells were cultured with exogenous CDEs (Figure 2H).

It is well established that exosomes contain noncoding RNAs (e.g. miRNAs) which can serve as a communication mechanism between stromal and cancer cells. We measured miRNA levels in the CDEs to determine whether miRNA underlay the molecular mechanism by which the exosomes exert the metabolic changes we observed. We extracted miRNAs from purified CDEs from three patients and measured the levels of a panel of 800 human miRNAs using NanoString technology. We grouped the miRNA by whether or not they target genes involved in oxidative phosphorylation (miRNA-mRNA interactions taken from starBase v2.0, starbase.sysu.edu.cn). The relative abundance of miRNAs that target oxidative phosphorylation genes is higher in exosomes than most other miRNA as seen in the density distribution plot (Figure 2I). The top 30 most abundant miRNAs across the exosomes sampled, target one or more OXPHOS genes, which is validated by measuring the decrease in mRNA levels of these genes after treatment with exosomes (Figure 2J). The miRNAs and their targets that we have identified are based on experimental miRNA abundance data from Nanostring assays followed by miRNA target prediction integrated with AGO-CLIP-SEQ data (Li et al., 2014). In order to confirm the inhibition of OXPHOS through miRNA we co-transfected mir-22, let7a and mir-125b present in the group of miRNA targeting OXPHOS, in PC3 cells, and measured OCR (Figure 2K). In line with our hypothesis, we see a decrease in OCR in PC3 cells co-transfected with miRNAs. Although, the reduction of OCR is moderate, this is due to the technical limitation of co-transfection experiments which can only allow using a small subset of the miRNAs that target OXPHOS in PC3. In summary, these results suggest that CDEs reduced mitochondrial oxidative phosphorylation and induced metabolic alterations in cancer cells mimicking hypoxia-induced alterations.

CDEs upregulate glucose metabolism in cancer cells

The above experiments showed that CDEs downregulate mitochondrial activity. We further investigated whether this reduced mitochondrial activity leads to increased glycolysis in cancer cells in presence of CDEs. We first measured levels of basal glycolysis (indicated by extracellular acidification rate, ECAR) in four prostate cancer cell lines in presence of exosomes from three independent prostate cancer patient CAFs (Figure 3A). As seen in the figure, these exosomes significantly increased glycolysis in cancer cells when compared to cancer cells cultured without exosomes. Notably, CytoD partially inhibited this increase of ECAR, thus confirming the role of exosomes in increase of glycolysis in cancer cells (Figure 3B). To expand our findings on the exosomes mediated increase of glycolysis in cancer cells; we measured both glucose uptake and lactate secretion in cancer cells cultured with and without exosomes for 24 h (Figures 3C,D). Consistent with above results, CDEs increased glucose uptake and lactate secretion when compared to cancer cells cultured without exogenously added exosomes.

Figure 3 with 1 supplement see all
CDEs upregulate glycolysis in cancer cells.

(A) Extracellular acidification rates (ECAR) of prostate cancer cells were measured after 24 hr culture with and without CAFs exosomes. ECAR is a measure of glycolytic capacity of cells. The ECAR was normalized with protein content inside cells. Four prostate cancer cell lines: PC3, DU145, 22RV1, E006AA were used. Six patients derived CAFs were used for exosomes isolation (n≥9). (B) ECAR of prostate cancer cells was measured. CytoD increased ECAR in prostate cancer cells when cultured with CAFs exosomes. CytoD concentration of 1.5 μg/ml was used. (n≥6). (C,D) Effect of CAFs-secreted exosomes on glucose uptake (C) and lactate secretion fluxes (D) in prostate cancer cells. (n=9). (E) Schematic of carbon atom transitions using 1:1 mixture of 13C6 glucose and 1-13C1-labeled glucose. (F) Relative lactate abundances were measured using GC-MS in PC3 cells cultured with and without CAFs-secreted exosomes for 24 hr. (n=4). (G–M) Contribution of glucose towards TCA cycle metabolites and glycolysis is measured using the labeled glucose. Comparison of mass isotopologue distributions (MID) of lactate, pyruvate, citrate, α-ketoglutarate, malate, fumarate, and glutamate in PC3 cancer cells cultured with and without CAFs-secreted exosomes. (n=4). (N) Percentage of glucose contribution to α-ketoglutarate in PC3 cells with and without CAFs-secreted exosomes. (n=4). Data information: data in (A,C and D) are expressed as mean ± SD, data in (B,FN) are expressed as mean ± SEM; *p<0.05, **p<0.01, ***p<0.001. Figure 3—figure supplement 1.

https://doi.org/10.7554/eLife.10250.007

To understand the underlying changes in metabolite abundances induced by CAF exosomes in cancer cells, we performed 13C GC-MS based isotope tracer analysis using a 1:1 mixture of U-13C6 glucose and 1-13C1 glucose (Figure 3E–N). GC-MS results are reported as mass isotopologue distributions (MIDs), which represent the relative abundance of different mass isotopologues of each metabolite; where M0 refers to the isotopologue with all 12C atoms and M1 and higher refer to heavier isotopologues with one or more 13C atoms derived from the tracer. It is well established that isotope tracer analysis can reveal the alterations in contributions of a substrate within a particular metabolic pathway (Figure 3E). We found that the CDEs increased the lactate levels in the cancer cells (Figure 3F, Figure 3—figure supplement 1). Further, the percentage of M3 pyruvate and M3 lactate was increased due to CDEs; however, there is a corresponding decrease in the percentage of M2 citrate and M3 citrate (Figure 3G-I). The increase of M3 pyruvate and M3 lactate indicates higher contribution of glucose to pyruvate and lactate in prostate cancer cells conditioned with CDEs. Moreover, there was a decrease of M0 pyruvate and M0 lactate with a corresponding increase of M1 pyruvate and M1 lactate, thus suggesting that the exosomes enhance glycolysis. The latter conclusion was based on the principle that M1 pyruvate is only produced by glucose-6-phosphate metabolized by phospho glucoisomerase. Consistent with the decreased OXPHOS observed in cancer cells due to CDEs, the percentage of M2 citrate, M2 α-ketoglutarate, M2 fumarate, M2 malate, and M2 glutamate was also significantly reduced in cancer cells in presence of CDEs (Figure 3I–M). In line with our observations, the percentage of 13C α-ketoglutarate from 13C labeled glucose was significantly reduced (Figure 3N). This further confirms that CDEs decreased the percentage contribution of glucose to a-ketoglutarate in cancer cells and instead diverted it towards lactate. The above results conclusively show that CDEs induce a Warburg type phenotype in cancer cells, by disabling normal oxidative mitochondrial function with a compensatory increase in glycolysis.

CDEs enhance reductive pathway of glutamine metabolism in cancer cells

Glutamine serves as an anaplerosis substrate to fuel the TCA cycle for energy generation and also provides nitrogen for protein synthesis (Dang, 2010; Wise and Thompson, 2010; DeBerardinis and Cheng, 2010; Daye and Wellen, 2012; Gaglio et al., 2009; Johnson et al., 2003; Rajagopalan and DeBerardinis, 2011; Shanware et al., 2011; Weinberg et al., 2010). To further unravel the mechanistic links between disabled normal oxidative mitochondrial function in cancer cells by CDEs and its influence on cancer cells’ mitochondrial metabolism, we analyzed glutamine’s contribution to the TCA cycle metabolite pools in cancer cells using labeled U-13C5 glutamine (Figure 4A). Proliferating cells under both normoxia and hypoxia can utilize glutamine by oxidative metabolism and produce pyruvate through malic enzyme and further combine oxaloacetate with acetyl-CoA to form M4 citrate (obtained by condensing of labeled oxaloacetate obtained from glutamine and unlabeled acetyl CoA). Alternatively, proliferating cells under hypoxia have been reported to predominantly reductively carboxylate glutamine generated α-ketoglutarate through IDH 1/2 to generate M5 citrate (Figure 4A) (Metallo, 2012). M5 citrate is further catalyzed to M3 fumarate and M3 malate in this reductive glutamine metabolism. Our MID data reveals that addition of CDEs increased M5 glutamate and M5 α-ketoglutarate in cancer cells thereby indicating that exosomes enhance glutamine’s entry into TCA cycle (Figure 4B,C). Notably, there was significant increase in M5 citrate, M3 fumarate and M3 malate in cancer cells in the presence of exogenously added exosomes thus suggesting that cancer cells rely critically on reductive glutamine metabolism when normal mitochondrial function is disrupted by stromal microenvironment (Figure 4D–F).

Figure 4 with 1 supplement see all
CDEs increase glutamine driven reductive carboxylation and lipogenesis in prostate cancer cells.

(A) Schematic of carbon atom transitions using 13C5 glutamine. Black color represents labeled carbon of glutamine before entering into TCA cycle. Blue color represents glutamine's direct effect on canonical TCA cycle and red color represents glutamine's effect on TCA cycle through reductive carboxylation. (B–F) Mass isotopologue distribution (MID) of glutamate, α-ketoglutarate, citrate, malate, and fumarate in PC3 cancer cells cultured with and without CDEs in U-13C5 glutamine (n=4). (G) Ratio of α-ketoglutarate and citrate pools in PC3 cancer cells cultured with and without CDEs measured using GC-MS. Higher ratio correlates with higher glutamine driven reductive carboxylation (n=4). (H–I) Ratio of glutamine contribution to citrate via oxidative and reductive pathways. Lower ratio indicates higher reductive carboxylation. CDEs increased reductive glutamine metabolism in PC3 cells (I). Oxidative contribution to citrate is determined by calculating M4 citrate percentage; reductive contribution to citrate is determined by M5 citrate percentage (n=4). (J) Glucose contribution to palmitate synthesis in PC3 cells cultured with or without CAFs exosomes for 72 hr was measured using GC-MS (n=6). (K) Glutamine contribution to palmitate synthesis in prostate cancer cells with or without CAFs exosomes measured using GC-MS (n=6). (L) Isotopologue spectral analysis (ISA) of both glucose and glutamine contribution to lipid synthesis in PC3 cells under control or CAFs exosomes culture conditions. CAFs exosomes enhance reductive carboxylation to lipid synthesis. However, total percentage of glucose and glutamine contribution to palmitate is about 60%. (M) Acetate concentration in cancer cell culture medium. (N) Acetate contribution to palmitate synthesis in PC3 cells with or without CAFs exosomes. Acetate spiked concentration was 500 μM (n=4). (O) Pyruvate contribution to palmitate synthesis in PC3 cells with or without CAFs exosomes (n=4). (P) ISA analysis of both pyruvate and acetate contribution to lipid synthesis in PC3 cancer cells under control or CAFs exosomes culture conditions. CAFs exosomes enhance acetate contribution to palmitate synthesis. Data information: data in (BP) are expressed as mean ± SEM,*p<0.05, **p<0.01, ***p<0.001.

https://doi.org/10.7554/eLife.10250.009

To obtain mechanistic understanding of CDEs induced increased reductive carboxylation in cancer cells; we measured the ratio of α-ketoglutarate to citrate abundance in cancer cells and found that exosomes increased this ratio significantly (Figure 4G). The increased ratio of α-ketoglutarate to citrate was recently shown to promote reductive glutamine metabolism and it correlated with reductive glutamine’s contribution to citrate (Fendt et al., 2013). The inhibition of respiratory chain components, or hypoxic conditions, was found to increase this ratio. Consistent with previous reports, the ratio of M4/M5 citrate, which represents the ratio of glutamine to citrate through oxidative metabolism over reductive metabolism, confirmed our above results that there is a significant increase in glutamine’s reductive metabolism in presence of exosomes (Figure 4H–I). This is further substantiated through significant increase in the percentage contribution of glutamine through reductive pathway in the TCA cycle in cancer cells in presence of exosomes when compared to control condition. Also, there was concomitant decrease in the percentage contribution of glutamine through oxidative pathway in the TCA cycle.

One of the key metabolic requirements of rapidly dividing cancer cells pertains to availability of adequate pool of fatty acids for enabling membrane synthesis. Therefore, to further investigate the effect of exogenous CDEs on nutritional substrates’ incorporation into lipogenesis; we used U-13C6 glucose or U-13C5 glutamine to estimate their conversion to cytosolic acetyl-CoA, which is the precursor for palmitate (fatty acid) synthesis. Similar to our earlier observation that CDEs reduced glucose contribution to TCA cycle metabolites in cancer cells, we found that exosomes also decreased glucose contribution to palmitate. This is evident from the shift in high mass isotopologues of palmitate to lower mass isotopologues derived from U-13C6 glucose, when cells are cultured with exosomes (Figure 4J). Conversely, there is a shift in the reverse direction, i.e. from low to high mass palmitate isotopologues derived from U-13C5 glutamine, in presence of CDEs (Figure 4K). To quantify the percentage contribution of these substrates to the lipogenic acetyl-CoA pool, we performed isotopologue spectral analysis (ISA) (Metallo, 2012; Kamphorst et al., 2014). In line with above results, ISA (Figure 4—figure supplement 1) indicated a significant decrease in the fraction of glucose contribution to lipogenic acetyl-CoA in cancer cells cultured with exosomes (Figure 4L). More importantly, there is a two-fold increase in glutamine’s contribution to lipogenic acetyl-CoA via the reductive carboxylation pathway. Additionally, these intriguing results suggest that there are likely to be other sources apart from glucose and glutamine that contribute to fatty acid synthesis.

Recently it was shown that acetate could be an important source for lipogenic acetyl-CoA in cancer cells, especially under hypoxic conditions(Kamphorst et al., 2014). To ascertain if acetate contributed to fatty acid synthesis, we measured acetate content in media (Figure 4M). Indeed, acetate concentrations were detected in our cancer cell media at significant levels. Since pyruvate was available in significant amounts in our cancer cell culture media, we included it in our estimates for palmitate synthesis. In order to quantify contribution from these alternative sources, we performed tracer experiments with U-13C3 pyruvate and U-13C2 acetate in cancer cells cultured with and without exosomes. We found that their contribution to lipogenic acetyl-CoA is significantly lower when compared to that of glucose and glutamine. This is evident from the low mass isotopologues of palmitate generated when cells are cultured with U-13C3 pyruvate or U-13C2 acetate (Figure 4N–O) in both control and exosomes-treated conditions. Interestingly, we noticed from the shift in palmitate mass isotopologues that CDEs increased acetate contribution to lipids (Figure 4N) but decreased the pyruvate contribution (Figure 4O). ISA results confirm these observations, where cancer cells cultured with CDEs showed an increase in acetate’s contribution with a concomitant decrease in pyruvate’s contribution to palmitate production (Figure 4P). These experiments collectively substantiate that exosomes have a significant effect on fatty acid synthesis in cancer cells by switching the carbon source from the oxidative glucose pathway to glutamine via the reductive carboxylation pathway in the TCA cycle.

Intra-exosomal metabolomics reveal that CDEs contain an 'off-the-shelf' pool of metabolite cargo

Exosomes are known to carry a complex cargo that includes proteins, lipids, and miRNAs (Costa-Silva et al., 2015; Simons and Raposo, 2009). Results from the previous sections indicate that exosomes may act as a source of metabolites and proliferating cancer cells use these metabolites for lipogenesis. To ascertain whether exosomes contain significant amount of de novo metabolites, we first measured lactate and acetate contained inside the prostate and pancreatic CDEs. We included intra-exosomal metabolic measurements of pancreatic CAFs in order to generalize our conclusions. Notably, we found high amounts of lactate and acetate in both prostate and pancreatic CDEs (Figure 5A,B). This suggests that exosomes can not only replenish TCA cycle metabolites but also act as source of lipids. Further, to prove these hypotheses we performed GC-MS analysis for intra-exosomal metabolites and found high concentrations of citrate and pyruvate along with significant presence of α-ketoglutarate, fumarate and malate (Figure 5C). To further expand our findings, we performed ultra-high-performance liquid chromatography (UPLC), and found markedly high levels of glutamine, arginine, glutamate, proline, alanine, threonine, serine, asparagine, valine, and leucine in prostate CAF exosomes (Figure 5D). Additionally, in pancreatic CDEs, we found high levels of glutamine, threonine, phenylalanine, valine, isoleucine, glycine, arginine, and serine (Figure 5E). Remarkably, through GC-MS analysis of intra-exosomal lipids, we found intact stearate (Figure 5F) and palmitate (Figure 5G) at high levels in both prostate and pancreatic CDEs. Our results offer definitive proof for the first time that exosomes harbor an 'off-the-shelf' pool of metabolite cargo, TCA cycle metabolites, amino acids, and lipids, which can fuel the metabolic activity of the recipient cells.

Prostate and pancreatic CAFs secreted exosomes carry metabolite cargo.

Intra-exosomal lactate (A) and acetate (B) concentrations were measured in exosomes isolated from three prostate and two pancreatic CAFs using enzymatic assays. Intra-exosomal metabolites were extracted by methanol/chloroform method and protein concentration was used for normalization. (n=3). (C) TCA cycle metabolites, including pyruvate, citrate, α-ketoglutarate, fumarate and malate were measured using GC-MS in exosomes isolated from pancreatic CAF35. (n=3). (D, E) Amino acids were measured using ultra-high performance liquid chromatography (UPLC) inside CDEs (prostate CAFs: [D]; pancreatic CAFs: [E]). Significant levels of amino acids were detected inside CDEs. (n=3). (F-G) Stearate and palmitate were detected at high levels using GC-MS inside pancreatic and prostate CDEs.(n3). Data information: data in (AC), (FG) are expressed as mean ± SEM.

https://doi.org/10.7554/eLife.10250.011

CDEs can supply amino acids to cancer cells in a manner similar to macropinocytosis

Recent studies have shown that macropinocytosis of circulating proteins (especially albumin) could supply amino acids to nutrient-deprived cancer cells (Commisso et al., 2013). Having established that CDEs could act as a source of metabolites, we further postulated that CDEs could act as source of TCA cycle metabolites for cancer cells. To establish whether metabolites contained in exosomes could fuel TCA cycle, we cultured patient-derived fibroblasts with 13C-labeled glucose, glutamine, pyruvate, leucine, lysine, and phenylalanine for 72 hr. We selected leucine, lysine and phenylalanine for labeling, because these were the most abundant amino acids in human serum albumin (Saifer and Palo, 1969). The extended timescale of 72 hr was adopted to allow detectable incorporation of labeling in proteins, amino acids, and lipids, and their compartmentalization within exosomal cargo. We then isolated labeled exosomes from the CAF culture spent medium. We postulated that nutrient deprived conditions will enhance cancer cells’ dependence on the nutrient cargo in exosomes and therefore tested our hypothesis under both nutrient replete and nutrient deprived conditions. The isolated CDEs were then spiked into complete or nutrient-deprived (without lysine, leucine, phenylalanine, glutamine, and pyruvate) cultures of prostate cancer cells for 48h. The intracellular metabolites were isolated from cancer cells and their 13C enrichment was determined. Notably, our results substantiate that exosomes can supply metabolites to cancer cells under both complete and nutrient deprivation conditions (Figure 6A). However, and in concordance with hypothesis, we found that there is a significant increase in the contribution of CDEs to cancer cells’ metabolites pools in nutrient deprived conditions, as compared to complete medium cultures. To definitively prove the direct export of metabolites by exosomes, we measured MIDs of metabolites in cancer cells when cultured with 13C labeled exosomes. We detected substantial labeling of intracellular amino acids in cancer cells, which included M5 glutamine, M6 lysine, and M6 leucine. We also detected M5 glutamate derived from mitochondrial glutaminolysis and labeled TCA cycle metabolites from labeled 13C-glutamine supplied by CDEs (Figure 6B). Further, it is important to note that substrates within CDEs will not be fully 13C labeled since 72 hr are not sufficient for CAFs to undergo at least one replication, and hence labeled metabolites in CDEs may be diluted with pre-existent unlabeled (M0) isoforms. This results in small, but significant levels of labeled metabolites within the cancer cells. Importantly, it provides a compelling proof-of-concept that CDEs can supply TCA cycle metabolites to cancer cells.

Figure 6 with 1 supplement see all
CDEs supply metabolites to cancer cells.

To label metabolites, proteins and lipids in CAFs-secreted exosomes, CAFs were cultured in RPMI with labeled 13C3 pyruvate (pyr), 13C5 glutamine (gln), 13C6 leucine (leu), 13C6 lysine (lys), 13C9-phenylalanine (phe) and U-13C6 glucose. After 72h of CAFs cultures, sufficient labeling was observed in metabolites, proteins and lipids contained in exosomes. Supply of metabolites to prostate cancer cells from labeled CDEs were measured under complete or deprivation medium cultures in culture media without labeling. (A) Percentage labeling (mean enrichment) observed in metabolites inside PC3 cells cultured with labeled CDEs. (n=4). Mean enrichment is calculated as ME=(i=1Ni×Mi)/N. where N is number of carbons in the metabolite and Mi is abundance of (M+i) isotopologue (B) Mass fraction of heaviest labeled isotopologues of TCA cycle metabolites enriched by labeled CDEs, in prostate cancer cells cultured under complete or nutrient-deprived (without lys, phe, gln, pyr, leu) unlabeled medium (n=4). (C) Effect of CDEs on PC3 cell viability under deprivation (without lys, phe, gln, pyr, leu) conditions and exosome uptake inhibitors. CDEs rescue loss of PC3 cell proliferation under deprivation medium. CytoD, (1.5 μg/ml), heparin(50 μg/ml), and CQ (chloroquine, 20 μM) inhibited this rescue of viability under deprivation conditions n=10. (D) EIPA(25 μM) inhibited rescue of PC3 viability by CDEs under deprivation conditions, (n≥7). Data information: data are expressed as mean ± SEM,*p<0.05,**p<0.01, ***p<0.001.

https://doi.org/10.7554/eLife.10250.012

To estimate the contribution of CDEs in labeling cancer cells’ metabolites, we determined MIDs of metabolites derived from isolated labeled CDEs and also from cancer cells cultured with labeled exosomes under nutrient deprivation conditions (Figure 6—figure supplement 1A). The deprivation and labeling conditions used were similar to Figure 6A and B. In order to quantify the contribution of metabolites from exosomes to cancer cells, we calculated the mean enrichment of 13C labeled amino acids and normalized them with their corresponding enrichment in exosomes. We observed that exosomes account for approximately 16% of phenylalanine, 14% of glutamine, 12% of lysine and 5% of leucine pools in PC3 cells (Figure 6—figure supplement 1A). Since MIDs are measured 48 hr after introduction of exosomes to cancer cells, several of the supplied metabolites would have been catabolized into other intermediates or incorporated into biomass precursors. Therefore, it is important to note that the contributions of essential amino acids estimated would be lower than their total contribution from exosomes over the course of 48 hr. In order to achieve higher detection of labeled isoforms, CAFs will have to be cultured with labeled substrates for multiple passages to completely replace unlabeled metabolite pools with their labeled isoforms. Nevertheless, these results substantiate that CDEs are key player in enriching metabolite pools in cancer cells under nutrient deprived conditions seen in the TME.

To evaluate the requirement of metabolites derived from exosomes for promoting tumor growth under nutrient deprivation conditions (without leucine, lysine, glutamine, pyruvate, and phenylalanine), we cultured cancer cells with exosomes under nutrient deprivation and complete media conditions with and without CytoD and heparin. Similar to CytoD, heparin has been recently shown to inhibit uptake of exosomes by cells (Christianson et al., 2013). CDEs were able to rescue reduction of proliferation under nutrient deprivation conditions (Figure 6C). However, this rescue effect is reduced to varying extents by adding CytoD, heparin and lysosomal degradation inhibitor choloroquine (Figure 6C). Similarly, addition of macropinocytosis inhibitor EIPA also counters the rescue of CDEs under deprivation (Figure 6D). These data suggest that uptake of exosomes and release of their cargo is necessary to rescue cell proliferation under nutrient deprived conditions. Taken together, these results provide evidence that CDEs can reprogram cancer cells’ metabolism by acting as source of amino acids under nutrient depleted conditions in the TME.

CDEs supply metabolites to pancreatic cancer cells via Kras-independent mechanism

We have shown that prostate CDEs can supply metabolites to prostate cancer cells. Our results were similar to the process of macropinocytosis, which was revealed as a mechanism to supply amino acids through extracellular proteins in oncogenic Ras-expressing pancreatic cancer cells. To expand the scope of our findings and understand whether Ras can similarly promote the supply of metabolites by CDEs in pancreatic cancer cells, we isolated exosomes from pancreatic CAFs cell line (CAF-19) and used them to study their metabolic influence in two pancreatic cancer cell lines: BxPC3 (wild type Kras) and MiaPaCa-2 (homozygous Kras). Since it was observed that CDEs supply metabolites to prostate cancer cells, we cultured both BxPC3 and MiaPaCa-2 cell lines, with and without CDEs under complete media and nutrient deprivation (without glutamine, leucine, lysine, phenylalanine, and pyruvate) conditions (Figure 7A). Notably, CDEs could rescue loss of proliferation in both cancer cell lines, thereby suggesting that internalization or uptake and supply of exosomes derived metabolites in cancer cells is Kras independent. Furthermore, this rescue of proliferation by CDEs in pancreatic cancer cell lines was inhibited by receptor mediated endocytosis inhibitor heparin (Figure 7B,C).

Figure 7 with 4 supplements see all
Pancreatic CDEs' metabolic reprogramming of pancreatic cancer cells is Kras independent.

(A) Effect of pancreatic CDEs on pancreatic cancer cell viability under nutrient deprivation (without lys, phe, gln, pyr, leu) conditions. CDEs rescue loss of both wild-type and activated Kras expressing pancreatic cancer cells proliferation under deprivation conditions. Viability of cancer cells with and without exosomes in deprivation condition was measured after 48 hr (n=10). (B,C) Heparin inhibit exosomes uptake and thus inhibit the rescue of proliferation by exosomes under nutrient deprived conditions. Heparin (50μg/ml) disrupts receptor-mediated endocytosis. Before adding exosomes, heparin was added to wells for incubation for at least 0.5 hr (n=5). (D) Basal OCR were measured for BxPC3 and MiaPaCa-2, pancreatic cancer cell lines cultured with pancreatic CAFs (CAF19) exosomes. OCR of both BxPC3 and MiaPaCa-2 were downregulated by CAF19 exosomes. (n=10). Maximal OCR and reserve OCR of BxPC3 and MiaPaCa-2 were downregulated by CAF19 exosomes (n=10). (E) ECAR of both BxPC3 and MiaPaCa-2 were upregulated by CAF19 exosomes (n=10). (F) Relative lactate abundances were measured using GC-MS in BxPC3 and MiaPaCa-2 cells cultured with and without CAF19-secreted exosomes for 24 hr (n=4). (G) Percentage of glucose contribution to α-ketoglutarate in BxPC3 and MiaPaCa-2 cells with and without CAF19-secreted exosomes (n=4). (H) Pancreatic CDEs increased reductive glutamine metabolism in wild-type and activated Kras expressing pancreatic cancer cells. Oxidative contribution to citrate is determined by calculating M4 citrate percentage; reductive contribution to citrate is determined by M5 citrate percentage (n=4). (I) Ratio of oxidative to reductive glutamine contribution to citrate in wild-type and activated Kras expressing pancreatic cancer cells with CAF19-secreted exosomes (n=4). (J) Mass isotopologue distributions (MID) of glutamate, α-ketoglutarate, citrate, malate, and fumarate in BxPC3 and MiaPaCa-2 cancer cells cultured with and without CAF19-secreted exosomes in U-13C5 glutamine (n=4). Higher reductive glutamine metabolism is detected through higher M5 citrate, M3 fumarate, M3 malate, M3 aspartate in pancreatic cancer cells cultured in presence of exosomes. Reductive glutamine metabolism (n=4). Data information: data in (A) are expressed as mean ± SD, data in (BJ) are expressed as mean ± SEM, *p<0.05, **p<0.01, ***p<0.001. Figure 7—figure supplements 14.

https://doi.org/10.7554/eLife.10250.014

Further, since both EIPA and CytoD could also inhibit rescue effect of proliferation in BxPC3 and MiaPaCa-2 cells (Figure 7—figure supplement 1), suggesting that endocytosis pathway dependent on macropinocytosis and caveolae mediated endocytosis are also associated with uptake of CDEs in pancreatic cancer cells. Additionally, CQ reduced rescue of proliferation by CDEs, thereby suggesting that release of exosomal content through lysosomes may play a role in some cancer cells. Nevertheless, these data suggest that CDEs internalization may happen to various modes of internalization.

To further substantiate the role of KRAS, we used doxycycline inducible Kras-G12D cell line (iKras-1, [Ying et al., 2012]) to test if enhancement of proliferation by CDEs indeed was KRAS independent. As shown in Figure 7—figure supplement 2, iKras-1 cells with or without doxycycline showed similar proliferation increases with CDEs, thereby suggesting that internalization or uptake and supply of exosome-derived metabolites in cancer cells is Kras independent. Clearly, these results suggest that exosomes derived from TME could drive proliferation of PDAC cells by supplying metabolites independent of activated Kras expression.

Previous studies have suggested that Kras can upregulate glycolysis and glutaminolysis. To further evaluate if exosomes mediated metabolic reprogramming is Kras mediated, we measured mitochondrial respiration in PDAC cells with and without pancreatic CDEs. In line with results obtained in prostate cancer cells, we found that OCR of both BxPC3 and MiaPaCa-2 cells were decreased in presence of pancreatic CDEs (Figure 7D). Both maximal and reserve mitochondrial capacity of pancreatic cancer cells were significantly reduced in presence of pancreatic CDEs further confirming mitochondrial respiratory capacity inhibition in cancer cells by CAF exosomes (Figure 7D). These results suggest that CDEs’ action of disabling normal oxidative mitochondrial function is exhibited also in pancreatic cancer and that this regulation is similar in both wild-type (BxPC3) and activated Kras (MiaPaCa-2) expressing cells. Furthermore, there was a corresponding increase in ECAR in both pancreatic cancer cell lines in presence of CDEs (Figure 7E). This was corroborated by increased lactate levels in the cancer cells in presence of exosomes using U-13C6 glucose labeling based isotope tracer analysis of pancreatic cancer cells in presence of CAF exosomes (Figure 7F). Concomitantly, CDEs decreased percentage contribution of glucose to α-ketoglutarate in both pancreatic cancer cell lines (Figure 7G, Figure 7—figure supplement 3).

To further elucidate the metabolic reprogramming induced by pancreatic CDEs in PDAC cells, we performed GC-MS based isotope labeling experiments using 13C5 labeled glutamine. In line with the results obtained in prostate cancers, we found that exosomes from pancreatic CAFs significantly increased the reductive glutamine metabolism (Figure 7H–J, Figure 7—figure supplement 4). Remarkably, this CAF exosomes-mediated increase of reductive glutamine metabolism was detected in both wild-type and activated Kras expressing pancreatic cancer cells, thus suggesting that metabolic reprogramming induced by stromal exosomes in cancer cells is not only Kras independent but is broadly observed in many cancers.

Discussion

Altered cell metabolism is one of the hallmarks of cancer. While much of the mechanistic underpinnings in this regard have focused on cell autonomous means of metabolic reprogramming, the objective of this study was to examine the critical role of TME, and in particular, CAFs, might be playing in this adaptive phenomenon. CAFs comprise the majority of cell types within the TME, and their reciprocal interactions with cancer cells have been well documented (Whiteside, 2008; Hazlehurst et al., 2003; Karnoub et al., 2007). Recently, Hu et al. have discovered that CDEs enhance drug resistance in colorectal cancer stem cells by regulating the Wnt pathway and this effect can be reversed by inhibiting exosome secretion (Hu et al., 2015). These studies suggest that CDEs may not only enhance cancer growth but may also induce chemoresistance. However, there continues to be sparse data vis-à-vis the role, if any, of the effects CAFs might have on altered cancer cell metabolism, and the channels of communication that enable such paracrine effects. The objective of our study was to determine whether exosomes released from CAFs might play a role in modulating cancer cell metabolism, allowing neoplastic cells to survive in the nutrient-deprived conditions, a characteristic of many tumors. Our results convincingly demonstrate that not only do exosomes enhance the phenomenon of 'Warburg effect' in tumors, but remarkably, contain de novo 'off-the-shelf' metabolites within their cargo that can contribute to the entire compendia of central carbon metabolism within cancer cells.

The Warburg effect, commonly observed characteristic of many cancer types, is identified as the reliance of cancer cells on aerobic glycolysis even under normoxia. This leads to diversion of glucose to lactate thereby creating low pH conditions which modulates TME (Warburg et al., 1927Gatenby et al., 2006Salimian Rizi et al., 2015). Although recent studies (Ishikawa et al., 2008; Santidrian et al., 2013), including our own (Yang et al., 2014), implicate mitochondrial activity in cancer metastasis, literature is abound with previous studies based on the hypothesis that aerobic glycolysis may be an outcome of impaired mitochondrial functions. The role of mitochondrial metabolism in tumorigenesis and cancer progression is likely to be organ-specific. Mitochondrial dysfunction could impair oxidative phosphorylation, the TCA cycle, fatty acid oxidation, the urea cycle, gluconeogenesis, and apoptotic pathways (King et al., 2006; Modica-Napolitano and Singh, 2004). TCA cycle dysfunction leads to oncogenesis by regulating signaling pathway and stabilizing HIF1-α (Selak et al., 2005). While many studies in cancer cell metabolism have highlighted higher compensatory glycolysis because of mitochondrial dysfunction; recent studies show that some tumors predominantly use glutamine under hypoxia or conditions mimicking hypoxia such as electron transport chain inhibition through reductive carboxylation for biosynthetic needs (Yang et al., 2014; Wise et al., 2008). In reductive carboxylation, glutamine is converted to α-ketoglutarate, followed by the conversion of α-ketoglutarate to isocitrate through isocitrate dehydrogenase (IDH), and isocitrate is converted to acetyl-CoA used for lipid synthesis. The upregulation of the reductive carboxylation pathway enhances cancer cells proliferation (Metallo, 2012; Mullen, 2012; Wise et al., 2011). Although less studied, pyruvate or acetate can act as an alternative source to glucose and glutamine for lipid biosynthesis. Pyruvate is converted to acetyl-CoA through mitochondrial pyruvate dehydrogenase (PDH), whereas acetate is transported into cells and converted to acetyl-CoA through acetyl-CoA synthase (De Schrijver et al., 2003; Feron, 2009; Koukourakis et al., 2005; Pizer et al., 1996; Yoshimoto et al., 2001; Zaidi et al., 2012). Acetyl-CoA is the first step in lipid biosynthesis, which serves biosynthetic needs of proliferating cells. Recent findings implicate TME in the induced metabolic rewiring in cancer cells (Cairns et al., 2011; Fiaschi and Chiarugi, 2012; Rattigan et al., 2012). However, role of exosomes in the metabolic crosstalk between cancer cells and TME is still unknown.

We first asked whether CDEs could reprogram cancer cell metabolism. To determine this metabolic regulation, we first cultured CDEs with prostate cancer cells and performed 13C based isotope tracing of metabolic fluxes and bioenergetics analysis. We have used 200 μg/ml for the different types of experiments conducted herein (proliferation assays, metabolic assays and tracer experiments). The working concentration was chosen to maintain a physiological ratio of CAFs to cancer cells which is reported to be between 1 and 10 (Brauer et al., 2013; Hu et al., 2015; Delinassios, 1987). A concentration of 200 μg/ml corresponded to ratio between 1 and 5 CAFs per cancer cell and hence, is within the range reported in the literature. However, for different CAFs and cancer cells system this ratio should be individually estimated based on the functional effect of stromal cells on cancer cells. It is to be noted that due to protracted purification steps during exosome isolation, degradation of metabolites can occur and hence, replenishment of exosomes for functional studies was followed and recommended. Additionally, to minimize degradation of metabolites in exosomes, we used fresh exosomes for all the experiments and avoided any freeze-thaw cycle in exosomes that were introduced to cancer cells cultures. Intriguingly, cancer cells cultured with exosomes had significantly reduced OXPHOS with a concomitant increase in glycolysis. Our results were further corroborated by observations regarding higher glucose uptake and lactate secretion by cancer cells in the presence of exosomes. We show for the first time that stromal exosomes shift cellular metabolism towards glycolysis in cancer cells (Figure 8). We further extended these observations in prostate cancer to pancreatic cancer and found similar inhibitory effect of pancreatic CDEs on mitochondrial respiration. Interestingly, this regulation was Kras independent in pancreatic cancers. In Figure 2, we observe a reduction of OCR in PC3 cells co-transfected with miR-22, let7a and miR-25b, in line with our hypothesis of the inhibitory effect of miRNAs from CDEs. However, the caveat associated with this is that due to limitations in genome engineering technologies, it is extremely difficult to insert multiple miRNAs in exosomes or transfect multiple miRNAs together without causing significant toxicity. Nonetheless, our results show that abundant miRNA in exosomes that have been previously shown to target OXPHOS, indeed cause inhibition of oxygen consumption in our system. In light of these results we believe further studies are needed to uncover the mechanism of ETC inhibition by CDEs-derived miRNAs that are out of scope for this study.

Pleiotropic regulation of cancer cell metabolism by CDEs.

Schematic shows the metabolic regulation of CDEs in cancer cells through inhibition of oxidative phosphorylation and contribution of metabolite cargo. This regulation leads to significant increase of reductive glutamine metabolism in cancer cells in presence of exosomes. CDEs are also cargo of amino acids, TCA cycle metabolites, and lipids. In nutrient starved TME metabolites derived from exosomes enrich cancer cells with biosynthesis building blocks and thereby promote tumor growth.

https://doi.org/10.7554/eLife.10250.019

Previous studies, reported that under disabled mitochondrial metabolic conditions such as hypoxia, or inhibition of electron transport complexes, cancer cells increasingly rely on reductive glutamine metabolism as compared to oxidative glutamine metabolism. To unravel the contribution of major nutrients to cancer cells, we performed 13C based metabolite tracing and isotopologue spectral analysis (ISA). Indeed, CDEs upregulated reductive carboxylation of glutamine in cancer cells. Previous reports showed that reductive carboxylation is a critical pathway to support the growth of tumor cells under hypoxia (Metallo, 2012; Mullen, 2012). This suggests that CDEs create hypoxia mimicking environment in cancer cells leading to an increase in reductive carboxylation of glutamine in cancer cells. Our ISA results of glucose and glutamine contribution to acetyl-CoA, a precursor for fatty acid synthesis, confirmed the increased reliance of cancer cells on reductive glutamine metabolism in presence of stromal exosomes. However, we were not able to balance palmitate synthesis through glucose and glutamine in cancer cells cultured with CDEs, which led us to measure contributions of acetate and pyruvate. Consistent with recent reports that acetate in media could serve as source of lipid synthesis in cancer cells, we found that acetate could contribute between 10–15% towards lipogenesis. However, pyruvate contribution was much lower and was between 3–8% in cancer cells. These results suggested that exosomes themselves might be acting as source of metabolites in a manner similar to macropinocytosis observed recently in Kras expressing pancreatic cancer cells (Commisso et al., 2013). CDE-mediated metabolic changes in cancer cells, metabolic flux analysis is warranted

Since exosomes contained carbon sources such as proteins and lipids, we inquired if exosomes could act as source of building blocks for biosynthesis and proliferation. To our remarkable surprise, we found that exosomes from pancreatic and prostate CAFs contained intact components of the intracellular metabolite pool, including amino acids, acetate, stearate, palmitate, and lactate. We provide previously unidentified evidence that these nutrients can enrich cancer cells under nutrient deprived or nutrient stress conditions. To label the metabolites, proteins and lipids contained in exosomes, we cultured CAFs in media supplemented with13C-labeled amino acids dominantly found in the serum (lysine, leucine, phenylalanine, and glutamine) along with nutrients such as glucose and pyruvate. We then cultured cancer cells under nutrient deprived conditions with these labeled exosomes and found that these labeled exosomes could indeed contribute to TCA cycle metabolites in cancer cells. These results conclusively showed that TME can supply metabolites directly to cancer cells through exosomes and these metabolites indeed can fuel TCA cycle in cancer cells. Recently published article by Lyden et al (Hoshino et al., 2015), showed that exosomes precondition specific organs for metastatic invasion. Hence, future studies may be directed towards determining organ-specific metabolic reprogramming of CDEs in cancer cells.

Having established that exosomes can fuel TCA cycle in a manner similar to macropinocytosis in prostate cancer, we further showed that this exosomes derived metabolite enrichment is independent of activated Kras expression. Previous studies have shown that tumor cells uptake extracellular nutrients through a mechanism regulated by Kras. From observations made in our pancreatic tumor cell lines, BxPC3 (wild type Kras) and MiaPaCa-2 (activated Kras) we showed that exosomes uptake pathways were independent of Kras expression levels. In our results, BxPC3 and MiaPaCa-2 showed similar extents of metabolic profile regulation as well as enhanced growth rate because of stromal exosomes. These results along with our exosomes uptake inhibition experiments suggest that exosomes uptake occurs in cancer cells through multiple pathways that are independent of activated Kras expression levels. Through endocytosis inhibitors CytoD and receptor mediated endocytosis inhibitor heparin, we inhibited exosomes uptake, and thereby repressed exosomes' influence on increasing growth rates of pancreatic cancer cells BxPC3 and MiaPaCa-2.

In summary, our results reveal insights into intercellular communication between tumor microenvironment and cancer cells. For the first time, we provide evidence that CAFs derived exosomes can reprogram cancer cell metabolism through a metabolite cargo based nutrient enrichment mechanism. Our results will invigorate development of targeted methods for disrupting the exosomes-mediated communication between cancer and stromal cells for in vivo studies and therapeutics based on the targeted inhibition of this crosstalk.

Materials and methods

Cells and reagents

PC3, DU145, 22RV1, BxPC3 and MiaPaCa-2 were received from ATCC and authenticated by STR profiling with online ATCC profile. E006AA was kindly povided by Dr. Denis Wirtz (Johns Hopkins University). Patient derived fibroblast cells were kindly provided by Drs. Donna Peehl and Anirban Maitra of Stanford University and MD Anderson, respectively and internal STR profiling is maintained and checked annually. All cell lines were mycoplasma free based on PCR based assays run every three months in the lab. PKH26 and PKH 67 fluorescent cell linker kits were from Sigma (St. Louis, MO). Exosome–Dynabeads Human CD63 Detection kit (10606D), sheep anti-rabbit IgG Dynabeads (11203D) were from Life technologies (Carlsbad, CA). 13C carbon-labeled isotopes were from Cambridge Isotope Laboratories (Tewksbury, MA). MiRCURYM RNA Isolation Kit was from Exiqon (Vedbaek, Denmark). Cytochalasin D was from Sigma. Cell counting kit-8 was from Dojindo (Rockville, MD). 3000 W spin columns were from Life technologies.

Heparin and EIPA were from Sigma. Chloroquine was from VWR (Radnor, PA). Synthetic liposomes (F60103F-DO) were from FormuMax Scientific (Sunnyvale, CA).

Cell culture

Prostate cancer cells and BxPC3 cells were cultured in RPMI containing 1 mM pyruvate, supplemented with 10% fetal bovine serum (Invitrogen, Carlsbad, CA), 100 U/ml penicillin and 100 U/ml streptomycin. Exosome-depleted FBS (Systems Biosciences, Palo Alto, CA) was used for cell culture when metabolic analysis or proliferation rate measurements were performed. CAF19, CAF35 and MiaPaCa-2 cells were cultured in DMEM. Prostate cancer patient derived fibroblast cells were cultured in MCDB105 (Sigma) supplemented with 5% fetal bovine serum (Invitrogen), 5 ng/ml fibroblast growth factor (FGF) (PeproTech, Rocky Hill, NJ), 5 ng/ml insulin (Sigma), 100 μg/ml gentamicin. All cells were incubated in 5% CO2, and 37°C incubator. CAFs were seeded in T75 flasks, and when the CAFs were 70% confluent, PBS was used to wash cell twice, then the fresh MCDB medium with exosomes depleted FBS (Systems Biosciences) was added to the flask. After 48 hr, exosomes were isolated from the spent medium, and added into the medium incubating prostate cancer cells. For 13C labeled RPMI medium, we used RPMI without amino acids and supplemented it with appropriate levels of labeled 13C3-pyruvate, 13C6-glucose, 13C5-glutamine, 13C6-leucine, 13C6-lysine, 13C9-phenylalanine; ultracentrifugation was used to remove possible exosomes in FBS of this medium; CAFs were cultured in this medium for 72 hr and labeled exosomes were isolated.

Exosomes isolation and utilization

To isolate exosomes, cells were cultured with exosome-depleted serum. We collected the conditioned medium to isolate exosomes according to the instructions of the protocol (Life technologies). The collected medium was centrifuged in 2000 xg for 30 min to remove cells and debris. We then transferred the supernatant containing the cell-free culture media to a new tube without disturbing the pellet. Next, we transferred the required volume of cell-free culture media to a new tube and added 0.5 volumes of the Total exosomes isolation (for cell culture media) reagent and mixed the culture media/reagent mixture well by vortexing until there was a homogenous solution. Incubate samples at 2°C to 8°C overnight. After incubation, the samples were centrifuged at 10,000 × g for 1 hr at 2°C to 8°C. The supernatant was aspirated and discarded. Exosomes were contained in the pellet at the bottom of the tube. Re-suspended the pellet in a convenient volume of working medium with exosomes depleted FBS (Systems Biosciences). The concentration of CDEs was measured by BCA kit, which represents the protein concentration of CDEs. The exosome concentration of 200 μg/ml was obtained by diluting an average yield of 270 μg exosome protein (which is equivalent to 5.5x1010 particles) which was produced from 120 ml of supernatant. This corresponds to 28000 particles per CAF over a period of 48 hr. Equivalent particle of exosomes was obtained from a measurement of 4.9 μg for 109 particles.

The utilization of exosomes in cancer cell cultures were based on application of 100–400 μg/ml of exosomes concentration that has been reported in literature (Christianson et al., 2013; Zhu et al., 2012). A working concentration of 200 μg/ml was chosen for most of the experiments in this study to maintain a physiological ratio of CAFs to cancer cells which is reported to be between 1–10 (Brauer et al., 2013; Hu et al., 2015; Delinassios, 1987). Hence, the number of CAFs required to secrete the amount of exosomes that the cancer cells are exposed to should reflect the ratio of CAFs to cancer cells in tumor. For the different types of experiments conducted herein (proliferation assays, metabolic assays and tracer experiments), this ratio was maintained between 1 and 5 CAFs per cancer cell, and hence is physiologically relevant.

Exosomes size distribution measurement

Exosomes size and particles density were measured by Zetaview (Particle Metrix, Diessen, Germany). Exosomes resuspended in PBS were diluted 1000 fold for measurement and size distribution. Briefly, 5 μl of exosomes in medium or PBS were added to the measurement system. According to particles’ Brownian motion, the diffusion constant is calculated and transferred into a size histogram via the Einstein Stokes relation between diffusion constant and particle size.

Flow cytometry

Enriched exosomes were captured using the CD63+ Dynabead exosomes isolation kit (Invitrogen, Life Technologies #10606D). The Flow Analysis of stromal exosomes bound to Dynabeads conjugated with antibody was done according to the manufacture’s protocol. Briefly, 10 µl of exosomes (200 µg/mL) were incubated with 90 µl of CD63+ Dynabeads overnight at 4°C. Dynabead magnet was then used to positively select for bound exosomes which were then stained with PE Mouse Anti-Human CD63 (BD Bioscience, San Jose, CA). Isotype control was stained by Simultest IgG2a/IgG1 (BD Bioscience, 340394). Flow cytometry was performed on a Accuri C6 System (BD Bioscience) and analyzed on Flow Jo software.

To analyze exosomes uptaken by prostate cancer cells, exosomes were pre-labeled by PKH67 dye (Sigma), and 3000 spin columns were used to remove extra dye. The dyed exosomes were added to RPMI medium to culture cancer cells for 3 hr and then flow cytometry was performed to measure fluorescence intensity of cells.

Fluorescence microscopy to image exosomes uptake by prostate cancer cells

Exosomes were pre-labeled according to PKH26 cell linker kit (Sigma). 3000 spin columns (Sigma) were used to remove extra dye. PC3 Cells were grown to 50% confluence in 8-well chamber slides and incubated with PKH26 labeled exosomes (200 µg/mL) for 3 hr. Cells were then washed two times with PBS solution and fixed with 4% PFA for 10 min. Nuclei were stained with 4’, 6-diamidino-2-phenylindole (DAPI) and slides were viewed under a Axio Observer Z1 Inverted fluorescence microscope (Zeiss) and analyzed on Zen software.

Viability assay

Cells viability was measured by Cell counting kit-8 (Dojindo Molecular Technologies, Inc., Rockville, MD). Cells were cultured on 96-well plate in the indicated conditions. Viability assay solution was added to the plate for incubation of 3 hr and absorbance was measured at 450 nm.

RNA purification and amplification for Illumina Microarrays

Total RNA was extracted from cells using the Quick-RNA MiniPrep (Zymo Research, Irvine, CA), following the manufacturer’s instructions. RNA amplification was performed using Illumina TotalPrepTM RNA amplification kit (AMIL1791, Life Technologies), according to the manufacturer’s instructions. Briefly, 500 ng total RNA was used to synthesize the first strand cDNA using a MyCycler thermal cycler (Bio-Rad, Hercules, CA). Subsequently, the second strand cDNA was synthesized and cDNA was purified with 20 µl of 55°C nuclease-free water. In vitro transcription for cRNA synthesis was carried out using 14 hr incubation at 37°C. cRNA was then eluted with 200 µl of 55°C nuclease-free water. Hybridization and imaging were done using the HumanHT-12 v4 Expression BeadChip Kit (Illumina, San Diego, CA) according to manufacturer’s protocol.

Analysis of gene expression using real-time PCR

Total RNA was isolated using a Zymo mini kit (Qiagen, Valencia, CA). High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA) was used to synthesize cDNA from 1 μg of total RNA. The levels of COX-1 and CYTB were examined by real-time PCR using 50 ng of the synthesized cDNA. Real-time PCR was performed with the SYBR Green PCR MasterMix (Applied Biosystems, Warrington, UK). All reactions with COX-1 and CYTB were normalized against glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Specific primer sets were as follows (listed 5’–3’; forward and reverse, respectively): COX-1, TCGCATCTGCTATAGTGGAG and ATTATTCCGAAGCCTGGTAGG; CYTB, TGAAACTTCGGCTCACTCCT and AATGTATGGGATGGCGGATA. Reactions were performed in a volume of 20 μl.

miRNA measurements from exosomes

Isolation of miRNA from exosomes was done with the MiRCURY RNA Isolation Kit. In brief, steps of lysis, precipitation, repeated washing, and elution were performed to isolate purified small RNAs and then miRNA expression levels were measured by NanoString miRNA assays. miRNA levels were measured using the nCounter Human V2 miRNA expression analysis kit (Nanostring), according to the manufacturer’s instructions. The data were corrected for loading using the relative geometric means of endogenous miRNA levels as a correction factor. The miRNAs were ranked by their average count across all exosomes samples.

Glucose assay

Glucose assay were done according to the instructions of assay kit (Wako Glucose kit, Wako Diagnostics, Mountain View, CA). In brief, a 250 μl of reconstituted Wako glucose reagent was added to a 96-well assay plate followed with 2 μl sample addition in each well. The plate was incubated at 37°C for 5 min. The change in absorbance, which indicates the amount of glucose present, was measured at 505 nm and 600 nm by using a spectrophotometer (SpectraMax M5; Molecular Devices, Sunnyvale, CA).

Lactate assay

Lactate secretion was determined using the Trinity Lactate Kit (Trinity Biotech Plc., Co Wicklow, Ireland). Media samples were diluted 1:10 in PBS, and lactate reagent was reconstructed and added to the diluted samples in an assay plate. The plate was incubated for 1 hr at 37°C, protecting from light. Afterwards the change in absorbance was read on a spectrophotometer at 540 nm.

Protein assay

Protein assays are used to do normalization in our experiment and is done according to Bicinchoninic Acid Protein Assay (Thermo Fisher Scientific, Waltham, MA) protocol. In brief, protein reagent was added to a 96-well assay plate and mix with samples or standard, and then incubated at 37°C for 30 min. The absorbance was read on a spectrophotometer at 562 nm.

Acetate assay

Acetate concentration was measured according to manufacturer’s instructions for acetate colorimetric assay kit (BioVision #K658, Milpitas, CA). Briefly, samples or acetate standards were mixed with reaction mixtures, incubated at room temperature for 40 min, and measured at OD450 nm.

Measurement of mitochondrial membrane potential

Mitochondrial permeability transition was determined by staining the cells with TMRM (Molecular Probes, Eugene, OR). The mitochondrial membrane potential was quantified by SpectraMax M5 (SpectraMax M5; Molecular Devices, Sunnyvale, CA).

Measurements of oxygen consumption rate and extracellular acidification rate

Mitochondrial oxygen consumption was monitored with an XF24 Extracellular Flux Analyzer (Seahorse Bioscience, North Billerica, MA). The cells were seeded in Seahorse 24-well microplates at a cell density of 70% confluent cells per well in 100 μL of culture media with indicated conditions. After overnight incubation at 37°C with 5% CO2, the media was replaced with 700 μL of assay medium. Then incubate the plate at 37°C without CO2 for at least 1 hr. The oxygen consumption rate (OCR) was then measured. The endogenous coupling degree of the OXPHOS system was assessed using oligomycin (2 μg/ml), an inhibitor of the F1FO-ATPsynthase. The uncoupled OCR was also measured in presence of 2.5 μM of FCCP. Finally, the cells were treated with a mitochondrial complex I inhibitor, Rotenone (2 μM) in order to assess the mitochondrial contribution to OCR. Extracellular acidification rate (ECAR) can be measured in a similar way to OCR. All OCR or ECAR value was normalized with protein content of cells.

Isotope labeling analysis using GC-MS

Metabolites extraction

Cancer cells were seeded in 6-well plates overnight, and replaced with medium containing U-13C6 glucose or U-13C5 glutamine. After 24/48/72 hr, medium was aspirated, and cells were washed with cold PBS once and quenched with 400 µl of cold methanol. Same volume of water containing 1 µg of norvaline (internal standard) was added, and cells were scraped into Eppendorf tubes. 800 µl of chloroform was added into the tubes, and vortexed at 4°C for 30 min, centrifuged at 7300 rpm for 10 min at 4°C. The aqueous layer was collected for metabolite analysis and the chloroform layer was collected for fatty acids analysis.

Derivatization

Aqueous samples were dried and dissolved in 30 μl of 2% methoxyamine hydrochloride in pyridine (Pierce, Waltham, MA), and sonicated for 10 mins. Afterwards, samples were kept in 37°C for 2 hr. Samples were kept for another 1 hr at 55°C after addition of 45 μl of MBTSTFA+1% TBDMCS (Pierce). Chloroform samples were dried and dissolved in 75 μl Methyl-8 Reagent (Pierce), and incubate at 60°C for 1 hr. Samples were transferred into vials containing 150 μl of insert (Thermo Fish Scientific).

GC/MS measurements

GC/MS analysis was performed using an Agilent 6890 GC equipped with a 30-m Rtx-5 capillary column for metabolites samples or 30 m DB-35 MS capillary column for fatty acids samples, connected to an Agilent 5975B MS. For metabolites samples, the following heating cycle was used for the GC oven: 100°C for three minutes, followed by a temperature increase of 5°C/min to 300°C for a total run time of 48 min. For fatty acids samples, the following heating cycle was used for the GC oven: 100°C for 5 min increased to 200°C at 15° min−1, then to 250°C at 5° min−1 and finally to 300°C at 15° min−1. Data was acquired in scan mode. The abundance of relative metabolites was calculated from the integrated signal of all potentially labeled ions for each metabolite fragment.

Intra-exosomal metabolites extraction

The exosomes pellet was extracted by adding 75 µl of cold methanol; 150 µl of cold water (with norvaline for GC-MS measurement, without norvaline for UPLC measurement) was added, which dissolved exosomes completely. 20µl of the liquid was stored for protein assay. Then 150 µl of cold chloroform was added into the tubes and vortexed at 4°C for 30 min, centrifuged at 7300 rpm for 10 min at 4°C. The aqueous layer was collected for intra-exosomal metabolite analysis. Chloroform layer was stored for lipid analysis.

Statistical analysis

The results presented are expressed in mean value of N experiments ± S.D or SEM, with N≥2, n≥ 3. Comparison of the data sets obtained from the different experiment conditions was performed with the Student t test. In the bar graphs, single asterisk (*) represents p<0.05, double asterisks (**) represent p<0.01 and triple asterisks (***) represent p<0.001.

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Decision letter

  1. Chi Van Dang
    Reviewing Editor; University of Pennsylvania, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your work entitled "Tumor Microenvironment Derived Exosomes Pleiotropically Modulate Cancer Cell Metabolism" for consideration by eLife. Your article has been favorably evaluated by Charles Sawyers (Senior editor) and three reviewers, one of whom, Chi Dang, is a member of our Board of Reviewing Editors.

The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission. Please note that many of the essential revisions are clarifications of data interpretation and acknowledgement of caveats and should not require lengthy additional experiments.

Summary:

The manuscript by Zhao et al. reports that cancer associated fibroblast(CAF)-derived exosomes (CDEs) changes epithelial cancer cell metabolism. The authors used CAFs from 4 prostate cancer cell lines, a number of primary prostate cancers, as well as 2 pancreas cancer cell lines and 2 primary pancreas cancers, to show that CDEs contain an array of metabolites including amino acids, TCA cycle metabolites, fatty acids and precursors/intermediates. Delivery of these exosomes to cancer cells inhibited oxidative phosphorylation (although this in turn leads to a series of downstream sequela including increased glycolysis, TCA reductive carboxylation, etc. through metabolic labeling using 13C-glucose or glutamine). By examining gene expression profiles, the authors found a decrease in OXPHOS gene expression in prostate cancer cells exposed to CDEs. Quantitative analysis suggests that 5-29% of various prostate tumor metabolites were derived from 13C-labeled CDEs. Extending these observations, the authors demonstrate that pancreatic cancer cells could also take up CDEs via a macropinocytosis mechanism that appears independent of K-ras mutational status. A number of major issues, if addressed, would make this manuscript meritorious for publication.

Essential revisions:

1) The main missing component of the paper is the mechanism of oxidative phosphorylation inhibition. This is hinted at in Figure 8—figure supplement 1. However, any mechanism acting through transcription would account for such a strong effect over only 24 h (oxidative phosphorylation proteins do not typically turnover so quickly). How can an increase of the intracellular metabolite pool by CDEs of less than 5% for most metabolites (Figure 6A) lead to such marked phenotypes in terms of growth and Glycolysis/OXPHOS? As the authors acknowledge in their Introduction, miRNAs are an important cargo of exosomes. The authors have found their CDEs to be enriched in several miRNAs targeting OXPHOS enzymes (Figure 8—figure supplement 1). These observations are in line with the presented hypothesis and it could explain how CDEs can decrease OXPHOS in cancer cells. Why is this point made "secondary", by showing some data in the last supplemental figure and not referring to it until one of the last discussion chapters? The reviewer believes that this set of data should be highlighted and possibly further explored in the manuscript.

2) Could the microsomes have an effect on tumor cell metabolism independent of the microsomal metabolite content? As a rigorous control for OXPHOS effects of microsomes, synthetic liposomes with a size distribution of microsomes should be used to rule out a metabolic response to liposome uptake that is independent of the contents of the microsomes.

3) Throughout the manuscript, authors use 200ug/mL of isolated exosomes for the different experiments based on the observation that this is the maximal up-taken concentration by cancer cells (Figure 5B). How does this value compare to what stromal cells can secrete? In other words, how physiological are the 200ug/ml of isolated CDEs? This is a very important point as it goes to what may actually be happening in the tumor itself.

4) While many of the tracer results are internally consistent (e.g. labeling of glutamate typically matches that of α-Ketoglutarate), the totality of the results are probably not easily quantitatively explainable with current metabolic maps. The authors should acknowledge that they are not doing flux identification and that some elements of the data seem inconsistent, e.g. too much M4 citrate relative to M3/M4 KG/Glu in Figure 4. A more specific concern regards the claims about the oxidative pentose phosphate pathway. There are more appropriate tracers for this purpose and the amount of PPP overflow is not enough to typically read out with the type of method they are using. Their data are indeed a little puzzling, particularly why there is so much more M0 than M1 lactate in all of their conditions, or why it changes with exosomes, but assuming that they do not want to investigate this thoroughly, they should acknowledge the oddity but not blame it on the PPP.

Do total pools of metabolites, other than lactate (Figure 3F) change significantly in cancer cells treated with CDEs? Are the total levels of TCA cycle metabolites (Figure 3) or amino acids (Figure 6), changed? Authors should also represent total ion currents in addition to the fractional labeling representation as it provides further information and would help answer these questions.

5) A limitation of this manuscript is the metabolomics on the CDEs themselves. The isolation of exosomes is quite protracted, opening up the possibility of artifactual changes in the metabolome. As the authors know, this is why the isolation of metabolites is extremely time sensitive and all proteins are denatured immediately with such reagents as ice cold methanol. This may be technical limitation of exosome isolation that could severely impact the results. This issue needs addressing. The quantitation of metabolites in the exosomes leads to some astronomical values per unit of protein. Perhaps this is because the exosomes are very low in protein? In a typical cell, which may be about 20% protein, the concentration of lactate of 50 umol/mg prot would correspond to a molar concentration of 10 M! The authors should carefully check this.

6) The main metabolite component of the exosomes seems to be Gln (Figure 5). This amino acid is a major source of carbon for the TCA cycle in different systems. Also, in Figure 6—figure supplement 1, the major labeled citrate isoform found in PC3 cells after treatment with "labeled" CDEs is M2 and not M6 – suggesting that the majority of this citrate is not coming directly from the exosome labeled citrate but rather from Acetyl-CoA (or precursors) delivered by exosomes, further suggesting feeding of the TCA cycle by exosome delivered metabolites – how do the authors reconcile this potentially higher TCA cycle activity with the decreased OXPHOS phenotype?

7) The authors do not elucidate how the tumor cells uptake the nutrient loaded exosomes. The effects of heparin and CytoD should be shown also on the control condition, to ensure that things like proliferation inhibition are specific to the deprivation/restoration with exsomes. The use of macropinocytosis inhibitors (EIPA) should provide an answer to this question. If the delivery of endosomal contents in the cell requires degradation of exosomes by fusion with lysosome (to release their cargo) in cancer cells, then treatment with drugs that block this process such as CQ or BafilomycinA should impair the phenotypes observed with cancer cells are treated with CDEs.

8) One of the sections points at excluding KRAS from playing a role in PDAC uptake of CDEs. However, to fully exclude KRAS role, the analysis should be based on more than comparison between two cell lines. For example, if the phenotype (decreased OCR or increased proliferation upon treatment with CDEs) is maintained after genetic ablation of KRAS in MiaPaCa cells.

9) The mitochondrial membrane potential is decreased in PC3 cells upon treatment with CDEs (Figure 2F), did the authors verify if total mitochondrial content and/or shape is altered in cancer cells treated with CDEs? Also, did the authors verify if CDEs contain mitochondria or fractions of mitochondria? This could explain the relatively high levels of malate and other TCA cycle intermediates found inside (Figure 5C).

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

Thank you for resubmitting your work entitled "Tumor Microenvironment Derived Exosomes Pleiotropically Modulate Cancer Cell Metabolism" for further consideration at eLife. Your revised article has been evaluated by Charles Sawyers (Senior editor), a Reviewing editor, and two reviewers. The manuscript has been improved. However, there are significant remaining issues that we would like for you to provide a response, within 1-2 weeks, on how you would address them to determine whether a second revision is invited.

While the effect of CAF-derived exosomes on OXPHOS (Figure 7) is impressive, the mechanisms by which this occurs remain undefined (issue 1, below), particularly with the issues surrounding the exosome metabolome (issue 2, below).

1) The miRNA data is still very correlative and quite weak (based on literature showing that particular miRNAs can regulate expression of various oxphos enzymes).

2) The concentrations of metabolites in the exosomes continue to be unrealistic, e.g. 1 moles glutamine, 5M lactate moles, per g protein, which would translate to (assuming exosomes are 20% protein), 200M and 1000M, which is physically impossible. What is the relationship between exosome protein, exosome volume, and exosome number? Complete analytical support is necessary, including raw data, for any metabolites that the authors claim are present in exosomes at concentrations exceeding 20 mM.

In addition to the two major issues above, there are other items that need addressing.

1) Figure 6 seems confusing. Since lysine is an essential amino acid, why are they seeing M+2 form? Why do lactate, pyruvate, and alanine, which all share the same carbon skeleton, all have different labeling patterns? How do they get to the conclusion that ~ 30% of KG is derived from exosome metabolites? It does not seem proper to normalize the KG labeling in cells to that in exosomes, when most KG in cells comes from glutamine or glutamate. It is also confusing how labeling of KG in cells would exceed glutamine and glutamate. I do not want to cause the authors excessive suffering on this point. To this end, they may be better served reporting less isotope labeling data, instead of reporting dubious data. The leucine data, for example, makes the point that the exosome contribution is modest while looking more straightforward than some other data. Similarly, it seems important to see results for glutamine. Perhaps a few well-chosen examples would suffice. Also, perhaps some funny labeling patterns can be cleaned up by more careful examination of the raw data to eliminate interfering peaks.

2) The authors provide some more rationale for choosing the concentration of exosomes used in their studies. However, there is still uncertainty as to how this represents the physiological state. The authors should at least write a sentence or 2 in the Discussion talking about this limitation. The limitation of this analysis (protracted purification protocol) should be mentioned in the Discussion.

3) The correction of the lactate levels to 1/10th what was originally reported does not seem "minor". Can the authors please explain this further?

4) It is still not clear why M+2 is the dominant citrate isotopomer in Figure 6—figure supplement 1. This continues to suggest that the majority of this citrate is not coming directly from the exosome labeled citrate but rather from Acetyl-CoA (or precursors) delivered by exosomes.

5) In the bar graphs looking at drug effects, the authors should not limit tests of statistical significance to the hypothesized effects (in the exosome rescue conditions), but also conduct the same tests in the non-rescue conditions, as it seems that the drugs sometimes have an effect also in those conditions. If those tests were all non-significant, there is no need to mark them on the figure, but that should be clearly reported.

6) The authors should be aware that oxoproline is most commonly found due to degradation of glutamate during sample processing. They should not make main text statements relating this to the glutathione pathway without further evidence.

https://doi.org/10.7554/eLife.10250.021

Author response

1) The main missing component of the paper is the mechanism of oxidative phosphorylation inhibition. This is hinted at in Figure 8—figure supplement 1. However, any mechanism acting through transcription would account for such a strong effect over only 24 h (oxidative phosphorylation proteins do not typically turnover so quickly). How can an increase of the intracellular metabolite pool by CDEs of less than 5% for most metabolites (Figure 6A) lead to such marked phenotypes in terms of growth and Glycolysis/OXPHOS? As the authors acknowledge in their Introduction, miRNAs are an important cargo of exosomes. The authors have found their CDEs to be enriched in several miRNAs targeting OXPHOS enzymes (Figure 8—figure supplement 1). These observations are in line with the presented hypothesis and it could explain how CDEs can decrease OXPHOS in cancer cells. Why is this point made "secondary", by showing some data in the last supplemental figure and not referring to it until one of the last discussion chapters? The reviewer believes that this set of data should be highlighted and possibly further explored in the manuscript.

We thank the reviewers for pointing this out and believe that the reviewers’ observation is correct in this regard. Based on the suggestion, we have moved the miRNA data from supplementary figure to the main Figure 2. The miRNA literature suggests that often times miRNAs act in clusters, however there are inherent technological limitations in co-transfecting multiple miRNAs into our exosome-based cell assays as we found these to be toxic to the cells. Based on our initial observations and technical difficulties, we realized that it was beyond the scope of the current work and hence, we had included the miRNA exosomal abundance data in supplementary figure in our previous version. Please see the corrections below, which were added in the revised manuscript.

We have moved the following text from Discussion to text. Subsection “CDEs downregulate mitochondrial function of prostate cancer cells”, last paragraph: It is well established that exosomes contain noncoding RNAs (e.g. miRNAs) which can serve as a communication mechanism between stromal and cancer cells. […] In summary, these results suggest that CDEs reduced mitochondrial oxidative phosphorylation and induced metabolic alterations in cancer cells mimicking hypoxia-induced alterations.

We have added the following new discussion in connection with the above observations. Discussion, third paragraph:“Further studies are needed to uncover the mechanism of ETC inhibition by CDEs-derived miRNAs.”

2) Could the microsomes have an effect on tumor cell metabolism independent of the microsomal metabolite content? As a rigorous control for OXPHOS effects of microsomes, synthetic liposomes with a size distribution of microsomes should be used to rule out a metabolic response to liposome uptake that is independent of the contents of the microsomes.

We thank the reviewers and editors for bringing this important control to our attention. To verify the effect of the metabolite content, we used synthetic liposomes [DOPC/CHOL (DOPC: 1,2-dioleoyl-sn-glycero-3-phosphocholine, CHOL: cholesterol) liposomes labeled with DiO, size 85-110nm] and cultured cancer cells with and without liposomes. The liposomes used were of similar size distribution as exosomes. We measured prostate and pancreatic cancer cells’ proliferation, oxygen consumption rate (OCR), and extracellular acidification rate (ECAR) with and without synthetic liposomes. Our results show that cancer cells indeed uptake synthetic liposomes (Figure 2—figure supplement 2). However, liposomes neither enhanced cancer cells proliferation or ECAR, nor inhibited OCR. Nevertheless, this was an important control indeed and it allowed us to conclusively link the CDEs induced changes in cancer cell metabolism and growth rate to metabolic content. We have updated our manuscript with these control results and made corrections as indicated below. The description of liposome results has been added on page 6, line 2 and Figure 2—figure supplement 2.

Subsection “CDEs downregulate mitochondrial function of prostate cancer cells“, second paragraph: “To conclusively associate CDEs induced metabolic reprogramming with metabolic content of exosomes, we verified if synthetic liposomes (DOPC/CHOL liposomes labeled with DiO, size 85-110nm; DOPC: 1,2-dioleoyl-sn-glycero-3-phosphocholine, CHOL: cholesterol) with a size distribution similar to exosomes could similarly modulate cancer cells. Our data suggests that liposomes did not alter cell proliferation, OCR and ECAR in both prostate (PC3) and pancreatic cancer cells (MiaPaCa-2 and BxPC3) (Figure 2—figure supplement 2). These results implicate metabolic content of exosomes towards observed changes in CDEs-induced increased cancer cell proliferation, mitochondrial dysfunction and increased glycolysis.”

3) Throughout the manuscript, authors use 200ug/mL of isolated exosomes for the different experiments based on the observation that this is the maximal up-taken concentration by cancer cells (Figure 5B). How does this value compare to what stromal cells can secrete? In other words, how physiological are the 200ug/ml of isolated CDEs? This is a very important point as it goes to what may actually be happening in the tumor itself.

We thank the reviewers and editors for pointing this out. Based on the application, 100-400 μg/ml of exosomes concentration has been reported in literature (Christianson et al., 2013; Zhu et al., 2012). A working concentration of 200 μg/ml was chosen for all the experiments in this study to maintain a physiological ratio of CAFs to cancer cells which is reported to be between 1-10 (Brauer et al., 2013; Delinassios, 1987; Hu et al., 2015). Hence, the number of CAFs required to secrete the amount of exosomes that the cancer cells are exposed to should reflect the ratio of CAFs to cancer cells in tumor. For the different types of experiments conducted herein (proliferation assays, metabolic assays and tracer experiments), this ratio was maintained between 1 and 5 CAFs per cancer cell, and hence is physiologically relevant. We have also clarified this in the Methods section of the manuscript as written below.

Methods, subsection "Exosome isolation and utilization":

“The utilization of exosomes in cancer cell cultures were based on application of 100-400 μg/ml of exosomes concentration that has been reported in literature (Christianson et al., 2013; Zhu et al., 2012). A working concentration of 200 μg/ml was chosen for all the experiments in this study to maintain a physiological ratio of CAFs to cancer cells which is reported to be between 1-10 (Brauer et al., 2013; Delinassios, 1987; Hu et al., 2015). Hence, the number of CAFs required to secrete the amount of exosomes that the cancer cells are exposed to should reflect the ratio of CAFs to cancer cells in tumor. For the different types of experiments conducted herein (proliferation assays, metabolic assays and tracer experiments), this ratio was maintained between 1 and 5 CAFs per cancer cell, and hence is physiologically relevant.”

4) While many of the tracer results are internally consistent (e.g. labeling of glutamate typically matches that of α-Ketoglutarate), the totality of the results are probably not easily quantitatively explainable with current metabolic maps. The authors should acknowledge that they are not doing flux identification and that some elements of the data seem inconsistent, e.g. too much M4 citrate relative to M3/M4 KG/Glu in Figure 4. A more specific concern regards the claims about the oxidative pentose phosphate pathway. There are more appropriate tracers for this purpose and the amount of PPP overflow is not enough to typically read out with the type of method they are using. Their data are indeed a little puzzling, particularly why there is so much more M0 than M1 lactate in all of their conditions, or why it changes with exosomes, but assuming that they do not want to investigate this thoroughly, they should acknowledge the oddity but not blame it on the PPP.

Do total pools of metabolites, other than lactate (Figure 3F) change significantly in cancer cells treated with CDEs? Are the total levels of TCA cycle metabolites (Figure 3) or amino acids (Figure 6), changed? Authors should also represent total ion currents in addition to the fractional labeling representation as it provides further information and would help answer these questions.

We completely agree with reviewers that we performed isotope tracing analysis and did not estimate metabolic fluxes. We have added a statement in Discussion section regarding this issue.

We are hoping that the question that reviewers had about M4 citrate is referred to Figure 4. Since Figure 4 describes experimental resultsobtained by using uniformly-labeled glutamine, levels of M4 citrate derived from oxidative metabolism of glutamine do correspond with M5 a-KG/Glu. Also, as is mentioned in the text and seen by previous researchers, mitochondrial ETC inhibition displays a response similar to hypoxia and thus increases glutamine-driven reductive carboxylation in cancer cells with CDEs. However, the reductive glutamine’s metabolism contributes to M5 citrate from M5 a-KG. The levels of M5 citrate are lower than M5 a-KG/Glu in our results.

We agree with reviewers that our tracing cannot clearly resolve pentose phosphate pathway and hence in the revised manuscript, we have removed that statement. The reason for high M0 lactate is because of pyruvate in the media, which results in dilution of labeled lactate.

We have included the total ions current for TCA cycle metabolites in Figure 3—figure supplement 1. As seen in Figure 3—figure supplement 1, there is an increase in lactate, pyruvate, and TCA cycle metabolites in cancer cells with CDEs. As discussed in the text, low citrate levels could be explained because of reduced glucose contribution to TCA cycle in the presence of exosomes. It is also to be noted that in Figure 4, we showed that in cancer cells cultured with CDEs there are other precursors of acetyl-CoA, which were found to maintain lipogenesis. There were non-significant changes in levels of amino acids in cancer cells with and without CDEs, which could be because of their utilization for nucleotides and protein synthesis.

Subsection “CDEs upregulate glucose metabolism in cancer cells“, second paragraph: “We found that the CDEs increased the lactate levels in the cancer cells (Figure 3F, Figure 3—figure supplement 1).”

5) A limitation of this manuscript is the metabolomics on the CDEs themselves. The isolation of exosomes is quite protracted, opening up the possibility of artifactual changes in the metabolome. As the authors know, this is why the isolation of metabolites is extremely time sensitive and all proteins are denatured immediately with such reagents as ice cold methanol. This may be technical limitation of exosome isolation that could severely impact the results. This issue needs addressing. The quantitation of metabolites in the exosomes leads to some astronomical values per unit of protein. Perhaps this is because the exosomes are very low in protein? In a typical cell, which may be about 20% protein, the concentration of lactate of 50 umol/mg prot would correspond to a molar concentration of 10 M! The authors should carefully check this.

We agree with the reviewers that technical limitations of the exosome isolation protocol could impact the metabolomic analysis of exosomes. We were cognizant of this issue and tried to minimize steps, which could cause degradation of the metabolites. However, our data clearly supported both (i) phenotypic changes observed by rescue of viability during nutrient deprivation conditions and (ii) metabolic contributions observed through isotopically enriched metabolites in cancer cells through labeled CDEs. Since the results were consistent for different experimental repeats and also across cell lines, we feel confident about the conclusions. Further, as seen in Figure 6figure supplement 1, the percentage contribution of labeled exosomes (estimated as mean enrichment normalized to exosome enrichment) is significant. This particular result could not have been impacted by isolation procedures. It is more likely that we are underestimating and not overestimating the levels of these metabolites.

We agree with the reviewer that low protein content in exosomes could lead to apparently higher concentrations. However, this is the best possible estimate of concentrations as is done with cellular or organelle level normalizations. We would like to point that we have made a minor correction in lactate concentration, which was because of range of standards used over the period of time. Currently lactate concentrations are 1/10th of the previous values.

6) The main metabolite component of the exosomes seems to be Gln (Figure 5). This amino acid is a major source of carbon for the TCA cycle in different systems. Also, in Figure 6—figure supplement 1, the major labeled citrate isoform found in PC3 cells after treatment with "labeled" CDEs is M2 and not M6 – suggesting that the majority of this citrate is not coming directly from the exosome labeled citrate but rather from Acetyl-CoA (or precursors) delivered by exosomes, further suggesting feeding of the TCA cycle by exosome delivered metabolites – how do the authors reconcile this potentially higher TCA cycle activity with the decreased OXPHOS phenotype?

We thank the reviewers for bringing forward this interesting point. Our results show that CDEs induce decreases of OXPHOS activity, which upregulates glutamine-driven reductive carboxylation. The upregulated reductive glutamine metabolism maintains higher citrate synthesis for lipogenesis and cell proliferation. However, as reviewer correctly pointed out acetyl CoA may also contribute to higher M2 citrate levels. Our data suggest that both reductive carboxylation and nutrient supply mechanism could collectively maintain higher levels of TCA cycle metabolites in cancer cells.

7) The authors do not elucidate how the tumor cells uptake the nutrient loaded exosomes. The effects of heparin and CytoD should be shown also on the control condition, to ensure that things like proliferation inhibition are specific to the deprivation/restoration with exsomes. The use of macropinocytosis inhibitors (EIPA) should provide an answer to this question. If the delivery of endosomal contents in the cell requires degradation of exosomes by fusion with lysosome (to release their cargo) in cancer cells, then treatment with drugs that block this process such as CQ or BafilomycinA should impair the phenotypes observed with cancer cells are treated with CDEs.

We thank the reviewers and editors for this suggestion. We have added the control conditions in Figures 6C–D and Figures 7B–C in prostate and pancreatic cancer cells, respectively. Please see the revised figures. Additionally, as suggested by the reviewers, we performed experiments with macropinocytosis inhibitor (EIPA) and lysosomal degradation inhibitor chloroquine (CQ) and have included them in Figures 6C–D and Figure 7—figure supplement 1. As seen in the figures, EIPA inhibits CDEs-induced increased cell proliferation, thus suggesting that CDEs uptake in cancer cells is also through macropinocytosis along with other endocytosis pathways. Further, Figure 6D and Figure 7—figure supplement 1 suggest that lysosomal degradation may not be the only pathway for release of exosomal content; other mechanisms may also be responsible for release of exosomal content.

Figure 6: Subsection “CDEs can supply amino acids to cancer cells in a manner similar to micropinocytosis”, last paragraph: “However, this rescue effect is reduced to varying extents by adding CytoD, heparin and lysosomal degradation inhibitor choloroquine (Figure 6C). Similarly, addition of macropinocytosis inhibitor EIPA also counters the rescue of CDEs under deprivation (Figure 6D). These data suggest that uptake of exosomes and release of their cargo is necessary to rescue cell proliferation under nutrient deprived conditions.”

Figure 7: Subsection "CDEs supply metabolites to pancreatic cancer cells via Kras-independent mechanism", first paragraph:

“Further, since both EIPA and CytoD could also inhibit rescue effect of proliferation in BxPC3 and MiaPaCa-2 cells (Figure 7—figure supplement 1), suggesting that endocytosis pathway dependent on macropinocytosis and caveolae mediated endocytosis are also associated with uptake of CDEs in pancreatic cancer cells. Additionally, CQ reduced rescue of proliferation by CDEs, thereby suggesting that release of exosomal content through lysosomes may play a role in some cancer cells. Nevertheless, these data suggest that CDEs internalization may happen through various modes of internalization.”

8) One of the sections points at excluding KRAS from playing a role in PDAC uptake of CDEs. However, to fully exclude KRAS role, the analysis should be based on more than comparison between two cell lines. For example, if the phenotype (decreased OCR or increased proliferation upon treatment with CDEs) is maintained after genetic ablation of KRAS in MiaPaCa cells.

Thank you for this suggestion. To exclude KRAS regulated effect on CDEs uptake in PDAC, we used doxycycline inducible Kras-G12D cell line (iKras-1, (Ying et al., 2012)) and measured proliferation of the cells with and without CDEs. As seen in Figure 7—figure supplement 2, CDEs could rescue loss of proliferation with and without oncogenic Kras expression, thereby suggesting that internalization or uptake and supply of exosomes derived metabolites in cancer cells is Kras independent. We have added the results in Figure 7—figure supplement 2 and the description in the second paragraph of the subsection “CDEs supply metabolites to pancreatic cancer cells via Kras-independent mechanism“:

To further substantiate the role of KRAS, we used doxycycline inducible Kras-G12D cell line (iKras-1, (Ying et al., 2012)) to test if enhancement of proliferation by CDEs indeed was KRAS independent. As shown in Figure 7—figure supplement 2, iKras-1 cells with or without doxycycline showed similar proliferation increases with CDEs, thereby suggesting that internalization or uptake and supply of exosome-derived metabolites in cancer cells is Kras independent.

9) The mitochondrial membrane potential is decreased in PC3 cells upon treatment with CDEs (Figure 2F), did the authors verify if total mitochondrial content and/or shape is altered in cancer cells treated with CDEs? Also, did the authors verify if CDEs contain mitochondria or fractions of mitochondria? This could explain the relatively high levels of malate and other TCA cycle intermediates found inside (Figure 5C).

As suggested, we measured total mitochondrial content through quantitative PCR by measuring expression level of mtDNA in cancer cells cultured with CDEs. Our data suggest that there is an insignificant change in total mitochondrial content in cancer cells with CDEs as seen below. Since the size of mitochondria is much bigger than exosomes, CDEs may not contain intact mitochondria. However, we did detect mitochondrial DNA in CDEs as seen in Author response image 1. We have not measured mitochondrial fractions in CDEs and feel that it will be beyond the scope of current work.

Author response image 1
DNA extracted from whole CAFs (samples 1-4) or CAF-derived exosomes (samples 5-8) was analyzed for the presence of 5 mitochondrial genes, MT-ATP6, MT-ND5, MT-RNR1, MT-CYB and MT-CO3 by PCR.

MT-ATP6: Mitochondrially Encoded ATP Synthase 6; MT-ND5: Mitochondrially Encoded NADH Dehydrogenase 5; MT-RNR1: Mitochondrially Encoded 12S RNA; MT-CYB: Mitochondrially Encoded Cytochrome B; MT-CO3: Mitochondrially Encoded Cytochrome C Oxidase III.

https://doi.org/10.7554/eLife.10250.020

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

The manuscript has been improved. However, there are significant remaining issues that we would like for you to provide a response, within 1-2 weeks, on how you would address them to determine whether a second revision is invited. While the effect of CAF-derived exosomes on OXPHOS (Figure 7) is impressive, the mechanisms by which this occurs remain undefined (issue 1, below), particularly with the issues surrounding the exosome metabolome (issue 2, below).

We have appropriately addressed the issue 2 below. It was an inadvertent error that resulted in higher concentrations. Please see below our response.

1) The miRNA data is still very correlative and quite weak (based on literature showing that particular miRNAs can regulate expression of various oxphos enzymes).

We agree that the miRNA data is correlative and transfecting multiple miRNAs into the cells to show mechanism has not been possible due to cells dying during multiple transfections. In the first submission we had included the miRNA data in supplementary text and in the Discussion. However, reviewers found that data to be interesting and asked us to bring it into the main text. Hence, in our revisions, we included the data in the main body. We had mentioned in our response that there were technical difficulties due to limitations in genome engineering technologies to either insert multiple miRNAs in exosomes or transfect multiple miRNAs together without causing significant toxicity to the cells. The miRNA literature suggests that often miRNAs act in clusters, however there are inherent technological limitations in co-transfecting multiple miRNAs into our exosome-based cell assays as we found these to be toxic to the cells.

In order to elucidate the effect of miRNAs on the oxidative phosphorylation, we have included the preliminary data obtained by co-transfecting highly expressed miRs in exosomes in PC3 cells as Figure 2K. In this figure, we measured oxygen consumption rate in PC3 cells co-transfected with miR22, let7a and miR125b and observe a significant reduction of OCR. In addition to these results, we have included the aforementioned caveats of co-transfecting miRNA in the Discussion section. We hope that the editors will agree that this data supports our hypothesis and conclusion.

The miRNAs and their targets that we have included are based on experimental miRNA abundance data from Nanostring assays followed by miRNA target prediction integrated with AGO-CLIP-SEQ data (Li et al., 2014). Therefore, the target genes we have identified are only those that have experimental evidence of interaction. We believe that our analytical approach and data is standard practice and with strong bioinformatic support lays the foundations for future experiments.

Inserted in the subsection “CDEs downregulate mitochondrial function of prostate cancer cells“: “The miRNAs and their targets that we have identified are based on experimental miRNA abundance data from Nanostring® assays followed by miRNA target prediction integrated with AGO-CLIP-SEQ data (Li et al., 2014). […] Although, the reduction of OCR is moderate, this is due to the technical limitation of co-transfection experiments, which can only allow using a small subset of the miRNAs that target OXPHOS in PC3.”

Inserted in the Discussion: “In Figure 2, we observe a reduction of OCR in PC3 cells co-transfected with miR-22, let7a and miR-25b, in line with our hypothesis of the inhibitory effect of miRNA from CDEs. […] In light of these results we believe further studies are needed to uncover the mechanism of ETC inhibition by CDEs-derived miRNAs that are out of scope for this study.”

Inserted in Figure 2 legend: “K. OCR of PC3 were measured after transfection of targeted miRNAs together into cells. (n=5). miRNAs were transfected into cells according to the manufacturer’s protocol (Lipofectamine® 2000 Transfection Reagent, Thermo fisher, cat. 11668-027). Cells were seeded in 6-well plate for 24h. Transfection was performed followed by incubation for 48h. Cells were then reseeded onto Seahorse plates for OCR measurements after the cells were attached.”

2) The concentrations of metabolites in the exosomes continue to be unrealistic, e.g. 1 moles glutamine, 5M lactate moles, per g protein, which would translate to (assuming exosomes are 20% protein), 200M and 1000M, which is physically impossible. What is the relationship between exosome protein, exosome volume, and exosome number? Complete analytical support is necessary, including raw data, for any metabolites that the authors claim are present in exosomes at concentrations exceeding 20 mM.

We thank the reviewers for pointing out this major issue in our data. We would like to apologize to the editors and reviewers for this confusion. Since the eLife required high quality figures during revisions, we used a newer version of illustrator on a computer that was missing several fonts. Hence, due to missing embedded fonts in the PDF files uploaded during revisions, several axis labels showing concentrations in µmol (micromoles) were converted automatically to mmol (milimoles). Due to this error, the data has been misinterpreted as being 1000 times higher than actual measurements. We would like to emphasize that this error only occurred in the revised submission and the original submission made in August 2015 had the correct units. The files on eLife server from August full submission can be checked for verifying our error. Further, we would be happy to provide the raw data and subsequent analyses of the data to definitively prove that this error occurred due to erroneous PDF files. Please note that we have made corrections in Figures 5A,C,D,E,F,G, Figure 4M, Figure 3C, D, Figures 2A,B and Figure 1E on this regard.

We would like to refer the editors and reviewers to the table below, which shows the comparison of exo-metabolome concentrations with intracellular concentrations of metabolites obtained from previous studies. As seen in the table, our exo-metabolite concentrations are in similar ranges as reported previously for intracellular metabolites.

C: intracellular metabolite concentration, mM;

P: cells protein content, which is 0.2 g/mL (20% protein);

X: metabolite concentration normalized with protein amount, μmol/g protein

X=C mmol(0.2g/mL)L= C 1000μmol(200g/L)L=5CμmolgFor example, in (Fan et al., 2013),

Gluintra=37mmol(0.2g/mL)L=371000μmol(200g/L)L=5×37μmolg=185μmolg
Amino acidMinimum concentration in Exo
(μmol/g protein)
Maximum concentration in Exo
(μmol/g protein)
Intracellular concentration obtained from previous studies (assuming 20% protein) (μmol/g protein)References
Glutamate30.8294585.3429185(Fan et al., 2013)
114.75(Baydoun et al., 1990)
Phe3.5357223.73383.8(Baydoun et al., 1990)
3.9(Hansen and Emborg, 1994)
Alanine39.4691191.361214.75(Baydoun et al., 1990)
Glycine60.8029168.640731.35(Baydoun et al., 1990)
41.8(Hansen and Emborg, 1994)
Tryptophan44.596451.1827.8(Baydoun et al., 1990)
Asparagine49.7664133.011793.35(Hansen and Emborg, 1994)
Leu10.3048174.23126.2(Baydoun et al., 1990)
Valine90.0222196.7996.15(Baydoun et al., 1990)
Lys24.451978.55655.4(Baydoun et al., 1990)
Gln462.80791693.0858100(Souba, 1993)

Importantly, our exosome metabolome data is accurate when measured in either micromoles or micrograms or micromoles/g protein. To avoid issues related to normalization, we propose to estimate the concentration of metabolites with respect to exosome particle number rather than exosomal protein. This will avoid the bias created by the apparently large concentrations of metabolites within exosomes. It is also in line with the preference of normalizing metabolite concentrations with cell number in the field of cell metabolism. To the best of our knowledge, most of the metabolomics and metabolic flux literature use cell number as the normalization factor.

As reported previously by us, protein content in exosomes is 4.9μg/ 109 particles ((El-Andaloussi et al., 2012), Nature Protocol estimates this number to be between 2-8μg/ 109 particles).

Furthermore, we may expect higher concentrations in vesicles. It is known that during receptor mediated endocytosis there is a significant increase in concentration. Another example of this phenomenon is the glutamate concentration inside synaptic vesicles, which can be as high as 100 mM (Danbolt, 2001). A previous study has stated (Mavelli and Stano, 2015) that during the process of micro-vesicles formation it could be possible for solutes, in this case metabolites, to become concentrated within vesicles as compared to within cells. In the aforementioned study, the authors observed this phenomenon in liposomes that are similar in size to exosomes. Hence, it is very likely that during vesicle formation process, concentrations of metabolites, proteins, etc. become higher than that in the cells. To the best of our knowledge, not much is known about concentration estimation inside exosomes. Collectively our data shows the strong effect of CDEs in the form of (a) rescue of cell viability under nutrient deprivation; (b) metabolic reprogramming of OXPHOS and reductive carboxylation; (c) cargo that supplies intact metabolites to the TCA cycle.

In addition to the two major issues above, there are other items that need substantive addressing. 1) Figure 6 seems confusing. Since lysine is an essential amino acid, why are they seeing M+2 form? Why do lactate, pyruvate, and alanine, which all share the same carbon skeleton, all have different labeling patterns? How do they get to the conclusion that ~ 30% of KG is derived from exosome metabolites? It does not seem proper to normalize the KG labeling in cells to that in exosomes, when most KG in cells comes from glutamine or glutamate. It is also confusing how labeling of KG in cells would exceed glutamine and glutamate. I do not want to cause the authors excessive suffering on this point. To this end, they may be better served reporting less isotope labeling data, instead of reporting dubious data. The leucine data, for example, makes the point that the exosome contribution is modest while looking more straightforward than some other data. Similarly, it seems important to see results for glutamine. Perhaps a few well-chosen examples would suffice. Also, perhaps some funny labeling patterns can be cleaned up by more careful examination of the raw data to eliminate interfering peaks.

We thank the reviewers for their suggestion in improving the clarity of data shown in Figure 6. We agree with the reviewers that indeed there are a few puzzling trends seen in the mass isotopologue distributions of lactate, pyruvate, alanine and lysine. We agree with the reviewer’s concern about the dissimilarity in the labeling pattern of pyruvate, alanine and lactate. This may be due to combined effect of compartmentalization of pyruvate pools, complex carbon transitions in TCA cycle and malic enzyme pathways and multiple sources of pyruvate leading to distinct labeling patterns in alanine and pyruvate. Since this experiment was performed as a proof-of-concept for showing that CDEs contribute TCA cycle metabolites to cancer cells, we now show the mass distributions of the heaviest isotopologues. The data now highlights M6 Glucose derived M3 lactate, M3 pyruvate and M3 alanine that can only be directly sourced from exosomes. Similarly, we only show M5 Glutamine, M5 Glutamate, M6 lysine, M6 leucine indicating the fraction of these metabolites derived from exosomes. We have modified the text for Figure 6 to reflect the changes in the figure (subsection “CDEs can supply amino acids to cancer cells in a manner similar to micropinocytosis”). If the reviewers are in agreement, to enhance clarity for the reader we propose to report only the 12C and 13C fractional enrichment for these metabolites as has been done in previous studies showing metabolite-tracing isotopic data.

In the case of citrate, α-ketoglutarate and malate, we show complete mass distributions as they are important TCA cycle metabolites, which are enriched by exosome-derived metabolites metabolized inside cancer cells. Due to branching and high turnover rates in the TCA cycle it is important that we show their complete mass distributions, which also support our hypothesis that exosome-derived metabolites are incorporated into the TCA cycle. Furthermore, we agree that the α-KG contribution is 30% and much higher than that of glutamine and glutamate, it is due to the low 13C enrichment of α-KG in exosome, which results in overestimating its contribution. We had reported these values based on the suggestion made in the previous review to quantify the contribution of exosome metabolites to cancer cells. It is important to note here that due to complexity of TCA cycle pathways it becomes extremely difficult to resolve contribution of metabolites that comes directly from exosomes and that from metabolites metabolized after uptake of exosomes by cancer cells. Hence, as suggested we have removed the figure (Figure 6—figure supplement 1, Panel B) that reported the estimated contributions of TCA metabolites by normalizing mean enrichment with exosomes enrichment.

2) The authors provide some more rationale for choosing the concentration of exosomes used in their studies. However, there is still uncertainty as to how this represents the physiological state. The authors should at least write a sentence or 2 in the Discussion talking about this limitation. The limitation of this analysis (protracted purification protocol) should be mentioned in the Discussion.

As per the reviewers’ suggestion, we have added a paragraph addressing this issue. We would like to stress that the physiological value of exosome concentrations should be considered from a perspective of a coculture or an organotypic model of tumors, which can mimic the in vivo ratio of stromal cells to cancer cells. The effect of tumor microenvironment cells on cancer cells can only be dissected accurately using a tumor-mimicking design. Apart from these considerations, previous studies have also reported similar concentrations of exosomes in tumors (Baranyai et al., 2015).

We have used 200 μg/ml for the different types of experiments conducted herein (proliferation assays, metabolic assays and tracer experiments). The working concentration was chosen to maintain a physiological ratio of CAFs to cancer cells which is reported to be between 1 and10 (Brauer et al., 2013; Delinassios, 1987; Hu et al., 2015). A concentration of 200 μg/ml corresponded to ratio between 1 and 5 CAFs per cancer cell and hence, is within the range reported in the literature. However, for different CAFs and cancer cells system this ratio should be individually estimated based on the functional effect of stromal cells on cancer cells. It is to be noted that due to protracted purification steps during exosome isolation, degradation of metabolites can occur and hence, replenishment of exosomes for functional studies was followed and recommended. Additionally, to minimize degradation of metabolites in exosomes, we used fresh exosomes for all the experiments and avoided any freeze-thaw cycle in exosomes that were introduced to cancer cells cultures.

3) The correction of the lactate levels to 1/10th what was originally reported does not seem "minor". Can the authors please explain this further?

During post-processing of all of our metabolites data whether it was obtained from UPLC, GC-MS or ELISA, we had initially used either mL or L (liters) for normalization. However, lactate measurements were obtained from colorometric assay which uses Trinity Bio's standard. This was the only standard that was in mg/dl instead of mg/l due to which an inadvertent error occurred. Hence, during normalization, lactate concentrations were ten times higher.Further, we would be happy to provide the raw data and subsequent analyses of the data.

4) It is still not clear why M+2 is the dominant citrate isotopomer in Figure 6—figure supplement 1. This continues to suggest that the majority of this citrate is not coming directly from the exosome labeled citrate but rather from Acetyl-CoA (or precursors) delivered by exosomes.

The reviewers make an appropriate observation that M2 citrate is the dominant isotopomer suggesting the contribution of Acetyl-CoA. We would like to point out that this data is meant to indicate the contribution of labeled metabolites (Glucose, Glutamine, Lysine, Phenylalanine, Pyruvate, and Leucine) derived from exosomes and their subsequent incorporation into central carbon metabolism. The TCA cycle metabolites citrate, α-ketoglutarate and malate are enriched by exosome-derived metabolites that are metabolized inside cancer cells. We would like to clarify that the contribution of citrate is not all directly from exosomes, rather most of the citrate is from other metabolites supplied by exosomes such as labeled pyruvate/acetyl-CoA (from glucose) and labeled malate (from glutamine and other precursors).

5) In the bar graphs looking at drug effects, the authors should not limit tests of statistical significance to the hypothesized effects (in the exosome rescue conditions), but also conduct the same tests in the non-rescue conditions, as it seems that the drugs sometimes have an effect also in those conditions. If those tests were all non-significant, there is no need to mark them on the figure, but that should be clearly reported.

We thank the reviewers for this suggestion and have addressed this issue by inserting the statistics in the control conditions. We have revised Figure 6, Figure 7 and Figure 7—figure supplement 1.

6) The authors should be aware that oxoproline is most commonly found due to degradation of glutamate during sample processing. They should not make main text statements relating this to the glutathione pathway without further evidence.

We have deleted the associated text in the revised manuscript.

https://doi.org/10.7554/eLife.10250.022

Article and author information

Author details

  1. Hongyun Zhao

    1. Laboratory for Systems Biology of Human Diseases, Rice University, Houston, United States
    2. Department of Chemical and Biomolecular Engineering, Rice University, Houston, United States
    Contribution
    HZ, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  2. Lifeng Yang

    1. Laboratory for Systems Biology of Human Diseases, Rice University, Houston, United States
    2. Department of Chemical and Biomolecular Engineering, Rice University, Houston, United States
    Contribution
    LY, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  3. Joelle Baddour

    1. Laboratory for Systems Biology of Human Diseases, Rice University, Houston, United States
    2. Department of Chemical and Biomolecular Engineering, Rice University, Houston, United States
    Contribution
    JB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  4. Abhinav Achreja

    1. Laboratory for Systems Biology of Human Diseases, Rice University, Houston, United States
    2. Department of Chemical and Biomolecular Engineering, Rice University, Houston, United States
    Contribution
    AA, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  5. Vincent Bernard

    1. Departments of Pathology and Translational Molecular Pathology, Ahmad Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center, Houston, United States
    Contribution
    VB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  6. Tyler Moss

    1. Department of Systems Biology, University of Texas, MD Anderson, Houston, United States
    Contribution
    TM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  7. Juan C Marini

    1. Baylor College of Medicine, Houston, United States
    Contribution
    JCM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  8. Thavisha Tudawe

    1. Department of Chemical and Biomolecular Engineering, Rice University, Houston, United States
    Contribution
    TT, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    The authors declare that no competing interests exist.
  9. Elena G Seviour

    1. Department of Systems Biology, University of Texas, MD Anderson, Houston, United States
    Contribution
    EGS, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    The authors declare that no competing interests exist.
  10. F Anthony San Lucas

    1. Departments of Pathology and Translational Molecular Pathology, Ahmad Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center, Houston, United States
    Contribution
    FASL, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    The authors declare that no competing interests exist.
  11. Hector Alvarez

    1. Departments of Pathology and Translational Molecular Pathology, Ahmad Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center, Houston, United States
    Contribution
    HA, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    The authors declare that no competing interests exist.
  12. Sonal Gupta

    1. Departments of Pathology and Translational Molecular Pathology, Ahmad Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center, Houston, United States
    Contribution
    SG, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  13. Sourindra N Maiti

    1. Department of Pediatrics, University of Texas MD Anderson Cancer Center, Houston, United States
    Contribution
    SNM, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  14. Laurence Cooper

    1. Department of Pediatrics, University of Texas MD Anderson Cancer Center, Houston, United States
    Contribution
    LC, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  15. Donna Peehl

    1. Department of Urology, School of Medicine, Stanford University, Stanford, United States
    Contribution
    DP, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    The authors declare that no competing interests exist.
  16. Prahlad T Ram

    1. Department of Systems Biology, University of Texas, MD Anderson, Houston, United States
    Contribution
    PTR, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  17. Anirban Maitra

    1. Departments of Pathology and Translational Molecular Pathology, Ahmad Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center, Houston, United States
    Contribution
    AM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  18. Deepak Nagrath

    1. Laboratory for Systems Biology of Human Diseases, Rice University, Houston, United States
    2. Department of Chemical and Biomolecular Engineering, Rice University, Houston, United States
    3. Department of Bioengineering, Rice University, Houston, United States
    Contribution
    DN, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    1. dn7@rice.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0002-8999-2282

Funding

No external funding was received for this work.

Acknowledgements

This work made possible in part through support from the Ken Kennedy institute for Information technology at Rice University to DN under the Collaborative Advances in Biomedical Computing 2011 seed funding program supported by the John and Ann Doerr Fund for the Computational Biomedicine.

Reviewing Editor

  1. Chi Van Dang, Reviewing Editor, University of Pennsylvania, United States

Publication history

  1. Received: July 21, 2015
  2. Accepted: February 26, 2016
  3. Accepted Manuscript published: February 27, 2016 (version 1)
  4. Version of Record published: April 13, 2016 (version 2)

Copyright

© 2016, Zhao 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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