. Author manuscript; available in PMC: 2026 Sep 18.

Published in final edited form as: Mol Cell. 2025 Sep 18;85(18):3486–3504.e7. doi: 10.1016/j.molcel.2025.08.021

mTOR inhibition reprograms cellular lipid homeostasis by inducing alternative lipid uptake and promoting cholesterol transport

Sejeong Shin ¹, Min-Joon Han ², Ishika Patel ³, Alia M Welsh ⁴, Maria Sverdlov ⁵, Wonhwa Cho ⁶, Stephanie M Cologna ⁶, Philippe P Roux ⁷, Sang-Oh Yoon ^1,^8,^*

PMCID: PMC12453599 NIHMSID: NIHMS2107770 PMID: 40972529

SUMMARY

The mechanistic target of rapamycin (mTOR) is a key regulator of lipid homeostasis by controlling processes including lipid uptake and biosynthesis. mTOR dysregulation and consequent altered lipid metabolism are common in various diseases, including cancers, making mTOR a promising therapeutic target. Therefore, it is crucial to understand how mTOR activation and inhibition reprogram lipid homeostasis. In human cancer cell lines, mTOR inhibition induces alternative lipid uptake through translation initiation factor 3D (eIF3D)-mediated LDLR-related protein 6 (LRP6) increase and activates liver X receptor β (LXRβ), promoting cholesterol release from lysosomes and its transport to the plasma membrane via NPC intracellular cholesterol transporter 1 (NPC1). This signaling supports tumor cell survival and stress resistance. In mouse xenograft models, combining mTOR inhibition with LRP6 knockdown or NPC1 targeting significantly suppresses tumor growth. Our findings highlight mTOR feedback signaling in reprogramming lipid homeostasis and its therapeutic potential to treat diseases characterized by dysregulated mTOR.

eTOC Blurb

Shin et al. find that mTOR inhibition reprograms lipid homeostasis in cancer cells by enhancing alternative lipid uptake and promoting cholesterol trafficking. These adaptations support survival signaling and stress resistance under metabolic stress. Dual targeting of mTOR and reprogrammed lipid metabolic pathways in tumor xenografts markedly suppresses tumor growth, suggesting a potential therapeutic strategy for cancer.

Graphical Abstract

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Introduction

Lipids are essential not only for maintaining cellular structures, such as plasma membrane and subcellular organelle membranes, but also for controlling the functionality of these structures ¹. For example, the major components of the plasma membrane are cholesterol and phospholipids. Changes in cholesterol levels in the plasma membrane alter the physical properties of the membrane (e.g., rigidity and integrity) and impact the expression, distribution, and functions of various plasma membrane proteins, including growth factor receptors and nutrient transporters ². Lipids such as triglycerides and fatty acids are integral to cellular energy production and storage. Overall, lipids play critical roles in cell growth, proliferation, and stress resistance ^1,3.

Cellular lipid levels are strictly regulated by their synthesis, degradation, uptake, and release, ensuring lipid homeostasis, termed lipidostasis. For instance, cancer cells not only acquire lipids from plasma lipoprotein uptake but also utilize lipid synthesis and degradation systems, such as fatty acid synthesis and oxidation, to support their proliferation, growth, and adaptation to stress ^3,4. Unlike most other molecules, cholesterol cannot be degraded in mammalian cells ⁵. Thus, cellular cholesterol levels are mainly regulated by cholesterol uptake, biosynthesis, and export.

A primary transcriptional regulator of proteins involved in lipid metabolism and uptake is the sterol regulatory element-binding protein (SREBP) family of transcription factors ⁶, which is primarily regulated by the mechanistic target of rapamycin (mTOR), a conserved serine/threonine kinase, that integrates nutrient and energy signals to regulate cellular metabolism, including lipid metabolism ⁷. The mTOR exists in two complexes, mTOR complex 1 (mTORC1) and mTORC2, each with unique and overlapping compositions and functions. mTORC1 promotes biosynthesis of fatty acid, triglyceride, and cholesterol through activating and increasing the levels of SREBP, which in turn upregulates the transcription of enzymes crucial for these processes ⁶. In adipocytes, inhibition of mTORC1 decreases lipogenesis but increases lipolysis and fatty acid oxidation ⁶. mTORC1 inhibition also reduces the expression of low-density lipoprotein receptor (LDLR), a primary cellular lipoprotein receptor, in an SREBP-dependent manner, thereby decreasing LDLR-mediated lipid uptake ^1,8. While the role and mechanism of mTORC2 in the regulation of lipid metabolism are less understood than those of mTORC1, mTORC2 is implicated in promoting lipid synthesis and uptake in the liver ^9–11.

As a key regulator of lipid metabolism, mTOR activity plays a significant role in programming lipid homeostasis in cells. Thus, various inputs for mTOR activity and the feedback loop between mTORC1 and mTORC2 in physiological conditions dynamically reprogram cellular lipid homeostasis. In pathological conditions, dysregulation of mTOR leads to aberrant lipid metabolism, contributing to the onset and progression of metabolic disorders such as obesity and cancer ⁶. Thus, clinical inhibition of mTOR activity is a promising approach in cancer treatment. Rapamycin and its analogs (rapalogs), the first-generation mTOR inhibitors, selectively but only partially inhibit mTORC1. These drugs are used in clinical settings and have shown anti-tumor effects in patients with tuberous sclerosis complex (TSC) and kidney cancer. However, their clinical effectiveness as single agents is limited in various cancer types, and resistance often develops ^12,13. Dual inhibition of mTORC1 and mTORC2 by the second-generation (e.g., Torin1, INK128) and third-generation (e.g., RapaLink-1) mTOR kinase inhibitors has shown promise in specific cancer types since in vitro and in vivo mouse studies demonstrate more potent and sustained anti-tumor effects compared to inhibition of mTORC1 alone ^14,15. Currently, several dual mTORC1/2 inhibitors are under active clinical trials to treat cancer patients. However, resistance to these second- and third-generation mTOR inhibitors remains a significant challenge in cancer treatment ^15–18.

Depending on physiological, pathological, and clinical inputs for mTOR activation and inhibition, cells dramatically reprogram their lipid metabolism to adapt to their environment. Given the significance of dysregulated hyperactive mTOR in cancer and its targeting in therapy, the mechanisms and functions of feedback signaling mediated by mTOR inhibition in lipid metabolism are of pathological and clinical importance. Recent studies, including ours, indicate that expression and activities of plasma membrane proteins, such as integrins and receptor tyrosine kinases (e.g., insulin-like growth factor 1 receptor (IGF1R) and insulin receptor (INSR)), play crucial roles in tumor resistance signaling and subsequent tumor survival/growth in response to mTORC1/2 inhibition ^15,19–21. Since both mTORC1 and mTORC2 are major positive regulators of lipid uptake and synthesis, our main question is how cancer cells activate survival signaling from the plasma membrane when its crucial component, lipids, especially cholesterol, may be deficient due to mTOR inhibition, especially by dual mTORC1/2 inhibition. Using first- to third-generation mTOR inhibitors and mTOR knockdown, our study suggests that cancer cells dynamically change their lipidostasis system in response to mTOR inhibition by activating alternative lipid uptake and promoting the lipid transport system. This leads to the activation of survival/growth signaling from the plasma membrane, including IGF1R/AKT signaling, which enables cells to adapt to metabolic stress induced by the inhibition of mTOR, a critical regulator of lipid metabolism.

RESULTS

mTOR inhibition decreases proteins involved in lipid biosynthesis and uptake but activates the IGF1R/AKT signaling pathway.

While rapamycin is a partial mTORC1 inhibitor, Torin1 and RapaLink-1 are dual mTORC1/2 inhibitors. mTORC1/2 inhibition initially blocks survival signaling, including abolishing phosphorylation of 4EBP1 and AKT (S473), substrates of mTORC1 and mTORC2, respectively ^15,20. However, long-term inhibition activates integrin and receptor tyrosine kinases, such as insulin-like growth factor 1 receptor (IGF1R), promoting AKT phosphorylation at T308 (activation loop) and restoring mTORC2-independent phosphorylation at S473 (hydrophobic motif) ^15,20 (Figure S1A). Once AKT phosphorylation increases following mTOR inhibition, re-treatment with mTOR inhibitors does not reduce the restored AKT phosphorylation ²⁰. Consistent with this, treatment with fresh media containing mTORC1/2 inhibitors does not significantly change AKT phosphorylation levels (Figure S1B). Dose-dependent studies showed that even at low concentrations of dual mTORC1/2 inhibitors suppressed 4EBP1 phosphorylation but induced IGF1R and AKT phosphorylation (Figure S1C). These findings suggest a feedback mechanism leading to mTORC2-independent AKT activation. Knockdown of mTOR in cells growing in complete media also led to AKT phosphorylation, with significantly higher levels compared to control shRNA cells ^15,20 (Figure S1D). Treatment with fresh media reduced AKT phosphorylation in mTOR knockdown cells; however, compared to control shRNA cells, phosphorylation at S473 was comparable, and phosphorylation at T308 was still higher (Figure S1E).

Integrins and receptor tyrosine kinases are plasma membrane proteins; thus, their activation depends on the integrity and functionality of the plasma membrane. Because AKT activation also requires a functional plasma membrane ²², we further characterized AKT phosphorylation under mTOR-inhibited conditions. Although T308 phosphorylation is sufficient for AKT activation, full activation requires phosphorylation at both S473 and T308 ²². To determine whether AKT phosphorylation at S473 is regulated by autophosphorylation or depends on T308 phosphorylation, we used the T308A AKT1 mutant, an inactive form of AKT. The T308A mutant still exhibited AKT phosphorylation at S473 following mTOR inhibition (Figure 1A), suggesting that S473 phosphorylation is independent of T308 phosphorylation and AKT activity. Our further studies reveal that inhibition or knockdown of RAF1, but not B-RAF, suppresses AKT phosphorylation induced by mTOR inhibition (Figures 1B and 1C). As PDK1 for AKT phosphorylation at T308 and RAF1 activity require plasma membrane localization ²³, these suggest that plasma membrane functionality is essential for AKT phosphorylation. Supporting this, AKT PH (Pleckstrin Homology)-domain inhibitors, which block AKT translocation to the plasma membrane, significantly reduced AKT phosphorylation following mTOR inhibition (Figure 1D). However, AKT ATP-competitive inhibitors, which block catalytic activity but do not interfere with membrane localization, still induced AKT phosphorylation at S473 (Figure 1D).

Figure 1. — **(A)** HA-AKT1 wild-type or mutants were expressed and treated with Torin1 (200 nM) for 24 hours. After immunoprecipitation with an HA antibody, samples were analyzed by immunoblot analysis. **(B)** A375 cells were treated with Torin1 for 6 hours, followed by LY3009120 (panRAF inhibitor: 2.5 μM) treatment for an additional 18 hours. Immunoblot analysis was performed. **(C)** Stable A375 control, B-RAF, or RAF1 knockdown cells were treated with Torin1 for 24 hours, and immunoblot analysis was performed. **(D)** A375 cells were treated with Torin1 for 6 hours, and AKT PH-domain targeting inhibitors (Perifosine: 10 μM; MK2206: 5 μM) or ATP-competitive inhibitors (GDC0068: 5 μM; AZD5363: 5 μM) were treated for an additional 18 hours. Immunoblot analysis was performed. **(E)** A375 cells grown in the presence of regular serum or lipid-depleted (LD) serum for 3 hours were treated with rapamycin (20 ng/ml), Torin1 (200 nM), and RapaLink-1 (10 nM) for an additional 24 hours, and immunoblot analysis was performed. **(F)** A375 cells were treated with mTOR inhibitors for 24 hours. Quantitative mass spectrometry for complete protein profiling and data analysis were performed. The heat map shows changes in protein levels. **(G)** A375 cells were treated with rapamycin (20 ng/ml), Torin1 (200 nM), and RapaLink-1 (10 nM) for 24 hours, and immunoblot analysis was performed. **(H)** Stable control or mTOR knockdown cells were lysed, and immunoblot analysis was performed. **(I-J)** A375 cells were treated with mTOR inhibitors for 24 hours. Mass spectrometry for untargeted lipid profiling and data analysis were performed. (I) The volcano plot shows changes in lipid levels. (J) Functional analysis of lipids elevated by mTORC1/2 inhibition. See also Figure S1.

Among major nutrients (e.g., glucose, amino acids, and lipids), lipids are crucial for membrane integrity and function. To understand the role of lipids in feedback signaling, we compared their effects with those of glucose and amino acids. Serum deprivation, which provides glucose and amino acids but lacks serum contents such as lipids and growth factors, dramatically decreased AKT phosphorylation, and mTORC1/2 inhibition did not restore it (Figure S1F). This suggests that serum components such as lipids and growth factors are essential for this phosphorylation following mTORC1/2 inhibition in cells grown in complete media. In the absence of lipids, mTORC1/2 inhibition resulted in a marked decrease in IGF1R and AKT phosphorylation (Figure 1E), indicating that lipids are critical for activating this pathway. In contrast, mTORC1/2 inhibition under glucose or amino acid deprivation induced AKT phosphorylation comparable to or higher than that observed in cells growing in complete media (Figures S1G and S1H). mTOR knockdown significantly increased AKT phosphorylation in cells growing in complete media (Figure S1D) ²⁰, which was further enhanced by glucose or amino acid deprivation (Figures S1I and S1J). In contrast, serum deprivation led to minimal AKT phosphorylation (Figure S1K). Our findings show that among major nutrients, only lipid deprivation, not glucose or amino acid deprivation, blocks mTOR inhibition-mediated AKT phosphorylation, suggesting lipids are essential for this process.

Our main question was how cancer cells activate IGF1R/AKT signaling when mTOR, a key regulator of cellular lipid biosynthesis and uptake, is inhibited, which could result in significantly reduced lipid levels. To address this, we analyzed quantitative proteomics data and found that critical enzymes involved in cholesterol biosynthesis, including HMG-CoA reductase (HMGCR) - a rate-limiting enzyme in cholesterol biosynthesis - were decreased by dual mTORC1/2 inhibition (Figure S1L and S1M). mTORC1/2 inhibition also decreased levels of SREBP1 (SREBF1) and SREBP2 (SREBF2) and transcriptional targets of SREBPs, including key lipid synthesis enzymes (e.g., fatty acid synthase (FASN) and HMGCR) and LDLR, a major protein involved in lipid/cholesterol uptake (Figure 1F). These results were further supported by immunoblot analysis (Figure 1G). Similar results were obtained using mTOR knockdown cells (Figure 1H). These findings indicate that mTORC1/2 inhibition could effectively reduce lipid biosynthesis and uptake. However, our untargeted lipid profiling revealed that mTORC1/2 inhibition substantially increased the levels of triglyceride (Figure 1I), storage and transport forms of lipids. Subsequent analysis of elevated lipids indicated an increase in cellular lipid droplets (Figure 1J), the main cellular storage organelles for triglycerides and cholesterol esters ⁴. These cholesterol esters are storage forms of cholesterol complexed with fatty acids.

mTORC1/2 inhibition increases cellular triglycerides while maintaining free cholesterol levels.

Consistent with the lipidomics data, mTORC1/2 inhibition and mTOR knockdown significantly increased cellular lipid droplet content (Figures 2A–2D). Lipid droplets also contain proteins, among which perilipin (PLIN) is one of the most abundant proteins ²⁴. PLIN2 is ubiquitously expressed in lipid droplets, and its increase is necessary and sufficient for the promotion of lipid droplet accumulation and cell survival ^24–26. Our quantitative proteomics data revealed that PLIN2 protein levels were profoundly increased by mTORC1/2 inhibition (Figure S2A), which was confirmed by immunoblot analysis (Figures 2E and 2F).

Figure 2. — Where applicable, data are the mean ± SD of at least three independent determinations. Statistical significance (*p < 0.05) was assessed by a t-test. **(A-B)** Lipid droplets in A375 cells treated with rapamycin (20 ng/ml), Torin1 (200 nM), and RapaLink-1 (10 nM) for 24 hours were visualized by confocal microscopy (A), and quantitative analysis of staining was performed (B). Scale bar, 20 μm. **(C-D)** Lipid droplets in stable control or mTOR knockdown A375 cells were visualized by confocal microscopy (C), and quantitative analysis of staining was performed (D). Scale bar, 20 μm. **(E)** Cells were treated with mTOR inhibitors for 24 hours, and immunoblot analysis was performed. **(F)** Stable control or mTOR knockdown cells were lysed, and immunoblot analysis was performed. **(G-H)** Unesterified cholesterol in A375 cells treated with mTOR inhibitors for 24 hours was stained with filipin and visualized by confocal microscopy (G), and quantitative analysis of staining was performed (H). Scale bar, 20 μm. **(I-J)** Unesterified cholesterol in stable control or mTOR knockdown A375 cells was stained with filipin and visualized by confocal microscopy (I), and quantitative analysis of staining was performed (J). Scale bar, 20 μm. **(K-L)** After A375 cells were treated with mTOR inhibitors for 24 hours, cholesterol in the outer plasma membrane was visualized by confocal microscopy (K), and quantitative analysis of staining was performed (L). Scale bar, 20 μm. **(M-N)** Using stable control or mTOR knockdown A375 cells, cholesterol in the outer plasma membrane was visualized by confocal microscopy (M), and quantitative analysis of the staining was performed (N). Scale bar, 20 μm. See also Figure S2.

Cholesterol exists as esterified (e.g., cholesterol esters in lipid droplets) or unesterified (e.g., free cholesterol in the plasma membrane and late endosomes/lysosomes) forms. Filipin staining is used to visualize unesterified cholesterol, which was confirmed with a lysosome marker (Figure S2B). This staining revealed that mTORC1/2 inhibition (Figures 2G and 2H) and mTOR knockdown (Figures 2I and 2J) did not significantly change unesterified free cholesterol levels. Using purified mCherry-perfringolysin D4H ²⁷, which labels cholesterol residing in the outer leaflet of the plasma membrane ^28–31, we also found that mTOR inhibition (Figures 2K and 2L) and knockdown (Figures 2M and 2N) did not significantly alter plasma membrane cholesterol levels. Together, these results indicate that although mTORC1/2 inhibition decreases enzymes involved in cholesterol synthesis and uptake, cells increase cellular triglyceride levels and maintain free cholesterol levels.

mTORC1/2 inhibition increases lipid uptake.

To determine how cells increase triglycerides following mTORC1/2 inhibition, we compared quantitative proteomic and transcriptomic data, which encompass approximately 8,000 proteins and 15,000 RNAs, respectively. Among about 8,000 proteins quantified, we analyzed around 7,100 proteins, excluding those quantified by only one peptide, to minimize errors in data analysis. Approximately 4% of proteins do not have corresponding mRNAs (Figure S3A), suggesting that these proteins may originate from an external source, such as serum used in cell culture. We next examined proteins increased by treatment with Torin1 or RapaLink-1 (Log_1.5(fold change) > 1, p < 0.05) and found that 6 proteins do not have corresponding mRNAs (Figure 3A). Among them, we were particularly interested in apolipoprotein B (ApoB) due to its role in cellular lipid uptake. In the liver, lipids, such as triglycerides and cholesterol, are combined with apolipoproteins, such as ApoB, forming lipoproteins, which are then secreted into the bloodstream ³². ApoB, a component of lipoproteins such as low-density lipoproteins (LDL), functions as a lipid carrier protein and acts as a ligand that binds to cellular lipid receptors, such as LDLR. ApoB is produced exclusively in the liver and, to a lesser extent, in the small intestine ^32,33 (Figure S3B). Consistent with this, our quantitative RNA analysis showed that APOB mRNA was detected only in liver cell lines, but not in melanoma cells (Figure S3C). Thus, our results suggest that increased cellular ApoB proteins originate from serum. Interestingly, quantitative proteomics analysis showed that ApoB levels were significantly increased by mTORC1/2 inhibition (Figure 3B). This increased ApoB protein following mTOR inhibition and mTOR knockdown was confirmed by confocal microscopy (Figures 3C and 3D).

Figure 3. — Where applicable, data are the mean ± SD of at least three independent determinations. Statistical significance (*p < 0.05) was assessed by a t-test. **(A)** A375 cells were treated with Torin1 (200 nM) and RapaLink-1 (10 nM) for 24 hours. RNA sequencing and quantitative mass spectrometry were performed for complete RNA and protein profiling, respectively. The Venn Diagram compares RNAs from transcriptome data and elevated proteins by both Torin1 and RapaLink-1 treatment obtained from proteome data. **(B)** The heatmap shows changes in ApoB protein levels following mTOR inhibition. **(C-D)** ApoB in (C) A375 cells treated with rapamycin (20 ng/ml), Torin1 (200 nM), and RapaLink-1 (10 nM) for 24 hours, and (D) stable control or mTOR knockdown A375 cells were visualized by confocal microscopy. Scale bar, 20 μm. **(E-H)** A375 cells grown in the presence of regular serum, lipid-depleted (LD) serum, and LDL-supplemented lipid-depleted serum were treated with mTOR inhibitors for 24 hours. (E) Lipid droplets were visualized by confocal microscopy (scale bar, 20 μm), and (F) quantitative analysis of staining was performed. (G-H) Cells were lysed, and immunoblot analysis was performed. See also Figure S3.

Since mTORC1/2 inhibition decreased LDLR (Figures 1F–1H), we suspected that cells might adopt an alternative LDL uptake system. We found that while lipid depletion reduced lipid droplet formation, mTORC1/2 inhibition increased lipid droplets in cells growing in the presence of fetal bovine serum (FBS) and in cells supplemented with LDL (Figures 3E and 3F). This suggests that cells maintain an active system for LDL uptake. We also found that the addition of LDL increased PLIN2 levels, and mTORC1/2 inhibition further increases its level (Figure 3G). In addition, dual mTORC1/2 inhibition significantly increased or restored IGF1R and AKT phosphorylation in cells growing in the presence of FBS and in cells supplemented with LDL, compared to the cells growing in lipid-depleted serum (Figure 3H), indicating that components of LDL, such as cholesterol and/or triglycerides, are essential for the activation of this signaling following mTOR inhibition. However, phosphorylation of S6K1 and ERK1/2, which does not require plasma membrane for their activation, was not significantly regulated by lipids. As anticipated, the phosphorylation of S6K1, a substrate of mTORC1, was completely inhibited by all mTOR inhibitors (Figure 3H).

mTORC1/2 inhibition increases lipid uptake through LDLR-related protein (LRP) 6.

Our quantitative proteomics revealed that cells express LDLR-related proteins (LRPs), and mTORC1/2 inhibition induced a slight but statistically significant (p<0.05) increase in LRP6 in cells growing in complete media (Figure S4A), which was confirmed by immunoblot analysis (Figures 4A, 4B, S4B, and S4C). However, LRP1 levels were not changed significantly (Figures 4A–4B). An increase in LRP6 expression following mTORC1/2 inhibition was also observed under serum-, glucose-, and amino acid-deprived conditions (Figures S4D–S4F), although glucose deprivation significantly reduced overall LRP6 levels. We next wondered whether the LRP6 increase following mTORC1/2 inhibition is due to either mTORC1 or mTORC2, or both. Using the knockdown of Raptor (a major mTORC1 component) and Rictor (a major mTORC2 component), we found that Raptor knockdown significantly increased LRP6 expression to a level comparable to Torin1 treatment (Figure 4C). Rictor knockdown led to a modest increase in LRP6 expression (Figure 4D).

To investigate LRP6’s role in lipid uptake under mTOR inhibition, we used LRP6 knockdown cells (Figure 4E), which showed reduced lipid droplet formation following mTOR inhibition (Figures 4F and 4G). LRP6 knockout also decreased lipid droplets (Figures S4G and S4H). Using LRP6 knockdown cells with and without LDLR knockout (Figure 4H), we then examined ApoB levels and lipid droplet formation. Dual mTORC1/2 inhibition increased ApoB levels and lipid droplet accumulation (Figures 4I–4K). While LDLR knockout partially attenuated these effects, LRP6 knockdown resulted in a more pronounced decrease (Figures 4I–4K), indicating that both LDLR and LRP6 play important roles in these processes, but LRP6 is more critical when mTOR is inhibited.

We next investigated the mechanisms underlying LRP6 protein increase following mTOR inhibition. mTORC1/2 inhibition did not significantly (fold change >2, p<0.05) increase LRP6 mRNA levels (Figure 4L). In addition to a modest increase in mRNA levels, we suspected the possibility of selective mRNA translation. Our previous report demonstrated that mTORC1/2 inhibition promotes selective mRNA translation through eukaryotic initiation factor 3D (eIF3D) ¹⁵. Knockdown of eIF3D regulated LRP6 protein levels in cells growing in complete media (Figure 4M). We next examined LRP6 protein level changes by eIF3D under specific conditions. As shown in Figures S4I–S4K, mTORC1/2 inhibition increased LRP6 levels under all conditions, although glucose deprivation significantly reduced LRP6 expression. eIF3D knockdown also suppressed LRP6 expression, except under glucose-deprived conditions (Figures S4I–S4K). These findings suggest that, except under specific conditions such as glucose-deprived conditions, which significantly reduce LRP6 proteins, mTORC1/2 inhibition increases LRP6 proteins through eIF3D. To investigate the interaction between the eIF3D protein and LRP6 mRNA, we performed RNA immunoprecipitation (RIP). As shown in Figure 4N, mTORC1/2 inhibition significantly increased their interaction, suggesting that eIF3D mediates LRP6 translation following mTOR inhibition. We also found that Raptor knockdown further increases the interaction of eIF3D protein and LRP6 mRNA (Figure 4O), indicating that mTORC1 inhibition contributes to eIF3D-mediated LRP6 upregulation following mTORC1/2 inhibition. As the increase in LRP6 protein is mediated by eIF3D, we examined whether eIF3D knockdown reduced LRP6 function and found that it decreased intracellular ApoB levels (Figure S4L).

LRP6 is critical for IGF1R/AKT activation and cell survival/proliferation following mTOR inhibition.

We found that eIF3D knockdown reduced AKT phosphorylation (Figure S5A). Subsequent experiments revealed that LRP6 knockdown and knockout significantly reduced the phosphorylation of IGF1R and AKT (Figures 5A, 5B, S5B, and S5C). Consistent with the specific role of LRP6, knockdown or knockout of LDLR or LRP1 did not inhibit IGF1R and AKT activation following mTORC1/2 inhibition (Figures 5C, 5D, and S5D).

Figure 5. — Where applicable, data are the mean ± SD of at least three independent determinations. Statistical significance (*p < 0.05) was assessed by a t-test. **(A-D)** A375 cells stably expressing (A-B) LRP6 shRNAs, (C) LDLR shRNAs, or (D) LRP1 shRNAs were treated with mTOR inhibitors for 24 hours, and immunoblot analysis was performed. **(E-F)** Clonogenic assays were performed using stable control or LRP6 knockdown A375 cells treated with Torin1 (100 nM) or RapaLink-1 (5 nM) with fresh media replenished every two days for 10 days. (E) Representative images of colony formation. (F) Quantification of relative colony intensity. **(G-K)** Mice were injected with A375-Luc2 cells stably expressing control or LRP6 shRNAs and treated with or without RapaLink-1 as described in Materials and Methods. Each group had 6 tumor sites. (G) Images show three representative tumors from each group. (H) The bioluminescent intensity of xenograft tumors and (I) mouse weight were measured. (J) Mouse tumor tissue samples were analyzed using hematoxylin and eosin (H & E) staining and immunohistochemistry for Ki67. Scale bar, 100 μm. (K) The percentage of Ki67-positive cells was calculated. See also Figure S5.

As LRP6 is critical for cell resistance signaling following mTOR inhibition, we examined whether their combined targeting could inhibit tumor survival/growth/proliferation. In clonogenic assays, mTORC1/2 inhibition or LRP6 knockdown alone reduced cell proliferation, and their combination led to a more pronounced reduction in colony formation (Figures 5E and 5F), suggesting significant inhibition of survival, growth, and proliferation. In vivo study using homozygous Foxn1^nu mice, RapaLink-1 or LRP6 knockdown dramatically suppressed tumor growth, and their combination exhibited an even more pronounced inhibitory effect (Figures 5G and 5H). RapaLink-1 treatment also reduced mouse body weight (Figure 5I), a known common on-target side effect of this inhibitor and rapamycin ^16,17,34. Tumor tissue staining for Ki67, a tumor proliferation index, showed that mTORC1/2 inhibition and/or LRP6 knockdown decreases tumor proliferation (Figures 5J and 5K).

mTORC1/2 inhibition promotes cholesterol transport through NPC1.

After binding to receptors, components of lipoproteins, such as cholesterol esters and triglycerides, are endocytosed, progressed to the lysosomes, hydrolyzed in the lysosomes, and released to the cytosol as free unesterified cholesterol and fatty acids. Cholesterol then relocates to the plasma membrane and ER/lipid droplets ^35–38. As mTOR inhibition maintains free cholesterol in the plasma membrane and increases lipid droplets, we suspected that mTOR inhibition might promote intracellular cholesterol trafficking. Lipid depletion decreased free cholesterol staining, whereas LDL supplementation increased free cholesterol in the plasma membrane and late endosome/lysosome (Figures 6A and 6B) and increased lipid droplets (Figures 3E and 3F) even though mTOR is inhibited, indicating active lipid trafficking.

Figure 6. — Where applicable, data are the mean ± SD of at least three independent determinations. Statistical significance (*p < 0.05) was assessed by a t-test. **(A-B)** A375 cells grown in the presence of regular serum, lipid-depleted (LD) serum, and LDL-supplemented lipid-depleted serum were treated with rapamycin (20 ng/ml), Torin1 (200 nM), and RapaLink-1 (10 nM) for 24 hours. (A) Unesterified cholesterol stained with filipin was visualized by confocal microscopy, and (B) quantitative analysis of staining was performed. Scale bar, 20 μm. **(C)** Cells were treated with mTOR inhibitors for 24 hours, and immunoblot analysis was performed. **(D)** Stable control or mTOR knockdown cells were lysed, and immunoblot analysis was performed. **(E)** A375 cells stably expressing control or NPC1 shRNAs were lysed, and immunoblot analysis was performed. **(F-G)** Stable A375 control or NPC1 knockdown cells were treated with/without Torin1 for 24 hours. (F) Unesterified cholesterol stained with filipin was visualized by confocal microscopy, and (G) quantitative analysis of staining was performed. Scale bar, 20 μm. **(H-I)** A375 cells were treated with Torin1 or RapaLink-1 for 6 hours, after which U18666A (5 μg/ml), an NPC1 inhibitor, was treated for an additional 18 hours. (H) Unesterified cholesterol stained with filipin was visualized by confocal microscopy, and (I) quantitative analysis of staining was performed. Scale bar, 20 μm. **(J-K)** Stable control or NPC1 knockdown A375 cells were treated with/without Torin1 for 24 hours. (J) Cholesterol in the outer plasma membrane was stained with mCherry-Perfringolysin D4H and visualized by confocal microscopy. Scale bar, 20 μm. (K) Quantitative analysis of staining was performed. **(L-M)** A375 cells were treated with Torin1 or RapaLink-1 for 6 hours, followed by U18666A (5 μg/ml) for an additional 18 hours. (L) Cholesterol stained with mCherry-Perfringolysin D4H was visualized by confocal microscopy, and (M) quantitative analysis of staining was performed. Scale bar, 20 μm. See also Figure S6.

A lysosomal protein, NPC Intracellular Cholesterol Transporter 1 (NPC1, Niemann-Pick disease type C1), releases free cholesterol from lysosomes. Thus, increased expression of NPC1 promotes the movement of cholesterol to the plasma membrane and ER/lipid droplets ^37,38. NPC1 is one of a few lipid-related proteins with increased expression following mTORC1/2 inhibition in our quantitative proteomics data (Figure S6A), which was confirmed by immunoblot analysis (Figures 6C and 6D). We used filipin in NPC1 knockdown cells (Figure 6E) to visualize free cholesterol. While NPC1 knockdown or knockout increased lysosomal cholesterol levels dramatically, it reduced plasma membrane cholesterol (Figures 6F, 6G, S6B, and S6C), which was also confirmed with an NPC1 inhibitor (Figures 6H, 6I, S6D, and S6E), U18666A ^39,40. We also used purified mCherry-perfringolysin D4H to visualize cholesterol in the outer leaflet of the plasma membrane. NPC1 inhibition by NPC1 knockdown (Figures 6J and 6K), NPC1 knockout (Figures S6F and S6G), and NPC1 inhibitor (Figures 6L and 6M) reduced cholesterol in the plasma membrane. These results suggest that mTORC1/2 inhibition maintains plasma membrane cholesterol levels by regulating NPC1.

mTOR inhibition increases NPC1 transcriptionally by liver X receptor (LXR)β.

We examined the mRNA expressions of NPC1 and found that mTORC1/2 inhibition significantly increased their mRNA levels (fold change>2, p<0.05) (Figure 7A). Since NPC1 is involved in lipid metabolism, localization, and transport, our pathway and process enrichment analysis of proteomic and transcriptomic data focused on lipid metabolic process and regulation of lipid transport/localization. We identified the liver X receptor (LXR)β, a transcription factor, as the main regulator of NPC1 gene expression. LXR plays a critical role in regulating the expression of specific genes involved in cholesterol trafficking and lipid metabolism ^41–43. LXRβ protein levels were increased following mTOR inhibition by mTORC1/2 inhibitors (Figure 7B) and mTOR knockdown (Figure 7C), without changes in nuclear localization (Figures S7A and S7B). Pharmacological LXR inhibition decreased the mTOR inhibition-mediated increase in NPC1 (Figure 7D). Since mTORC1/2 inhibition increases LXRβ and NPC1 protein levels, we investigated whether this is from inhibition of mTORC1, mTORC2, or both. As shown in Figure S7C, Raptor knockdown increased LXRβ and NPC1 levels. Rictor knockdown also increased their levels, but to a lesser extent than Raptor knockdown (Figure S7D). This suggests that mTORC1/2 inhibition increases NPC1 and LXRβ through the inhibition of both mTORC1 and mTORC2, with mTORC1 inhibition playing a more dominant role in their regulation.

We found that mTOR inhibition profoundly increased PLIN2 (Figures 2E and 2F). We wondered whether LXRβ mediates this increase. mTORC1/2 inhibition increased PLIN2 mRNA levels (Figure 7E), and LXR inhibition reduced PLIN2 expression increased by mTORC1/2 inhibition (Figure 7F). LXR also reduces LDLR protein levels by promoting its degradation ^42,44. LXR inhibition mitigated mTOR inhibition-mediated LDLR reduction to some extent (Figure 7G), suggesting that mTOR inhibition decreases LDLR protein levels through SREBP suppression and LXRβ activation. These results suggest that LXR regulates key molecules involved in cholesterol uptake and trafficking when mTOR is inhibited. In clonogenic assays, stable LXRβ knockdown combined with mTORC1/2 inhibition profoundly reduced colony formation (Figures S7E– S7G).

We next investigated the regulation of LXRβ expression. mTORC1/2 inhibition increased LXRβ mRNA levels (Figure 7H), suggesting transcriptional regulation. LRP6 is a major regulator of β-catenin ^45,46, a transcription factor that controls lipid homeostasis in part by regulating lipid droplet formation and the LXR pathway ^47–50. Thus, we investigated whether β-catenin regulates LXRβ. As shown in Figures 7I–7J, inhibition of β-catenin by inhibitors (Figure 7I) and knockdown (Figure 7J) decreased expression of LXRβ and its downstream target, NPC1. In addition, mTOR inhibition increased LXRβ activity (Figure 7K), and inhibition of β-catenin suppressed LXRβ activity under this condition (Figure 7K). These findings suggest that β-catenin is a key regulator of LXRβ under mTOR-inhibited conditions.

The combination of mTORC1/2 and NPC1 inhibition suppresses tumor growth.

We assessed whether NPC1 is crucial for IGF1R and AKT activation following mTOR inhibition. NPC1 knockdown (Figures 7L and 7M) and knockout (Figures S7H and S7I) significantly reduced IGF1R and AKT phosphorylation under mTORC1/2-inhibited conditions. In clonogenic assays, combined NPC1 knockdown and mTORC1/2 inhibition significantly reduced colony formation (Figures 7N and 7O). We also assessed the in vivo effects of mTORC1/2 and NPC1 inhibition using homozygous Foxn1^nu mice. Using the NPC1 inhibitor, U18666A, we first confirmed that NPC1 inhibition blocked AKT phosphorylation induced by mTOR inhibition in vitro (Figure S7J). In mice, RapaLink-1 or U18666A alone significantly suppressed tumor growth, and combined treatment with RapaLink-1 and U18666A at half doses compared to single treatments showed an even more substantial reduction in tumor growth (Figures 7P and 7Q). The reduced dose of RapaLink-1 was sufficient to increase NPC1 expression in tumor tissues (Figures S7K and S7L). Although RapaLink-1 treatment reduced mouse body weight ^16,17,34, U18666A did not cause noticeable side effects, including no significant weight loss or gain (Figure 7R). Tumor tissue staining with Ki67 showed that inhibition of mTORC1/2 and/or NPC1 decreases tumor proliferation (Figures S7M and S7N). In summary, our results suggest that mTOR inhibition-mediated promotion of lipid uptake and cholesterol transport enables cancer cells to activate tumor growth and survival signaling under stress conditions induced by mTOR inhibition (Figure 7S).

Discussion

We found that cancer cells increase LRP6 in response to mTOR inhibition, thereby increasing lipid uptake and activating cell survival signaling. LRP6, a co-receptor with LRP5 for Wnt ligands, also initiates Wnt signaling ⁵¹. Thus, dysregulation of LRP6 is linked to Wnt signaling-associated diseases such as cancer, neurodegeneration, and metabolic syndrome ^51–53. The relationship between Wnt signaling and mTORC1 is complex. Inhibition of mTORC1 by rapamycin inhibits ⁵⁴ or activates ⁵⁵ Wnt signaling. In melanocytes and melanoma, loss of TSC, a negative regulator of mTORC1, suppresses Wnt signaling, and thus, rapamycin activates Wnt signaling-mediated melanogenesis ⁵⁶. Little is known about the relationship between mTORC1/2 targeting and Wnt signaling. In our phospho-proteomics data, we found that mTORC1/2 inhibition increases LRP6 phosphorylation at S1490, which is known to activate Wnt signaling ^51,57. In addition to this plasma membrane LRP6, we showed that the activity of GSK3, the key regulator of Wnt signaling in the cytoplasmic segment, was also inhibited by mTORC1/2 inhibition ²⁰. In regard to Wnt signaling in lipid metabolism, Wnt stimulates endosomal flux of LDL-derived cholesterol, trafficking of cholesterol, and lipid droplet homeostasis ^48,58. It will be interesting to study if cancer cells modulate LRP6 function and Wnt signaling components in response to dual mTORC1/2 inhibition and the functional outcomes in cellular lipid homeostasis and tumor resistance.

Heritable mutations in the NPC1 gene impair cholesterol release, thereby causing massive cholesterol accumulation within the late endosome/lysosome, which causes metabolic diseases and premature death ^40,59. Increased expression of NPC1 promotes the trafficking of LDL cholesterol to the plasma membrane and ER/lipid droplets ^37,38. We found that NPC1 expression was regulated by LXRβ, which was increased following mTOR inhibition. SREBPs and LXRs are key transcription factors in lipid metabolism ⁴². SREBPs promote cholesterol biosynthesis and LDLR expression, whereas LXRs inhibit these processes ^42,44. Our findings indicate that mTORC1/2 inhibition decreases SREBPs but increases LXRβ, which induces NPC1 expression. Similar to our findings, metabolites such as desmosterol, an intermediate in the cholesterol metabolic pathway, activate LXR but suppress SREBP activity ⁶⁰. Additional studies on the interplay between mTOR, LXR, and SREBP will reveal the mechanisms by which mTOR contributes to cellular lipid homeostasis and its potential as a therapeutic target in lipid metabolism.

NPC1 affects mTORC1 activity positively ⁶¹ or negatively ^62–64. In the case of negative regulation, NPC1 null cells induce hyperactive mTORC1, and their lysosomes show an accumulation of cholesterol and proteolytic and structural defects ⁶³. In these cells, mTORC1/2 inhibition restores the integrity and proteolytic function of lysosomes without altering cholesterol accumulation ⁶⁴. This suggests the critical functions of NPC1 in cholesterol release from lysosomes and highlights NPC1/mTOR-mediated lysosome function modulation. Recently, it was shown that Wnt5a, a Wnt signaling ligand, directly interacts with lysosomal NPC1, which downregulates mTORC1 activity and promotes cholesterol release from lysosomes ⁶⁵. Our study suggests that mTORC1/2 inhibition promotes cholesterol release from lysosomes by increasing NPC1 protein levels. These findings collectively highlight the intricate connections among mTOR, NPC1, and cholesterol transport mechanisms.

As lipids are essential for membrane integrity and function, energy, hormones, and signaling, cancer cells reprogram the lipid homeostasis system to accommodate their needs for sustained proliferation, survival, and metastasis. Especially under stress conditions such as hypoxia, acidosis, low levels of nutrients, and anti-cancer drug treatment, dynamic reprogramming of lipid metabolism is essential for cancer cells to survive and escape stress ⁶⁶. For example, under hypoxic conditions, cells increase LDL uptake and endocytosis, lipid droplets, and lipid accumulation by regulating hypoxia-inducible factors (HIFs), which enables cancer cells to maintain endoplasmic reticulum homeostasis and increase survival and aggressive behavior ^67–69. Therefore, understanding reprogramming mechanisms provides therapeutic potential with a synthetic lethal approach to block tumors’ resistance to stress, including therapy-induced stress. Our study suggests that cells reprogram their lipid homeostasis by orchestrating alternative lipid uptake and promoting cholesterol transport. We found that the levels of LRP6 are increased by eIF3D in response to mTOR inhibition. Little is known about the eIF3D binding motif in mRNAs. Similar to eIF4E, eIF3D has been found to directly bind to the 5′ cap of mRNAs ^70,71. We showed that while mTOR inhibition suppresses eIF4E-dependent mRNA translation, it activates eIF3D-dependent selective mRNA translation ¹⁵. Our findings were supported by other reports showing that while mTORC1/eIF4E preferentially promotes the translation of mRNAs with 5’ TOP (terminal oligopyrimidine: cytidine residue followed by a stretch of 5-15 pyrimidine nucleotides) motifs, eIF3D represses their translation ⁷². Also, eIF4E-independent translation is largely dependent on eIF3D, which binds mRNAs with longer 5’ UTR (untranslated region) ⁷³. Most 5’ UTRs typically have around 50 ~ 200 nucleotides (e.g., NPC1: 163 and PLIN2: 96 nucleotides), but LRP6 has a longer 5’ UTR (309 nucleotides). Our results suggest that selective mRNA translation is essential for alternative lipid uptake when mTORC1/2 is inhibited. Currently, small-molecule inhibitors of eIF3D or LRP6 are not available. Therefore, we used LRP6 knockdown and NPC1 inhibitor to block lipid uptake and trafficking. Our results suggest that targeting mTOR, in combination with restricting the supply of lipoproteins, inhibiting the uptake of lipoproteins, or inhibiting cholesterol release/trafficking in cancer cells, may enhance the efficacy of mTOR targeting. The liver is the main organ that synthesizes most lipoproteins and more than 80% of cholesterol. Since lipids, including cholesterol, are essential for cancer progression and aggressiveness, statins, which inhibit the synthesis of cholesterol and lipoproteins in the liver, suppress cancer progression and reduce cancer-related mortality ^74,75. In addition to NPC1 inhibitors, statins in combination with dual mTORC1/2 inhibitors may further increase the drug efficacy of mTORC1/2 inhibitors, as statins lower plasma LDL-cholesterol that is available for uptake by cancer cells for their survival and metastasis. It will be important to develop and/or use drugs targeting lipid uptake/trafficking/metabolism and determine which combinatorial treatment with mTORC1/2 inhibitors gives higher efficacy and lower side effects.

In summary, we explored the impact of mTOR inhibition on lipid homeostasis in tumor cells. Our findings highlight the adaptive responses of cancer cells to mTORC1/2 inhibition and provide insights into the interplay between mTOR signaling and lipid uptake/metabolism in cancer. We believe our detailed mechanistic studies on this interaction provide opportunities for developing more effective therapeutic strategies targeting lipid metabolism in cancer.

Limitations of the study

Although our data indicate that RAF1 contributes to AKT phosphorylation, it remains unclear whether this regulation is direct or indirect. Further studies are needed to elucidate the precise molecular mechanisms underlying this regulation.

Cellular LDLR binds and internalizes ApoB-containing LDL, which mediates the clearance of LDL from the blood ⁷⁶. Therefore, loss-of-function mutations in LDLR are a major cause of familial hypercholesterolemia, which induces atherosclerosis and a higher risk of cardiovascular disease, suggesting the importance of LDLR in regulating lipid levels in blood ^77,78. LRP6 regulates cholesterol and triglyceride metabolism by binding to ApoB, modulating LDLR function, and facilitating the uptake of lipoproteins, particularly LDL cholesterol ^79,80. LRP6 mutation is found in patients with coronary artery disease, which is linked to hyperlipidemia with increased plasma cholesterol/triglyceride and cardiovascular disease ^76,80,81. These suggest that both LDLR and LRP6 are critical for lipid homeostasis. However, little is known about the relationship between LDLR and LRP6, whether they have distinct or cooperative functions in mediating LDL cholesterol regulation, whether specific tissues or cells have a preference for LDLR, LRP6, or other LRPs to obtain LDL, whether cells change their LDL uptake systems in response to stress, and whether affinity or uptake efficiency of LDLR and LRP6 changes under different metabolic or therapeutic conditions. LDLR also binds to ApoE. While ApoB transports LDL, VLDL (very-low-density lipoproteins), and IDL (intermediate-density lipoproteins), ApoE transports chylomicrons, VLDL, IDL, and some HDL (high-density lipoprotein)³². It remains unclear whether LRP6 can mediate the uptake of ApoE-containing lipoproteins. Unlike ApoB, which is primarily expressed in the liver, ApoE is produced in multiple tissues and organs, including the liver, adipose tissues, and brain. Therefore, depending on the tumor microenvironment, such as anatomical location, lipid availability, and surrounding cell types, tumors may access different sources of lipoproteins. Therefore, further studies are needed to assess the roles of LRP6 and LDLR in mediating the uptake of specific versus diverse classes of lipoproteins, including those containing ApoE. A better understanding of LRP6’s selectivity and function will help identify tumor-specific lipid dependencies and support the development of more effective targeting strategies in combination with mTOR inhibition.

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Sang-Oh Yoon (syoon1@uic.edu).

Materials availability

Materials generated in this study are available from the lead contact upon request.

Data and code availability

The proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD049052 and 10.6019/PXD049052. RNA-seq data have been deposited to GEO with accession number GSE304701. The lipidomics data have been deposited in the MetaboLights repository with the study identifier MTBLS12837. Original images have been deposited at Mendeley (http://doi.org/10.17632/v7gdh9prbj.1). All deposited datasets will be publicly available upon publication.
This paper does not report any original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

STAR METHODS

Experimental models and study participant details

Cell lines

The cell lines, A375, A375-Luc2, MDA-MB-435, LN229, and A172, were obtained from ATCC. HEK293T and liver cancer cell lines (Huh7 and HepG2) were generously provided by Dr. Andrew L. Kung (Memorial Sloan Kettering Cancer Center, NY) and Dr. Alan McLachlan (University of Illinois, IL). ELT3 and RT4 cells were kindly provided by Dr. Jane Yu (University of Cincinnati, OH). ELT3 and RT4 cells were grown in DMEM/F12 media supplemented with 10% FBS, 50 nM sodium selenite, 25 μg/mL insulin, 200 nM hydrocortisone, 10 μg/mL transferrin, 1 nM triiodothyronine, 10 μU/mL vasopressin, 10 nM cholesterol, 1.6 μM ferrous sulfate, and 20 ng/mL EGF. Other cell lines were maintained in DMEM containing 10% FBS at 37°C with 5% CO₂.

Mice

5-7-week-old female nude mice (Homozygous Foxn1^nu, The Jackson Laboratory) were used for the cancer xenograft model. Animal studies were conducted in accordance with Institutional Animal Care and Use Committee (IACUC)-approved protocols and institutional guidelines.

METHOD DETAILS

Immunoblot analysis

Immunoblot analysis was performed as described previously ⁸². Cells were extracted in lysis buffer containing Triton X-100 (25 mM HEPES (pH 7.4), 150 mM NaCl, 1% Triton X-100, 5 mM EDTA, 1 mM EGTA, 10 mM β-glycerophosphate, proteases inhibitor cocktail, and phosphatase inhibitor cocktail), and samples were resuspended in SDS sample buffer (Thermo Fisher Scientific). Samples were boiled and electrophoresed by SDS-PAGE. Proteins were then transferred to nitrocellulose membranes (Whatman). The membranes were blocked with TBST (25 mM Tris-HCl, pH 7.5, 150 mM NaCl, 0.05% Tween 20) containing 5% nonfat dried milk or 5% BSA and probed overnight with primary antibodies, followed by 2 h incubation with secondary antibodies coupled to peroxidase. Blots were developed by using enhanced chemiluminescence.

RT-qPCR analysis

RT-qPCR analysis was performed as described previously ⁸². Total RNA was isolated using the RNeasy mini kit (Qiagen), and the first-strand cDNA was prepared using the Superscript III first-strand synthesis supermix kit (Invitrogen) according to the manufacturer’s protocol. qPCR was performed using QuantiTech SYBR green PCR kit (Qiagen) on ABI ViiA7 real-time PCR systems (Life Technologies). Pre-designed KiCqStart SYBR Green Primers were purchased from Sigma, and melting curve analysis was performed at the end of PCR.

Tandem mass tag (TMT) mass spectrometry

- Sample preparation for tandem mass tag (TMT) mass spectrometry

Samples for protein analysis were prepared as previously described ^83,84. Following lysis, protein precipitation, reduction/alkylation, and digestion, peptides were quantified by micro-BCA assay, and 300μg of peptide per sample were labeled with TMTPro reagents (Thermo Fisher Scientific) for 2 hours at room temperature. Labeling reactions were quenched with 0.5% hydroxylamine and acidified with TFA. Acidified peptides were combined and desalted by Sep-Pak (Waters).

- Basic pH reversed-phase separation (BPRP) of TMT-labeled peptides

TMT-labeled peptides were solubilized in 5% ACN/10 mM ammonium bicarbonate, pH 8.0, and 300 μg of TMT-labeled peptides were separated by an Agilent 300 Extend C18 column (3.5 μm particles, 4.6 mm ID and 250 mm in length). An Agilent 1260 binary pump coupled with a photodiode array (PDA) detector (Thermo Fisher Scientific) was used to separate the peptides. A 45-minute linear gradient from 10% to 40% acetonitrile in 10 mM ammonium bicarbonate pH 8.0 (flow rate of 0.6 mL/min) separated the peptide mixtures into a total of 96 fractions (36 seconds). A total of 96 Fractions were consolidated into 24 samples in a checkerboard fashion, acidified with 20 μL of 10% formic acid, and vacuum dried to completion. Each sample was desalted via Stage Tips and re-dissolved in 5% FA/ 5% ACN for LC-MS3 analysis.

- Liquid chromatography separation and tandem mass spectrometry (LC-MS3)

Proteome data were collected on an Orbitrap Eclipse mass spectrometer (ThermoFisher Scientific) coupled to a Proxeon EASY-nLC 1200 LC pump (ThermoFisher Scientific). Fractionated peptides were separated using a 120 min gradient at 500 nL/min on a 35 cm column (i.d. 100 μm, Accucore, 2.6 μm, 150 Å) packed in-house. High-field asymmetric-waveform ion mobility spectrometry (FAIMS) was enabled during data acquisition with compensation voltages set as −40, −60, and −80 V ⁸⁵. MS1 data were collected in the Orbitrap (60,000 resolution; maximum injection time 50 ms; AGC 4 × 105). Charge states between 2 and 5 were required for MS2 analysis, and a 120 s dynamic exclusion window was used. Cycle time was set at 1.2 s. MS2 scans were performed in the ion trap with CID fragmentation (isolation window 0.5 Da; Turbo; NCE 35%; maximum injection time 35 ms; AGC 1 × 104). An online real-time search algorithm (Orbiter) was used to trigger MS3 scans for quantification ⁸⁶. MS3 scans were collected in the Orbitrap using a resolution of 50,000, NCE of 45%, maximum injection time of 150 ms, and AGC of 1.5 × 105. The close-out was set at two peptides per protein per fraction ⁸⁶.

Phosphorylated peptides were separated using a 120-minute gradient with FAIMS enabled during data acquisition with compensation voltages set as −40, −60, and −80 V for the first shot and −45 and −65 V for the second shot ⁸⁵. MS1 data were collected in the Orbitrap (120,000 resolution; maximum injection time 50 ms; AGC 4 × 105). Charge states between 2 and 5 were required for MS2 analysis, and a 90 s dynamic exclusion window was used. Cycle time was set at 1.25 s. MS2 scans were performed in the Orbitrap with HCD fragmentation (isolation window 0.5 Da; 50,000 resolution; NCE 36%; maximum injection time 250 ms; AGC 1.5 × 105).

-TMT data analysis

Raw files were converted to mzXML, and monoisotopic peaks were re-assigned using Monocle ⁸⁷. Searches were performed using the Comet search algorithm against a human database downloaded from Uniprot in May 2021. We used a 50 ppm precursor ion tolerance and 0.9 Da product ion tolerance for MS2 scans collected in the ion trap and 0.02 Da product ion tolerance for MS2 scans collected in the Orbitrap. TMTpro on lysine residues and peptide N-termini (+304.2071 Da) and carbamidomethylation of cysteine residues (+57.0215 Da) were set as static modifications, while oxidation of methionine residues (+15.9949 Da) was set as a variable modification. For phosphorylated peptide analysis, +79.9663 Da was set as a variable modification on serine, threonine, and tyrosine residues. Each run was filtered separately to 1% False Discovery Rate (FDR) on the peptide-spectrum match (PSM) level. Then, the proteins were filtered to the target 1% FDR level across the entire combined data set. For reporter ion quantification, a 0.003 Da window around the theoretical m/z of each reporter ion was scanned, and the most intense m/z was used. Reporter ion intensities were adjusted to correct for isotopic impurities of the different TMTpro reagents according to manufacturer specifications. Peptides were filtered to include only those with a summed signal-to-noise (SN) ≥ 100 across all TMT channels. An extra filter of an isolation specificity (“isolation purity”) of at least 0.5 in the MS1 isolation window was applied for the phosphorylated peptide analysis. For each protein or phosphorylation site, the filtered peptide TMTpro SN values were summed to generate protein or phosphorylation site quantification values. To control different total protein loading within a TMTpro experiment, the summed protein quantities of each channel were adjusted to be equal within the experiment.

Untargeted lipid profiling

- Sample preparation

Cell samples were spiked with an internal lipid standard mix, and ice-cold methanol and chloroform in a 1:2 v/v ratio were added directly to the samples. After sonication for 5 minutes, the samples were incubated on ice for 30 minutes and vortexed occasionally. Ice-cold water was added for a final ratio of chloroform: methanol: water of 8:4:3 (v/v/v), and the samples were incubated on ice for an additional 10 minutes. Following centrifugation at 10,000 rpm for 5 minutes at 4 °C to separate the aqueous and organic layers, the lower organic phase was carefully transferred to a new tube. The remaining upper aqueous layer was re-extracted and combined with the organic phases. The organic layer was then dried down under nitrogen at 30 °C. The dried lipid extract was reconstituted with a 1:1 mixture of methanol and chloroform and transferred to an LC vial with a 200 μL glass insert. A QC sample was prepared by mixing an aliquot of all samples. The QC sample and the blanks were injected during the sample analysis.

- Instrumentation and data acquisition

MS analysis of the lipid extracts was performed using an Agilent 6545 Q-TOF LC-MS system controlled by the Agilent Mass Hunter acquisition software. The mass spectrometer was operated in a 2 GHz extended dynamic range mode employing precursor ion analysis for relative quantification experiments in positive ion mode. Internal references were used for calibration. Mobile phase A was 90:10 H₂O: methanol (v/v) with 10 mm ammonium acetate plus 0.5 mm ammonium fluoride, and B was (5:3:2) isopropanol: methanol: acetonitrile v/v/v with 10 mm ammonium acetate plus 0.5 mm ammonium fluoride. The resuspended lipid extracts were loaded onto a 2.1 × 100 mm Agilent Poroshell C18, 2.7 μm column (Agilent Technologies Inc., Santa Clara, CA, USA). Separation was performed using an Agilent 1290 UPLC system using the following gradient: 70% B at 0-1 min, 86% B at 3.5-10 min, and 100% B at 11-17 min at a flow rate of 300 μl/minute. A post-column equilibration time of 5 minutes was used for all runs. Source parameters were as follows: gas temperature (200 °C), drying gas (11 L/min), nebulizer (35 psi), sheath gas temperature (350 °C), sheath gas flow (12 L/min), VCap (3000 V), and fragmentor (145 V). Data was collected for relative quantification using a scan speed of 4 MS spectra per second. Pooled samples were used in lipid database creation, and an iterative MS/MS workflow was performed in the Mass Hunter acquisition software across multiple injections of the pooled samples with a scan speed of 10 MS and 3 MS/MS spectra per second.

- Data analysis

Raw LC-MS/MS data was used to create a fragmentation-based (MS/MS) library containing m/z precursors and retention times for all MS/MS identified lipids using the Lipid Annotator software (Agilent Technologies Inc., Santa Clara, CA, USA) and the following settings: lipid species for positive ([M + H]+, [M + Na]+, [M + NH4]+, [M + H - H2O]+, [M + Na - H2 O]+, [M + NH4 - H2O]+), Q-Score ≥ 60, and mass deviation ≤ 10 ppm. Raw LC-MS data files were processed using the Profinder software (vB.10.00, Agilent Technologies Inc., Santa Clara, CA, USA). Here, molecular features were extracted for peaks ≥ 5000 counts in the positive mode ([M + H]+ , [M + Na]+ , [M + NH4 ]+ , [M + H - H2 O]+, [M + Na - H2 O]+ , [M + NH4 - H2 O]+ ) using an isotope model of common organic molecules. The resulting compound list was further filtered for those having an absolute height of ≥10,000 counts, a quality score of ≥60, and compounds having two or more isotopes present. Additionally, the retention time for each compound was aligned to ±0.1 min using a mass accuracy window of ≤5.0 ppm and peaks integrated using the Agile integrator in the Profinder software. Each integrated peak was manually reviewed for retention time and fragmentation matching. The processed data file was then exported and imported into the Mass Profiler Professional software (v15.1, Agilent Technologies Inc., Santa Clara, CA, USA), where each data set was analyzed separately. All compound abundance values were baseline-corrected to the median abundance and normalized by the class of lipids in IS. If the class is unavailable in IS, it will use the closest IS by retention time.

Lentivirus production and generation of stable cells

Lentivirus and stable knockdown cells were generated as described previously ²⁰. Briefly, HEK293T cells were transfected with shRNA plasmids using Lipofectamine 2000 (Thermo Fisher Scientific) with the plasmids for lentiviral packaging and envelope, and the media were changed the following day. Around 30 hours after transfection, viral supernatants were collected, and new media were added. Additional viral supernatants were collected the next day. To generate cells stably expressing shRNAs, cells were infected with viral supernatants in the presence of a serum-containing media supplemented with 8 μg/ml polybrene. After 24 hours, the virus-containing media were replaced with fresh serum-containing media, and cells were grown for another day. Cells were then treated with puromycin (2 μg/ml) for selection. The knockdown efficiency was assessed by immunoblot analysis.

For gene knockout, target sequences were obtained from the Human GeCKO lentiviral sgRNA v2 library. The following sgRNA sequence was used: LDLR- GTGGCCCAGCGAAGATGCGA; LRP6-ATACGCCGTTCATTTATAGA; NPC1- CGTCAGCGTCCTTCCCACAC. Oligonucleotides were annealed and ligated into lentiCRISPR V2 (a gift from Drs. Feng Zhang and Brett Stringer), digested with BSMBI. The same method was used to generate lentiviruses for knockout, as described in the generation of knockdown cells. Lentiviruses for gene knockout were generated using the same method described for producing knockdown viruses.

Purification of mCherry-perfringolysin D4H

His-tagged mCherry-Perfringolysin D4H proteins were purified as previously described ³⁰. Briefly, the plasmids were transformed into the Escherichia coli strain BL21-DE3 (Thermo Fisher Scientific). E. coli growing with kanamycin (50 μg/ml) were treated with 0.1 mM isopropyl-D-thiogalactopyranoside (IPTG). After 18 hours at 18°C, E. coli was harvested and lysed by sonication. mCherry-Perfringolysin D4H proteins were purified with HisPur Cobalt spin columns (Thermo Fisher Scientific) according to the manufacturer’s protocol. Eluted proteins were dialyzed extensively with Slide-A-Lyzer dialysis cassettes in buffer (20 mM HEPES, 300 mM NaCl, 10% glycerol, and 0.5 mM TCEP, pH 7.5) and concentrated with a Protein Concentrator (Thermo Fisher Scientific).

Confocal microscopy

- Filipin staining

Cells were fixed with 4% paraformaldehyde for 10 minutes at room temperature, after which they were washed four times with PBS and incubated with filipin solution (50 μg filipin/ml) for 1.5-2 hours. After washing three times with PBS, the cells were incubated with RedDot2 far-red nuclear staining dye. After washing with PBS three times, cells were mounted with EverBrite mounting media (Biotium) and imaged using a Zeiss LSM 880 confocal microscope. Quantitative analysis of staining was performed using Fiji image processing software.

- mCherry-perfringolysin D4H staining

Cells were washed with warm serum-free DMEM media and incubated with purified mCherry-perfringolysin D4H in serum-free DMEM for 20 minutes at 37°C. Cells were washed with warm PBS and fixed with 4% paraformaldehyde for 10 min at room temperature. Cells were washed with PBS three times, mounted with EverBrite mounting media, and imaged immediately using a Zeiss LSM 880 confocal microscope. Quantitative analysis of staining was performed using Fiji image processing software.

- Lipid droplet staining

Lipid droplet staining was performed using LipidSpot Lipid Droplet Stains (Biotium) according to the manufacturer’s protocol. Briefly, cells were fixed using 4% paraformaldehyde for 10 minutes at room temperature and rinsed four times with PBS. The cells were then incubated with LipidSpot Lipid Droplet Stains for 15 minutes. After washing three times with PBS, the cells were incubated with DAPI. Cells were then washed three times with PBS, mounted with EverBrite mounting media (Biotium), and imaged immediately with a Zeiss LSM 880 confocal microscope. Quantitative analysis of staining was performed using Fiji image processing software.

- Lysosome/filipin staining

Cells were incubated with LysoBrite Red at 37°C for 30 minutes and then washed with warm PBS. After fixing with 4% paraformaldehyde, cells were washed with PBS three times. Filipin staining and subsequent imaging using a Zeiss LSM 880 confocal microscope were performed immediately.

- ApoB staining

Cells were fixed with 4% paraformaldehyde for 10 min at room temperature, then rinsed four times with PBS. Cells were permeabilized with 0.2% Triton X-100 in PBS for 5 min and rinsed four times with PBS. Cells were incubated with a blocking solution containing 3% BSA in PBS for 15 min, followed by incubation with primary antibodies in a blocking solution for 2 hr. After PBS washing three times, cells were incubated with a secondary antibody conjugated to a fluorochrome (Alexa Fluor, Thermo Fisher Scientific) in a blocking buffer for 1 hr. Cells were then rinsed with PBS four times and incubated with DAPI solution (DNA staining) for 15 minutes. Following PBS washing four times, cells were mounted with mounting media (Biotium) and examined using a Zeiss LSM 880 laser scanning confocal microscope.

RNA-binding protein immunoprecipitation

RNA-binding protein immunoprecipitation (RIP) was performed using an imprint RIP kit (Sigma) according to the manufacturer’s protocol with some modifications. Briefly, stable cells infected with vector control or HA-eIF3D plasmids under various conditions were lysed. Immunoprecipitation was performed using anti-HA magnetic beads (Thermo Scientific). After washing with RIP wash buffer five times, proteins and DNA were removed by proteinase K and DNase I, respectively. RNA was collected by acidic phenol extraction followed by precipitation with ethanol, sodium acetate, and GlycoBlue.

Clonogenic assays

Cells (1,000 per well) were seeded into six-well plates and grown for 10 days, feeding new media and mTOR inhibitors every 2 days. Cells were rinsed with PBS, fixed with methanol, and stained with 0.5% crystal violet.

Dual luciferase assays

Cells were transfected with pTAL-LXRE-firefly luciferase and pRL-TK-Renilla luciferase plasmids. Following treatment with inhibitors, dual luciferase activity was measured using the Dual Luciferase Reporter Assay System (Promega), according to the manufacturer’s instructions.

Animal studies

Animal studies were performed in accordance with Institutional Animal Care and Use Committee (IACUC)-approved protocols and institutional guidelines.

- Combination of mTOR and LRP6 targeting

A375-Luc2 cells (3 x 10⁶ cells) stably expressing control or LRP6 shRNAs were injected subcutaneously into 6-7-week-old female nude mice (Homozygous Foxn1^nu, The Jackson Laboratory). After tumor volume reached ~100 mm³, mice were treated with or without RapaLink-1 (1.5 mg/kg body weight, every 5 days ^16,88). Mice were monitored twice weekly for tumor size and body weight. Using a LAGO-X imaging system, the bioluminescent intensity in xenograft tumors was recorded and quantified following the injection of RediJect D-luciferin bioluminescent substrate (Perkin Elmer).

- Combination of mTOR and NPC1 targeting

For single or combination drug treatment studies, A375-Luc2 cells (3x10⁶ cells) were injected subcutaneously into 5-6-week-old female nude mice (Homozygous Foxn1^nu, The Jackson Laboratory). After tumor volume reached ~100 mm³, mice were randomly divided into 4 groups for intraperitoneal injection with control, RapaLink-1 (1.5 mg/kg body weight, every 5 days ^16,88), U18666A (2 mg/kg body weight, every 2 days), or a combination of RapaLink-1 (0.75 mg/kg body weight, every 5 days) and U18666A (1 mg/kg body weight, every 2 days). Mice were monitored twice weekly for tumor size and body weight. Using a LAGO-X imaging system, the bioluminescent intensity in xenograft tumors was recorded and quantified following injection of RediJect D-luciferin bioluminescent substrate (Perkin Elmer).

Immunohistochemistry

Immunohistochemistry was performed on formalin-fixed paraffin-embedded (FFPE) tissue samples. The adhered sections were deparaffinized on Leica Autostainer XL automated stainer (Leica Biosystems) using xylene and descending ethanol series, rehydrated in tap water, and stained with hematoxylin (Vintage Hematoxylin, StatLab) and eosin (Vintage Eosin, StatLab). Staining was performed with BOND Polymer Refine Detection Kit (Leica Biosystems) on BOND RX automated stainer (Leica Biosystems) according to the preset protocol. After deparaffinization, sections were subjected to heat-based antigen retrieval with BOND Epitope retrieval buffer 1 (pH 6.0, Leica Biosystems) for 40 min at 99°C. Endogenous peroxidase activity and non-specific binding were blocked by sequentially treating samples with peroxidase block (BOND Polymer Refine Detection Kit) and protein block (Background Sniper, Biocare Medical) for 15 min at room temperature. Sections were then incubated with Ki67 antibody for 30 min. After several washes, the signal detection was performed with anti-rabbit-Poly-HRP and DAB from the BOND Polymer Refine Kit by incubating sections for 15 min and 10 min correspondingly followed by hematoxylin counterstain. Slides were dehydrated on Autostainer XL and mounted with Micromount media (Leica Biosystems). Images were scanned using Aperio AT2 (Leica Biosystems). Staining intensity was quantified using the image processing software Fiji.

Quantification and statistical analysis

Statistical analysis was performed using Microsoft Excel or GraphPad Prism 9. Data from qPCR, fluorescence quantification, clonogenic assay, luciferase activity assay, in vivo tumor growth, and mouse weight were statistically analyzed using a t-test. Data were presented as means and error bars representing standard deviations. Statistical significance was defined as a p-value less than 0.05.

Supplementary Material

NIHMS2107770-supplement-1.pdf^{(5.2MB, pdf)}

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Anti-phospho-T308-AKT	Cell Signaling Technology	13038, RRID: AB_2629447
Anti-phospho-S473-AKT	Cell Signaling Technology	4060, RRID: AB_2315049
Anti-total AKT	Cell Signaling Technology	4691, RRID: AB_915783
Anti-phospho-S65-4E-BP1	Cell Signaling Technology	9451, RRID: AB_330947
Anti-4E-BP1	Cell Signaling Technology	9644, RRID: AB_2097841
Anti-phospho-T389-S6K1	Cell Signaling Technology	9234, RRID: AB_2269803
Anti-S6K1	Cell Signaling Technology	2708, RRID: AB_390722
Anti-phospho-Y980-IGF1R	Cell Signaling Technology	4568S, RRID: AB_2122279
Anti-IGF1R	Cell Signaling Technology	3018, RRID: AB_560943
Anti-phospho-T202/Y204-ERK1/2	Cell Signaling Technology	4370, RRID: AB_2315112
Anti-ERK1/2	Cell Signaling Technology	9102, RRID: AB_330744
Anti-mTOR	Cell Signaling Technology	2983, RRID: AB_2105622
Anti-RICTOR	Cell Signaling Technology	2140, RRID: AB_2179961
Anti-RAPTOR	Cell Signaling Technology	2280, RRID: AB_561245
Anti-LXRβ	Cell Signaling Technology	13519, RRID: AB_2798244
Anti-NPC1	Cell Signaling Technology	33422, RRID: AB_3697652
Anti-LRP6	Cell Signaling Technology	2560, RRID: AB_2139329
Anti-HA	Cell Signaling Technology	3724, RRID: AB_1549585
Anti-IGF1R	Proteintech	20254, RRID: AB_10667455
Anti-NPC1	Proteintech	13926, RRID: AB_2152050
Anti-PLIN2	Proteintech	15294, RRID: AB_2878122
Anti-APOB	Proteintech	20578, RRID: AB_10732938
Anti-SREBF1	Proteintech	14088, RRID: AB_2255217
Anti-SREBF2	Proteintech	28212, RRID: AB_2881091
Anti-LDLR	Proteintech	10785, RRID: AB_2281164
Anti-FASN	Proteintech	10624, RRID: AB_2100801
Anti-FDFT1	Proteintech	13128, RRID: AB_2294094
Anti-LXRβ	Proteintech	60345, RRID: AB_2881454
Anti-HA	Proteintech	51064, RRID: AB_11042321
Anti-ACTIN	Millipore Sigma	A5441, RRID: AB_476744
Anti-EIF3D	Santa Cruz Biotechnology	SC-271515, RRID: AB_10659103
Anti-LRP1	Abclonal	A0633, RRID: AB_2861470
Chemicals, peptides, and recombinant proteins
Rapamycin	Selleckchem	S1039
Torin1	Selleckchem	S2827
Perifosine	Selleckchem	S1037
MK-2206	Selleckchem	S1078
GDC-0068	Selleckchem	S2808
AZD5363	Selleckchem	S8019
LY3009120	Selleckchem	S7842
Filipin	Millipore Sigma	F9765
Rapalink1	MedChemExpress	HY-111373
U18666A	MedChemExpress	HY-107433
GSK2033	MedChemExpress	HY-108688
SR9243	MedChemExpress	HY16972
ICG001	MedChemExpress	HY-14428
XAV939	MedChemExpress	HY-15147
LipidSpot lipid droplet stain	Biotium	70065
RedDot2 nuclear stain	Biotium	40061
EverBrite mount media	Biotium	23001
DAPI	Biotium	40043
Critical commercial assays
Dual Luciferase Reporter Assay	Promega	E1910
Imprint RNA immunoprecipitation kit	Sigma	RIP-12RXN
Deposited data
Proteomics data	This paper	ProteomeXchange: PXD049052
RNA-seq data	This paper	GEO: GSE304701
Lipidomics data	This paper	MetaboLights: MTBLS12837
Original images	This paper	Mendeley Data: http://doi.org/10.17632/v7gdh9prbj.1
Experimental models: Cell lines
A375	ATCC	CRL-1619
A375-Luc2	ATCC	CRL-1619-Luc2
MDA-MB-435	ATCC	HTB-129
LN229	ATCC	CRL-2611
A172	ATCC	CRL-1620
ELT3	Dr. Jane Yu	N/A
RT4	Dr. Jane Yu	N/A
293T	Dr. Andrew L. Kung	N/A
Experimental models: Organisms
Athymic Foxn1^nu mice	The Jackson Laboratory	002019
Recombinant DNA
pLKO.1-mTOR shRNAs	Millipore Sigma	SHCLNG-NM_004958
pLKO.1-eIF3D shRNAs	Millipore Sigma	SHCLNG-NM_003753
pLKO.1-NPC1 shRNAs	Millipore Sigma	SHCLNG-NM_000271
pLKO.1-LRP6 shRNAs	Millipore Sigma	SHCLNG-NM_002336
pLKO.1-LRP1 shRNAs	Millipore Sigma	SHCLNG-NM_002332
pLKO.1-LXRβ shRNAs	Millipore Sigma	SHCLNG-NM_007121
pLKO.1-LDLR shRNAs	Millipore Sigma	SHCLNG-NM_000527
pLKO.1-RICTOR shRNAs	Millipore Sigma	SHCLNG-NM_152756
LentiCRISPR V2 puro	Addgene	52961
LentiCRISPR V2 neo	Addgene	98292
pLKO.1-RAPTOR shRNAs	Dr. David Sabatini	N/A
pLKO.1-RICTOR shRNAs	Dr. David Sabatini	N/A
plentiCRISPR V2 sg_LDLR	This study	N/A
plentiCRISPR V2 sg_LRP6	This study	N/A
plentiCRISPR V2 sg_NPC1	This study	N/A
pNIC28-mCherry-Perfringolysin D4H	Dr. Yasunori Saheki	N/A
pTAL-LXRE-Luc	Dr. David Mangelsdorf	N/A
Software and algorithms
Metascape	Open source	https://metascape.org
Fiji	Open source	https://imagej.net/software/fiji/
GraphPad Prism	GraphPad Software, Inc.	RRID: SCR_002798
Excel	Microsoft	RRID: SCR_016137
Biorender	Biorender	RRID: SCR_018361
Other
Mass Spectrometry	Multiplexed Proteomics Center at Harvard Medical School	N/A
DMEM	Corning	10-013-CV
DMEM/F12	Corning	10-092-CV
DMEM, no glucose	Gibco	11966025
DMEM, no amino acids	US Biological	D9800-13
MEM amino acids solution	Gibco	11130051
Glucose solution	Gibco	A2494001
Fetal bovine serum, dialyzed	Cytiva HyClone	SH30079
Lipid-depleted fetal bovine serum	Biowest	S181L

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Highlights.

mTOR inhibition induces lipid uptake via eIF3D-dependent LRP6 mRNA translation
mTOR inhibition promotes cholesterol trafficking through LXRβ-mediated NPC1 expression
LRP6 and NPC1 are essential for IGF1R/AKT signaling activation following mTOR inhibition
Co-targeting mTOR and LRP6 or NPC1 enhances the anti-tumor efficacy of mTOR inhibitors

ACKNOWLEDGMENTS

We thank Dr. Hui Chen from the Mass Spectrometry Core at the University of Illinois Chicago for assistance with lipidomic analysis. In addition, we thank the Research Histology and Tissue Imaging Collaborative at the University of Illinois Chicago and the Mouse Histology and Phenotyping Laboratory at Northwestern University for assistance with histology studies. The graphical abstract was created with BioRender.com (https://BioRender.com/beviot0). This work was supported by NIH grant R01 CA240835 (S.O.Y.), University of Illinois Cancer Center Pilot Program (S.O.Y.), LAM Foundation Pilot Research grant LAM0159P01-1001549-23 (S.O.Y.), NIH R01 NS124784 and R01 NS114413 (S.M.C), and NIH R35 GM122530 (W.C.).

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

DECLARATION OF INTERESTS

The authors declare no competing interests.

REFERENCES

1.Ricoult SJ, and Manning BD (2013). The multifaceted role of mTORC1 in the control of lipid metabolism. EMBO Rep 14, 242–251. 10.1038/embor.2013.5. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Meza U, Delgado-Ramirez M, Romero-Mendez C, Sanchez-Armass S, and Rodriguez-Menchaca AA (2020). Functional marriage in plasma membrane: Critical cholesterol leveloptimal protein activity. Br J Pharmacol 177, 2456–2465. 10.1111/bph.15027. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Rohrig F, and Schulze A (2016). The multifaceted roles of fatty acid synthesis in cancer. Nat Rev Cancer 16, 732–749. 10.1038/nrc.2016.89. [DOI] [PubMed] [Google Scholar]
4.Pellerin L, Carrie L, Dufau C, Nieto L, Segui B, Levade T, Riond J, and Andrieu-Abadie N (2020). Lipid metabolic Reprogramming: Role in Melanoma Progression and Therapeutic Perspectives. Cancers (Basel) 12. 10.3390/cancers12113147. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Lee MS, and Bensinger SJ (2022). Reprogramming cholesterol metabolism in macrophages and its role in host defense against cholesterol-dependent cytolysins. Cell Mol Immunol 19, 327–336. 10.1038/s41423-021-00827-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Szwed A, Kim E, and Jacinto E (2021). Regulation and metabolic functions of mTORC1 and mTORC2. Physiol Rev 101, 1371–1426. 10.1152/physrev.00026.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Lamming DW, and Sabatini DM (2013). A Central role for mTOR in lipid homeostasis. Cell Metab 18, 465–469. 10.1016/j.cmet.2013.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Kurdi A, Martinet W, and De Meyer GRY (2018). mTOR Inhibition and Cardiovascular Diseases: Dyslipidemia and Atherosclerosis. Transplantation 102, S44–S46. 10.1097/TP.0000000000001693. [DOI] [PubMed] [Google Scholar]
9.Guri Y, Colombi M, Dazert E, Hindupur SK, Roszik J, Moes S, Jenoe P, Heim MH, Riezman I, Riezman H, and Hall MN (2017). mTORC2 Promotes Tumorigenesis via Lipid Synthesis. Cancer Cell 32, 807–823 e812. 10.1016/j.ccell.2017.11.011. [DOI] [PubMed] [Google Scholar]
10.Hagiwara A, Cornu M, Cybulski N, Polak P, Betz C, Trapani F, Terracciano L, Heim MH, Ruegg MA, and Hall MN (2012). Hepatic mTORC2 activates glycolysis and lipogenesis through Akt, glucokinase, and SREBP1c. Cell Metab 15, 725–738. 10.1016/j.cmet.2012.03.015. [DOI] [PubMed] [Google Scholar]
11.Yuan M, Pino E, Wu L, Kacergis M, and Soukas AA (2012). Identification of Aktindependent regulation of hepatic lipogenesis by mammalian target of rapamycin (mTOR) complex 2. J Biol Chem 287, 29579–29588. 10.1074/jbc.M112.386854. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Yoon SO, and Roux PP (2013). Rapamycin resistance: mTORC1 substrates hold some of the answers. Curr Biol 23, R880–883. 10.1016/j.cub.2013.08.030. [DOI] [PubMed] [Google Scholar]
13.Panwar V, Singh A, Bhatt M, Tonk RK, Azizov S, Raza AS, Sengupta S, Kumar D, and Garg M (2023). Multifaceted role of mTOR (mammalian target of rapamycin) signaling pathway in human health and disease. Signal Transduct Target Ther 8, 375. 10.1038/s41392-023-01608-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Rodrik-Outmezguine VS, Okaniwa M, Yao Z, Novotny CJ, McWhirter C, Banaji A, Won H, Wong W, Berger M, de Stanchina E, et al. (2016). Overcoming mTOR resistance mutations with a new-generation mTOR inhibitor. Nature 534, 272–276. 10.1038/nature17963. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Shin S, Han MJ, Jedrychowski MP, Zhang Z, Shokat KM, Plas DR, Dephoure N, and Yoon SO (2023). mTOR inhibition reprograms cellular proteostasis by regulating eIF3D-mediated selective mRNA translation and promotes cell phenotype switching. Cell Rep 42, 112868. 10.1016/j.celrep.2023.112868. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Zhang Z, Fan Q, Luo X, Lou K, Weiss WA, and Shokat KM (2022). Brain-restricted mTOR inhibition with binary pharmacology. Nature 609, 822–828. 10.1038/s41586-022-05213-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Ehinger Y, Zhang Z, Phamluong K, Soneja D, Shokat KM, and Ron D (2021). Brainspecific inhibition of mTORC1 eliminates side effects resulting from mTORC1 blockade in the periphery and reduces alcohol intake in mice. Nat Commun 12, 4407. 10.1038/s41467-021-24567-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Kim JW, Milowsky MI, Hahn NM, Kwiatkowski DJ, Morgans AK, Davis NB, Appleman LJ, Gupta S, Lara PLN, Hoffman-Censits JH, et al. (2021). Sapanisertib, a dual mTORC1/2 inhibitor, for TSC1- or TSC2-mutated metastatic urothelial carcinoma (mUC). Journal of Clinical Oncology 39, 431–431. 10.1200/JCO.2021.39.6_suppl.431. [DOI] [Google Scholar]
19.Rodrik-Outmezguine VS, Chandarlapaty S, Pagano NC, Poulikakos PI, Scaltriti M, Moskatel E, Baselga J, Guichard S, and Rosen N (2011). mTOR kinase inhibition causes feedback-dependent biphasic regulation of AKT signaling. Cancer Discov 1, 248–259. 10.1158/2159-8290.CD-11-0085. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Yoon SO, Shin S, Karreth FA, Buel GR, Jedrychowski MP, Plas DR, Dedhar S, Gygi SP, Roux PP, Dephoure N, and Blenis J (2017). Focal Adhesion- and IGF1R-Dependent Survival and Migratory Pathways Mediate Tumor Resistance to mTORC1/2 Inhibition. Mol Cell 67, 512–527. 10.1016/j.molcel.2017.06.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Yu Y, Yoon SO, Poulogiannis G, Yang Q, Ma XM, Villen J, Kubica N, Hoffman GR, Cantley LC, Gygi SP, and Blenis J (2011). Phosphoproteomic analysis identifies Grb10 as an mTORC1 substrate that negatively regulates insulin signaling. Science 332, 1322–1326. 10.1126/science.1199484. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Manning BD, and Toker A (2017). AKT/PKB Signaling: Navigating the Network. Cell 169, 381–405. 10.1016/j.cell.2017.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Marais R, Light Y, Paterson HF, and Marshall CJ (1995). Ras recruits Raf-1 to the plasma membrane for activation by tyrosine phosphorylation. EMBO J 14, 3136–3145. 10.1002/j.1460-2075.1995.tb07316.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Zadoorian A, Du X, and Yang H (2023). Lipid droplet biogenesis and functions in health and disease. Nat Rev Endocrinol 19, 443–459. 10.1038/s41574-023-00845-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Qiu B, Ackerman D, Sanchez DJ, Li B, Ochocki JD, Grazioli A, Bobrovnikova-Marjon E, Diehl JA, Keith B, and Simon MC (2015). HIF2alpha-Dependent Lipid Storage Promotes Endoplasmic Reticulum Homeostasis in Clear-Cell Renal Cell Carcinoma. Cancer Discov 5, 652–667. 10.1158/2159-8290.CD-14-1507. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Najt CP, Devarajan M, and Mashek DG (2022). Perilipins at a glance. J Cell Sci 135. 10.1242/jcs.259501. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Maekawa M (2017). Domain 4 (D4) of Perfringolysin O to Visualize Cholesterol in Cellular Membranes-The Update. Sensors (Basel) 17. 10.3390/s17030504. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Wilhelm LP, Voilquin L, Kobayashi T, Tomasetto C, and Alpy F (2019). Intracellular and Plasma Membrane Cholesterol Labeling and Quantification Using Filipin and GFP-D4. Methods Mol Biol 1949, 137–152. 10.1007/978-1-4939-9136-5_11. [DOI] [PubMed] [Google Scholar]
29.Maekawa M, and Fairn GD (2015). Complementary probes reveal that phosphatidylserine is required for the proper transbilayer distribution of cholesterol. J Cell Sci 128, 1422–1433. 10.1242/jcs.164715. [DOI] [PubMed] [Google Scholar]
30.Ercan B, Naito T, Koh DHZ, Dharmawan D, and Saheki Y (2021). Molecular basis of accessible plasma membrane cholesterol recognition by the GRAM domain of GRAMD1b. EMBO J 40, e106524. 10.15252/embj.2020106524. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Naito T, Ercan B, Krshnan L, Triebl A, Koh DHZ, Wei FY, Tomizawa K, Torta FT, Wenk MR, and Saheki Y (2019). Movement of accessible plasma membrane cholesterol by the GRAMD1 lipid transfer protein complex. Elife 8. 10.7554/eLife.51401. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Feingold KR (2000). Introduction to Lipids and Lipoproteins. In Endotext, Feingold KR, Anawalt B, Boyce A, Chrousos G, de Herder WW, Dhatariya K, Dungan K, Hershman JM, Hofland J, Kalra S, et al. , eds. [Google Scholar]
33.Mehta A, and Shapiro MD (2022). Apolipoproteins in vascular biology and atherosclerotic disease. Nat Rev Cardiol 19, 168–179. 10.1038/s41569-021-00613-5. [DOI] [PubMed] [Google Scholar]
34.Fang Y, Westbrook R, Hill C, Boparai RK, Arum O, Spong A, Wang F, Javors MA, Chen J, Sun LY, and Bartke A (2013). Duration of rapamycin treatment has differential effects on metabolism in mice. Cell Metab 17, 456–462. 10.1016/j.cmet.2013.02.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Trinh MN, Brown MS, Goldstein JL, Han J, Vale G, McDonald JG, Seemann J, Mendell JT, and Lu F (2020). Last step in the path of LDL cholesterol from lysosome to plasma membrane to ER is governed by phosphatidylserine. Proc Natl Acad Sci U S A 117, 18521–18529. 10.1073/pnas.2010682117. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Infante RE, and Radhakrishnan A (2017). Continuous transport of a small fraction of plasma membrane cholesterol to endoplasmic reticulum regulates total cellular cholesterol. Elife 6. 10.7554/eLife.25466. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Xu H, Zhou S, Tang Q, Xia H, and Bi F (2020). Cholesterol metabolism: New functions and therapeutic approaches in cancer. Biochim Biophys Acta Rev Cancer 1874, 188394. 10.1016/j.bbcan.2020.188394. [DOI] [PubMed] [Google Scholar]
38.Millard EE, Srivastava K, Traub LM, Schaffer JE, and Ory DS (2000). Niemann-pick type C1 (NPC1) overexpression alters cellular cholesterol homeostasis. J Biol Chem 275, 38445–38451. 10.1074/jbc.M003180200. [DOI] [PubMed] [Google Scholar]
39.Lu F, Liang Q, Abi-Mosleh L, Das A, De Brabander JK, Goldstein JL, and Brown MS (2015). Identification of NPC1 as the target of U18666A, an inhibitor of lysosomal cholesterol export and Ebola infection. Elife 4. 10.7554/eLife.12177. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Pfeffer SR (2019). NPC intracellular cholesterol transporter 1 (NPC1)-mediated cholesterol export from lysosomes. J Biol Chem 294, 1706–1709. 10.1074/jbc.TM118.004165. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Bilotta MT, Petillo S, Santoni A, and Cippitelli M (2020). Liver X Receptors: Regulators of Cholesterol Metabolism, Inflammation, Autoimmunity, and Cancer. Front Immunol 11, 584303. 10.3389/fimmu.2020.584303. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Wang B, and Tontonoz P (2018). Liver X receptors in lipid signalling and membrane homeostasis. Nat Rev Endocrinol 14, 452–463. 10.1038/s41574-018-0037-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Rigamonti E, Helin L, Lestavel S, Mutka AL, Lepore M, Fontaine C, Bouhlel MA, Bultel S, Fruchart JC, Ikonen E, et al. (2005). Liver X receptor activation controls intracellular cholesterol trafficking and esterification in human macrophages. Circ Res 97, 682–689. 10.1161/01.RES.0000184678.43488.9f. [DOI] [PubMed] [Google Scholar]
44.Zelcer N, Hong C, Boyadjian R, and Tontonoz P (2009). LXR regulates cholesterol uptake through Idol-dependent ubiquitination of the LDL receptor. Science 325, 100–104. 10.1126/science.1168974. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Maurice MM, and Angers S (2025). Mechanistic insights into Wnt-beta-catenin pathway activation and signal transduction. Nat Rev Mol Cell Biol. 10.1038/s41580-024-00823-y. [DOI] [PubMed] [Google Scholar]
46.Liu J, Xiao Q, Xiao J, Niu C, Li Y, Zhang X, Zhou Z, Shu G, and Yin G (2022). Wnt/beta-catenin signalling: function, biological mechanisms, and therapeutic opportunities. Signal Transduct Target Ther 7, 3. 10.1038/s41392-021-00762-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Behari J, Yeh TH, Krauland L, Otruba W, Cieply B, Hauth B, Apte U, Wu T, Evans R, and Monga SP (2010). Liver-specific beta-catenin knockout mice exhibit defective bile acid and cholesterol homeostasis and increased susceptibility to diet-induced steatohepatitis. Am J Pathol 176, 744–753. 10.2353/ajpath.2010.090667. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Scott CC, Vossio S, Vacca F, Snijder B, Larios J, Schaad O, Guex N, Kuznetsov D, Martin O, Chambon M, et al. (2015). Wnt directs the endosomal flux of LDL-derived cholesterol and lipid droplet homeostasis. EMBO Rep 16, 741–752. 10.15252/embr.201540081. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Chee WY, Kurahashi Y, Kim J, Miura K, Okuzaki D, Ishitani T, Kajiwara K, Nada S, Okano H, and Okada M (2021). beta-catenin-promoted cholesterol metabolism protects against cellular senescence in naked mole-rat cells. Commun Biol 4, 357. 10.1038/s42003-021-01879-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Vergara D, Stanca E, Guerra F, Priore P, Gaballo A, Franck J, Simeone P, Trerotola M, De Domenico S, Fournier I, et al. (2017). beta-Catenin Knockdown Affects Mitochondrial Biogenesis and Lipid Metabolism in Breast Cancer Cells. Front Physiol 8, 544. 10.3389/fphys.2017.00544. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Jeong W, and Jho EH (2021). Regulation of the Low-Density Lipoprotein Receptor-Related Protein LRP6 and Its Association With Disease: Wnt/beta-Catenin Signaling and Beyond. Front Cell Dev Biol 9, 714330. 10.3389/fcell.2021.714330. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Liu CC, Prior J, Piwnica-Worms D, and Bu G (2010). LRP6 overexpression defines a class of breast cancer subtype and is a target for therapy. Proc Natl Acad Sci U S A 107, 5136–5141. 10.1073/pnas.0911220107. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.De Ferrari GV, Papassotiropoulos A, Biechele T, Wavrant De-Vrieze F, Avila ME, Major MB, Myers A, Saez K, Henriquez JP, Zhao A, et al. (2007). Common genetic variation within the low-density lipoprotein receptor-related protein 6 and late-onset Alzheimer’s disease. Proc Natl Acad Sci U S A 104, 9434–9439. 10.1073/pnas.0603523104. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Inoki K, Ouyang H, Zhu T, Lindvall C, Wang Y, Zhang X, Yang Q, Bennett C, Harada Y, Stankunas K, et al. (2006). TSC2 integrates Wnt and energy signals via a coordinated phosphorylation by AMPK and GSK3 to regulate cell growth. Cell 126, 955–968. 10.1016/j.cell.2006.06.055. [DOI] [PubMed] [Google Scholar]
55.Zeng H, Lu B, Zamponi R, Yang Z, Wetzel K, Loureiro J, Mohammadi S, Beibel M, Bergling S, Reece-Hoyes J, et al. (2018). mTORC1 signaling suppresses Wnt/beta-catenin signaling through DVL-dependent regulation of Wnt receptor FZD level. Proc Natl Acad Sci U S A 115, E10362–E10369. 10.1073/pnas.1808575115. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Cao J, Tyburczy ME, Moss J, Darling TN, Widlund HR, and Kwiatkowski DJ (2017). Tuberous sclerosis complex inactivation disrupts melanogenesis via mTORC1 activation. J Clin Invest 127, 349–364. 10.1172/JCI84262. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Pan W, Choi SC, Wang H, Qin Y, Volpicelli-Daley L, Swan L, Lucast L, Khoo C, Zhang X, Li L, et al. (2008). Wnt3a-mediated formation of phosphatidylinositol 4,5-bisphosphate regulates LRP6 phosphorylation. Science 321, 1350–1353. 10.1126/science.1160741. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Boucher P, Matz RL, and Terrand J (2020). atherosclerosis: gone with the Wnt? Atherosclerosis 301, 15–22. 10.1016/j.atherosclerosis.2020.03.024. [DOI] [PubMed] [Google Scholar]
59.Zhang JR, Coleman T, Langmade SJ, Scherrer DE, Lane L, Lanier MH, Feng C, Sands MS, Schaffer JE, Semenkovich CF, and Ory DS (2008). Niemann-Pick C1 protects against atherosclerosis in mice via regulation of macrophage intracellular cholesterol trafficking. J Clin Invest 118, 2281–2290. 10.1172/JCI32561. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Muse ED, Yu S, Edillor CR, Tao J, Spann NJ, Troutman TD, Seidman JS, Henke A, Roland JT, Ozeki KA, et al. (2018). Cell-specific discrimination of desmosterol and desmosterol mimetics confers selective regulation of LXR and SREBP in macrophages. Proc Natl Acad Sci U S A 115, E4680–E4689. 10.1073/pnas.1714518115. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Xu J, Dang Y, Ren YR, and Liu JO (2010). Cholesterol trafficking is required for mTOR activation in endothelial cells. Proc Natl Acad Sci U S A 107, 4764–4769. 10.1073/pnas.0910872107. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Castellano BM, Thelen AM, Moldavski O, Feltes M, van der Welle RE, Mydock-McGrane L, Jiang X, van Eijkeren RJ, Davis OB, Louie SM, et al. (2017). Lysosomal cholesterol activates mTORC1 via an SLC38A9-Niemann-Pick C1 signaling complex. Science 355, 1306–1311. 10.1126/science.aag1417. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Lim CY, Davis OB, Shin HR, Zhang J, Berdan CA, Jiang X, Counihan JL, Ory DS, Nomura DK, and Zoncu R (2019). ER-lysosome contacts enable cholesterol sensing by mTORC1 and drive aberrant growth signalling in Niemann-Pick type C. Nat Cell Biol 21, 1206–1218. 10.1038/s41556-019-0391-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Davis OB, Shin HR, Lim CY, Wu EY, Kukurugya M, Maher CF, Perera RM, Ordonez MP, and Zoncu R (2021). NPC1-mTORC1 Signaling Couples Cholesterol Sensing to Organelle Homeostasis and Is a Targetable Pathway in Niemann-Pick Type C. Dev Cell 56, 260–276 e267. 10.1016/j.devcel.2020.11.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Awan S, Lambert M, Imtiaz A, Alpy F, Tomasetto C, Oulad-Abdelghani M, Schaeffer C, Moritz C, Julien-David D, Najib S, et al. (2022). Wnt5a Promotes Lysosomal Cholestero Egress and Protects Against Atherosclerosis. Circ Res 130, 184–199. 10.1161/CIRCRESAHA.121.318881. [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Jin HR, Wang J, Wang ZJ, Xi MJ, Xia BH, Deng K, and Yang JL (2023). Lipid metabolic reprogramming in tumor microenvironment: from mechanisms to therapeutics. J Hematol Oncol 16, 103. 10.1186/s13045-023-01498-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Du W, Zhang L, Brett-Morris A, Aguila B, Kerner J, Hoppel CL, Puchowicz M, Serra D, Herrero L, Rini BI, et al. (2017). HIF drives lipid deposition and cancer in ccRCC via repression of fatty acid metabolism. Nat Commun 8, 1769. 10.1038/s41467-017-01965-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Bensaad K, Favaro E, Lewis CA, Peck B, Lord S, Collins JM, Pinnick KE, Wigfield S, Buffa FM, Li JL, et al. (2014). Fatty acid uptake and lipid storage induced by HIF-1alpha contribute to cell growth and survival after hypoxia-reoxygenation. Cell Rep 9, 349–365. 10.1016/j.celrep.2014.08.056. [DOI] [PubMed] [Google Scholar]
69.Ackerman D, Tumanov S, Qiu B, Michalopoulou E, Spata M, Azzam A, Xie H, Simon MC, and Kamphorst JJ (2018). Triglycerides Promote Lipid Homeostasis during Hypoxic Stress by Balancing Fatty Acid Saturation. Cell Rep 24, 2596–2605 e2595. 10.1016/j.celrep.2018.08.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Lee AS, Kranzusch PJ, Doudna JA, and Cate JH (2016). eIF3d is an mRNA cap-binding protein that is required for specialized translation initiation. Nature 536, 96–99. 10.1038/nature18954. [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Lamper AM, Fleming RH, Ladd KM, and Lee ASY (2020). A phosphorylation-regulated eIF3d translation switch mediates cellular adaptation to metabolic stress. Science 370, 853–856. 10.1126/science.abb0993. [DOI] [PubMed] [Google Scholar]
72.Herrmannova A, Jelinek J, Pospisilova K, Kerenyi F, Vomastek T, Watt K, Brabek J, Mohammad MP, Wagner S, Topisirovic I, and Valasek LS (2024). Perturbations in eIF3 subunit stoichiometry alter expression of ribosomal proteins and key components of the MAPK signaling pathways. Elife 13. 10.7554/eLife.95846. [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Roiuk M, Neff M, and Teleman AA (2024). eIF4E-independent translation is largely eIF3d-dependent. Nat Commun 15, 6692. 10.1038/s41467-024-51027-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Deng CF, Zhu N, Zhao TJ, Li HF, Gu J, Liao DF, and Qin L (2022). Involvement of LDL and ox-LDL in Cancer Development and Its Therapeutical Potential. Front Oncol 12, 803473. 10.3389/fonc.2022.803473. [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Nielsen SF, Nordestgaard BG, and Bojesen SE (2012). Statin use and reduced cancer-related mortality. N Engl J Med 367, 1792–1802. 10.1056/NEJMoa1201735. [DOI] [PubMed] [Google Scholar]
76.Go GW (2015). Low-Density Lipoprotein Receptor-Related Protein 6 (LRP6) Is a Novel Nutritional Therapeutic Target for Hyperlipidemia, Non-Alcoholic Fatty Liver Disease, and Atherosclerosis. Nutrients 7, 4453–4464. 10.3390/nu7064453. [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Srivastava RAK (2023). A Review of Progress on Targeting LDL Receptor-Dependent and - Independent Pathways for the Treatment of Hypercholesterolemia, a Major Risk Factor of ASCVD. Cells 12. 10.3390/cells12121648. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Zhao H, Li Y, He L, Pu W, Yu W, Li Y, Wu YT, Xu C, Wei Y, Ding Q, et al. (2020). In Vivo AAV-CRISPR/Cas9-Mediated Gene Editing Ameliorates Atherosclerosis in Familial Hypercholesterolemia. Circulation 141, 67–79. 10.1161/CIRCULATIONAHA.119.042476. [DOI] [PubMed] [Google Scholar]
79.Ye ZJ, Go GW, Singh R, Liu W, Keramati AR, and Mani A (2012). LRP6 protein regulates low density lipoprotein (LDL) receptor-mediated LDL uptake. J Biol Chem 287, 1335–1344. 10.1074/jbc.M111.295287. [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Liu W, Mani S, Davis NR, Sarrafzadegan N, Kavathas PB, and Mani A (2008). Mutation in EGFP domain of LDL receptor-related protein 6 impairs cellular LDL clearance. Circ Res 103, 1280–1288. 10.1161/CIRCRESAHA.108.183863. [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Mani A, Radhakrishnan J, Wang H, Mani A, Mani MA, Nelson-Williams C, Carew KS, Mane S, Najmabadi H, Wu D, and Lifton RP (2007). LRP6 mutation in a family with early coronary disease and metabolic risk factors. Science 315, 1278–1282. 10.1126/science.1136370. [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Shin S, Buel GR, Wolgamott L, Plas DR, Asara JM, Blenis J, and Yoon SO (2015). ERK2 Mediates Metabolic Stress Response to Regulate Cell Fate. Mol Cell 59, 382–398. 10.1016/j.molcel.2015.06.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Navarrete-Perea J, Yu Q, Gygi SP, and Paulo JA (2018). Streamlined Tandem Mass Tag (SL-TMT) Protocol: An Efficient Strategy for Quantitative (Phospho)proteome Profiling Using Tandem Mass Tag-Synchronous Precursor Selection-MS3. J Proteome Res 17, 2226–2236. 10.1021/acs.jproteome.8b00217. [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Li J, Cai Z, Bomgarden RD, Pike I, Kuhn K, Rogers JC, Roberts TM, Gygi SP, and Paulo JA (2021). TMTpro-18plex: The Expanded and Complete Set of TMTpro Reagents for Sample Multiplexing. J Proteome Res 20, 2964–2972. 10.1021/acs.jproteome.1c00168. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Schweppe DK, Prasad S, Belford MW, Navarrete-Perea J, Bailey DJ, Huguet R, Jedrychowski MP, Rad R, McAlister G, Abbatiello SE, et al. (2019). Characterization and Optimization of Multiplexed Quantitative Analyses Using High-Field Asymmetric-Waveform Ion Mobility Mass Spectrometry. Anal Chem 91, 4010–4016. 10.1021/acs.analchem.8b05399. [DOI] [PMC free article] [PubMed] [Google Scholar]
86.Schweppe DK, Eng JK, Yu Q, Bailey D, Rad R, Navarrete-Perea J, Huttlin EL, Erickson BK, Paulo JA, and Gygi SP (2020). Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics. J Proteome Res 19, 2026–2034. 10.1021/acs.jproteome.9b00860. [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Rad R, Li J, Mintseris J, O’Connell J, Gygi SP, and Schweppe DK (2021). Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage. J Proteome Res 20, 591–598. 10.1021/acs.jproteome.0c00563. [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Fan Q, Aksoy O, Wong RA, Ilkhanizadeh S, Novotny CJ, Gustafson WC, Truong AY, Cayanan G, Simonds EF, Haas-Kogan D, et al. (2017). A Kinase Inhibitor Targeted to mTORC1 Drives Regression in Glioblastoma. Cancer Cell 31, 424–435. 10.1016/j.ccell.2017.01.014. [DOI] [PMC free article] [PubMed] [Google Scholar]

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