PPAR suppresses ALDH1A3 to inhibit lung cancer progression
INTRODUCTION
Lung cancer is the most frequently diagnosed cancer and the leading cause of cancer-related deaths globally Despite extensive research into lung cancer therapies, patients often experience drug resistance and tumor relapse within 10 to 14 months To enhance clinical outcomes, a comprehensive understanding of lung cancer pathology and biology is essential.
Peroxisome proliferator-activated receptor gamma (PPARγ) plays a crucial role in regulating lipid and glucose metabolism, and it has been identified as a potential tumor suppressor Despite its significance, the precise metabolic functions of PPARγ remain poorly understood, necessitating further research This study aims to investigate the biochemical role of PPARγ in managing oxidative stress by focusing on the regulation of aldehyde dehydrogenases 1 family member A3.
1.1 Peroxisome proliferator-activated receptor gamma (PPAR)
PPAR is a member of the nuclear receptor transcription factor superfamily, which regulates target genes through specific ligand binding There are three types of PPARs: PPAR, PPAR/, and PPAR The structure of PPAR includes an N-terminal transactivation domain (AF1), a DNA-binding domain (DBD), a hinge region, and a ligand-binding domain (LBD) that features a ligand-dependent transactivation function at the C-terminal The AF1 domain varies significantly among nuclear receptors, while the DBD is highly conserved, consisting of two zinc fingers that recognize peroxisome proliferator response elements (PPREs) Each zinc finger contains four cysteine residues that form a complex with a zinc ion PPAR binding to DNA facilitates the regulation of gene expression, allowing for both activation and suppression, with the hinge region providing flexibility between the DBD and LBD upon ligand binding.
2 into the LBD, the stable structure of the LBD facilitates the interaction of PPAR with co- regulator molecules, resulting in gene expression activation or inhibition (5, 6)
The ligand-binding domain (LBD) of PPARγ exhibits a promiscuous nature, leading to incomplete identification of its endogenous ligands Unlike synthetic PPARγ agonists, these endogenous ligands demonstrate lower selectivity for the PPAR subtype Key endogenous ligands include mono- and polyunsaturated fatty acids, 5-oxo-15-(S)-hydroxyeicosatetraenoic acid, 5-oxo-eicosatetraenoic acid, essential eicosanoids such as 8-(S)-hydroxyeicosatetraenoic acid, 15-deoxy-D12,14-prostaglandin J2 (15d-PGJ2), and serotonin (5-hydroxytryptamine).
1.1.3 Thiazolidinediones (TZDs) as synthetic ligands of PPAR
Thiazolidinediones (TZDs) are synthetic agonists of PPARγ known for their role as insulin sensitizers in the treatment of type 2 diabetes The first TZD, troglitazone (Rezulin®), was introduced in 1997 but was withdrawn from the market due to liver toxicity Rosiglitazone (Avandia®), the second TZD launched in 1999, faced removal in Europe and limited use in the US due to concerns over heart failure Pioglitazone (Actos®), also released in 1999, has been restricted due to potential bladder cancer risks Despite these concerns, pioglitazone remains the preferred TZD treatment option, although its usage has declined due to associated side effects.
PPARγ serves as a crucial regulator of lipid metabolism, influencing processes such as adipogenesis, the browning of white adipocytes, lipolysis, and insulin sensitivity Unlike PPARα or PPARβ/δ, which primarily focus on fatty acid oxidation, PPARγ is predominantly expressed in adipocytes and enhances their differentiation from fibroblasts The use of PPARγ agonists, such as thiazolidinediones (TZDs), further underscores its significant role in metabolic regulation.
The upregulation of various genes related to glucose metabolism, including CAP and GLUT4, as well as lipid metabolism genes like CD36, aP2, LPL, FATP, and acyl-CoA synthetase, GyK, UCP2, and UCP3, is influenced by PPARγ activation This activation occurs not only in adipose tissue but also in other organs Additionally, PPARγ plays a crucial role in inhibiting inflammation by reducing inflammatory cytokines and promoting the differentiation of immune cells into anti-inflammatory phenotypes.
1.1.5 Functions of PPAR in lung cancer
Lung cancer is a leading cause of cancer-related deaths, primarily divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) NSCLC accounts for approximately 85% of cases and is further categorized into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, while SCLC makes up the remaining 15% PPARγ is recognized as a tumor suppressor in lung cancer, although the roles of PPARα and PPARβ/δ remain less understood.
PPARγ plays a crucial role in cancer by promoting tumor cell differentiation, inducing apoptosis, and inhibiting cell growth Research has shown that the activation of PPARγ leads to morphological changes and an increased expression of differentiation markers such as gelsolin, Mad, and p21 Furthermore, treatment with ciglitazone induces a mature differentiation-like status in these cells In adenocarcinoma cells, PPARγ activation facilitates their conversion into a more differentiated state, underscoring its involvement in cellular differentiation.
PPARγ plays a crucial role in inhibiting cell proliferation and promoting apoptosis in lung cancer cells Troglitazone has been shown to induce growth arrest and trigger apoptosis through the activation of GADD153 Additionally, ciglitazone and 15d-PGJ2 enhance apoptosis and inhibit cell proliferation by regulating p21 and cyclin D1 Furthermore, rosiglitazone inhibits the PI3K/Akt signaling pathway while activating PTEN in various lung cancer cell lines, including H1838, H1792, and A549 Notably, high levels of PPARγ expression are observed in a specific subset of lung cancer cases.
Non-small cell lung cancer (NSCLC) cells exhibit sensitivity to PPARγ activation, and treatments with PPARγ activators like thiazolidinediones (TZDs) have been shown to decrease tumorigenesis and slow cancer progression in lung cancer xenograft models.
PPARγ plays a crucial role not only in suppressing cell proliferation but also in regulating the tumor microenvironment While normal angiogenesis involves the formation of new blood vessels, cancer cells exploit this process to promote tumor growth and metastasis, primarily through the action of vascular endothelial growth factor (VEGF) Research has shown that rosiglitazone can decrease VEGF secretion in Lewis lung carcinoma cells, thereby inhibiting angiogenesis and cancer progression in mice Additionally, myofibroblasts, which are stromal cells surrounding tumors, secrete various factors that support tumor growth Activation of PPARγ by ligands such as troglitazone, ciglitazone, and rosiglitazone promotes myofibroblast differentiation and inhibits the production of type I collagen in lung fibroblasts Given the importance of extracellular matrix remodeling in cancer progression, targeting the tumor microenvironment alongside angiogenesis inhibition using PPARγ agonists presents a promising strategy for lung cancer treatment Epidemiological studies further support this approach, revealing that among diabetic patients, those using thiazolidinediones (TZDs) experienced a 33% reduction in lung cancer diagnoses compared to non-users, highlighting the potential of TZDs in lung cancer therapy.
Aldehyde dehydrogenases (ALDHs) are NAD(P)+-dependent enzymes that facilitate the conversion of aldehydes into carboxylic acids The accumulation of aldehydes can disrupt cellular homeostasis and potentially lead to cell death, highlighting the crucial role of ALDHs in managing reactive aldehyde levels in high malignancy cases Additionally, high expression of ALDH is recognized as a significant cancer prognostic marker.
High levels of aldehyde dehydrogenases (ALDHs) are associated with poor prognosis in various cancers, including gastric, breast, and glioblastoma ALDHs, which consist of 19 subtypes, play significant roles beyond detoxifying harmful aldehydes In lung cancer, the aldehyde oxidation reaction facilitated by ALDHs contributes to NADH production, promoting energy generation that supports cancer growth Additionally, elevated ALDH activity serves as a marker for stem and progenitor cells, influencing their self-renewal and expansion capabilities.
1.3 Aldehyde dehydrogenases 1 family member A3 (ALDH1A3)
ALDH1A3, a key enzyme in the ALDH1 family, plays a crucial role in regulating various cellular functions in cancer It is notably upregulated in melanoma compared to normal melanocytes, and its depletion results in toxic aldehyde accumulation, leading to increased apoptosis and reduced tumor growth Additionally, ALDH1A3 is highly expressed in Ki67+ proliferating breast cancer cells, with higher levels correlating with poorer survival rates in breast cancer patients Furthermore, ALDH1A3 regulation has been observed in chemoresistant pleural mesothelioma cells, where its suppression not only decreases cancer cell survival but also enhances the effectiveness of chemotherapy.
HYPOTHESIS
Research indicates that ALDH1A3 plays a crucial role in generating retinoic acid from retinaldehyde, which in turn promotes the expression of PPARγ during adipogenesis This raises questions about the feedback regulation and biological relationship between PPARγ and ALDHs in lung cancer I propose a hypothesis suggesting that PPARγ activation may negatively regulate ALDH1A3, thereby enhancing PPARγ's tumor suppressor function in lung cancer.
MATERIALS AND METHODS
H1993, H1299, and HBEC cells were cultured in RPMI 1640 or DMEM medium supplemented with 5% fetal bovine serum (FBS), along with 50 U/mL of penicillin and 50 U/mL of streptomycin, at 37°C in a 5% CO2 environment Pioglitazone (sc-204848) was sourced from Santa Cruz, while diethylaminobenzaldehyde (DEAB) (D86256) was obtained from Sigma-Aldrich.
Expression of PPARG and multiple ALDH genes were analyzed from GEO database (GSE accession number: GSE4824 (47)) using Matrix 1.29 (25)
NUBIscan was utilized to identify potential PPARγ binding sites on the ALDH1A3 promoter, focusing on the PPARγ responsive elements directed repeat 1 (DR1) sequences within 5000 base pairs upstream of the transcription start site Sequences with a P-value of ≤ 0.05 were deemed potential binding sites for PPARγ on the ALDH1A3 promoter.
HBEC cells were treated with tetracycline to induce PPARγ for a ChIP assay The cells underwent crosslinking with 1% formaldehyde for 10 minutes, followed by lysis and sonication to achieve chromatin fragments ranging from 200 to 1000 base pairs PPARγ antibody, along with an IgG control antibody and protein A/G-coupled agarose beads from Invitrogen, were utilized to precipitate the PPARγ-chromatin complex The resulting purified DNA was analyzed for potential binding sites (BS) using PCR, with the binding site of PPARγ on the FABP4 promoter serving as a positive control Primer sequences are detailed in the accompanying table.
Cells were lysed with RIPA buffer containing NaCl, Triton X-100, sodium deoxycholate, SDS, Tris, and protease and phosphatase inhibitors Protein concentrations were determined using the BCA Protein Assay Kit, and samples were prepared in SDS-sample buffer Following SDS-PAGE, proteins were transferred to a nitrocellulose membrane and blocked with 5% skim milk in TBST for 1 hour at room temperature Primary antibodies were incubated overnight at 4°C, followed by multiple washes and incubation with secondary antibodies for 1 hour at room temperature Membranes were washed again before development using X-ray film or a Bio-Rad Laboratories imaging system.
The article discusses various antibodies used in research, including β-actin (Ab6276), ALDH1A3 (Ab129815), and 4HNE (Ab46545) sourced from Abcam, as well as PPARγ (#2435) and PARP (#9542) from Cell Signaling Technology Additionally, it mentions cyclin A (sc-239) and cyclin B1 (sc-245) obtained from Santa Cruz For secondary antibodies, HRP conjugated anti-mouse IgG (ab6728) from Abcam and anti-rabbit IgG (G21234) from Invitrogen are highlighted.
RNA extraction was performed using TRIzol (Invitrogen), followed by cDNA synthesis utilizing RT Master Mix (Toyobo) The Ct values were assessed with SYBR Green (Life Technologies), and the delta-delta Ct method was employed for analysis, using 18S rRNA as the reference gene For primer sequences, please refer to Table 2.
Statistical significance was determined by one-way ANOVA (Tukey’s post-hoc test), two-tailed unpaired t-test and Pearson correlation analysis Values are mean ± SEM (n 3) P 0.05 was considered as statistically significant
Table 1 Primer sequences for ChIP assay
Table 2 Primer sequences for RT-PCR
RESULTS
4.1 Expressional correlation between ALDH1A3 to PPARG
Recent findings indicate that retinoic acid produced by ALDH1 isoforms promotes the expression of PPARγ A correlation analysis between ALDHs and PPARγ was conducted using expression data from 78 lung cancer cell lines sourced from the GEO database Out of 18 ALDH genes examined, five—ALDH1A3, ALDH2, ALDH3B1, ALDH3B2, and ALDH8A1—showed a statistically significant correlation with PPARG Notably, ALDH1A3 exhibited the strongest correlation, with a Pearson coefficient of r = 0.5640 Additionally, the expression levels of ALDH1A3 and PPARG are illustrated in the accompanying figures.
4.2 ALDH1A3 is an inverse target of PPAR
This study investigates the regulation of ALDH1A3 transcription by PPARγ in lung cancer cells, based on the positive correlation between ALDH1A3 and PPARG Utilizing the NUBIscan program, two potential PPARγ binding sites were identified on the ALDH1A3 promoter Experiments with HBEC cells induced PPARγ expression, revealing that PPARγ directly binds to the BS2 region of the ALDH1A3 promoter This interaction functionally suppresses ALDH1A3 expression in both H1993 and HBEC cells Specifically, in H1993 cells, ALDH1A3 expression decreased in a dose-dependent manner upon pioglitazone treatment, while in HBEC cells, suppression occurred only with tetracycline-induced PPARγ activation Overall, the data indicates that PPARγ binds to the BS2 region on the ALDH1A3 promoter, leading to reduced ALDH1A3 expression.
4.3 ALDH suppression decreases lung cancer cell proliferation
This study investigated the impact of ALDH1A3 modulation on cancer cell growth using two lung cancer cell lines: H1299 (PPARγ negative) and H1993 (PPARγ positive) Both cell lines exhibited confirmed expression of ALDH1A3 and PPARγ Treatment with pioglitazone, a PPARγ agonist, effectively inhibited growth in PPARγ positive H1993 cells, while H1993 cells showed greater sensitivity to the ALDH inhibitor 4-Diethylaminobenzaldehyde (DEAB) compared to H1299 The study found that PPARγ expression is induced by retinoic acid produced by the ALDH1 family, and inhibition of ALDH1A3 by DEAB led to a decrease in PPARγ expression, which could be reversed by all-trans retinoic acid (ATRA) These findings suggest that pioglitazone and DEAB may target overlapping signaling pathways involving PPARγ and ALDH1A3.
4.4 Inhibition of ALDH1A3 by pioglitazone induces lipid peroxidation
ALDH1A3 plays a crucial role in converting reactive aldehydes to carboxylic acids, thereby limiting lipid peroxidation Treatment with pioglitazone led to increased lipid peroxidation, evidenced by elevated 4HNE-protein adducts in H1993 cells, while H1299 cells showed no such increase The high levels of 4HNE induced by pioglitazone may contribute to cytotoxicity, resulting in reduced cell proliferation and increased cell death Collectively, these findings support the notion of PPARγ acting as a tumor suppressor by promoting 4HNE production through the downregulation of ALDH1A3 expression.
Table 3 List of 78 lung cancer cell lines used to analyze Pearson correlation
Non-Small-Cell Lung Cancer
Table 4 Pearson correlation of PPARG to individual ALDHs
Gene Full name Pearson correlation coefficient P-value
ALDH1A1 Aldehyde dehydrogenase 1 family, member A1
ALDH1A2 Aldehyde dehydrogenase 1 family, member A2
ALDH1A3 Aldehyde dehydrogenase 1 family, member A3
ALDH1B1 Aldehyde dehydrogenase 1 family, member B1
ALDH1L1 Aldehyde dehydrogenase 1 family, member L1
ALDH3A1 Aldehyde dehydrogenase 3 family, memberA1
ALDH3A2 Aldehyde dehydrogenase 3 family, member A2
ALDH3B1 Aldehyde dehydrogenase 3 family, member B1
ALDH3B2 Aldehyde dehydrogenase 3 family, member B2
ALDH4A1 Aldehyde dehydrogenase 4 family, member A1
ALDH5A1 Aldehyde dehydrogenase 5 family, member A1
ALDH6A1 Aldehyde dehydrogenase 6 family, member A1
ALDH7A1 Aldehyde dehydrogenase 7 family, member A1
ALDH8A1 Aldehyde dehydrogenase 8 family, member A1
ALDH9A1 Aldehyde dehydrogenase 9 family, member A1
ALDH16A1 Aldehyde dehydrogenase 16 family, member A1
ALDH18A1 Aldehyde dehydrogenase 18 family, member A1
A positive correlation exists between ALDH1A3 and PPARG expression across various lung cancer cell lines, as demonstrated in the lung cancer panel analysis The correlation is illustrated by the expression levels of ALDH1A3 and PPARG, with the x-axis representing different cell lines and the y-axis indicating the relative expression signal sourced from the GEO dataset (GSE4824).
Figure 2 ALDH1A3 is regulated by PPAR (A) Potential binding sites (BS) 1 and 2 of
The interaction of PPARγ with the ALDH1A3 promoter was analyzed using the NUBIscan program, revealing binding at the BS2 site To validate this interaction, a ChIP assay was performed on HBEC cell lines, with the FABP4 promoter included as a positive control.
The study examines the suppression of ALDH1A3 by PPARγ, highlighting the effects of pioglitazone treatment at varying doses on H1993 cells Additionally, it investigates the expression of ALDH1A3 in response to 3 µM of pioglitazone and troglitazone in the presence of tetracycline.
The ON and OFF conditions in HBEC cells were assessed, with ALDH1A3 expression measured through RT-PCR and immunoblot assays The results are presented as mean ± SEM, and statistical differences were analyzed using one-way ANOVA with Tukey’s post-hoc test Significant findings include ***P ≤ 0.001 and ****P ≤ 0.0001 when compared to pioglitazone at 0 µM, and **P ≤ 0.01 when compared to the Tet OFF Vehicle (Veh).
###P 0.001 vs Tet ON Vehicle (Veh)
Figure 4 PPAR activator shows the same growth inhibitory effect with ALDH inhibition (A) mRNA expression of PPAR and ALDH1A3 in H1299 and H1993 (B)
A cell proliferation assay was conducted to evaluate the effects of pioglitazone and DEAB treatment at various doses over a period of 10 days Additionally, the expression of PPARγ was assessed in H1993 cells after 48 hours of treatment with DEAB, both alone and in combination with ATRA Results are presented as mean ± SEM, and statistical significance was determined using a two-tailed unpaired t-test, with **P ≤ 0.01 and ***P ≤ 0.001 indicating significant differences compared to H1299 cells.
Figure 5 PPAR activation increases lipid peroxidation and reduces cell growth
4HNE-adducts (A) and cell cycle or apoptosis related proteins (B) were determined in H1993 and H1299 cells treated with 0, 3, 10, 30, 50, and 100 M of pioglitazone
Figure 6 Proposed model of the molecular interaction between PPAR and ALDH1A3
DISCUSSION
PPARγ, like many nuclear receptors, is activated through its binding with retinoic X receptor (RXR), which facilitates the recruitment of coactivators or corepressors to modulate gene expression As a tumor suppressor, PPARγ plays a critical role in inducing genes such as PTEN, p21, AP-2α, and p53, while also inhibiting inflammatory cytokines and angiogenesis factors This study identifies a novel inverse target of PPARγ that may reduce lung cancer cell progression, indicating a complex relationship where, despite the positive correlation between ALDH1A3 and PPARG, the activation of PPARγ can lead to decreased cancer cell proliferation.
TZDs can suppress ALDH1A3 at the transcriptional level I then figured out the potential
PPAR responsive element in ALDH1A3 promoter and confirmed the binding of PPAR by ChIP assay As a result, PPAR directly binds to ALDH1A3 promoter to decrease
The expression of ALDH1A3 is linked to increased levels of the lipid peroxidation marker 4HNE, which contributes to the inhibition of cancer cell growth This finding supports the tumor suppressor role of PPARγ by elucidating the underlying mechanism.
It was reported that ALDH1A3 is the dominant ALDH isozyme that is important for
ALDH activity in most NSCLC cell lines Consistently, high expression of ALDH1A3 is also linked to shorter overall survival of NSCLC (53) Therefore, my findings showed that
PPAR activation decreased ALDH1A3 expression hence suppressed lung cancer progression
Regarding cancer metabolism, anti-tumor function of PPAR was previously reported to induce metabolic switch and increase ROS level to decrease lung cancer cell growth
Activation of PPARγ by pioglitazone enhances lipid oxidation, as evidenced by increased levels of 4HNE protein adducts However, the accumulation of 4HNE can disrupt cellular metabolism, ultimately leading to cell death This study elucidates the tumor suppressor function of PPARγ in regulating reactive oxygen species (ROS), thereby supporting its role in cancer metabolism.
CONCLUSION
This study reveals that PPARγ acts as a tumor suppressor in lung cancer by directly reducing ALDH1A3, highlighting its significant role in cancer metabolism These findings enhance our understanding of PPARγ's anti-tumorigenic properties and support the potential use of TZDs as a therapeutic option for lung cancer treatment.
FUTURE DIRECTION
Recent data indicates that PPARγ directly interacts with the ALDH1A3 promoter, playing a crucial role in regulating its transcription Additionally, further research is needed to explore the involvement of co-regulators that enhance PPARγ's function in inhibiting ALDH1A3 expression.
Lung cancer lipid metabolism regulation by PPAR – FABP4 is mediated by
Reprogramming lipid metabolism in cancer
Lipids, which include phospholipids, fatty acids, triglycerides, sphingolipids, cholesterol, and cholesteryl esters, play crucial biochemical roles in tumor growth They contribute to the formation of lipid rafts in cell membranes, facilitating the recruitment of signaling proteins for effective signal transduction The composition of lipids, particularly the ratio of saturated to unsaturated fatty acids, influences cell membrane flexibility and protein dynamics For example, saturated phospholipids can mitigate oxidative stress from lipid peroxidation and inhibit the uptake of chemotherapeutic drugs Additionally, lipids act as second messengers in cellular signal transduction and serve as vital energy sources during nutrient scarcity Consequently, dysregulation of lipid metabolism can lead to the development and progression of various metabolic disorders, including cancer.
1.1.1 Lipid biosynthesis in cancer cells
Lipid synthesis is the process that produces fatty acids from carbon sources derived from nutrients These fatty acids can be transformed into various types of lipids, such as diacylglycerides, triacylglycerides, and phosphoglycerides Primarily, triacylglycerides play a crucial role in energy storage and metabolism.
Lipid droplets serve as a vital energy source, while sterols, particularly cholesterol and cholesteryl esters, play a crucial role in membrane function Cholesterol, synthesized through the mevalonate pathway, is essential for maintaining the fluidity of the lipid bilayer and acts as a structural backbone for steroid hormones like estrogen and progesterone Additionally, various lipids derived from fatty acids include sphingolipids, phosphoinositides, and eicosanoids, which contribute to cellular functions and signaling.
Cancer cells can produce citrate for fatty acid biosynthesis via the reductive carboxylation of glutamine, in addition to the acetyl-CoA derived from glucose, which is primarily used for de novo fatty acid and cholesterol synthesis.
Tumors can produce lipids, such as fatty acids and phospholipids, through de novo lipid synthesis, exhibiting lipid biosynthesis levels comparable to liver tissue, known for its high fatty acid production The critical role of de novo lipogenesis in tumor growth is underscored by findings that inhibiting various enzymes in the fatty acid biosynthesis pathway significantly suppresses cancer cell proliferation.
1.1.2 Fatty acid catabolism in cancer cells
Fatty acid catabolism is crucial for cell survival and can contribute to chemotherapy resistance, alongside the upregulation of lipogenic pathways The transport of fatty acids into mitochondria for beta-oxidation relies on the carnitine palmitoyl transferase (CPT) system, which comprises three components: CPT1, carnitine acylcarnitine translocase (CACT), and CPT2 CPT1 has several isoforms, including CPT1A, CPT1B, and CPT1C, which are distributed across various tissues.
CPT1A depletion has been shown to inhibit mTOR activity, trigger apoptosis, and enhance the responsiveness of prostate cancer cells to anti-androgen therapies, indicating its role in promoting prostate cancer cell growth Additionally, CPT1A is positively correlated with histone deacetylase activity, contributing to breast cancer progression Furthermore, CPT1A plays a crucial role in the proliferation of leukemic cells, making its knockdown a significant area of interest in cancer research.
Etomoxir-induced inhibition enhances the sensitivity of leukemic cells to cytarabine treatment In contrast to CPT1A, the dysregulation of CPT1B expression has been less frequently documented in research It has been proposed that Stat3 regulates CPT1B expression, which plays a role in promoting chemoresistance in breast cancer.
Research on CPT2 and CACT in cancer is limited compared to CPT1 Studies indicate that CPT2 knockdown can inhibit the growth of breast cancer cells, suggesting its role as a tumor promoter in this type of cancer Conversely, a meta-analysis has shown a positive correlation between CPT2 expression and prognosis in colorectal cancer The role of CACT in cancer progression is still not well-defined; however, it has been reported to be upregulated in prostate cancer and downregulated in bladder cancer.
1.1.3 Lipid droplets as protective factor for cancer cells
Dysregulated lipid metabolism, particularly the accumulation of lipid droplets, is linked to cancer aggressiveness and drug resistance Colon cancer stem cells exhibit higher lipid content compared to their differentiated counterparts, and the accumulation of lipid droplets in colorectal cancer cells treated with 5-fluorouracil and oxaliplatin can lead to chemoresistance and tumor progression In glioblastoma, proteins like adipophilin and fatty acid binding proteins 3 and 7 facilitate lipid droplet formation under hypoxic conditions, providing ATP support during reoxygenation Furthermore, lipid droplets help protect cells from oxidative stress caused by polyunsaturated fatty acids (PUFAs), and inhibiting their formation can enhance PUFA-induced cell death in triple-negative breast cancer.
Cell membranes are composed of various lipids, with lipid rafts primarily formed by cholesterol and sphingolipids These lipid rafts play a crucial role in supporting the normal functions of membrane proteins, as well as regulating cell proliferation and apoptosis Consequently, targeting lipid rafts has emerged as a potential strategy in cancer therapy.
The use of membrane-depleting agents or cholesterol synthesis inhibitors disrupts oncogene activity, as their activation relies heavily on lipid rafts These lipid rafts play a crucial role in regulating cytoskeletal remodeling and focal adhesion, which are essential for cancer cell migration Additionally, lipid rafts contribute to the formation of clusters of apoptotic signaling molecule-enriched rafts (CASMERs), vital for transmitting apoptotic signals Research indicates that cholesterol synthesis inhibitors can prevent CASMER formation, particularly in leukemia and non-small cell lung cancer (NSCLC).
Non-receptor tyrosine kinase Src
Src is a member of a 9-gene family that includes Src, Blk, Fgr, Fyn, Hcy, Lck, Lyn, Yes, and Yrk, all of which play crucial roles in cancer progression The first viral oncogene discovered, V-Src, has a cellular counterpart, c-Src, which is well-documented in various human cancers The structure of Src features a unique NH2-terminal region with the Src homology domain (SH4), two conserved SH2 and SH3 domains, a protein tyrosine kinase (SH1) domain, and a C-terminal tail with a negative-regulatory tyrosine residue Upon activation, c-Src influences numerous substrates like STAT3, JNK, and FAK, promoting cancer progression In non-small cell lung cancer (NSCLC), Src is implicated in cancer proliferation, metastasis, and drug resistance, suggesting that targeting Src could potentially delay cancer progression While Src is known to facilitate key signaling pathways in cancer, its role in driving metabolic changes associated with cancer remains unclear Previous research indicates that Src may be essential for lipid accumulation and the suppression of white adipocyte browning, suggesting its involvement in cancer-related lipid metabolism.
Fatty acid-binding protein 4 (FABP4)
FABP4 belonging to a large gene family of FABPs is a direct target of PPAR to regulate lipid fluxes and trafficking (101) While the activation of PPAR could promote
30 lipid accumulation (102), lipolysis (103) and white-to-brown program in white adipocytes
PPARγ plays a crucial role in lipid mobilization, while FABP4 primarily facilitates lipolysis Research indicates that mice lacking FABP4 exhibit reduced lipolysis both at baseline and when stimulated by isoproterenol or dibutyryl cAMP, highlighting FABP4's essential function in this process FABP4 may decrease lipid droplets by transporting free fatty acids to the plasma membrane or by interacting with hormone-sensitive lipase (HSL) to enhance its activation on lipid droplet surfaces Although FABP4's role in metabolic regulation is well established, its involvement in cancer remains contentious In prostate cancer, elevated levels of FABP4 have been linked to apoptosis, suggesting a tumor-suppressive role Conversely, in ovarian cancer, FABP4 is upregulated in omental metastases, aiding tumor progression by transferring fatty acids from adipocytes Additionally, FABP4 has been associated with both poor prognostic outcomes and tumor suppressor functions in lung cancer, indicating the need for further investigation into its dual roles.
The activation of PPARγ induces FABP4, a key regulator of lipolysis, prompting an investigation into whether this function is influenced by oncogenic signaling Recent studies indicate that the oncogene Src plays a significant role in modulating PPARγ activity, particularly in the context of adipose tissue inflammation and insulin sensitivity Therefore, I hypothesize that the lipid metabolism associated with lung cancer, mediated by the PPARγ-FABP4 axis, is regulated by the oncogene Src.
Cells were cultured at 37°C with 5% CO2 in RPMI 1640 or DMEM medium supplemented with 5% or 10% fetal bovine serum (FBS), along with 50 U/mL penicillin and 50 U/mL streptomycin The following reagents were used: SU6656 (sc-203286A) from Santa Cruz or SU6656 (S7774) from Selleckchem, pioglitazone (sc-204848) from Santa Cruz, PP2 (#1767-1) from BioVision, HTS01037 (10699-10) from Cayman Chemical, and Thiazolyl Blue Tetrazolium Blue (M2128), Oil-red O (O1391), and Stattic (S7947) from Sigma-Aldrich.
Plasmids
The pCDNA-BLRP-wtPPARγ and pCDNA vector have been previously documented in research (24, 112) Additionally, the wtSrc-GFP was supplied by Margaret Frame from The Beatson Institute for Cancer Research in Scotland and Yoav I Henis from Tel Aviv University, Israel The pCDNA c-Abl Δ1-81 was also mentioned in this context.
Yosef Shaul from the Weizmann Institute of Science in Israel provided pCMV-Stat3, while pCMV control was a gift from Ki Woo Kim at Yonsei University, Korea Jong Bae Park from the National Cancer Center, Korea, supplied pLL-EGFR-vIII, and Yes-EGFP was acquired from Addgene (plasmid #110497), originally contributed by Bernardo Mainou and Lars Rửnnstrand Additionally, multiple mutated plasmids, including SrcY527F-GFP, SrcK295M-GFP, SrcR175A-GFP, SrcW118A-GFP, and PPARγY78F, were generated using Pfu Plus 5X PCR Master Mix from Elpis Biotech, as previously described For primer sequences, refer to Table 5.
siRNA transfection
Calu6 cells were transfected with indicated siRNA using lipofectamine 3000 Included are various siRNA purchased from Bioneeer for negative control siRNA (SN-
1002) or Dharmacon for SRC siRNA (M-003175-03-0005) and FABP4 siRNA (M-008853-00-0005) Refer to table 6 for target sequence of siRNA.
Immunoblot and immunoprecipitation assays
Samples were prepared using RIPA buffer and analyzed through immunoblot assays as outlined in Chapter I The primary antibodies utilized included β-actin (Ab6276) and 4HNE (Ab46545) from Abcam, FYN (#610164) from BD, Cyclin B1 (SC245) from Santa Cruz, and various antibodies from Cell Signaling Technology, such as phospho-Stat3 (Y705) (#9131), Stat3 (#9139), pSrc (Y416) (#6943), Src (#2108), PPARγ (#2435), FABP4 (#3544T), GFP (#2955), Cyclin A2 (#4656), PARP (#9542), pAMPK (#2535), AMPK (#5832), cleaved caspase 9 (#9501), and cleaved caspase 3 (#9664) Secondary antibodies included HRP-conjugated anti-mouse IgG (ab6728) from Abcam and anti-rabbit IgG (G21234) from Invitrogen.
In the immunoprecipitation assay, HEK293 cells were transfected with biotin-protein ligase (BirA), pCDNA-BLRP-wtPPARγ or pCDNA, along with wtSrc-GFP, SrcK295M-GFP, or pEGFP, followed by a 1-day treatment with SU6656 The assay was conducted as previously described, with PPARγ being pulled down using streptavidin beads (Thermo) at 4°C overnight The beads were washed four times with phosphate-buffered saline containing 0.5% NP40 Subsequently, the immunoprecipitates were analyzed using SDS-PAGE and probed with antibodies for PPARγ (#2435) and GFP (#2955) from Cell Signaling Technology, with ImageJ utilized for quantifying the blots.
Relative RT-PCR analysis
mRNA and cDNA were prepared according to the methods outlined in Chapter I The data analysis utilized the delta-delta Ct method, with 18S rRNA serving as the internal reference For primer sequences, please refer to Table 7.
Oil-red O (ORO) staining
Cells were treated according to the specified protocols, followed by fixation with 3.7% formaldehyde for 30 minutes After washing with water and 60% isopropanol, a working solution of Oil Red O (ORO) was prepared to a final concentration of 0.2% by diluting a 0.5% ORO solution with deionized water in a 3:2 ratio and filtering through a 0.8 µm syringe filter The staining process involved 10 minutes of ORO staining, subsequent washing with water, and counterstaining with hematoxylin Quantification of the staining was performed using ImageJ software, as previously described.
Luciferase assay
The Luciferase assay was conducted in HEK293 cells transfected with renilla luciferase, TK-PPRE3x-Luc plasmid, and specific plasmids of interest or control Transfection efficiency was assessed through immunoblotting for each assay The Dual Luciferase Reporter Assay System (Promega) was utilized to quantify renilla or luciferase activity, with PPARγ transcriptional activity indicated by relative light units (RLU).
Cell growth assay
Cell growth assay was evaluated by staining cells with 0.4% methylene blue in 50% methanol or MTT assay after treatment Regarding MTT assay, cells were incubated with
5 mg/mL of Thiazolyl Blue Tetrazolium Blue in PBS for 2 hours and the blue formazan crystal was dissolved in DMSO and optical density was measured at 570 nm
Intracellular reactive oxygen species (ROS) levels were quantified following established protocols After specific treatments, H1993 or Calu6 cells were incubated with 1 or 2.5 µM of 2′-7′dichlorofluorescin diacetate (CM-H2DCF-DA) solution for 15 minutes The cells were then washed multiple times in KRB, and images were captured using an inverted microscope equipped with a Nipkow spinning disk laser array Data analysis was performed using MetaMorph software from Molecular Devices.
Cells were seeded at a density of 2500 cells per well and treated accordingly The oxygen consumption rate was measured using the Seahorse XFe96 Analyzer (Agilent) during a mitochondrial stress test Measurements were taken with and without drug injections, which included 2 µM oligomycin, 0.5 µM FCCP, and 0.5 µM each of rotenone and antimycin.
A Oxygen consumption rate was normalized by protein amounts determined by BCA assay
The microarray dataset GSE4824 was utilized to investigate lipid metabolic genes in lung cancer cell lines Gene expression analysis for Src, FABP4, and LPL was conducted using provisional TCGA data from cBioPortal.com A correlation analysis was performed on patients exhibiting gene expression upregulation, as indicated by a z-score greater than 2.
Frozen samples were provided by Department of Pathology in Wonju Severance Christian Hospital under the approval of Committee of Institutional Review Board (Approval number: CR318314)
A prognostic analysis was conducted using data from the TCGA and GEO databases, specifically the Provisional TCGA database, GSE8894, and GSE11117 for lung cancer, as well as the GDC TCGA for renal cancer, focusing on kidney clear cell carcinoma To determine statistical significance, a log-rank test was applied to compare the survival outcomes among three groups categorized by low, medium, and high expression levels of FABP4 or LPL.
A xenograft model was created with the approval of the Institutional Animal Care and Use Committee (IACUC) at Wonju College of Medicine, Yonsei University (Approval number: YWC-170907-3), where Calu6 cells were injected subcutaneously into the right flank.
36 region of Balb/c nude mice at five millions of cells Treatment started when tumors were visible Mice were intraperitoneally injected for 23 days with vehicle (n=5) or SU6656 at
20 mg/kg (n=7) for 23 days Tumor volumes were measured by the digital caliper and determined by the formula ẵ ì (width 2 ì length)
Two-tailed Student’s t-test, one-way and two-way ANOVA, Pearson correlation coefficient and log-rank test were performed using GraphPad Prism version 6.0 Data were displayed as mean ± SEM (n 3)
Table 5 Primer sequences for site-directed mutagenesis
SrcY527F-GFP wtSrc-GFP Forward:
SrcK295M-GFP wtSrc-GFP Forward:
SrcW118A-GFP wtSrc-GFP Forward:
SrcR175A-GFP wtSrc-GFP Forward:
The underline sequence are mutation site
Table 6 Target sequences of siRNA siRNA Target sequence
The SMARTpool siGENOME FABP4 siRNA includes four unique sequences: D-008853-01 (GUAGGUACCUGAAACUUG), D-008853-02 (GAAAUGGGAUGGAAAAUCA), D-008853-03 (GAUGUGAuCACCAUUAAAU), and D-008853-04 (GAAAGUCAAGAGCACCAUA) Additionally, the SMARTpool siGENOME SRC siRNA comprises five distinct sequences: D-0031750-05 (GAGAACCUGGUGUGCAAAG), D-0031750-06 (CGUCCAAGCCGCAGACUCA), D-0031750-07 (CCUCAGGCAUGGCGUACGU), D-0031750-12 (CCAAGGGCCUCAACGUGAA).
Table 7 Primer sequences for RT-PCR
Reverse: ATCTGTCACCACATAATTACCT SRC Forward: TTCAGAGGAGCCCATTTACATC
4.1 Regulation of Src on lipid content in lung cancer cells
The investigation into the relationship between Src and lipid metabolism revealed that lung cancer cell lines A549 and H2347 exhibited high levels of both Src and pSrc expression, in contrast to H1993 and Calu6 Notably, Oil Red O (ORO) staining indicated that cells with elevated Src expression accumulated more lipid droplets Further analysis of lipid metabolic genes using a microarray dataset indicated that FABP4 and LPL were highly expressed in Calu6 and H1993, aligning with RT-PCR results A significant negative correlation between Src and the expression of FABP4 and LPL was confirmed through TCGA data analysis from cBioPortal Treatment with the Src-specific inhibitor SU6656 resulted in increased mRNA and protein levels of FABP4 in low Src expression cell lines, while LPL levels remained unchanged Additionally, Src knockdown led to a significant increase in FABP4 expression, and exogenous Src transfection suppressed FABP4, a response that was mitigated by SU6656, but did not affect LPL levels.
The treatment with SU6656 specifically inhibited Src, as evidenced by the reduction of pStat3 levels (Figure 11B) This data indicates that Src negatively regulates lipolytic genes, potentially influencing lipid content in lung cancer.
4.2 Inhibition of Src increased PPAR-induced FABP4
The involvement of Src in lipid metabolism pushed me to investigate the mechanism how Src inhibits FABP4 to regulate lipid droplets Since many studies have found that
FABP4 is identified as a direct target gene of PPARγ, leading to the hypothesis of a relationship between Src and PPARγ function in regulating lipid droplet levels in lung cancer A positive correlation between PPARγ and FABP4 expression in lung cancer cells, along with the upregulation of FABP4 following PPARγ activation by pioglitazone, confirms that PPARγ is upstream of FABP4 Notably, LPL, another reported PPARγ target gene, is not induced by pioglitazone treatment Two independent systems, including a human bronchial epithelial cell line expressing PPARγ and PPARγ-negative H1299 cells transfected with PPARγ, demonstrated that Src inhibition specifically induces FABP4 expression The HBEC-C1-PPARγ system revealed significant induction of FABP4, but not LPL, upon pioglitazone treatment Furthermore, Src inhibition through SU6656 treatment significantly upregulated FABP4 expression, with additional confirmation of Src activity suppression via Stat3 in HBEC cells Overexpression of PPARγ in H1299 cells also led to significant FABP4 upregulation, which was further enhanced by SU6656 treatment These findings indicate that Src suppression enhances PPARγ activity, resulting in increased FABP4 expression in cancer.
4.3 Src suppresses PPAR activity in a PPAR-Tyr 78 and Src kinase activity independent manner
Based on the observations, I investigated the molecular mechanism by which Src negatively regulates FABP4 expression Inhibition of Src resulted in the release of PPARγ activity, leading to the upregulation of FABP4 To confirm this, I conducted a luciferase assay to assess PPARγ transcriptional activity The results, illustrated in Figure 14A, demonstrated that the PPRE-driven construct was activated over 20-fold upon PPARγ transfection into HEK293 cells, but this activation was significantly suppressed by the co-overexpression of Src in a dose-dependent manner.
The study reveals that Src specifically suppresses PPAR activity, as demonstrated by luciferase assays that did not show similar effects with other kinases like Yes and Fyn Treatment with Src inhibitors SU6656 and PP2 restored PPAR activity, indicating the pivotal role of Src in this regulatory mechanism Notably, even the phosphodead PPAR Y78F mutant exhibited reduced transcriptional activity compared to wildtype PPAR, yet Src still inhibited PPAR Y78F, suggesting that Src's suppression is not solely dependent on tyrosine phosphorylation at position 78 Furthermore, various Src kinase mutants, including constitutively active and kinase-dead variants, demonstrated similar abilities to suppress PPAR transcriptional activity, implying that Src's inhibitory effect may extend beyond its kinase activity Although Stat3 is a well-known downstream target of Src, its overexpression and Stattic treatment did not influence PPAR transactivation, indicating that Src's suppression of PPAR is independent of Stat3 Additionally, both wildtype and kinase-dead Src were found to interact with PPAR, suggesting that protein-protein interactions play a role in Src's regulation of PPAR activity Overall, these findings suggest that Src regulates FABP4 by inhibiting PPAR transcriptional activity through mechanisms that do not rely on Stat3, phosphorylated PPAR, or Src's kinase activity.
4.4 Src inhibition reduces lipid content in an FABP4 dependent manner in in vitro and in vivo model
The relationship between Src, FABP4, and lipid metabolism prompted an investigation into lipid regulation following Src suppression The Src inhibitor SU6656 was found to decrease lipid content, but this effect diminished when treated with a FABP4 inhibitor.
Knockdown of Src reduced lipid content, an effect that was mitigated by FABP4 inhibition or knockdown To validate these in vitro findings, an in vivo Calu6 xenograft tumor model was established, where SU6656 (20 mg/kg) was administered intraperitoneally every other day for 23 days Consistent with the in vitro results, SU6656 significantly suppressed tumor weight and growth, while body weight remained stable Notably, SU6656 also led to a decrease in lipid accumulation.
24), which was associated with the upregulation of FABP4 Taken together, my data suggests that Src negatively regulates FABP4 to control lipid accumulation and possibly cancer progression
4.5 FABP4-dependent accumulation of intracellular ROS upon Src suppression
Lipid droplets play a crucial role in cancer cell survival through their antioxidant activity, prompting an investigation into how Src suppression-induced lipid droplet reduction impacts intracellular ROS levels Using the redox-sensitive dye CM-H2DCF-DA, I found that SU6656 treatment led to ROS generation, which was diminished by the presence of HTS01037, indicating that FABP4 is essential for ROS production Additionally, ROS accumulation was observed with Src knockdown, which was reversed by FABP4 knockdown Given that ROS accumulation can activate AMPK and lipid peroxidation, I explored these pathways in response to Src and FABP4 inhibition Notably, SU6656 treatment increased ROS levels, activating AMPK without inducing lipid peroxidation, and this effect was mitigated by the FABP4 inhibitor.
FABP4 plays a role in lipolysis and β-oxidation; however, Src suppression does not affect the oxygen consumption rate (OCR) in Calu6 and H1993 cells, indicating that FABP4-induced lipolysis does not lead to further β-oxidation In terms of cell growth, the Src inhibitor SU6656 significantly inhibits cancer cell proliferation, while the FABP4 inhibitor HTS01037 fails to rescue and even reduces cell growth when combined with SU6656 These findings suggest that Src inhibition may upregulate FABP4, resulting in decreased lipid droplets and an increase in endogenous reactive oxygen species (ROS) levels.
4.6 Lipolytic genes serve as prognostic factors in lung and renal cancer
Oxygen consumption rate measurement
Cells were seeded at a density of 2500 cells per well and treated accordingly The oxygen consumption rate was then assessed using the Seahorse XFe96 Analyzer (Agilent) through a mitochondrial stress test Measurements were taken with and without drug injections, including 2 µM oligomycin, 0.5 µM FCCP, and 0.5 µM each of rotenone and antimycin.
A Oxygen consumption rate was normalized by protein amounts determined by BCA assay.
Gene expression analysis
The microarray dataset GSE4824 was utilized to investigate lipid metabolic genes in lung cancer cell lines Gene expression levels of Src, FABP4, and LPL were examined using provisional TCGA data sourced from cBioPortal.com A correlation analysis was performed on patients exhibiting gene expression upregulation, as indicated by a z-score greater than 2.
Patient samples
Frozen samples were provided by Department of Pathology in Wonju Severance Christian Hospital under the approval of Committee of Institutional Review Board (Approval number: CR318314).
Survival analysis
Prognostic analysis for lung and renal cancers was conducted using data from the TCGA and GEO databases, including the Provisional TCGA database, GSE8894, and GSE11117 for lung cancer, as well as GDC TCGA for kidney clear cell carcinoma Statistical significance was determined using the log-rank test to compare the survival outcomes among three groups based on low, medium, and high expression levels of FABP4 or LPL.
Xenograft experiment
The xenograft model was approved by the Institutional Animal Care and Use Committee (IACUC) at Wonju College of Medicine, Yonsei University (Approval number: YWC-170907-3) Calu6 cells were injected subcutaneously into the right flank of the subjects.
36 region of Balb/c nude mice at five millions of cells Treatment started when tumors were visible Mice were intraperitoneally injected for 23 days with vehicle (n=5) or SU6656 at
20 mg/kg (n=7) for 23 days Tumor volumes were measured by the digital caliper and determined by the formula ẵ ì (width 2 ì length).
Statistical analysis
Two-tailed Student’s t-test, one-way and two-way ANOVA, Pearson correlation coefficient and log-rank test were performed using GraphPad Prism version 6.0 Data were displayed as mean ± SEM (n 3)
Table 5 Primer sequences for site-directed mutagenesis
SrcY527F-GFP wtSrc-GFP Forward:
SrcK295M-GFP wtSrc-GFP Forward:
SrcW118A-GFP wtSrc-GFP Forward:
SrcR175A-GFP wtSrc-GFP Forward:
The underline sequence are mutation site
Table 6 Target sequences of siRNA siRNA Target sequence
The SMARTpool siGENOME FABP4 siRNA includes four distinct sequences: D-008853-01 (GUAGGUACCUGAAACUUG), D-008853-02 (GAAAUGGGAUGGAAAAUCA), D-008853-03 (GAUGUGAuCACCAUUAAAU), and D-008853-04 (GAAAGUCAAGAGCACCAUA) Additionally, the SMARTpool siGENOME SRC siRNA comprises five unique sequences: D-0031750-05 (GAGAACCUGGUGUGCAAAG), D-0031750-06 (CGUCCAAGCCGCAGACUCA), D-0031750-07 (CCUCAGGCAUGGCGUACGU), D-0031750-12 (CCAAGGGCCUCAACGUGAA).
Table 7 Primer sequences for RT-PCR
Reverse: ATCTGTCACCACATAATTACCT SRC Forward: TTCAGAGGAGCCCATTTACATC
Regulation of Src on lipid content in lung cancer cells
In a study examining the relationship between Src and lipid metabolism, high levels of both pSrc and Src were detected in lung cancer cell lines A549 and H2347, in contrast to lower levels in H1993 and Calu6 ORO staining indicated that cells with elevated Src expression accumulated more lipid droplets Analysis of lipid metabolic genes through a microarray dataset revealed that FABP4 and LPL were highly expressed in Calu6 and H1993, corroborated by RT-PCR findings A significant negative correlation between Src and FABP4/LPL was identified using TCGA data from cBioPortal Treatment with the Src-specific inhibitor SU6656 resulted in increased mRNA and protein levels of FABP4 in low Src expression cell lines, while LPL expression remained unchanged Additionally, Src knockdown led to a significant increase in FABP4, and exogenous Src transfection suppressed FABP4, an effect that was reversed by SU6656, but LPL levels were unaffected.
The specific inhibition of Src by SU6656 treatment was evidenced by the reduced levels of pStat3, as shown in Figure 11B Overall, the findings indicate that Src negatively regulates lipolytic genes, potentially influencing lipid content in lung cancer.
Inhibition of Src increased PPAR-induced FABP4
The involvement of Src in lipid metabolism pushed me to investigate the mechanism how Src inhibits FABP4 to regulate lipid droplets Since many studies have found that
FABP4 is identified as a direct target gene of PPARγ, suggesting a potential relationship between Src and PPARγ function in regulating lipid droplet levels in lung cancer A positive correlation between PPARγ and FABP4 expression in lung cancer cells, along with FABP4 upregulation upon PPARγ activation by pioglitazone, confirms that PPARγ acts upstream of FABP4 Notably, LPL, another reported PPARγ target gene, is not induced by pioglitazone treatment Two independent systems, including a human bronchial epithelial cell line that conditionally expresses PPARγ and PPARγ-negative H1299 cells transfected with PPARγ, demonstrated that Src inhibition specifically induces FABP4 expression In the HBEC-C1-PPARγ system, FABP4 expression significantly increased upon PPARγ activation with pioglitazone, while LPL did not show a similar response Furthermore, treatment with a Src-specific inhibitor significantly upregulated FABP4 expression, and the effect of SU6656 on Src activity was confirmed by Stat3 suppression in HBEC cells Additionally, overexpression of PPARγ in PPARγ-negative H1299 cells led to a significant increase in FABP4 levels, which was further enhanced by SU6656 treatment These findings indicate that Src suppression enhances PPARγ activity, resulting in increased FABP4 expression in cancer.
Src suppresses PPAR activity in a PPAR-Tyr 78 and Src kinase activity
Based on previous observations, I investigated the molecular mechanism by which Src negatively regulates FABP4 expression I found that inhibiting Src enhances PPARγ activity, leading to increased FABP4 levels To confirm this, I conducted a luciferase assay to assess PPARγ transcriptional activity The results, shown in Figure 14A, indicate that the PPRE-driven construct was activated more than 20-fold upon PPARγ transfection into HEK293 cells; however, this activation was significantly suppressed by the co-overexpression of Src in a dose-dependent manner.
Src specifically suppresses PPARγ activity, as demonstrated by luciferase assays showing no similar effects from other kinases like Yes and Fyn Treatment with Src inhibitors SU6656 and PP2 rescued PPARγ activity, indicating that Src's inhibitory effect is significant Although Src phosphorylates PPARγ at tyrosine 78, the phosphodead PPARγ Y78F still showed reduced transcriptional activity, yet Src could inhibit it further Various Src kinase mutants, including constitutively active and kinase-dead forms, similarly suppressed PPARγ activity, suggesting that Src's regulatory role may extend beyond its kinase activity Notably, the involvement of Stat3 in this suppression was ruled out, as its overexpression and Stattic treatment did not affect PPARγ transactivation Additionally, both wildtype and kinase-dead Src were found to bind to PPARγ, implying that protein interaction plays a role in Src's suppression of PPARγ Overall, Src regulates FABP4 by inhibiting PPARγ transcriptional activity independently of Stat3, phosphorylated PPARγ, or Src's kinase activity.
Src inhibition reduces lipid content in an FABP4 dependent manner in in vitro and in vivo model
The connection between Src, FABP4, and lipid metabolism prompted an investigation into lipid regulation following Src suppression The Src inhibitor SU6656 was found to decrease lipid content; however, this reduction was diminished when FABP4 inhibition was applied.
Knockdown of Src resulted in a reduction of lipid content, an effect that was mitigated by FABP4 inhibition or knockdown To validate these in vitro findings, an in vivo study was conducted using a Calu6 xenograft tumor model, where SU6656 or DMSO was administered intraperitoneally at a dosage of 20 mg/kg every other day for 23 days Consistent with the in vitro results, SU6656 significantly suppressed tumor growth and weight, while body weight remained relatively stable Notably, SU6656 also led to a decrease in lipid accumulation within the tumors.
24), which was associated with the upregulation of FABP4 Taken together, my data suggests that Src negatively regulates FABP4 to control lipid accumulation and possibly cancer progression.
FABP4-dependent accumulation of intracellular ROS upon Src suppression 44 4.6 Lipolytic genes serve as prognostic factors in lung and renal cancer
This study investigated the impact of Src suppression on lipid droplet reduction and intracellular ROS levels, utilizing the redox-sensitive dye CM-H2DCF-DA Results indicated that SU6656 treatment led to increased ROS generation, which was diminished by the presence of the FABP4 inhibitor HTS01037, highlighting the necessity of FABP4 for ROS production Additionally, Src knockdown also resulted in ROS accumulation, which was reversed by FABP4 knockdown The findings revealed that while SU6656 enhanced ROS levels and activated AMPK, it did not induce lipid peroxidation, and this AMPK activation was mitigated by FABP4 inhibition.
FABP4 plays a role in lipolysis and β-oxidation; however, suppression of Src did not affect the oxygen consumption rate (OCR) in Calu6 and H1993 cells, indicating that FABP4-induced lipolysis does not promote β-oxidation In terms of cell growth, the Src inhibitor SU6656 significantly reduced cancer cell proliferation, while the FABP4 inhibitor HTS01037 did not reverse this effect and instead further diminished cell growth when combined with SU6656 These findings suggest that Src inhibition may upregulate FABP4, resulting in decreased lipid droplets and an increase in endogenous reactive oxygen species (ROS) levels.
4.6 Lipolytic genes serve as prognostic factors in lung and renal cancer
Src suppression enhances PPARγ, which in turn upregulates FABP4, promoting lipolysis and potentially reducing cancer progression through reactive oxygen species (ROS) Analysis of five pair-matched normal lung and lung tumor tissues demonstrated significantly higher Src expression in tumors compared to normal tissues, while PPARγ and FABP4 showed an inverse expression pattern Additionally, elevated levels of FABP4 and LPL were associated with improved survival rates in lung cancer and renal cell carcinoma across multiple independent datasets This suggests that lipolytic genes may function as tumor suppressors in these cancers A proposed model illustrating the regulation of PPARγ activity by Src, affecting lipid content and redox balance, is presented Overall, targeting Src regulation in lipolysis presents a promising avenue for cancer therapy development.
Table 8 Analysis of protein expression in lung cancer patients
Sample Blot intensity Relative blot intensity
(Normalized to -actin) Src FABP4 PPAR -actin Src FABP4 PPAR P1-N 5963.116 8881.262 3978.426 6274.79 0.950 0.634 1.415 P1-T 14656.29 698.678 3302.962 10278.08 1.426 0.321 0.068 P2-N 3781.217 6190.56 11426.67 6907.196 0.547 1.654 0.896 P2-T 11334.51 913.335 3651.477 7573.376 1.497 0.482 0.121 P3-N 3527.439 7506.279 7693.497 8076.891 0.437 0.953 0.929 P3-T 10646.07 2821.033 6317.134 10164.59 1.047 0.621 0.278 P4-N 262.092 3097.711 7008.205 910.991 0.288 7.693 3.400 P4-T 4028.167 2084.276 13259.5 10192.28 0.395 1.301 0.204 P5-N 429.698 5491.024 8940.761 5938.134 0.072 1.506 0.925 P5-T 5435.539 3327.317 3728.841 6634.61 0.819 0.562 0.502 P: Patient; N: Normal; T: Tumor
* Pearson correlation coefficient (r) of relative blot intensity between proteins of interest
Figure 7 Involvement of Src in lipid metabolism (A) Protein expression of pSrc and
In lung cancer cells, the role of Src and lipid droplets is significant, as highlighted by the analysis of metabolic genes using the GEO dataset (GSE4824) Additionally, RT-PCR analysis reveals the expression levels of FABP4 and LPL mRNA in these cancer cells, providing insights into their metabolic pathways.
Figure 8 Src negatively correlates with FABP4 and LPL Pearson’s correlation was analyzed using data from lung cancer patients obtained from cBioPortal database
Figure 9 FABP4 expression in response to Src inhibitor treatment (A) mRNA expression (left) and protein expression (right) of FABP4 upon 5M of SU6656 treatment
(B) mRNA expression of LPL upon the treatment of SU6656 5M Data show mean ± SEM of triplicates Difference was analyzed using two-tailed unpaired t-test **P 0.01,
In a study examining the effects of Src knockdown in Calu6 cells, the expression levels of FABP4 mRNA and protein were assessed after transfection with either siControl (siCtr) or siSrc for 48 hours The results, presented as mean ± SEM of triplicate experiments, indicated significant differences in expression levels, with statistical analysis revealing **P ≤ 0.01 and ****P ≤ 0.0001.
Src overexpression in Calu6 cells led to a significant suppression of FABP4 expression, as evidenced by RT-PCR analysis following 24-hour treatment with 5 µM SU6656 The transfection efficiency in these cells was validated through immunoblot assays, with statistical differences evaluated using one-way ANOVA and Tukey’s post-hoc test, showing results with **P ≤ 0.01 and ****P ≤ 0.0001.
FABP4 is identified as a target gene of PPARγ, with basal levels of both FABP4 and PPARγ observed in lung cancer cells Following a 24-hour treatment with pioglitazone, mRNA expression levels of FABP4 and LPL were measured in PPARγ-negative (H1299) and PPARγ-positive cells (A549, H1993, Calu6) Additionally, protein and mRNA expression of FABP4, along with LPL mRNA levels, were assessed in HBEC-C1-PPARγ cells after 24 hours of pioglitazone treatment, with or without overnight tetracycline exposure The data, presented as mean ± SEM from triplicate experiments, were statistically analyzed using two-tailed unpaired t-tests and one-way ANOVA with Tukey’s post-hoc test, revealing significant differences (***P ≤ 0.001; ****P ≤ 0.0001).
Figure 13 FABP4 expression upon Src inhibition in HBEC-C1-PPAR (A) or H1299 (B) cells (A) mRNA expression (upper, left) and protein expression of FABP4 (lower, left)
In a study involving HBEC-C1-PPARγ cells, treatment with 5 µM SU6656 for 24 hours, with or without overnight tetracycline pretreatment, confirmed PPARγ overexpression and SU6656 activity through immunoblot analysis Additionally, FABP4 mRNA expression was assessed in H1299 cells transfected with an empty vector of PPARγ, also in the presence or absence of 5 µM SU6656 for 24 hours Transfection efficiency was validated by immunoblotting The data, presented as mean ± SEM from triplicate experiments, were analyzed using one-way ANOVA with Tukey’s post-hoc test, revealing significant differences indicated by asterisks (***P ≤ 0.001; ****P ≤ 0.001).
The luciferase assay using the PPRE3x-luc construct demonstrated the suppression of PPARγ transcriptional activity by Src The experiment involved varying amounts of PPARγ (0 or 600 ng) and wtSrc-GFP (ranging from 0 to 600 ng) Additionally, the transcriptional activity of PPARγ was restored by Src inhibitors, such as SU6656 and PP2, with transfection efficiency confirmed through immunoblotting.
Figure 15 illustrates the specific suppression of PPARγ by Src, highlighting the evaluation of PPARγ transcriptional activity in the presence of cAbl (Δ1-81), EGFRvIII, and wtSrc-GFP (A), as well as wtYes-GFP, wtFyn, and wtSrc-GFP (B) The transfection efficiency for these experiments was validated through immunoblot assays.
Figure 16 PPAR activity in various conditions including PPARY78F construct with wtSrc-GFP (A) or wt-PPAR with Src mutation (B and C) Included are Src K295M-
GFP, Src Y527F-GFP, SrcW118A and SrcR175A known for kinase dead, constitutive active, SH2 domain and SH3 domain mutation, respectively Transfection efficiency was confirmed by immunoblot assay
Figure 17 PPAR activity suppressed by Src is not dependent on Src kinase activity and Stat3 (A) PPAR transactivation in the presence of wildtype Src or Src K295M upon
SU6656 and PP2 treatment (B) PPAR transactivation in the presence of Stat3 co- transfection upon 2 M of Stattic treatment Transfection efficiency was confirmed by immunoblot assay
Figure 18 Src interacts with PPAR Co-IP was performed in HEK293 transfected with genes of interest with or without 5 M of SU6656 in 24 hour treatment
The study analyzed lipid content in HBEC-C1-PPARγ and Calu6 cells after treating them with 5 μM SU6656, 20 μM HTS01037, or a combination of both for three days Oil Red O (ORO) staining was performed to assess lipid accumulation, with results expressed as mean ± SEM Statistical significance was indicated with asterisks: *P ≤ 0.05, **P ≤ 0.01, and ***P ≤ 0.001, determined by one-way ANOVA followed by Tukey’s post-hoc test.
Figure 20 Lipid content upon Src knockdown in Calu6 Lipid content (left) and quantification (right) in Calu6 cells upon Src knockdown in the presence or absence of 20
M HTS 01037 (HTS) treatment for 3 days Values are mean ± SEM Asterisks refer to *P
0.05; **P 0.01 (one-way ANOVA, Tukey’s post-hoc test)
In Calu6 cells, the lipid content was assessed following Src knockdown, both with and without the concurrent knockdown of FABP4 The efficiency of the knockdown was determined using immunoblot assays after the transfection of siSrc and/or siFABP4 The results are presented as mean ± SEM, with significant findings indicated by asterisks.
*P 0.05 (one-way ANOVA, Tukey’s post-hoc test)
Src inhibition significantly reduces cancer growth both in vitro and in vivo In a study involving Calu6 cells, cell proliferation and MTT assays demonstrated decreased growth after treatment over seven days Additionally, Calu6 cells were injected into athymic nude mice, which were treated with either a vehicle or SU6656 at a dosage of 20 mg/kg every other day for 23 days The results showed a marked reduction in tumor volume and weight in the SU6656-treated group, indicating its efficacy in inhibiting tumor growth Data were presented as mean relative tumor size or weight ± SEM, with statistical analysis conducted using a two-tailed method.
67 unpaired t-test (A, B, right) or two-way ANOVA (Sidak’s post-hoc test) (B, left) Asterisks refer to *P 0.05; ***P 0.001; ****P 0.0001
In a study involving athymic nude mice, Calu6 cells were injected into the flank region, followed by the administration of either a vehicle (n=5) or SU6656 at a dosage of 20 mg/kg (n=7) every other day for 23 days The relative tumor volume was assessed by normalizing the growth to the initial tumor volume of each mouse.
In a study examining the effects of SU6656 treatment on xenograft tumors, Calu6 cells were injected into athymic nude mice, which subsequently received either a vehicle or SU6656 at a dosage of 20 mg/kg every other day for 23 days The analysis revealed lipid content and FABP4 protein levels in the remaining tumor tissues at the conclusion of the in vivo experiment, with results expressed as mean ± SEM Statistical significance was determined using a two-tailed Student's t-test.
Figure 25 ROS measurement in response to Src and/or FABP4 regulation (A)
FUTURE STUDY
Recent studies have indicated that Src can induce phosphorylation of PPARγ at tyrosine 78, influencing inflammation and insulin sensitivity However, my findings reveal that Src regulates PPARγ activity independently of this phosphorylation site This raises intriguing questions about the mechanisms through which Src inhibits PPARγ activity, warranting further investigation.
Although LPL has been identified as a target gene of PPARγ, my study shows that it is not regulated by PPARγ agonists This raises the question of which potential transcription factors may play a role in the regulation of LPL transcription in lung cancer.