Fatostatin promotes anti-tumor immunity by reducing SREBP2 mediated cholesterol metabolism in tumor-infiltrating T lymphocytes

doi:10.21203/rs.3.rs-2372341/v1

Download PDF

Article

Fatostatin promotes anti-tumor immunity by reducing SREBP2 mediated cholesterol metabolism in tumor-infiltrating T lymphocytes

https://doi.org/10.21203/rs.3.rs-2372341/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 29 Feb, 2024

Read the published version in European Journal of Pharmacology →

Version 1

posted

You are reading this latest preprint version

Aberrant lipid metabolism affects intratumoral T cells mediated immune response and tumor growth. Fatostatin, a chemical inhibitor of sterol regulatory element binding proteins (SREBPs) activation was found that it can inhibit cancer cell proliferation, invasion, migration, G2/M phase arrest under SREBP-dependent processes and block mitotic cell division not depending on the SREBPs mediated lipogenesis. However, the complicated intervention effect of fatostatin on lipids metabolism in the TME, and its influence on anti-tumor immunity of T cells remains unclear. Here we found that fatostatin effectively inhibited the growth of B16 melanoma, MC38 colon cancer, and Lewis lung cancer (LLC) transplanted tumor in mice through reducing SREBPs mediated lipids metabolism in the tumor tissue, especially cholesterol levels. The effects of fatostatin on the overall metabolic level of TME mainly focus on tryptophan metabolism, glycolysis and gluconeogenesis metabolism, cysteine and methionine metabolism. Mechanically, fatostatin decreased intracellular cholesterol accumulation and inhibited XBP1-mediated ER stress, thereby suppressing the proportion of Treg cells and CD8⁺T cells exhaustion in the TME, exerting an anti-tumor function. Our study suggests that targeting SREBP2-mediated cholesterol metabolism could be a potential strategy for anti-tumor immunotherapy and confirmed the application potential of fatostatin in tumor immunotherapy.

Biological sciences/Cancer/Cancer metabolism

Biological sciences/Cancer/Cancer microenvironment

Biological sciences/Cancer/Tumour immunology

Aberrant lipid metabolism is one of the most significant metabolic alterations in cancer^{1, 2}. Dysregulated lipogenesis or lipid uptake contributes to the rapid growth of cancer cells and tumor formation³. Lipids contain thousands of different types of molecules, including phospholipids, fatty acids (FFAs), triglycerides, sphingolipids, cholesterol and cholesteryl esters (CEs) ^{4, 5}. Aberrant lipid metabolism is not only driven by the metabolic demands of the tumor's own proliferation, but also affects the function and metabolism of surrounding immune cells and is influenced by them⁶. Tumor infiltrating CD8⁺ cytotoxic T lymphocytes (CTLs), as the leader killer in immune checkpoint inhibitory (ICI) therapy and adoptive cell therapy, can be exhausted by the accumulation of lipids⁷. Increased lipid metabolism in intratumoral CD4⁺ regulatory T (Treg) cells maintains their stability and suppressive functions which attenuates the efficacy of ICI immunotherapies⁸. To better reveal the inhibitory mechanisms of vigorous lipid metabolism on anti-tumor immunity and to identify new potential therapeutic strategies are the hot pursuits in current research⁹.

Cholesterol plays an important role in the anti-tumor response of T cells and has received increasing attention^{10, 11}. As a major component of membrane lipids, cholesterol can directly regulate T cell signaling and function^{12, 13, 14}, affecting T cell receptor (TCR) aggregation and T cell immune synapse formation^{15, 16, 17}. High cholesterol levels in the TME can increase endoplasmic reticulum (ER) stress, induce expression of immune checkpoint molecules, cause exhaustion of CD8⁺ T cells and inhibit their tumor killing function¹⁸. The cholesterol signaling pathway mediated by sterol regulatory element binding proteins 2 (SREBP2) has been shown to be critical for CD8⁺ T cell proliferation and effector functions¹⁴. It has been demonstrated that the SCAP/SREBP pathway is significantly enriched in Treg cells¹⁹. Additionally, it regulates lipid metabolism in Treg cells and coordinates the cellular program that inhibits receptor signaling, contributing to the functional maturation of Treg cells and their immunosuppressive effects¹⁹. Thus, intratumoral T cells mediated immune response could be regulated by sophisticated regulation of cholesterol metabolism²⁰.

Fatostatin is a diarylthiazole compound originally identified from a library of synthetic small molecules as a chemical inhibitor of insulin-induced adipogenesis. Fatostatin can block the transport of SCAP from the ER to the Golgi apparatus, thereby inhibiting SREBPs activation²¹. Current studies have shown that fatostatin possesses anti-tumor effects in prostate, breast, and endometrial cancers by inhibiting cancer cell proliferation, invasion, migration, G2/M phase arrest under SREBP-dependent processes^{22, 23, 24, 25}. In addition, fatostatin also has SREBP-independent properties, such as blockade of cell division through the inhibition of microtubule formation and endoplasmic reticulum protein processing^{26, 27, 28}. It has recently been shown that fatostatin can indeed reduce cholesterol accumulation in liver tissues and cultured tumor cells by inhibiting SREBP2-mediated de novo cholesterol synthesis and uptake, but does not exert effective pharmacologic effects in reducing blood cholesterol levels like other statins^{22, 29, 30}. Surprisingly, fatostatin has even been reported to cause lipid accumulation, rather than inhibit lipogenesis in breast cancer cells²⁸. At present, the intervention effect of fatostatin on lipids metabolism, especially cholesterol in tumor tissues, and its influence on anti-tumor immunity of T cells remains unknown. Besides, the inhibitory effect of fatostatin on lung cancer which has the highest mortality rate and second incidence rate worldwide, colon cancer which ranks second and third, and melanoma which is the most serious skin cancer worldwide, has rarely been reported ^{31, 32}.

Herein, we examined how fatostatin inhibits transplanted tumor growth metabolically and how such metabolic alterations influence the effector functions of CTLs. We found that fatostatin effectively inhibited the growth of B16 melanoma, MC38 colon cancer, and Lewis lung cancer (LLC) transplanted tumors in mice through reducing SREBPs mediated lipids metabolism in the subcutaneous grafted tumor tissue, especially cholesterol levels. Medium and high doses of fatostatin showed significantly changes in metabolic levels, which mainly focused on tryptophan metabolism, glycolysis and gluconeogenesis metabolism, cysteine and methionine metabolism. Mechanically, fatostatin decreased intracellular cholesterol accumulation and inhibited XBP1-mediated ER stress, thereby reducing the proportion of Treg cells in the TME and suppressing CD8⁺ T cells exhaustion, exerting an anti-tumor function.

2.1 Fatostatin effectively inhibits the growth of B16, MC38 and LLC transplanted tumors in mice

To observe the potential effect of fatostatin on the growth of melanoma, colon cancer and lung cancer, we established B16, MC38, LLC transplanted models and administered intraperitoneal injection of high, medium, and low doses (7.5, 15, 30 mg/kg) of fatostatin for sequential drug intervention. The results found that all doses of fatostatin in these three transplanted models indicated significant tumor-suppressive effects (Fig. 1). In the B16 model, the low dose of fatostatin had the best tumor inhibitions (Fig. 1B). Different from the B16 model, the MC38 and LLC models both showed the best tumor inhibitions in the medium dose group (Fig. 1D-F).

Fatostatin has been reported to exert an anti-tumor effect in some cancers through directly inhibiting cell proliferation and promoting apoptosis and cell cycle arrest in cancer cells. Here, MC38 and B16 tumor tissues were digested enzymologically and separated into single cells for flow cytometry to detect the proportion of Annexin Ⅴ /PI apoptotic cells and prepared into frozen sections for immunofluorescence to evaluate the cleaved-caspase 3 expression in tumor tissue. The results showed that fatostatin induced apoptosis in cancer cells as it tended to increase the proportion of apoptotic tumor cells with the increased doses (Fig. 1G-I).

In addition, to preliminary investigate the toxic effects of fatostatin on mice, we measured the changes in body weight and vital organ weight coefficients of mice in each dose group. The results showed that only high doses of fatostatin (30 mg/kg) caused significant weight loss in mice, while the weight coefficients of major organs did not change obviously (Fig.S1A-F). The HE staining data of liver and kidney tissues also showed no obvious pathological changes (Fig.S1G).

The above results indicated that low and medium doses of fatostatin can significantly inhibit the growth of the three types of tumors without significant toxic effects, and this suppressive effect may be partially dependent on the induction of apoptosis in tumor cells.

2.2 Effect of fatostatin on the overall metabolic level in the TME

Fatostatin is widely believed to be able to act by inhibiting SREBP activation, rather than by inhibiting lipogenesis. The accumulation of various lipids including triacylglycerol, ceramide, and dihydroceramide, has been observed in breast cancer cells. To date, the exact metabolic changes induced by fatostatin in the local tumor microenvironment are unclear. In order to comprehensively observe the effect of fatostatin on the overall metabolic level in the TME, we performed a pseudotargeted metabolomics analysis of MC38 and B16 tumor tissue based on HPLC-MS ³³.. An unsupervised PCA analysis was conducted to visualize the intervention effects of different concentrations of fatostatin, showed that no obvious differences between the control group and low dose group, however, the medium and high dose groups were significantly distinguished from the control group (Fig. 2A, D). Next, the OPLS-DA model showed that the control group was significantly distinguished from the medium and high dose groups in both the B16 and MC38 models, respectively (Fig. 2B, E). Therefore, volcano plot analysis could be used to screen for differential metabolites (Fig. 2C, F).

The results of the layer clustering heat map analysis indicated a clear distinction between metabolites in the medium dose group compared with the control group (Fig. 3A, C). Pathway and enrichment analyses of the differential metabolites between the control group and the medium dose group showed that the main biochemical pathways affected by fatostatin in the MC38 model include the purine metabolism, methionine metabolism and methionine metabolism, glycolysis and gluconeogenesis, tryptophan metabolism, meanwhile, mitochondrial beta-oxidation of long chain saturated fatty acids and phospholipid biosynthesis also impacted (Fig. 3B). In the B16 model, the main metabolic pathways affected by fatostatin were Warburg effect, glutamate metabolism, gluconeogenesis and glycolysis, tryptophan metabolism, cysteine and methionine metabolism (Fig. 3D).

These data suggest that the most significant metabolic changes induced by fatostatin in the MC38 and B16 subcutaneous grafted tumor tissue are mainly concentrated in the metabolic pathways of tryptophan metabolism, glycolysis and gluconeogenesis metabolism, cysteine and methionine metabolism.

2.3 Fatostatin significantly downregulates lipid metabolism in the TME

To further confirm whether fatostatin caused lipid metabolism changes in local tumor tissues, we first examined the mRNA levels of SREBP1 and SREBP2 as well as protein expression levels in tumors. The results showed that fatostatin downregulated the expression levels of activated sheared SREBP1 and SREBP2 proteins but did not effectively reduce the mRNA expression levels of Srebf1 and Srebf2 in tumor tissues (Fig. 4A-D).

We reanalyzed metabolomic data related to fatty acid, tryptophan, and cholesterol metabolites and examined the free and total cholesterol levels in tumors using Amplex Red cholesterol Assay kit. The results showed that some lipid metabolites were indeed significantly reduced in the medium and high dose groups compared with the control group (Fig. 4E and Fig.S2). Notably, the free cholesterol and total cholesterol levels in MC38 tumors were decreased significantly in all dose groups. (Fig. 4F, G).

The above results indicated that fatostatin effectively inhibited SREBPs mediated lipid metabolism levels in tumors, especially the content of total and free cholesterol in the TME, suggesting that the inhibitory effect of fatostatin on tumor growth could be more closely related to its reduction of cholesterol metabolism in tumors.

2.4 Fatostatin significantly reduces the proportion of Treg cells and inhibits CD8 ⁺ T cells exhaustion in the TME

Tumor-infiltrating T lymphocytes have been shown to play a key role in tumor development and influence the clinical prognosis of cancer patients. However, the large number of Treg cells in the tumor immune microenvironment is thought to be the "culprit" of tumor cells evading the body's immune surveillance ³⁴. CD8⁺ T cells are often unable to control tumor growth due to functional exhaustion or dysfunction caused by the tumor immunosuppressive environment, particularly by Treg cells ³⁵. Exhausted CD8⁺ T cells tend to overexpress suppressor receptors such as programmed cell death-1 (PD-1), lymphocyte activation gene-3 (LAG-3), T cell immunoglobulin-3 (TIM-3), 2B4 and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) and have increased levels of apoptosis in CD8⁺ T cells³⁶.

Since cholesterol metabolism in tumor microenvironment affects CD8⁺ T cell-mediated tumor immunity, we hypothesized that fatostatin-reduced cholesterol levels in the TME might enhance the antitumor effect of CD8⁺ T cells. To test this conjecture, we performed flow cytometry and immunofluorescence to examine the effect of fatostatin on Treg cells and CD8⁺ T cells. The data showed that fatostatin significantly reduced the proportion of Treg cells infiltrating the tumor in MC38 and B16 models (Fig. 5A-D). Immunofluorescence results also showed that the protein expression of Foxp3 in tumor tissues was significantly decreased by low and medium doses of fatostatin (Fig. 5E, F).

We also used flow cytometry to examine the exhaustion and apoptosis of CD8⁺ T cells in TIL, revealing that fatostatin significantly decreased the proportion of apoptotic and PD-1⁺ depleted T cells in tumor-infiltrating CD8⁺ T cells. Fatostatin can effectively reduce the proportion of Treg cells in the TME and inhibit the exhaustion and apoptosis of CD8⁺ T cells in the MC38 model (Fig. 5A-C, G-I) and the B16 model (Fig. 5B-D, J-K and Fig.S3B), but not inhibit the apoptosis of CD8⁺ T cells of the high dose group in the B16 model compared with the control group (Fig. 5J, K and Fig.S3B).

The spleen is the centre of cellular and humoral immunity and can perform anti-tumor, anti-viral and other immune functions through a variety of mechanisms. Next, we also examined the effects of fatostatin on Treg cells and CD8⁺ T cells in the spleens of MC38 model and B16 model mice. The data showed that fatostatin did not exert significant influence on the Treg cells and the exhaustion and apoptosis of CD8⁺ T cells in the spleen of MC38 and B16 model mice, except for the high dose group where fatostatin slightly increased the apoptosis of CD8⁺ T cells in the spleen of MC38 model mice (Fig.S3E, F). This suggests that the regulatory effect of fatostatin on Treg cells and CD8⁺ T cells is tumor specific.

To further validate the effect of fatostatin on Treg cells and CD8⁺ T cells in the TME, we constructed an in vitro cell model in which the supernatant of MC38 cells stimulated the differentiation of primary cultured splenic lymphocytes^{18, 35}. The subsequent flow cytometry confirmed that fatostatin can significantly reduce the proportion of Treg cells, PD-1 ⁺ CD8⁺ T cells and Annexin V⁺ CD8⁺ T cells enhanced by the TS group (Fig. 6A, B).

Together, these data revealed that fatostatin can effectively reduce the proportion of Treg cells infiltrating tumors and inhibit the exhaustion and apoptosis of CD8⁺ T cells in the TME, promoting the CD8⁺ T cell-mediated antitumor immunity both in vivo and in vitro.

2.5 Fatostatin downregulates cholesterol levels in T lymphocytes in the TME

Recent evidence has shown that cholesterol accumulation in the TME can cause the exhaustion of CD8⁺ T cells ¹⁸. In vivo experiments described above have identified that fatostatin significantly decreased cholesterol levels in transplanted tumors, reducing the proportion of Treg cells and apoptosis and exhaustion of CD8⁺ T cells in TIL. The inhibitory effects of fatostatin on cholesterol levels within lymphocytes proliferation and the percentages of Treg cells and CD8⁺ T cells were observed in vitro. The results showed that fatostastin was able to reduce the protein levels of SREBP2 with the decrease in cholesterol levels in splenic lymphocytes (Fig. 6C, D). Additionally, knockdown of SREBP2 in splenic lymphocytes reduced the proportion of Treg cells and exhaustion of CD8⁺ T cells (Fig.S3G-K). To reduce intracellular cholesterol, the addition of β-cyclodextrin (β-CD) alone, consistent with fatostatin, was found to lower intracellular cholesterol levels while significantly inhibiting the TS-induced increase in Treg cells ratio and CD8⁺ T cells exhaustion (Fig. 6F, G) ^{35, 37, 38}. Furthermore, β-CD did not further enhance the inhibitory effect of fatostatin on the proportion of Treg cells and CD8⁺ T cells exhaustion, and consequent changes in intracellular cholesterol levels (Fig. 6E-G).

Our results suggest that fatostatin attenuates the immunosuppressive microenvironment by effectively reducing the cholesterol content in lymphocytes, significantly decreasing the proportion of Treg cells and the exhaustion of CD8⁺ T cells.

2.6 Fatostatin inhibits XBP1-mediated ER stress by reducing intracellular cholesterol accumulation

Cholesterol accumulation in tumor-infiltrating lymphocytes was found to induce an endoplasmic reticulum (ER) stress response and unfolded protein response (UPR) response. We next investigated whether fatostatin exerts immunomodulatory effects depended on the inhibition of ER stress caused by SREBP2 mediated cholesterol metabolism. Real-time PCR results showed that the mRNA levels of ER related genes Eif2ak3, Atf4, Atf6, Xbp1s (the active form of XBP1) were significantly upregulated in the TS group compared with the control group, however, only one of these genes, Xbp1s, was reduced by fatostatin (Fig. 7A). Meanwhile, fatostatin was also found to inhibit the expression of UPR related gene Ddit3 (Fig. 7A). In addition, the results of flow cytometry and immunofluorescence of reactive oxygen species (ROS) levels in ER also showed a similar inhibitory effect after fatostatin intervention (Fig. 7B-D). To further verify that the immunomodulatory effect of fatostatin depends on the XBP1-mediated ER stress response, XBP1-specific inhibitors STF-083010 (STF) were used. The results showed that STF intervention alone can significantly inhibit the ratio of the Treg cells and exhaustion of CD8⁺ T cells, but STF used in combination with fatostatin did not enhance the inhibitory effect of fatostatin remarkably (Fig. 7E, F). Immunoblotting results also confirmed that fatostatin effectively reduced the protein levels of XBP1s in the transplanted tumor tissue and the splenic lymphocytes (Fig. 7G, H). Moreover, knockdown of XBP1s in splenic lymphocytes also decreased the proportion of Treg cells and exhaustion of CD8⁺ T cells (Fig. 7I-K).

Altogether, our data revealed that fatostatin can inhibit the activation of XBP1 induced by cholesterol accumulation in the TME and suppress ER stress response, thereby leading to the decrease in the proportion of Treg cells and CD8⁺ T cells exhaustion, ultimately exert anti-tumor effects.

Alterations in tumor lipid metabolism contribute to the initiation and progression of tumor, as well as maintaining an immunosuppressive microenvironment ^{39, 40}. Despite recent advances in targeting lipid metabolism to interfere with tumor growth and metastasis, there are still key challenges in developing cancer therapies by targeting aberrant lipid metabolism, mainly due to the limited understanding of the mechanisms regulating lipid synthesis, storage, utilization and efflux in cancer cells. And targeting SREBPs could be a promising therapeutic strategy for tumor suppression by effectively regulating fatty acid and cholesterol metabolism ^{41, 42}.

Fatostatin, a SCAP/SREBP inhibitor, can bind to SCAP and inhibit SREBP1/2 activation. Our in vivo and in vitro experiments have confirmed that fatostatoin can reduce the lipid metabolism level through inhibiting the activation of SREBP-1 and SREBP-2, especially the cholesterol content under a dose-dependent manner. Notably, in the three transplanted models, a better tumor suppression rate was achieved instead at lower doses (7.5 mg/kg, 15 mg/kg), suggesting that excessive inhibition of lipid metabolism in the body is not conducive to tumor control. Previous studies have found the antitumor activity of fatostatoin is not completely dependent on the regulation of lipid metabolism level, but also directly induces apoptosis of tumor cells and affects mitotic cell division. Here, we also found that the tumor suppressive effect of fatostatoin was partially dependent on the induction of apoptosis.

Next, we are the first to demonstrate that fatostatin can inhibit Treg cells and the exhaustion of CD8⁺ T cells, by reducing the cholesterol metabolism of lymphocytes in the TME. Immunosuppressive cells, represented by Treg cells, mediate the suppression of the effector function of infiltrating T cells in the TME. Tumor-specific CD8⁺ T cells are exposed to continuous antigenic stimulation, resulting in a dysfunctional state known as " exhaustion ", leading to the inability of CD8⁺ T cells to control tumor progression⁴³. Exhausted CD8⁺ T cells will overexpress suppressor receptors such as PD-1, TIM-3 and CTLA-4. Currently, antibodies against inhibitory receptors, particularly against CTLA-4 and PD-1, have successfully enhanced T-cell effector function, leading to improved clinical efficacy in the treatment of several solid tumors, including advanced melanoma ⁴⁴, non-small cell lung cancer⁴⁵, renal cell carcinoma and metastatic bladder cancer⁴⁶. Despite its great clinical success, most patients have not experienced complete remission and have even developed resistance to the drug⁴⁷. Our data suggest that fatostatin can reduce the proportion of Treg cells and inhibit the exhaustion of CD8⁺ T cells, thereby reversing the immunosuppressive microenvironment. Therefore, fatostatin may be able to be used as an adjuvant to ICIs, reducing the development of their drug resistance and enhancing their anti-tumor function.

Cholesterol accumulation in the TME has been shown to trigger ER stress and further increase T cell exhaustion. It has been shown that high levels of cholesterol can activate the IRE1α-XBP1 signaling pathway in intratumoral T cells, thereby increasing PD-1 expression⁴⁸. Minkyung Song et al. also found that selective knockdown of XBP1 on T cells significantly enhanced anti-tumor immunity in ovarian cancer transplanted tumor-bearing mice⁴⁹. Our data also suggest that inhibition of XBP1 activation facilitates a reduction in the proportion of Treg cells and suppresses CD8⁺ T cells exhaustion in the TME, supporting the possibility of XBP1 as a target for enhanced immunotherapy. Thus, controlling ER stress or targeting the IRE1α-XBP1 signaling pathway may help restore the metabolic fitness and anti-tumor capacity of T cells in cancer hosts.

Recent evidence supported that as being a key regulatory protein of lipid metabolism, SCAP is also an important sensor connected to glucose metabolism and glutamine metabolism⁵⁰. The function of SCAP to stabilize SREBP in ER was interfered with by fatostatin, which may directly change other related glucose and amino acid metabolism pathways. Our metabolomics data also found that the differential metabolites caused by fatostatin more concentrated on the metabolic pathways of amino acids such as glycolysis, cysteine, arginine and tryptophan, as well as intermediate metabolites of glycolysis and gluconeogenesis. We were surprised to notice that fatostatin is likely to increase tryptophan (TRP) metabolized into kynurenine (KYN) and reduce its conversion to serotonin and indoles, while lowering cholesterol levels in tumor tissue. It was well known that indoleamine 2,3-dioxygenase 1 (IDO1)-mediated TRP-KYN pathway is one of the most widely studied metabolic pathways involved in cancer immune tolerance and evasion. The depletion of TRP and the accumulation of KYN can have a direct suppressive effect on CD8⁺ CTLs function, contributing strongly to immunosuppressive tumor microenvironment. The Tregs attenuation and CD8⁺ T cells inhibition by high dose of fatostatin is likely to be influenced by enhanced IDO1-mediated KPN pathway metabolites, and whether the reduced cholesterol is involved in the regulation needs to be further explored.

In conclusion, our results revealed that fatostatin exerts anti-tumor effects by reducing cholesterol levels in tumor-infiltrating lymphocytes and inhibiting XBP1-mediated ER stress, thereby reducing the proportion of Treg cells, and suppressing CD8⁺ T cells exhaustion in the TME. Our results highlight the important role of SREBP2 mediated cholesterol in the inhibition of lipid synthesis induced by fatostatin and suggest that the use of fatostatin to target SREBP2/SCAP may represent a potential anti-tumor therapeutic strategy in the treatment of malignant melanoma, lung cancer, and colon cancer.

4.1 Reagents and antibodies

Fatostatin was purchased from Selleck Chemicals (S8284, Houston, USA). β-cyclodextrin was purchased from Solarbio (C8511, Beijing, China). STF-083010 was purchased from Biochempartner (307543-71-1, Shanghai, China). Annexin V-FITC/PI Apoptosis Detection Kit (A211-01), RNA-easy Isolation Reagent (R701-01), HiScript II 1st Strand cDNA Synthesis Kit (R211-01) and AceQ qPCR SYBR Green Master Mix (Low ROX Premixed) (Q131-02) were purchased from Vazyme Biotechnology (Nanjing, China). RIPA lysis buffer (FD011) was purchased from FUDE Biological Technology (Hangzhou, China). Anti-β-Actin (60008-1-Ig), anti-SREBF1 (14088-1-AP), anti-SREBF2 (28212-1-AP), anti-XBP1s (24868-1-AP) and anti-FOXP3 (22228-1-AP, 1:500 for immunofluorescence (IF)) antibodies were purchased from Proteintech (Wuhan, China). Anti-Cleaved Caspase-3 (9664S, 1:400 for IF) antibody was purchased from Cell Signaling Technology (MA, USA). HRP* Goat Anti Rabbit IgG(H + L) (RS0002) and HRP* Goat Anti Mouse IgG(H + L) (RS0001) were purchased from ImmunoWay Biotechnology (TX, USA). Anti-CD45 (45-0451-82), anti-CD4 (11-0041-85), anti-CD25 (17-0251-82), anti-Foxp3 (12-5773-82) and anti-PD-1 (17-9985-82) antibodies for flow cytometry, Amplex Red cholesterol Assay kit (A12216), Foxp3 / Transcription Factor Fixation/Permeabilization Concentrate and Diluent (00-5521-00) and Pierce™ BCA Protein Assay Kit (23227) were purchased from Thermo Fisher Scientific (MA, USA). Anti-CD8 (100708) antibodies for flow cytometry was purchased from Biolegend (CA, USA). 4,6-Diamidino-2-phenylindole (DAPI; KGA215-10) was purchased from KeyGen Biotech (Nanjing, China). Reactive Oxygen Species Assay Kit (S0033S), Lipo8000™ Transfection Reagent (C0533) and ER-Tracker Red (C1041) were purchased from Beyotime Biotechnology (Shanghai, China). Polybrene (40804ES76) was purchased from YEASEN Biotech (Shanghai, China).

4.2 Cell culture and establishment of in vitro co-culture model

Mouse colorectal cancer (MC38), Lewis lung cancer (LLC), Melanoma (B16) and 293T cell lines were purchased from the Chinese Academy of Sciences Cell Bank (Shanghai, China). The MC38, LLC and 293T cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, CA, USA) with 10% fetal bovine serum (FBS; Gibco, CA, USA). The B16 cells were cultured in Roswell Park Memorial Institute 1640 medium (RPMI-1640, Gibco, CA, USA) with 10% FBS. The primary lymphocytes isolated from the spleen of C57/B6J mice were cultured in RPMI-1640 with 10% FBS. Tumor supernatant (TS) was obtained from s.c. grown MC38 tumor (1 mg tumor in 1 mL DMEM medium with 10% FBS, filtered). All the cells were maintained at 37 ℃ in a humidified atmosphere with 5% CO2. The TS was added to the primary lymphocytes, the appropriate reagents were added, and the cells were cultured for 48 hours before being collected for detection.

4.3 Animal experiments

C57/B6J mice (5–6 weeks of age) were obtained from Cavens-biogle (Suzhou, China), and kept in the animal facility under standard specific pathogen-free (SPF) conditions. All animal experiments were performed in consistent with protocols approved by the Animal Welfare and Ethics Committee of China Pharmaceutical University. After one week of acclimatization, the mice were randomly divided into four groups for the experiment. Mice were inoculated with 1 x 10⁵ B16 cells or 1 × 10⁵ MC38 cells or 5 × 10⁵ LLC cells per mouse in the axillary. After 5 days, the corresponding dose of fatostatin was administered intraperitoneally to the mice for 21 days. Weight and tumor volumes (mm³) were measured by the formula (V=п /6× (Longest diameter) × (Shortest diameter) ^²) every 2 days. Tumor growth inhibitory rates were measured by the formula ((1-Treated group/Control group) × 100%). At the end of the experiment, the mice were sacrificed. The subcutaneous grafted tumors were removed, photographed, and sectioned for follow-up experiments.

4.4 Quasi targeted metabolomics

Tumor tissue was homogenized in 80% methanol, the supernatant liquid was dried to powder under a stream of nitrogen at room temperature. The redissolved samples (methanol: acetonitrile: water = 5:3:2) were detected by liquid mass spectrometry QTRAP 6500+(SCIEX) with VanGuard column + BEH Amide column (100 mm × 2.1 mm I.D., 1.7µm). After the detection, the peak area of the material to be measured in the sample can be obtained and imported into Excel. GraphPad 8.0 (GraphPad Software, CA, USA) was used to analyze the data.

4.5 RNA extraction and quantitative Real-time polymerase chain reaction (qRT-PCR) analysis

The total RNA of cells and tumor tissues was extracted using RNA-easy Isolation Reagent according to the instructions of the manufacturer and reverse-transcribed into cDNA following the protocols of HiScript II 1st Strand cDNA Synthesis Kit. qRT-PCR was performed using the AceQ qPCR SYBR Green Master Mix (Low ROX Premixed) and specific PCR primers (Sangon, Shanghai, China). The expressions of the indicated genes were normalized to the expression of the housekeeping gene β-Actin by using the 2^−ΔΔCt method. The sequences of the primers used for qRT-PCR in this study were:

β-Actin (F: GGCTGTATTCCCCTCCATCG; R: CCAGTTGGTAACAATGCCATGT)

Srebf1 (F: TGACCCGGCTATTCCGTGA; R: CTGGGCTGAGCAATACAGTTC)

Srebf2 (F: GCAGCAACGGGACCATTCT; R: CCCCATGACTAAGTCCTTCAAT).

4.6 Western blot assay

The total proteins of cells and tumor tissues were extracted from the RIPA lysis buffer for 1 hour on the ice and then centrifuged at 13,000 rpm for 30 minutes at 4°C. The supernatant was obtained and the protein concentration was detected by Pierce™ BCA Protein Assay Kit, following the manufacturer's protocol. Then, an equal amount of sample was run on 8% sodium dodecyl sulfate polyacrylamide gel electrophoresis. The proteins were transferred to a nitrocellulose filter membrane (66485, Pall Corporation, NY, USA). Proteins were incubated with primary antibodies of SREBF1 (1:1000), SREBF2 (1:1000), XBP1s (1:1000) and β-Actin (1:5000) overnight at 4℃ and then incubated with corresponding secondary antibodies for 1 hour at room temperature. Signals were detected by ECL detection reagents (Tanon, Shanghai, China).

4.7 Cell apoptosis

The tumor tissues were cut into small pieces and digested with collagenase IV (V900893, Sigma) and DNase I (10104159001, Roche, Mannheim, Germany) at 200 rpm for 1 hour at 37°C to liberate cell populations. The cells were collected and labeled with Annexin V and PI according to the protocols of Annexin V/PI Cell Apoptosis Detection Kit. Results were acquired by BD FACSCelesta™ flow cytometer (NJ, USA) and the data were analyzed with FlowJo software (NJ, USA).

4.8 Flow cytometry analysis

The tumor tissue digestion was separated by centrifugation on a 30%/70% Percoll (BS909, Biosharp, Beijing, China) gradient. Cells at the 30%/70% interface were collected as tumor-infiltrating lymphocytes. Harvested cells were stained with combinations of the following antibodies against CD45, CD4, CD25, Foxp3, CD8, PD-1, and Annexin V. For staining Foxp3, cells were stained with surface markers before fixed and permeabilized following the manufacturer instructions of Foxp3 / Transcription Factor Fixation/Permeabilization Concentrate and Diluent. Results were detected by flow cytometry.

4.9 ROS levels detection

The cells after treatment were harvested and stained with DCFH-DA (10 mM, diluted in the serum-free medium into 10 µmol/L). The cells were incubated at 37 ℃ for 20 minutes. Then cells were washed in the serum-free medium 3 times to remove the remaining DCFH-DA. Finally, the cells were resuspended in phosphate-buffered saline (PBS) and detected by flow cytometry.

4.10 Immunofluorescence

Frozen sections were left at room temperature for 30 minutes. Then the frozen sections were fixed in 4% paraformaldehyde for 15 minutes and permeabilized in 0.15% Triton X-100 for 30 minutes. After blocking with 5% bovine serum albumin (BSA) for 1 hour at room temperature, the frozen sections were incubated with primary antibody overnight at 4 ℃ followed by incubated with Alexa Fluor secondary antibody for 1 hour and DAPI. The frozen sections were observed with a confocal laser scanning microscope (LSM700, Zeiss, Oberkochen, Germany).

4.11 Detection of ER and ROS by immunofluorescence

The cells after treated were harvested and incubated with the ER-Tracker Red (1 µl/ml) for 30 minutes at 37℃, then washed in the medium twice to remove the remaining staining working solution. Next, DCFH-DA was loaded according to the steps mentioned above. The cells were observed with a confocal laser scanning microscope.

4.12 Viral Transduction

Plasmid pLKO.1-shSREBP2, Plasmid pLKO.1-shXBP1s and Plasmid pLKO.1-shNC were purchased from Tsingke Biotechnology (Beijing, China). Viruses were packaged into 293T cells transfected with Lipo8000. Viral supernatant was harvested from day 2 through day 3, filtered with a 0.45-µM filter (Biosharp, Beijing, China), and stored at -80℃ until use. Lymphocytes were mixed with virus and 10 µg/ml Polybrene in a 6-well plate, using the flat angle centrifuge for incubation at 1800 rpm at 32℃ for 2 hours. After that, the medium was replaced with a fresh culture medium. The cells were harvested and used for the following experiments after 3 days.

ShRNA target sequence:

SREBP2: AATGAACAAGGCTTAGTCAGG

XBP1: GGTTGAGAACCAGGAGTTAAG

4.13 Statistical analysis

All experiments were repeated at least three independent experiments performed in a parallel manner. Tumor volume change results were expressed as mean ± standard error of the mean (mean ± SEM) and the rest of the calculated data were expressed as mean ± standard deviation (mean ± SD). Two-tailed unpaired t tests were used when comparing two groups. When comparing multiple groups, ANOVA tests were used with Tukey’s multiple comparisons tests to calculate adjusted P values. A value of P < 0.05 was considered statistically significant, nonsignificant P values were indicated as “ns”. All statistical analyses were conducted using GraphPad 8.0.

Acknowledgement

We acknowledge the Cellular and Molecular Biology Center of China Pharmaceutical University for the use of instrumentation facilities, and thank Ms. Minhui Sun, Ms. Xiaonan Ma, and Mr. Yingjian Hou for their technical guidance.

Conflict of interest

The authors declare no competing financial interests.

Author contributions

L. Zhu: Data curation, validation, investigation, software, writing-original draft; Y. Shi: Data curation, validation, investigation, visualization, writing-original draft; Z. Feng: Validation, investigation, formal analysis; D. Yuan: Validation, investigation; S. Guo: Validation, investigation; Y. Wang: Methodology, formal analysis; H. Shen: Methodology, formal analysis; Y. Li: Writing-review and editing; F. Yan: Supervision; Y. Wang: Project administration, funding acquisition, supervision, writing-original draft, writing-review and editing.

Ethics statement

All animal work procedures were approved by the Animal Welfare and Ethics Committee of China Pharmaceutical University.

Funding statement

This study was supported by the National Natural Science Foundation of China (No. 81872903), the “Double First-Class” University project of China Pharmaceutical University (CPU2018GY03).

Bian X, Liu R, Meng Y, Xing D, Xu D, Lu Z. Lipid metabolism and cancer. J Exp Med 2021, 218(1).
Martínez-Reyes I, Chandel NS. Cancer metabolism: looking forward. Nat Rev Cancer 2021, 21(10): 669–680.
Ackerman D, Simon MC. Hypoxia, lipids, and cancer: surviving the harsh tumor microenvironment. Trends Cell Biol 2014, 24(8): 472–478.
Yu W, Lei Q, Yang L, Qin G, Liu S, Wang D, et al. Contradictory roles of lipid metabolism in immune response within the tumor microenvironment. J Hematol Oncol 2021, 14(1): 187.
Yu X, Mi S, Ye J, Lou G. Aberrant lipid metabolism in cancer cells and tumor microenvironment: the player rather than bystander in cancer progression and metastasis. J Cancer 2021, 12(24): 7498–7506.
Snaebjornsson MT, Janaki-Raman S, Schulze A. Greasing the Wheels of the Cancer Machine: The Role of Lipid Metabolism in Cancer. Cell Metab 2020, 31(1): 62–76.
Manzo T, Prentice BM, Anderson KG, Raman A, Schalck A, Codreanu GS, et al. Accumulation of long-chain fatty acids in the tumor microenvironment drives dysfunction in intrapancreatic CD8 + T cells. J Exp Med 2020, 217(8).
Wang R, Liu H, He P, An D, Guo X, Zhang X, et al. Inhibition of PCSK9 enhances the antitumor effect of PD-1 inhibitor in colorectal cancer by promoting the infiltration of CD8(+) T cells and the exclusion of Treg cells. Front Immunol 2022, 13: 947756.
Xu S, Chaudhary O, Rodriguez-Morales P, Sun X, Chen D, Zappasodi R, et al. Uptake of oxidized lipids by the scavenger receptor CD36 promotes lipid peroxidation and dysfunction in CD8(+) T cells in tumors. Immunity 2021, 54(7): 1561–1577 e1567.
Baek AE, Yu YA, He S, Wardell SE, Chang CY, Kwon S, et al. The cholesterol metabolite 27 hydroxycholesterol facilitates breast cancer metastasis through its actions on immune cells. Nat Commun 2017, 8(1): 864.
Pathan-Chhatbar S, Drechsler C, Richter K, Morath A, Wu W, OuYang B, et al. Direct Regulation of the T Cell Antigen Receptor's Activity by Cholesterol. Front Cell Dev Biol 2020, 8: 615996.
Xu C, Gagnon E, Call ME, Schnell JR, Schwieters CD, Carman CV, et al. Regulation of T cell receptor activation by dynamic membrane binding of the CD3epsilon cytoplasmic tyrosine-based motif. Cell 2008, 135(4): 702–713.
Shi X, Bi Y, Yang W, Guo X, Jiang Y, Wan C, et al. Ca2 + regulates T-cell receptor activation by modulating the charge property of lipids. Nature 2013, 493(7430): 111–115.
Kidani Y, Elsaesser H, Hock MB, Vergnes L, Williams KJ, Argus JP, et al. Sterol regulatory element-binding proteins are essential for the metabolic programming of effector T cells and adaptive immunity. Nat Immunol 2013, 14(5): 489–499.
Molnár E, Swamy M, Holzer M, Beck-García K, Worch R, Thiele C, et al. Cholesterol and sphingomyelin drive ligand-independent T-cell antigen receptor nanoclustering. J Biol Chem 2012, 287(51): 42664–42674.
Schamel WW, Arechaga I, Risueño RM, van Santen HM, Cabezas P, Risco C, et al. Coexistence of multivalent and monovalent TCRs explains high sensitivity and wide range of response. J Exp Med 2005, 202(4): 493–503.
Zech T, Ejsing CS, Gaus K, de Wet B, Shevchenko A, Simons K, et al. Accumulation of raft lipids in T-cell plasma membrane domains engaged in TCR signalling. Embo j 2009, 28(5): 466–476.
Ma X, Bi E, Lu Y, Su P, Huang C, Liu L, et al. Cholesterol Induces CD8(+) T Cell Exhaustion in the Tumor Microenvironment. Cell Metab 2019, 30(1): 143–156.e145.
Lim SA, Wei J, Nguyen TM, Shi H, Su W, Palacios G, et al. Lipid signalling enforces functional specialization of T(reg) cells in tumours. Nature 2021, 591(7849): 306–311.
Huang B, Song BL, Xu C. Cholesterol metabolism in cancer: mechanisms and therapeutic opportunities. Nat Metab 2020, 2(2): 132–141.
Shao W, Machamer CE, Espenshade PJ. Fatostatin blocks ER exit of SCAP but inhibits cell growth in a SCAP-independent manner. J Lipid Res 2016, 57(8): 1564–1573.
Gao S, Shi Z, Li X, Li W, Wang Y, Liu Z, et al. Fatostatin suppresses growth and enhances apoptosis by blocking SREBP-regulated metabolic pathways in endometrial carcinoma. Oncol Rep 2018, 39(4): 1919–1929.
Li X, Chen YT, Hu P, Huang WC. Fatostatin displays high antitumor activity in prostate cancer by blocking SREBP-regulated metabolic pathways and androgen receptor signaling. Mol Cancer Ther 2014, 13(4): 855–866.
Li X, Wu JB, Chung LW, Huang WC. Anti-cancer efficacy of SREBP inhibitor, alone or in combination with docetaxel, in prostate cancer harboring p53 mutations. Oncotarget 2015, 6(38): 41018–41032.
Ma X, Zhao T, Yan H, Guo K, Liu Z, Wei L, et al. Fatostatin reverses progesterone resistance by inhibiting the SREBP1-NF-κB pathway in endometrial carcinoma. Cell Death & Disease 2021, 12(6): 544.
Gholkar AA, Cheung K, Williams KJ, Lo YC, Hamideh SA, Nnebe C, et al. Fatostatin Inhibits Cancer Cell Proliferation by Affecting Mitotic Microtubule Spindle Assembly and Cell Division. J Biol Chem 2016, 291(33): 17001–17008.
Liu Y, Zhang N, Zhang H, Wang L, Duan Y, Wang X, et al. Fatostatin in Combination with Tamoxifen Induces Synergistic Inhibition in ER-Positive Breast Cancer. Drug Des Devel Ther 2020, 14: 3535–3545.
Brovkovych V, Izhar Y, Danes JM, Dubrovskyi O, Sakallioglu IT, Morrow LM, et al. Fatostatin induces pro- and anti-apoptotic lipid accumulation in breast cancer. Oncogenesis 2018, 7(8): 66.
Kamisuki S, Mao Q, Abu-Elheiga L, Gu Z, Kugimiya A, Kwon Y, et al. A small molecule that blocks fat synthesis by inhibiting the activation of SREBP. Chem Biol 2009, 16(8): 882–892.
Li M, Lu Q, Zhu Y, Fan X, Zhao W, Zhang L, et al. Fatostatin inhibits SREBP2-mediated cholesterol uptake via LDLR against selective estrogen receptor alpha modulator-induced hepatic lipid accumulation. Chem Biol Interact 2022, 365: 110091.
Arnold M, Singh D, Laversanne M, Vignat J, Vaccarella S, Meheus F, et al. Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040. JAMA Dermatol 2022.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71(3): 209–249.
Zheng F, Zhao X, Zeng Z, Wang L, Lv W, Wang Q, et al. Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry. Nat Protoc 2020, 15(8): 2519–2537.
Mellman I, Coukos G, Dranoff G. Cancer immunotherapy comes of age. Nature 2011, 480(7378): 480–489.
Yang W, Bai Y, Xiong Y, Zhang J, Chen S, Zheng X, et al. Potentiating the antitumour response of CD8(+) T cells by modulating cholesterol metabolism. Nature 2016, 531(7596): 651–655.
Wherry EJ. T cell exhaustion. Nat Immunol 2011, 12(6): 492–499.
Abe M, Kobayashi T. Imaging cholesterol depletion at the plasma membrane by methyl-β-cyclodextrin. J Lipid Res 2021, 62: 100077.
Ma X, Bi E, Huang C, Lu Y, Xue G, Guo X, et al. Cholesterol negatively regulates IL-9-producing CD8(+) T cell differentiation and antitumor activity. J Exp Med 2018, 215(6): 1555–1569.
Cheng C, Geng F, Cheng X, Guo D. Lipid metabolism reprogramming and its potential targets in cancer. Cancer Commun (Lond) 2018, 38(1): 27.
Mullen PJ, Yu R, Longo J, Archer MC, Penn LZ. The interplay between cell signalling and the mevalonate pathway in cancer. Nat Rev Cancer 2016, 16(11): 718–731.
Alannan M, Fayyad-Kazan H, Trézéguet V, Merched A. Targeting Lipid Metabolism in Liver Cancer. Biochemistry 2020, 59(41): 3951–3964.
Shao W, Espenshade PJ. Expanding roles for SREBP in metabolism. Cell Metab 2012, 16(4): 414–419.
Jiang W, He Y, He W, Wu G, Zhou X, Sheng Q, et al. Exhausted CD8 + T Cells in the Tumor Immune Microenvironment: New Pathways to Therapy. Front Immunol 2020, 11: 622509.
Olson DJ, Eroglu Z, Brockstein B, Poklepovic AS, Bajaj M, Babu S, et al. Pembrolizumab Plus Ipilimumab Following Anti-PD-1/L1 Failure in Melanoma. J Clin Oncol 2021, 39(24): 2647–2655.
Yu H, Boyle TA, Zhou C, Rimm DL, Hirsch FR. PD-L1 Expression in Lung Cancer. J Thorac Oncol 2016, 11(7): 964–975.
Aggen DH, Drake CG. Biomarkers for immunotherapy in bladder cancer: a moving target. J Immunother Cancer 2017, 5(1): 94.
Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 2012, 12(4): 252–264.
Chen X, Cubillos-Ruiz JR. Endoplasmic reticulum stress signals in the tumour and its microenvironment. Nat Rev Cancer 2021, 21(2): 71–88.
Song M, Sandoval TA, Chae CS, Chopra S, Tan C, Rutkowski MR, et al. IRE1α-XBP1 controls T cell function in ovarian cancer by regulating mitochondrial activity. Nature 2018, 562(7727): 423–428.
Cheng C, Ru P, Geng F, Liu J, Yoo JY, Wu X, et al. Glucose-Mediated N-glycosylation of SCAP Is Essential for SREBP-1 Activation and Tumor Growth. Cancer Cell 2015, 28(5): 569–581.

(Not answered)

Download PDF

Journal Publication

published 29 Feb, 2024

Read the published version in European Journal of Pharmacology →

Version 1

posted

You are reading this latest preprint version

Fatostatin promotes anti-tumor immunity by reducing SREBP2 mediated cholesterol metabolism in tumor-infiltrating T lymphocytes

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Results

2.1 Fatostatin effectively inhibits the growth of B16, MC38 and LLC transplanted tumors in mice

2.2 Effect of fatostatin on the overall metabolic level in the TME

2.3 Fatostatin significantly downregulates lipid metabolism in the TME

2.5 Fatostatin downregulates cholesterol levels in T lymphocytes in the TME

2.6 Fatostatin inhibits XBP1-mediated ER stress by reducing intracellular cholesterol accumulation

3. Discussion

4. Materials And Methods

4.1 Reagents and antibodies

4.2 Cell culture and establishment of in vitro co-culture model

4.3 Animal experiments

4.4 Quasi targeted metabolomics

4.5 RNA extraction and quantitative Real-time polymerase chain reaction (qRT-PCR) analysis

4.6 Western blot assay

4.7 Cell apoptosis

4.8 Flow cytometry analysis

4.9 ROS levels detection

4.10 Immunofluorescence

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1