RHOJ Derived Peptide Promotes Chemosensitivity by Inhibiting Glutamine Metabolism in Gastric Cancer

RHOJ Derived Peptide Promotes Chemosensitivity by Inhibiting Glutamine Metabolism in Gastric Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article RHOJ Derived Peptide Promotes Chemosensitivity by Inhibiting Glutamine Metabolism in Gastric Cancer Jian Li, Huanqing Li, Jiayi Wang, Fangzhou Ye, Fan Li, Songhua Bei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7691539/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 5 You are reading this latest preprint version Abstract Background Chemoresistance is a cause of the failure of chemotherapy in gastric cancer (GC) treatment. Recent studies have highlighted that dysregulation of glutamine metabolism plays a pivotal role in promoting chemoresistance. While small molecule inhibitors targeting glutamine metabolism have been investigated, peptide-based compounds have gained increasing attention due to their high specificity and low toxicity. Endogenous or rationally designed peptides have shown potential in inducing apoptosis, disrupting cancer-related signaling pathways, and overcoming drug resistance in various cancers. However, the potential of functional peptides to target glutamine metabolism and reverse drug resistance in GC has not been thoroughly explored. Methods We performed proteomic profiling to identify proteins upregulated in cisplatin-sensitive GC cells, from which peptides were derived for functional screening. A RHOJ-derived peptide (peptide 1) was identified and validated as a candidate chemosensitizer. Untargeted metabolomics, flow cytometry, molecular docking, molecular dynamics simulations, fluorescence imaging, and a subcutaneous xenograft model were employed to investigate the mechanism by which peptide 1 modulates GLUL-mediated glutamine metabolism and reverses cisplatin resistance. Results In this study, we found glutamine metabolism was enhanced in the cisplatin resistant GC cells, and identified a peptide which derived from RHOJ (named peptide 1) could increase the sensitivity of resistant cells to chemotherapy in GC. Molecular docking analysis revealed that this peptide could bind to the key enzyme glutamine synthetase in glutamine metabolism pathway. Mechanistically, peptide 1 inhibited glutamine production, increased ROS levels, induced DNA damage, and promoted apoptosis in resistant cells, ultimately restoring cisplatin sensitivity both in vitro and in vivo. Conclusions Our study demonstrated that glutamine metabolism plays a vital role in chemoresistance of GC, and RHOJ-derived peptide 1 enhances the chemosensitivity of drug-resistant GC cells through targeting GLUL, depleting glutamine, inducing ROS accumulation, and promoting DNA damage. This mechanism ultimately restores chemosensitivity in drug-resistant cells and highlights peptide 1 as a promising therapeutic strategy for overcoming chemoresistance in GC. Gastric cancer Peptide Glutamine metabolism ROS Cisplatin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Gastric cancer (GC) remains a significant global health burden, with substantial variability in its incidence and mortality rates worldwide. Based on the latest epidemiological data, there were over 968,000 new cases of GC in 2022 and close to 660,000 deaths, ranking the disease as fifth in terms of both incidence and mortality worldwide [ 1 ]. While incidence rates have declined in many high-income countries over the last half century, GC continues to pose a considerable public health challenge, particularly in regions of Eastern Asia, Central and South America, Eastern Europe, and parts of Africa [ 2 ]. Helicobacter pylori infection remains a major risk factor for GC. Furthermore, genetic predisposition, chronic gastritis, and certain occupational exposures have been implicated in gastric carcinogenesis [ 3 ]. Despite advancements in diagnosis and treatment modalities, prognosis for GC remains guarded, particularly for patients diagnosed at advanced stages. The treatment of GC typically involves a multimodal approach, incorporating surgery, chemotherapy, immunotherapy and targeted therapies. Chemotherapy remains the first-line treatment approach in advanced or metastatic GC. However, chemotherapy resistance remains a significant obstacle in the management of GC, contributing to disease progression, metastasis, and poor clinical outcomes [ 4 ]. Therefore, understanding the mechanisms underlying chemotherapy resistance is crucial for developing effective therapeutic strategies and improving patient survival. Chemotherapy resistance can arise through various mechanisms, including alterations in drug transporters, activation of DNA repair mechanisms, dysregulation of apoptosis pathways, and emergence of cancer stem cells with enhanced self-renewal [ 5 ]. Moreover, emerging evidence suggests that alterations in glutamine metabolism may play a critical role in the development of chemotherapy resistance, highlighting the importance of understanding the interplay between metabolic reprogramming and drug resistance mechanisms in GC. Glutamine is an amino acid that serves as a crucial nutrient for cancer cell growth and proliferation [ 6 ]. In addition to its role as a building block for protein synthesis, glutamine fuels various metabolic pathways, including the tricarboxylic acid (TCA) cycle, nucleotide synthesis, and antioxidant defense mechanisms[ 7 ]. Dysregulation of glutamine metabolism has been implicated in cancer progression and resistance to chemotherapy agents, suggesting that targeting glutamine-dependent pathways may represent a promising therapeutic strategy for overcoming drug resistance [ 8 ]. Several studies have indicated a close association between glutamine metabolism and drug resistance in cancers. A study on breast cancer revealed that the tumor-associated fibroblasts in tumor microenvironment (TME) can produce and secrete glutamine, which promotes the energy metabolism of cancer cells and thus leading to tamoxifen resistance [ 9 ]. Glutamine metabolism in colorectal cancer cells can also affect signal pathway transduction to promote metformin resistance [ 10 ]. Meanwhile, glutamine played a significant role on metabolic enzymes such as glutaminase 1 (GLS1) and glutamate dehydrogenase (GDH) to promote drug resistance [ 11 ]. Another study also demonstrated that that tumor cells secrete glutaminase 1 (GLS1) to promote glutamine metabolism thus contributing to acquired trastuzumab resistance in HER2-positive GC. Anti-glutamine metabolism therapy may provide a new insight into reversing trastuzumab resistance [ 12 ]. Targeting glutamine metabolism using small molecule inhibitors or metabolic modulators may sensitize chemoresistant tumors to standard chemotherapy agents [ 13 ], offering new avenues for improving treatment outcomes and overcoming therapeutic resistance in cancer patients. Small molecule peptide compounds have become one of the hotspots in cancer treatment research due to their advantages such as small molecular weight, strong targeting ability, high bioactivity, low toxicity, and easy transmembrane absorption [ 14 ]. Researchers have confirmed that various peptide compounds can act on targets within tumor cells. For instance, a specific RAGE-binding peptide RP7 could induce apoptosis and inhibit epithelial-mesenchymal transition (EMT) in TNBC cells through blocking of Erk1/2/NF-κB pathway [ 15 ]. Pep5-based antitumor peptides also exhibit remarkable antitumor activity towards tumor cells (HepG2, A549) and animal models through promoting the apoptosis and necrosis [ 16 ]. Moreover, they can also exert anti-tumor effects by targeting neovascularization and immune cells in the TME [ 17 , 18 ]. The above studies provided compelling evidence and valuable insights into the development of peptide-based antitumor agents. Their unique structures and mechanisms of action making them a potential choice for cancer therapy. Despite the increasing attention on peptide-based anticancer research, the role of peptide in GC drug resistance remains unclear, and there is limited reporting on the role of endogenous peptides in sensitizing chemotherapy. Therefore, exploring the role and mechanisms of peptides in GC drug resistance has become a key focus of our research attention. In this study, we first performed proteomic analysis to identify proteins that were highly expressed in cisplatin-sensitive GC cells. Peptides derived from RHOJ were then synthesized and subjected to functional screening to assess their potential role in reversing cisplatin resistance. We screened and identified six peptides form RHOJ, among which peptide 1 (AVFDEAILTIFHPK) exhibited antitumor effects and significantly enhanced the sensitivity of GC resistant cells to chemotherapy drugs. Subsequently, non-targeted metabolomics revealed differences in glutamine metabolism between GC resistant and sensitive cells, which hints us that this could potentially serve as a mechanism for peptide 1 to enhance chemosensitivity. Through in vivo , in vitro experiments and molecular simulations, we found that peptide 1 could inhibit glutamine metabolism by binding to the key enzyme GLUL, further inducing intracellular oxidative stress imbalance, thereby promoting apoptosis and DNA damage in resistant cells to enhance their sensitivity to chemotherapy. 2. Results 2.1. Screening and Validation of Chemotherapy-Sensitizing Peptides in gastric cancer. To explore the molecular mechanisms underlying cisplatin resistance in GC, we performed proteomic profiling of cisplatin-sensitive (SGC7901, HGC27) and cisplatin-resistant (SGC7901/DDP, HGC27/DDP) cells. The analysis revealed a significant upregulation of 17 proteins in the cisplatin-sensitive cell lines, with Ras homolog family member J (RHOJ) identified as one of elevated proteins compared to the resistant cells (Fig. 1 A and B). As a member of the Rho GTPase family, RHOJ is known to participate in cytoskeletal regulation and cell motility, which may be linked to chemotherapy resistance. Its higher expression in cisplatin-sensitive cells prompted us to explore its potential involvement in the regulation of chemosensitivity. Based on the identification of RHOJ as a candidate protein associated with cisplatin response, we sought to evaluate the functional relevance of RHOJ-derived peptides in modulating cisplatin sensitivity. To this end, six peptide fragments were selected from the RHOJ protein sequence using a random selection strategy. These peptides (Peptide 1–6) were synthesized based on different functional domains of RHOJ and tested for their potential to enhance cisplatin sensitivity in SGC7901/DDP cells (Table 1 ). Table 1 The sequences of RHOJ derived peptides and corresponding PeptideRanker Scores Peptide ID Sequence (14 aa) PeptideRanker Score Peptide 1 AVFDEAILTIFHPK 0.421969 Peptide 2 LSGGAGGGGGGSRV 0.500732 Peptide 3 QFFVDHPGAVPITT 0.532915 Peptide 4 DLKQFFVDHPGAVP 0.434701 Peptide 5 AALSGGAGGGGGGS 0.474199 Peptide 6 GKTCLLISYTTNQF 0.423952 First of all, in order to evaluate the cytotoxicity of these peptides on normal human gastric epithelial cells, we treated GES-1 cells with each peptide under the treatment of cisplatin. The apoptosis rate was assessed using Annexin V-FITC/PI staining followed by flow cytometric analysis. The results revealed that treatment with peptides 3 and 5 induced a significantly higher apoptosis rate in GES-1 cells compared to the control group, whereas the other peptides exhibited no significant cytotoxic effects (Fig. 1 C). These findings indicate that peptides 3 and 5 may exert toxicity on normal gastric epithelial cells, limiting their potential as therapeutic candidates. Next, we investigated whether these six peptides could enhance cisplatin sensitivity in cisplatin-resistant GC cells (SGC7901/DDP). Using the CCK-8 assay, we assessed cell viability following peptide treatment in combination with cisplatin. The results showed that peptides P2 to P6 had no significant effect on the IC50 of cisplatin in resistant cells compared to the untreated control. In contrast, peptide P1 significantly decreased the IC50 in SGC7901/DDP cells, rendering their sensitivity to cisplatin comparable to that of parental SGC7901 cells (Fig. 1 D). Based on these results, peptide 1 was identified as a potential candidate for overcoming cisplatin resistance in gastric cancer and was selected for further investigation in subsequent studies. 2.2 Glutamine metabolism was enhanced in the cisplatin resistant gastric cancer cells To explore the underlying mechanism by which peptide 1 reverses cisplatin resistance, we investigated its impact on glutamine metabolism, a pathway previously implicated in chemoresistance[ 19 , 20 ]. To investigate the relationship between metabolic reprogramming and cisplatin resistance in GC, untargeted metabolomics was carried out on both cisplatin-sensitive and cisplatin-resistant GC cell lines. A heat map was created to display the metabolic differences between cisplatin resistant and sensitive GC cells, illustrating the variance in metabolite levels between the two cell types. As it shown in Fig. 2 A, the disparity in glutamine levels was found to be significant between the cisplatin sensitive and cisplatin resistant GC cell lines. Notably, glutamine abundance was significantly elevated in the resistant cells compared to their sensitive counterparts (Fig. 2 B). This finding suggests a potential metabolic reprogramming toward enhanced glutamine utilization or synthesis in the development of chemoresistance. In addition, KEGG pathway analysis showed that amino acid metabolism pathways were among the most significantly enriched in cisplatin-resistant cells compared to sensitive cells (Fig. 2 C), aligning with findings from earlier studies [ 21 , 22 ]. Given the critical role of glutamine in supporting tumor cell proliferation, redox balance, and survival under stress conditions[ 23 ], we hypothesized that glutamine metabolism may contribute to the maintenance of cisplatin resistance in GC. To further validate the impact of glutamine on chemotherapy resistance, we measured the cell viability via CCK8 assay with or without the deprivation of glutamine both in sensitive and resistant cells under cisplatin treatment. The results revealed that there was no significant difference in cell viability in cisplatin sensitive cells line following glutamine deprivation (Fig. 2 D). However, a significant reduction in cell viability in the resistant cells was observed with the absence of glutamine, indicating a more pronounced dependency on glutamine in the resistant cells (Fig. 2 E). Given the distinct differences in glutamine metabolism between resistant and sensitive cells, and the previously identified peptide 1 could reduce the IC50 of cisplatin in resistant cells, we hypothesized that peptide 1 might enhance chemosensitivity by affecting cellular glutamine metabolism. We first assessed the glutamine levels in both SGC7901 and SGC7901/DDP, and discovered that the glutamine content was significantly higher in the resistant cells compared to the sensitive ones, which was consistent with our metabolomic sequencing results. We then measured the glutamine levels after peptide1 treatment in SGC7901/DDP. GLN assay revealed that the glutamine levels in the resistant cells significantly decreased following peptide 1 treatment when compared to the non-treatment group (Fig. 2 F). The above data suggested that glutamine metabolism played a significant role in GC cisplatin resistant, and peptide1 treatment could reduce the glutamine level in resistant cells, but the specific mechanism need further elucidation. 2.3. Peptide 1 treatment increased the sensitivity of resistant cells to chemotherapy in gastric cancer. Previous data has demonstrated that heightened glutamine metabolism is characteristic of chemotherapy resistance in GC. It has also been observed that peptides derived from chemo-sensitive cells are capable of reducing intracellular glutamine levels. In pursuit of further clarification on whether peptide 1 could enhance the efficacy of cisplatin (DDP) treatment, we first try to confirm that whether peptide 1 is a transmembrane peptide or not. By synthesizing the peptide tagged with FITC and treating both resistant and sensitive cells, immunofluorescence assays confirmed the efficient cellular uptake of peptide 1(Fig. 3 A). Subsequently, in order to evaluate the peptide's role in enhancing the efficacy of platinum-based drugs, we examined the impact of peptide 1 on cell viability in the presence of four generations of platinum drugs. Our findings revealed that peptide 1 significantly increased the cytotoxic effects of these drugs (Fig. 3 B). Apoptosis assays further confirmed that peptide 1 could enhance the sensitivity of GC cells to the platinum drug DDP (Fig. 3 C). Additionally, cell apoptosis typically involves alterations in apoptosis-related proteins. To delineate the specific molecular mechanism by which peptide 1 promotes apoptosis in resistant cells, we conducted Western blot analyses focusing on key proteins involved in the apoptotic process including cleaved Caspase-3, a critical executor of apoptosis, alongside Bcl-2, an anti-apoptotic protein, and Bax, a pro-apoptotic protein. The results showed that treatment with DDP led to apoptosis in resistant cells, which was evidenced by a marked upregulation of Bax and a downregulation of Bcl-2. The expression pattern of Cleaved Caspase-3 followed the same trend as Bax. Notably, the addition of peptide 1 to the treatment regimen further amplified the expression trends of these proteins, resulting in a higher apoptosis rate compared to treatment with cisplatin alone (Fig. 3 D). In conclusion, these experiments demonstrated that peptide 1 function as a transmembrane peptide and can significantly enhance the sensitivity of chemo-resistant cells to DDP by activating apoptotic signaling pathways. 2.4. Peptide 1 restores chemosensitivity by inhibiting enzymes within the glutamine metabolism pathway and inducing reactive oxygen species (ROS) production in gastric cancer both in vitro and in vivo. Metabolic reprogramming is identified as one of the hallmark features of cancers [ 24 ]. In the aspect of amino acid metabolism, an elevated uptake of nutrients such as glutamine in tumor cells was observed. Researches indicated that the consumption rate of glutamine in tumor cells is 5 to 10 times that of normal cells, underscoring their dependency on glutamine. Glutamine metabolism supports the excessively activated glycolysis and oxidative phosphorylation in tumor cells by providing essential raw materials. Additionally, it directly contributes to chemotherapy resistance by impacting the homeostatic balance of glucose, lipid, and protein metabolism. The glutamine metabolic pathway plays a role in tumor cell resistance through several mechanisms: dynamic changes in glutamine transporter activity directly affect intracellular glutamine levels and impact cell resistance; metabolites in the tumor microenvironment mediate resistance through immune responses; changes in the expression and activity of key enzymes in the glutamine metabolic pathway are also crucial for the development of tumor cell resistance [ 13 ]. Given the role of peptide 1 in altering glutamine levels in resistant cells and its potential to enhance the efficacy of DDP in GC treatment, we conducted further investigations to elucidate the effect of peptide 1 on enzymes within the glutamine metabolic pathway. We began by assessing the enzyme changes via RT-qPCR for GLUL, SLC1A5, GGT1, and CPS1, which had significant role in glutamine metabolism. Our findings revealed that DDP or peptide 1 treatment alone significantly inhibited the mRNA levels of these enzymes, and this inhibitory trend was further enhanced when both treatments were combined (Fig. 4 A). The Western blot results for these proteins were consistent with the PCR findings, as it shown in Fig. 4 B and C. Furthermore, we then measured the glutamine changes caused by the expression of the above proteins. Glutamine assays demonstrated that treatment with either DDP or peptide 1 alone significantly reduced the glutamine content within cells, with a further decrease observed when used in combination (Fig. 4 D). One of the roles of glutamine metabolism is to provide important carbon and nitrogen sources for cells; due to the activation of glycolysis, the surge in demand for glutamine by tumor cells is crucial for providing essential nutrients and is significant for the survival of tumor cells. Another important role of glutamine metabolism is to maintain the homeostasis of ROS within cells. Glutamine is a precursor for the antioxidant glutathione (GSH), a potent intracellular antioxidant that effectively scavenges redundant ROS within cells. Preliminary data suggest that peptide 1 could induce a reduction in intracellular glutamine levels by inhibiting enzymes involved in the glutamine metabolic pathway. To further confirm whether changes in glutamine levels influenced ROS levels within resistant cells, we utilized flow cytometry to assess ROS levels across different treatment groups. Experimental results showed that treatments with either DDP or peptide 1 alone led to a significant increase in ROS levels in resistant cells, with their combination further destabilizing the ROS balance evidenced by the highest ROS level among different groups (Fig. 4 E and F). According to previous studies, elevated intracellular ROS can directly damage cellular DNA, leading to DNA breaks. When DNA damage reaches a certain level that cannot be repaired, the apoptotic processes will initiate, which aligns with our observations. Next, we assessed in vivo cooperativity between peptide 1 and DDP in nude mice bearing SGC7901/DDP xenografts (Fig. 4 G and H). Compared to the control group, treatment with peptide 1 or DDP resulted in a reduction in tumor weight and volume in mice. However, treatment of SGC7901/DDP-bearing mice with DDP plus P1 led to a marked reduction in tumor weight and growth when compared with treatment with DDP or P1 alone (Fig. 4 I and J). Of note, western blot analysis shown that compare to control group, the levels of GLUL, SLC1A5, GGT1, CPS1 were significantly lower expressed in xenograft tumor tissues with peptide 1 or DDP treatment, and the expression of the above proteins showed a further decrease by combined treatment with peptide 1 and DDP (Fig. 4 K and L). In summary, our research demonstrates that peptide 1 can inhibit the glutamine metabolic pathway, resulting in decreased glutamine synthesis and further promoting an imbalance in cellular ROS homeostasis. This triggers ROS accumulation and ultimately initiates cell apoptosis, thereby enhancing the sensitivity of resistant cells to platinum-based chemotherapy drugs. 2.5. Identification of GLUL as the molecular target of peptide 1. Glutamine synthetase (GLUL), also known as glutamate-ammonia ligase, plays a pivotal role in glutamine metabolism. It is a key enzyme responsible for catalyzing the combination of ammonia and glutamate to form glutamine [ 25 ]. This process is an ATP-dependent synthetic reaction and crucial for regulating intracellular glutamine levels. To explore the specific regulatory mechanisms of peptide 1 on the glutamine metabolic pathway, we first examined the expression differences of GLUL in sensitive and resistant cells. Western blot analysis revealed that the expression level of GLUL was significantly higher in resistant cells compared to sensitive SGC7901 cells, correlating with the increased need for glutamine in these cells to maintain drug resistance. Subsequently, we employed molecular docking and microscale thermophoresis (MST) experiments to investigate the potential interaction between peptide 1 and GLUL. Molecular docking is a computer simulation technique used to predict the mode of interaction between a molecule and a target molecule, playing a significant role in drug design and discovery, as well as molecular biology research. In our study, we initially retrieved the structural data of GLUL from the Protein Data Bank (PDB) and constructed the three-dimensional structure of peptide 1 using GaussView software, as shown in Fig. 5 A. Molecular docking results indicated that peptide P1 binds to an active site on GLUL, and the structure of the resultant complex is depicted in Fig. 5 B. This docked complex structure was then used for subsequent molecular dynamics simulations. The Root Mean Square Deviation (RMSD) results suggest rapid increase in RMSD values within 0–10 ns, indicating significant conformational changes in the complex systems, which stabilized after 100 ns indicating minor main chain atomic displacements and suggesting that the peptide-protein complex can stably exist in solution (Fig. 5 C). The root mean square fluctuation (RMSF) was calculated to assess the flexibility of Peptide 1 and individual amino acid residues in the GLUL protein throughout the molecular docking simulation (Fig. 5 D and E). Additionally, we further utilized surface plasmon resonance (SPR) to study biomolecular interactions. SPR results indicated that with the introduction of different concentrations of peptide P1, the response unit (RU) values began to increase, and upon cessation of peptide injection, the RU values started to decline. These experimental findings suggest that peptide 1 binds to GLUL protein immobilized on the chip, with a dissociation constant (K_D) of 5.310E-5, demonstrating high affinity between peptide P1 and GLUL protein (Fig. 5 F and G). Furthermore, to validate the results of molecular docking, differential scanning fluorimetry (DSF) was employed, a technique used to assess the thermal stability and folding state of proteins by detecting changes in fluorescence signals as the temperature increases. This technique determined the thermal denaturation temperature of proteins, thus assessing the corresponding stability. In our study, this technique was utilized to evaluate changes in the Tm of the protein before and after the addition of peptide P1. DSF results showed that the Tm of GLUL protein changed by more than 0.4°C after the addition of peptide P1, indicating binding between peptide 1 and GLUL (Fig. 5 H). Collectively, these findings provide converging evidence that peptide 1 directly binds to GLUL. This interaction was consistently supported by multiple complementary approaches. The results confirm that peptide 1 interacts with GLUL in a stable and specific manner, laying a mechanistic foundation for its role in modulating glutamine metabolism and chemosensitivity in gastric cancer cells. 2.6. The peptide 1 increases the sensitivity of cisplatin-resistant gastric cancer cells to cisplatin by targeting GLUL-mediated glutamine metabolism. In order to identify whether peptide 1 influences DDP sensitivity through GLUL-mediated glutamine metabolism, we utilized the cisplatin-resistant cell line SGC7901/DDP to construct cell lines with stable overexpression and knockdown of GLUL (Fig. 6 A, B and C). CCK8 assays revealed that, compared to the control group, cell viability decreased upon addition of peptide 1. However, the proliferative capacity of the resistant cells significantly increased when GLUL was overexpressed; this trend of increased cell viability was reversed under peptide 1 treatment, with the most notable reduction in cell viability occurred when peptide 1 was combined with shRNA-mediated GLUL suppression (Fig. 6 D). ROS assay showed that peptide 1 led to an accumulation of cellular ROS. Overexpression of GLUL reduced ROS levels, but their levels increased upon treatment with peptide 1. Further elevation in ROS levels was observed when GLUL was suppressed with shRNA and combined with peptide 1 treatment (Fig. 6 E). Our results demonstrated that treatment with peptide 1 significantly reduced intracellular glutamine levels in cisplatin-resistant GC cells. In contrast, GLUL overexpression led to a marked increase in glutamine abundance. Notably, co-treatment with peptide 1 and GLUL overexpression partially reversed the p1-induced glutamine depletion. Furthermore, knockdown of GLUL combined with peptide 1 treatment resulted in a further reduction in glutamine levels (Fig. 6 F). Flow cytometry analysis of cell apoptosis demonstrated that peptide 1 significantly promoted cell apoptosis. Compared to the control group, the apoptosis rate significantly decreased after GLUL overexpression, but this trend was reversed following the addition of peptide 1. Combining shRNA-mediated GLUL suppression with peptide 1 significantly increased the apoptosis rate (Fig. 6 G and H). Subsequent analysis of apoptosis-related proteins confirmed these levels of apoptosis (Fig. 6 I). Furthermore, Western blot analysis examined the changes in key enzymes of the glutamine metabolic pathway after overexpression or suppression of GLUL. Overexpression of GLUL promoted the expression of enzymes in the metabolic pathway, whereas the addition of peptide 1 significantly inhibited their expression. Suppression of GLUL further enhanced this trend of inhibited expression (Fig. 7 A and B). Although CPS1 and GGT1 do not directly participate in DNA repair mechanisms, their roles in maintaining cellular environmental stability and antioxidative defenses indirectly affect the processes of DNA damage and repair. γ-H2AX is a key component of the cellular mechanism to recognize and respond to DNA damage. It is often served as an early marker for the presence of double-strand breaks. We then assessed the levels of DNA damage in cells treated with peptide 1 combined with shGLUL or oeGLUL via immunofluorescence, finding that peptide 1 treatment could promote DNA damage. Overexpression of GLUL significantly mitigated DNA damage, while suppression of GLUL significantly increased DNA damage (Fig. 7 C and D). To further determine whether the effect of Peptide 1 on cisplatin sensitivity was mediated through GLUL, we evaluated tumor growth and metabolic changes in xenograft models with GLUL knockdown or overexpression. As shown in Fig. 8 , GLUL knockdown significantly inhibited tumor growth compared with control tumors, while the combination of GLUL knockdown and Peptide 1 treatment further suppressed tumor progression, leading to the smallest tumor volumes and weights (Fig. 8 A, B and C). Consistently, glutamine levels were markedly reduced in GLUL-silenced tumors, and Peptide 1 treatment enhanced this effect (Fig. 8 D). Flow cytometry analysis further revealed that combined GLUL knockdown and Peptide 1 treatment resulted in a significant increase in apoptosis compared with either intervention alone (Fig. 8 E and F). Conversely, GLUL overexpression promoted tumor growth, increased glutamine abundance, and reduced apoptosis in resistant cells (Fig. 8 G, H and I). Importantly, Peptide 1 treatment partially abrogated the pro-tumorigenic effects of GLUL overexpression, as evidenced by decreased tumor volume, reduced glutamine levels, and enhanced apoptosis in oeGLUL + P1 tumors compared with oeGLUL alone (Fig. 8 J, K and L). These findings provide compelling evidence that Peptide 1 exerts its chemosensitizing effect by targeting GLUL-mediated glutamine metabolism. By lowering intracellular glutamine levels, Peptide 1 disrupts metabolic homeostasis, increases oxidative stress, and enhances apoptosis in cisplatin-resistant GC cells. Moreover, the ability of Peptide 1 to counteract the effects of GLUL overexpression highlights its potential as a metabolic vulnerability–targeting therapeutic strategy. Together, these results support the concept that inhibition of GLUL is central to overcoming cisplatin resistance and that RHOJ-derived Peptide 1 functions as an effective GLUL-targeting sensitizer in vivo. 3. Materials and methods 3.1. Cell lines and cell culture SGC7901, HGC27 and corresponding cisplatin-resistant SGC7901/DDP and HGC27/DDP cells were purchased from Cell Bank, Chinese Academy of Sciences (Shanghai, China). Cells were maintained in RPMI 1640 (Gibco, Grand Island, USA) supplemented with 10% fetal bovine serum (FBS) in a humidified atmosphere at 37°C with 5% CO2. Cisplatin-resistant SGC7901/DDP and HGC27/DDP cells were cultured in medium supplemented with cisplatin at a final concentration of 4 µM, to maintain drug resistance. 3.2. Peptide selection and synthesis Based on our proteomic analysis, RHOJ was identified as a significantly downregulated protein in cisplatin-resistant gastric cancer cells. To investigate whether endogenous peptides derived from RHOJ could influence chemosensitivity, we designed a set of peptide fragments based on the full-length amino acid sequence of human RHOJ (UniProt ID: Q9H4E5). Using a sliding window approach (14 amino acids in length), multiple overlapping peptide fragments were generated across the RHOJ sequence. The selection of peptide segments was based on (i) uniform coverage across the RHOJ protein sequence, (ii) predicted biological activity (via PeptideRanker), and (iii) suitability for synthesis and solubility. From these, six peptide fragments were randomly selected for synthesis and functional screening. These peptides, designated as peptide 1 to peptide 6, were synthesized at > 95% purity (GL Biochem, Shanghai) and used in subsequent in vitro assays to evaluate their effect on cisplatin sensitivity. 3.3. CCK-8 assay Logarithmic phase GC cells were seeded at a density of 4,000 cells per well in a 96-well cell culture plate with 200 µL medium per well and cultured overnight. Cells were treated according to experimental requirements (3 parallel wells per group, independently repeated 3 times), with an equal volume of PBS added as a control. After incubation in the cell culture incubator for 48 hours, the culture medium was removed, and 100 µL of pre-prepared 10% CCK-8 solution was added to each well. After incubating for 2 hours, the absorbance of each well was measured at a wavelength of 450 nm. 3.4. Apoptosis A total of 6 × 10 5 cells per well were seeded in 6-well plates, with each well containing 2 mL of growth medium. The cells were allowed to adhere overnight to achieve approximately 70–80% confluence. After achieving the desired confluence, the cells were subjected to experimental treatments. Various concentrations of DDP were added to the respective wells. Control wells received equivalent volumes of vehicle or PBS. Each treatment condition was performed in triplicate wells. Following treatment, the cells were incubated at 37°C in a CO2 incubator for 48 hours. After 48 hours incubation, apoptosis was assessed using Annexin V-FITC/propidium iodide (PI) staining. Cells were harvested, washed with cold PBS, and stained with Annexin V-FITC and PI according to the manufacturer's instructions. Stained cells were then analyzed using flow cytometry to quantify the percentage of apoptotic cells. Data acquisition and analysis were performed using Flowjo. 3.5. Western blot Proteins were extracted from cells using lysis buffer to disrupt cell membranes and release proteins. The concentration of extracted proteins was measured using protein quantification methods BCA assay (P0009, Beyotime, Shanghai). 30 µg of protein samples were loaded onto sodium dodecyl sulfate-polyacrylamide gels (SDS-PAGE) and separated by electrophoresis. The separated proteins were then transferred onto a polyvinylidene difluoride (Merck Millipore, Boston, MA, USA) using semi-dry method. Membranes were blocked by 5% non-fat milk for 2 h and incubated with the indicated primary antibody at 4°C overnight. Membranes were then washed with buffer to remove unbound primary antibody and followed by HRP-conjugated secondary antibody (A0208 and A0192, Beyotime, Shanghai) incubation for 2 h at room temperature. Mixed enhanced chemiluminescence (ECL) was added to produce a visible signal when it reacts with the protein-antibody-secondary antibody complex after the membrane was washed again to remove unbound secondary antibody. A chemiluminescence detection system (Tanon, Shanghai, China) was used to visualize the image of the membrane and the signals were quantified using Image J. Antibodies for WB were list as follows: anti-GLUL (1:1,000; Cat#80636S), anti-ASCT2 or SLC1A5 (1:1,000; Cat#8057S), anti-bax (1:1,000; Cat#5023S), anti-bcl2 (1:1,000; Cat#3498S), anti-C-capase3 (1:1,000; Cat# 9661T) were obtained from Cell Signaling Technology (Beverly, MA, USA); anti-GGT1 (1:1,000; Cat#ab55138), anti-CPS1 (1:1,000; Cat#ab129076), were obtained from Abcam (Cambridge, England); anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH; 1:5,000; 60004-1-Ig) were obtained from Proteintech (Rosemont, IL, USA). 3.6. Quantitative real-time PCR (qRT-PCR) Total RNA was isolated from cells using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. The extracted RNA was reverse transcribed into complementary DNA (cDNA) using the HiScript II Q RT SuperMix for qPCR (Vazyme, Nanjing, Jiangsu, China) reverse transcription kit. qRT-PCR was performed on a QuantStudio 6 Pro Real-Time PCR System (Thermo Fisher). The amplification program consisted of an initial denaturation step at 95°C for 3 minutes, followed by 39 cycles of denaturation at 95°C for 10 seconds and annealing/extension at 55°C for 30 seconds. Relative gene expression levels were determined using the 2^–ΔΔCt method, with GAPDH expression serving as the internal control for normalization. 3.7. Glutamine (Gln) assay Glutamine levels were measured using a commercially available assay kit (BC5305 Solabio, Beijing) following the manufacturer's instructions. Briefly, cells were collected into centrifuge tubes, discard the supernatant after centrifugation, resuspend cells in 1 mL of extraction buffer per 10^4 cells, and then disrupt the cells by ultrasonication. Centrifuge at 12,000 × g for 5 minutes at 4°C, transfer the supernatant to a new tube, add 500 µL of extraction buffer two, vigorously vortex for 5 minutes, centrifuge at 12,000 × g for 5 minutes at 4°C, and collect the upper layer of liquid for further analysis on ice. Glutamine concentrations were determined by a spectrophotometer comparing to a standard curve generated using known concentrations of glutamine standards provided in the kit. Data analysis was performed using Graphpad prism. 3.8. ROS detection Cultivate cells in complete medium until reaching desired confluence. After treated cells with the experimental conditions, harvest treated cells and wash them with phosphate-buffered saline (PBS). Incubate cells with a ROS-sensitive fluorescent dye, 2',7'-dichlorofluorescein diacetate (DCF-DA), at a final concentration of 10 µM in serum-free medium for 20 minutes at 37°C in the dark. After staining, wash the cells with PBS to remove excess dye. Analyze the fluorescence intensity of stained cells using a flow cytometer. Set the appropriate compensation controls using unstained and single-stained cells to correct for spectral overlap between channels. Gate the cell population of interest based on forward scatter (FSC) and side scatter (SSC) to exclude debris and aggregates. Acquire at least 10000 cells per sample to ensure statistical robustness. Analyze flow cytometry data using dedicated software to quantify the mean fluorescence intensity (MFI) of ROS-stained cells. Compare the MFI of treated cells with untreated control cells to assess changes in ROS levels. Perform statistical analyses to determine the significance of observed differences. 3.9. Nano differential scanning fluorimetry assay (NanoDSF) PrometheusTM Series NT.48 was used to determine start temperatures, melt temperature, and end temperature for GLUL and the mixture of GLUL and peptide 1. Samples were filled in NanoDSF Standard-grade capillaries and transferred into the device. Thermal unfolding was tested during in a linear temperature slope (7°C/min; 20–95°C). Experiments were conducted in triplicates. If the change of temperature was great than 0.4°C or 3 times of stander error, the protein conformation is considered to have changed. 3.10. Surface plasmon resonance (SPR) GLUL was immobilized to the series S sensor chip CM5. Protein fixation requires specific acidic conditions (pH values of 4.0, 4.5, and 5.0 were tested). Ligand proteins were diluted to 20 µg/ml using 10 mM sodium acetate at different pH levels. Samples were manually injected in sequence in manual mode, and the pH environment yielding the highest signal was selected. Protein fixation parameters were set as follows: concentration of 30–50 µg/ml, pH environment of 4.0, flow rate of 10 µl/min, and temperature of 25°C. The solution required for fixation includes 100 µl NHS, 100 µl EDC, and 150 µl Ethamolamine. The instrument automatically activates the chip surface by mixing EDC and NHS. After protein fixation, the reference channel was subjected to blocking treatment (using EDC, NHS, and Ethamolamine). Peptide 1 at 3.125 µM to 200 µM in running buffer (1 x PBS with 0.05% Tween 20 ) were introduced and allowed to contact for 120 s and disassociate for 300 s. Biacore T200 system was used to perform SPR experiments. 3.11. Molecular docking Structure of GLUL protein (PDB ID 2OJW) was obtained from the Protein Data Bank (PDB), which comprises 14 subunits. Each subunit's conformation is extracted individually to represent the structure of the GLUL protein. The peptides sequence is AVFDEAILTIFHPK. The structural model of peptide was predicted by Robetta Web protein structure prediction server. Using deep learning methods, the peptides sequence undergoes fragment assembly or homology modeling to obtain initial peptide conformations. The initial peptide structure undergoes energy optimization of side chain and backbone positions to find the lowest energy conformation and score it. Rosetta program suite was used to perform molecular docking of peptides and CLNA, and Monte Carlo algorithm-based global docking system was employed to explore the relative conformational space of peptides and protein. In this system, Rosetta centers proteins as spheres and uniformly places peptides on 26 initial positions on the sphere's surface, enabling parallel docking simulations. The docking approach is semi-flexible, utilizing empirical energy functions within Rosetta, which include van der Waals interactions, electrostatic interactions, hydrogen bonds, and entropy contributions. 3.12. Molecular dynamics simulations The system established was a fully atomic explicit solvent model of GLUL using the GROMACS-2022 software. The parameters of the system were set as follows: temperature, 310 K; simulation time, 100 ns; force field, AMBER99SB; water model, TIP3P. The C-terminus and N-terminus were modeled as COO- and NH3+, respectively. The minimum protein-box distance was 1 nm, and the system was contained within a rectangular box with dimensions of 5.979 × 6.599 × 7.250 nm³. The GLNA protein system comprised a total of 49,260 atoms, including two sodium ions. All simulations utilized periodic boundary conditions. Prior to molecular dynamics simulations, energy minimization was performed using 50,000 steps of steepest descent to alleviate spatial conflicts within the system. Subsequently, simulations were conducted under NVT and NPT (1 bar) ensembles for 0.1 ns each to equilibrate the system. The Parrinello-Rahman method was employed to maintain pressure at 1 bar, while temperature was controlled at 310 K using separate temperature coupling for protein and non-protein moieties via velocity rescaling. The Verlet cutoff scheme updated the neighbor list every 10 steps with a cutoff distance of 1.0 nm. Bond lengths were constrained using the LINCS method (protein) and SETTLE algorithm (water molecules) allowing for a time step of 2 fs. Van der Waals interactions were truncated at 1.0 nm, and electrostatic interactions were computed using the particle mesh Ewald (PME) method with a real-space cutoff of 1.0 nm. The structural and conformational properties of the GLUL and peptide were characterized using all-atom RMSD and RMSF parameters based on residues. 3.13. Proteomic profiling of cisplatin-sensitive and -resistant Gastric Cancer Cells To identify differentially expressed proteins associated with cisplatin resistance, proteomic analysis was performed on cisplatin-sensitive gastric cancer cells (SGC7901, HGC27) and their cisplatin-resistant counterpart (SGC7901/DDP, HGC27/DDP). Cells were cultured under standard conditions, harvested at 70–80% confluence, and washed twice with cold PBS. Total proteins were extracted using RIPA lysis buffer containing protease and phosphatase inhibitors. Lysates were sonicated on ice and centrifuged at 12,000 × g for 15 minutes at 4°C to remove debris. Protein concentration was determined using a BCA protein assay kit. Equal amounts of protein from each sample were reduced with dithiothreitol (DTT), alkylated with iodoacetamide (IAA), and digested with trypsin overnight at 37°C. The resulting peptides were desalted and fractionated using high-pH reverse-phase liquid chromatography to improve proteome coverage. Each peptide fraction was analyzed by liquid chromatography–tandem mass spectrometry (LC-MS/MS) using a high-resolution Orbitrap mass spectrometer coupled to an ultra-performance liquid chromatography (UPLC) system. The mass spectrometer was operated in data-dependent acquisition (DDA) mode. MS/MS spectra were searched against the human UniProt database using the Sequest search engine in Proteome Discoverer (Thermo Fisher Scientific). Protein identification and quantification were performed with a false discovery rate (FDR) < 1%. Label-free quantification (LFQ) was used to compare protein abundance across samples. Differentially expressed proteins between cisplatin-sensitive and -resistant cells were identified based on fold change and statistical significance (p < 0.05). Selected proteins with consistent upregulation were further analyzed and used for downstream peptide design. 3.14. Non-target metabolomics Cells were harvested and washed with phosphate-buffered saline (PBS) to remove extracellular metabolites. Metabolites were extracted using a methanol: water (4:1) solvent mixture. Samples were vortexed and incubated on ice for 10 minutes to ensure efficient extraction. Subsequently, samples were centrifuged at 14,000 rpm for 10 minutes at 4°C, and the supernatants were collected for analysis. Metabolite profiling was performed using a high-performance liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) system. Chromatographic separation was achieved using a reverse-phase C18 column with a binary solvent system consisting of water with 0.1% formic acid (solvent A) and acetonitrile with 0.1% formic acid (solvent B). Gradient elution was employed at a flow rate of 0.3 mL/min. Mass spectrometric detection was performed in both positive and negative ionization modes over a predefined mass range. Raw LC-MS data were acquired and processed using dedicated software platforms. Peak detection, retention time alignment, and peak integration were performed to generate a list of detected metabolites. Peak areas were normalized to internal standards to correct for variations in sample preparation and instrument response. Subsequently, statistical analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), were conducted to identify metabolite features that distinguish between experimental groups. Metabolite features were annotated by comparing retention times and mass spectra with reference standards available in public databases such as METLIN and HMDB. Putative metabolite identifications were further validated using tandem mass spectrometry fragmentation patterns and isotopic patterns. Metabolite identities were confirmed by comparing experimental data with authentic standards or by elucidating chemical structures using additional analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy. 3.15. Virus production, infection, and selection Viral particles were produced using a lentiviral packaging system. Briefly, HEK293T cells were seeded in 10 cm culture dishes at a density of 5×10^6 cells per dish. The following day, cells were transfected with the desired lentiviral vector along with the packaging plasmids psPax2 and pMD2.G (Addgene plasmids #12260 and #12259) using a calcium phosphate transfection method. Twenty-four hours post-transfection, the culture medium was replaced with fresh medium, and supernatants containing viral particles were harvested 48 hours later. Supernatants were then filtered through a 0.45 µm filter to remove cellular debris. Target cells were seeded in six-well plates at a density sufficient to reach 50% confluence at the time of infection. Viral particles were added to the cells at a concentration of 40 µl/ml supplemented with 5 mg/ml polybrene to enhance infection efficiency. After 24 hours of viral exposure, the medium was replaced with fresh growth medium. Following viral infection, cells were subjected to selection with puromycin to enrich for stably transduced cells. Puromycin selection was initiated 24 hours post-infection at a concentration of 2 µg/ml and continued for at least one week. Surviving cells were considered successfully infected and selected for RT-qPCR and WB validation. 3.16. Fluorescence detection Cells were fixed with 4% paraformaldehyde for 15 minutes at room temperature and permeabilized with 0.1% Triton X-100 for 10 minutes. After blocking with 5% bovine serum albumin (BSA) for 1 hour, cells were incubated with primary antibodies overnight at 4°C. Primary antibodies against the target protein were diluted according to manufacturer recommendations. The following day, cells were washed and incubated with fluorophore-conjugated secondary antibodies for 1 hour at room temperature. Nuclei were counterstained with 4',6-diamidino-2-phenylindole (DAPI). Images were acquired using a fluorescence microscope. 3.17. Subcutaneous tumor model establishment To establish the subcutaneous tumor model, male BALB/c nude mice aged 6–8 weeks were obtained from the experimental animal center, school of pharmacy, Fudan university. Animals were housed in a controlled environment with a 12-hour light/dark cycle and provided with standard rodent chow and water ad libitum. Prior to tumor inoculation, mice were acclimatized to the laboratory conditions for seven days. SGC7901/DDP-shNC, SGC7901/DDP-shGLUL, SGC7901/DDP-Vector, SGC7901/DDP-oeGlUL were cultured RPMI 1640 complete medium at 37°C in a humidified atmosphere with 5% CO2 until reaching logarithmic growth phase. Cells were subcutaneously injected into the left flank of each mouse using a sterile syringe with a 27-gauge needle. Tumor growth was monitored every 3 days using digital calipers, and tumor volume was calculated using the formula: tumor volume = 0.5 × L × W 2 , where V is the tumor volume, L is the longest diameter, and W is the shortest diameter. Eleven days after tumor implantation, the mice were randomly separated into eight experimental groups (5 mice per group): SGC7901/DDP-shNC, SGC7901/DDP-shGLUL, SGC7901/DDP-NC, SGC7901/DDP-shGLUL; SGC7901/DDP-Vector, SGC7901/DDP-oeGLUL, SGC7901/DDP-Vector, SGC7901/DDP oeGLUL, the following 3-week treatments were given as one or as a combination of the following: PBS, 100 µL per mice, intraperitoneal injection, once a week; cisplatin, 5 mg/kg, intraperitoneal injection, once a week; peptide 1, 10 mg/kg, intraperitoneal injection, once every two days. Mice were euthanized and tumors were excised for further analysis after three weeks later. 3.18. Statistical analysis The quantitative data were presented as mean ± standard deviation (SD). GraphPad Prism version10.0.0 software (GraphPad Software, Boston, Massachusetts, USA) was used for statistical analysis. The statistical differences between experimental groups were analyzed by one-way analysis of variance (ANOVA) or Student's t-test. Differences were considered statistically significant if P values < 0.05. 4. Discussion In recent years, peptide drugs have become a research hotspot due to their advantages such as low molecular weight, high sensitivity, low resistance, and ease of modification. Currently, there are approximately 80 peptide drugs available on the global market, and over 150 peptides in clinical development and another 400–600 peptides in preclinical studies [ 26 , 27 ]. The discovery of novel peptides has become a significant focus in biomedical research due to their potential therapeutic applications. Currently, the discovery of novel peptides primarily relies on the following methods: Phage Display Technology, Computational Design and Modeling, Natural Product Screening, Mass Spectrometry-Based Proteomics. Although many anti-tumor peptides have been discovered, and numerous studies have elucidated their potential molecular mechanisms in cancer biology, the role of peptides in the context of drug resistance remains limited. Based on the advantages of peptides, this class of small molecules could provide new insights into solving clinical problems of tumor drug resistance. Therefore, in order to discover novel peptides that might played a significant role in reverse GC chemoresistance. We initially employed proteomic analysis to compare the protein expression profiles of cisplatin-sensitive and -resistant GC cells, identifying candidate proteins for subsequent peptide design and functional screening. By focusing on peptides that were upregulated in sensitive cells, we hypothesized that these molecules might play a role in mediating sensitivity to chemotherapeutic agents. Subsequently, we conducted experiments to verify the role of these six peptides in sensitizing gastric cancer cells to chemotherapy and found one of these peptides significantly reduced the IC50 of cisplatin in the resistant cells. At present, studies investigating the mechanisms of chemotherapy resistance in GC primarily concentrate on molecular changes, epigenetic modifications, metabolic reprogramming, microenvironment influences. Metabolic reprogramming allows cancer cells to adapt to and survive the harsh conditions imposed by factors such as anti-tumor agents. Glucose metabolism and lipid metabolism have been reported closely linked to chemotherapy resistance [ 28 , 29 ], and targeted metabolism can increase trastuzumab sensitivity [ 12 , 30 , 31 ]. Moreover, an elevated glutamine metabolism was observed trastuzumab resistance in gastric cancer, and modulation of glutamine metabolism can reverse trastuzumab resistance both in vitro and in vivo [ 12 ]. In this study, metabolomic analysis of cisplatin-sensitive and cisplatin‐resistant cell lines showed that glutamine metabolism was markedly enhanced in the cisplatin-resistant group. As a critical nutrient for cancer cells, glutamine supports both their energetic and biosynthetic needs. In drug-resistant GC cells, we also observed an upregulated glutamine metabolism, which is consistent with previous studies that have highlighted the role of glutamine in promoting resistance to chemotherapy [ 12 , 32 ]. Interestingly, when treated the cisplatin-resistant cells with peptide1, the glutamine content in resistant cells was significantly reduced compared to non-treatment group, suggesting that peptide 1 can inhibit glutamine synthesis. Moreover, the combination of peptide 1 and DDP enhances apoptosis in resistant cells, thereby restoring chemotherapy sensitivity. Our finding suggests that the peptides not only directly impact cell survival but also potentiate the effects of chemotherapeutic drugs. This provides a mechanistic direction for studying how peptide1 promotes chemotherapy sensitivity. Therefore, we first conducted PCR and WB analyses of key enzymes in the glutamine metabolism pathway, the results revealed that the combination of peptide 1 and DDP significantly downregulated the expression of these enzymes. Consistent results were obtained from glutamine content measurements across different treatment groups. Glutamine not only provides additional nutrients to tumor cells but also plays a crucial role in maintaining intracellular ROS balance. Glutamine is an important precursor for the synthesis of glutathione, a potent intracellular antioxidant capable of scavenging excessive ROS. Through the conversion of glutamine, cells can maintain adequate glutathione levels, thereby regulating and neutralizing ROS. Therefore, we measured ROS levels in resistant cells treated with peptide 1 using flow cytometry. The results showed that ROS levels increased after peptide 1 treatment, further leading to apoptosis in resistant cells. Similar results were observed in animal experiments. ROS are known to induce oxidative stress and damage cellular components, including lipids, proteins, and DNA, which can lead to cell death if not adequately managed. The inhibition of glutamine metabolism by anti-tumor peptides led to a significant accumulation of ROS within the gastric cancer cells. The accumulation of ROS in the context of inhibited glutamine metabolism creates a state of metabolic stress that drug-resistant cells are unable to compensate for, making them susceptible to chemotherapy agents. Our experiments showed that treatment with anti-tumor peptides significantly reduced the viability of drug-resistant gastric cancer cells when combined with conventional chemotherapeutics. Our experiments showed that increased glutamine metabolism is associated with cisplatin resistance in gastric cancer, and targeting glutamine metabolism by anti-tumor peptide can effectively reverse chemoresistance in vitro and in vivo. GLUL plays a crucial role in glutamine metabolism, and its dysregulation has been implicated in various cancers, including liver cancer, lung cancer, breast cancer, pancreatic cancer, ovarian cancer, colon cancer, and gastric cancer [ 33 – 41 ]. However, its role in drug-resistant remains unclear. In this study, we found that GLUL is highly expressed in cisplatin-resistant cell lines, which indicates more glutamine is needed in drug resistant cells. We therefore hypothesized that GLUL might be a target of peptide 1 and conducted subsequent studies to investigate the binding of peptide 1 and GLUL. Our study demonstrates that the small molecule peptide 1 interacts specifically with the target protein GLUL, inhibiting its activity and thereby modulating downstream signaling pathways. Peptide 1 was shown to bind to the active site of GLUL with high affinity, as evidenced by our molecular docking and MST experiments, which revealed significant binding interactions. In our cell experiments, with the presence of DPP, peptide 1 treatment could decrease the GLN level and increase the level of ROS, apoptosis and DNA damage. The overexpression of GLUL by lentivirus could alleviate the above tendency. peptide 1 treatment with oeGLUL further decreased the level of ROS, apoptosis and DNA damage. Dual inhibition of GLUL by peptide 1 and shRNA significantly lead to an increased the level of ROS, apoptosis and DNA damage. The above data suggested that GLUL is a critical regulator of glutamine metabolism, and played a significant role in GC drug resistance. In summary, peptide 1 enhances the sensitivity of GC cells to DDP by inhibiting GLUL-mediated glutamine metabolism, which subsequently contributes to ROS imbalance and DNA damage and eventually induces the apoptosis of cisplatin-resistant cells. An increasing number of studies have shown that glutamine metabolism plays a crucial role in tumor resistance. Both basic and clinical trials have demonstrated that targeting tumor-dependent glutamine metabolism can effectively inhibit tumor growth, overcome tumor immune evasion and reverse drug resistance [ 42 – 44 ]. Inhibitors targeting glutamine metabolism are gradually being developed, with the latest generation, CB839, having passed Phase I/II clinical trials, potentially bringing new hope and breakthroughs in future cancer treatments [ 45 , 46 ]. In this study, we explored the potential of anti-tumor peptides to enhance the chemosensitivity of drug-resistant gastric cancer cells by inhibiting glutamine metabolism pathways, ultimately leading to the accumulation of ROS and DNA damage within the cells. Our findings provide new insights into the mechanisms underlying the reversal of chemoresistance and suggest novel therapeutic approaches for treating gastric cancer. In summary, the peptides studied in this research, by targeting glutamine metabolism, have broad prospects for clinical application in overcoming chemotherapy resistance. They have the potential to be used as part of combination therapies to overcome resistance to platinum-based drugs, making them a promising first-line treatment for advanced gastric cancer, especially those patients who have developed resistance to standard chemotherapy regimens. Our study had some limitations. Firstly, the differences in peptidomes and glutamine metabolism characteristics between chemotherapy-sensitive and resistant patients need to be clarified. Due to limitations in current experimental conditions and resources, this study only discusses these aspects based on cell lines. Secondly, further investigation is needed to clarify the differences in GLUL expression at the tissue and serum levels between sensitive and resistant patients. This is crucial for future precise treatments of gastric cancer based on GLUL expression to distinguish between chemotherapy resistance and sensitivity patients. Lastly, we only constructed CDX model in this study. In future research, obtaining gastric cancer tissues from resistant and sensitive patients and constructing PDX models would greatly provide more solid evidence to this study. In conclusion, our study highlights the potential of anti-tumor peptides to inhibit glutamine metabolism, induce ROS accumulation and DNA damage, thus enhancing the chemosensitivity of drug-resistant gastric cancer cells. This approach not only sheds light on novel therapeutic targets but also offers a new strategy to improve the efficacy of chemotherapy in gastric cancer treatment. Declarations Author Contributions: Conceptualization, Xiaohong Zhang, Jun Jiang and Li Feng; Data curation, Jian Li, Fangzhou Ye, Jiayi Wang and Jun Jiang; Formal analysis, Jian Li, Huanqing Li, Fangzhou Ye, Jiayi Wang and Jun Jiang; Funding acquisition, Huanqing Li and Li Feng; Investigation, Jian Li, Huanqing Li, Fangzhou Ye and Jun Jiang; Methodology, Jian Li, Huanqing Li, Fangzhou Ye, Jiayi Wang, Songhua Bei and Jun Jiang; Project administration, Xiaohong Zhang, Jun Jiang and Li Feng; Resources, Jiayi Wang, Xiaohong Zhang, Jun Jiang and Li Feng; Software, Huanqing Li, Jiayi Wang and Songhua Bei; Supervision, Xiaohong Zhang, Jun Jiang and Li Feng; Validation, Huanqing Li and Fangzhou Ye; Visualization, Fan Li and Songhua Bei; Writing–original draft, Jian Li; Writing –review & editing, Jian Li, Fan Li, Songhua Bei, Xiaohong Zhang and Li Feng. Funding: This research was funded by the National Nature Science Foundation of China (Grant number: 8217100675 and 82503570), Major Discipline Construction of Minhang District, Shanghai (Grant number: 2020MWDXK03), High-Level Specialist Physician Training Program under the Minhang District Integrated Medical-Education-Research Health Service System (Grant number: 2024MZYS16), and the APC was funded by National Nature Science Foundation of China (Grant number: 8217100675). Institutional Review Board Statement: This study was approved by the Ethics Committee of Minhang hospital, Fudan University, and all animal experiments were approved by Laboratory Animal Center, Fudan University and performed in accordance with the guidelines. Data Availability Statement: The data generated from this study are available upon request from the corresponding author. Acknowledgments: We thank Jun Jiang for assisting with peptide selection and preparation for subsequent functional experiments. 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Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted Editorial decision: Major revision 29 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 07 Oct, 2025 Editor assigned by journal 30 Sep, 2025 First submitted to journal 27 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7691539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525811397,"identity":"3c1103ac-b077-48ad-b713-5baba19761ef","order_by":0,"name":"Jian Li","email":"","orcid":"","institution":"Fudan University Minhang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Li","suffix":""},{"id":525811398,"identity":"5c664d65-be99-48ce-850e-7f3d90a510e3","order_by":1,"name":"Huanqing Li","email":"","orcid":"","institution":"Minhang 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proteins across the cisplatin-resistant and cisplatin-sensitive gastric cancer cell lines. Notably, RHOJ was significantly downregulated in both resistant cell lines. \u003cstrong\u003e(C)\u003c/strong\u003e Six peptides were synthesized based on the amino acid sequence of the RHOJ protein. Flow cytometry analysis of apoptosis in GES-1 cell line treated with candidate peptides to evaluate potential cytotoxicity. \u003cstrong\u003e(D)\u003c/strong\u003e The IC50 value of six peptides in SGC7901 and SGC7901/DDP cells following treatment with each peptide was determined by CCK8 assay. \u003cem\u003eNS\u003c/em\u003e, no significant, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/4cc93022d3be6e4a0c3d5321.jpeg"},{"id":93915664,"identity":"fb7dd633-213e-41c4-9da9-21fa2d42e31c","added_by":"auto","created_at":"2025-10-20 08:45:25","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200669,"visible":true,"origin":"","legend":"\u003cp\u003eGlutamine metabolism was enhanced in the cisplatin resistant gastric cancer cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The heatmap of differential metabolite analysis. \u003cstrong\u003e(B)\u003c/strong\u003e The different abundance of L-Glutamine between the cisplatin-sensitive and cisplatin-resistant gastric cancer cell lines. \u003cstrong\u003e(C)\u003c/strong\u003eKEGG pathway enrichment analysis of differentially expressed metabolites between cisplatin-sensitive and cisplatin-resistant gastric cancer cells. The analysis highlights the most significantly enriched metabolic pathways in resistant cells, with amino acid metabolism ranking among the top.\u003cstrong\u003e (D) \u003c/strong\u003eThe cell viability of SGC7901 and SGC7901/DDP \u003cstrong\u003e(E) \u003c/strong\u003ewith or without the deprivation of glutamine.\u003cstrong\u003e (F)\u003c/strong\u003e Relative Gln concentration in SGC7901 and SGC7901/DDP cells treated with peptide 1. * \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/af2925253db6c769a347a679.jpeg"},{"id":93913540,"identity":"f67cb215-00dd-4636-895c-5ec0078d9d5a","added_by":"auto","created_at":"2025-10-20 08:37:25","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":196553,"visible":true,"origin":"","legend":"\u003cp\u003ePeptide 1 increased the sensitivity of resistant cells to chemotherapy.\u003cstrong\u003e (A)\u003c/strong\u003e Immunofluorescence assay using FITC-labeled peptide 1. \u003cstrong\u003e(B) \u003c/strong\u003eCell viability of SGC7901/DDP cells which were treated with four generations of platinum drugs in SGC7901/DDP cell lines (with or without peptide 1). \u003cstrong\u003e(C)\u003c/strong\u003e Apoptotic cells were quantified by using Annexin V-FITC. Combined treatment of peptide 1 and cisplatin induced apoptosis in SGC7901/DDP cells compared with either alone. \u003cstrong\u003e(D) \u003c/strong\u003eApoptosis-related proteins (Bax, Bcl2, C-caspase3) were detected by western blot. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/b52bcfa4a9453c9c0907b801.jpeg"},{"id":93913542,"identity":"e3dd7bf8-a1f0-4ad8-904c-5a29c6e54daf","added_by":"auto","created_at":"2025-10-20 08:37:25","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":295562,"visible":true,"origin":"","legend":"\u003cp\u003ePeptide 1 restores cisplatin sensitivity via inhibition of glutamine metabolism and induction of ROS. \u003cstrong\u003e(A)\u003c/strong\u003e mRNA expression levels of key enzymes involved in the glutamine metabolism pathway. \u003cstrong\u003e(B, C)\u003c/strong\u003e Western blot analysis showing protein expression of key glutamine metabolism enzymes. \u003cstrong\u003e(D)\u003c/strong\u003e Intracellular glutamine (Gln) concentration in SGC7901/DDP cells treated with peptide 1 or cisplatin. \u003cstrong\u003e(E, F)\u003c/strong\u003eMeasurement of reactive oxygen species (ROS) levels in SGC7901/DDP cells following treatment with peptide 1 or cisplatin. \u003cstrong\u003e(G)\u003c/strong\u003e Representative bioluminescence images of nude mice bearing subcutaneous SGC7901/DDP xenografts treated with vehicle, cisplatin (5 mg/kg, once per week), peptide 1 (10 mg/kg, every other day), or their combination, recorded at weeks 1, 2, and 3.\u003cstrong\u003e (H)\u003c/strong\u003ePhotographic images of tumors excised from each group at the end of the 21-day treatment period (n = 5 per group). \u003cstrong\u003e(I) \u003c/strong\u003eTumor weights measured at the end of the experiment. \u003cstrong\u003e(J) \u003c/strong\u003eTumor growth curves of subcutaneous xenografts during the treatment period. Tumor volumes were measured every three days. \u003cstrong\u003e(K)\u003c/strong\u003e Western blot analysis of key enzymes involved in glutamine metabolism in tumor tissues obtained from subcutaneous xenografts after 21 days of treatment. Mice were treated with vehicle, peptide 1 (10 mg/kg, every other day), cisplatin (5 mg/kg, once per week), or a combination of both agents. GAPDH was used as a loading control to normalize protein expression levels. \u003cstrong\u003e(L)\u003c/strong\u003e Quantification of protein expression levels of key enzymes involved in glutamine metabolism in xenograft tumor tissues based on densitometric analysis of Western blot bands shown in (K). * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/a30d3685ebd2a95897b88ad2.jpeg"},{"id":93913545,"identity":"9c78a85f-4759-4c12-b22a-8bea7db0fb70","added_by":"auto","created_at":"2025-10-20 08:37:25","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":231115,"visible":true,"origin":"","legend":"\u003cp\u003eBinding interaction between Peptide 1 and GLUL as revealed by molecular docking and molecular dynamics simulations. \u003cstrong\u003e(A)\u003c/strong\u003eCartoon view of molecular docking between peptide and GLUL. GLUL and Peptide 1 is represented in light purple and yellow, respectively.\u003cstrong\u003e (B)\u003c/strong\u003e Representative snapshot of the Peptide 1–GLUL complex at 100 ns during molecular docking simulation. At this time point, peptide residues 1–2, 5, 11, and 13–14 form interactions with GLUL residues 192, 199, 212, 274, 355, and 357. \u003cstrong\u003e(C) \u003c/strong\u003eRMSD was calculated to evaluate the structural stability of the Peptide 1–GLUL complex over the course of a 100 ns molecular dynamics simulation. The black curve represents the RMSD of the GLUL protein, and the red curve corresponds to the RMSD of the peptide. \u003cstrong\u003e(D)\u003c/strong\u003e RMSF was calculated to assess the flexibility of Peptide 1 throughout the MD simulation. \u003cstrong\u003e(E)\u003c/strong\u003e RMSF was calculated to evaluate the flexibility of individual amino acid residues in the GLUL protein over the course of the simulation. \u003cstrong\u003e(F, G)\u003c/strong\u003e SPR analysis of the interaction between Peptide 1 and GLUL protein. GLUL was immobilized, and Pepide 1 was flowed over the chip surface at various concentrations. The binding response curves indicate a dose-dependent interaction, confirming direct binding between Peptide 1 and GLUL. \u003cstrong\u003e(H)\u003c/strong\u003e DSF analysis of GLUL thermal stability in the presence or absence of Peptide 1. A shift in the melting temperature (Tm) of GLUL was observed upon Peptide 1 binding, suggesting a physical interaction that affects protein stability.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/1bd4605d69c81d2ddb38bcad.jpeg"},{"id":93913543,"identity":"79c56123-06ad-4ff2-9f09-14dad0ec5ea5","added_by":"auto","created_at":"2025-10-20 08:37:25","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":233245,"visible":true,"origin":"","legend":"\u003cp\u003ePeptide 1 modulates cisplatin sensitivity via GLUL-mediated glutamine metabolism in gastric cancer cells. \u003cstrong\u003e(A) \u003c/strong\u003eProtein levels of GLUL in in cisplatin-sensitive (SGC7901) and cisplatin-resistant (SGC7901/DDP) gastric cancer cells. \u003cstrong\u003e(B, C)\u003c/strong\u003e Verification of GLUL overexpression and knockdown efficiency in SGC7901/DDP cells by Western blot. \u003cstrong\u003e(D)\u003c/strong\u003eCell viability of GLUL-overexpressing and GLUL-knockdown SGC7901/DDP cells treated with peptide 1, measured by CCK-8 assay. \u003cstrong\u003e(E)\u003c/strong\u003e Intracellular ROS levels in GLUL-overexpressing and GLUL-knockdown SGC7901/DDP cells following peptide 1 treatment. \u003cstrong\u003e(F)\u003c/strong\u003e Glutamine concentrations in GLUL-overexpressing and GLUL-knockdown SGC7901/DDP cells after peptide 1 treatment.\u003cstrong\u003e (G-H\u003c/strong\u003e) Apoptosis rates of SGC7901/DDP cells with GLUL overexpression or knockdown upon peptide 1 treatment, assessed by flow cytometry.\u003cstrong\u003e (I)\u003c/strong\u003e Expression of apoptosis-related proteins (Cleaved caspase-3, Bax, Bcl-2) in GLUL-overexpressing and knockdown cells after peptide 1 treatment, as determined by Western blot. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/8da611e4feb75e855c5132ce.jpeg"},{"id":93915663,"identity":"7bce9679-54ec-4459-b8e5-24c37e43fb9c","added_by":"auto","created_at":"2025-10-20 08:45:25","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":275609,"visible":true,"origin":"","legend":"\u003cp\u003ePeptide 1 induces DNA damage by targeting GLUL-mediated glutamine metabolism in cisplatin-resistant gastric cancer cells. \u003cstrong\u003e(A, B)\u003c/strong\u003e Western blot analysis of downstream enzymes (SLC1A5, GGT1, CPS1) in the glutamine metabolism pathway in SGC7901/DDP cells with GLUL overexpression or knockdown with or without peptide 1 treatment. \u003cstrong\u003e(C) \u003c/strong\u003eRepresentative immunofluorescence images of SGC7901/DDP cells treated with peptide 1, stained for DNA damage marker γ-H2AX (green) and nuclei (DAPI, blue). \u003cstrong\u003e(D)\u003c/strong\u003e Quantification of γ-H2AX fluorescence intensity to evaluate DNA damage levels. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/340dcdc07fd9b91d1acfef6b.jpeg"},{"id":93917258,"identity":"c03b82ee-94b5-4058-bdc3-383b0eed4667","added_by":"auto","created_at":"2025-10-20 09:01:25","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":237465,"visible":true,"origin":"","legend":"\u003cp\u003ePeptide 1 modulates cisplatin sensitivity via GLUL-mediated glutamine metabolism in vivo. \u003cstrong\u003e(A) \u003c/strong\u003eTumors harvested from nude mice treated with shNC, shGLUL, peptide 1 combined with shNC, or peptide 1 combined with shGLUL. Tumors were collected at the end of the 21-day treatment period. \u003cstrong\u003e(B)\u003c/strong\u003e Growth curves of subcutaneous xenograft tumors in nude mice bearing SGC7901/DDP cells under different treatment conditions over a 21-day period. \u003cstrong\u003e(C)\u003c/strong\u003e Tumor weights measured at the end of the treatment period. \u003cstrong\u003e(D) \u003c/strong\u003eRelative glutamine levels in SGC7901/DDP cells with GLUL knockdown (shGLUL) or control (shNC), treated with or without peptide 1 for 48 hours, as measured by a glutamine assay kit. \u003cstrong\u003e(E) \u003c/strong\u003eFlow cytometry analysis of apoptosis in tumors from different treatment groups, with Annexin V-FITC and PI staining. \u003cstrong\u003e(F)\u003c/strong\u003e Quantification of apoptosis percentage in different treatment groups, as determined by flow cytometry. \u003cstrong\u003e(G) \u003c/strong\u003eTumors harvested from nude mice treated with vector, oeGLUL, peptide 1 combined with vector, or peptide 1 combined with oeGLUL. Tumors were collected at the end of the 21-day treatment period.\u003cstrong\u003e (H)\u003c/strong\u003e Growth curves of subcutaneous xenograft tumors in nude mice bearing SGC7901/DDP cells under different treatment conditions over a 21-day period.\u003cstrong\u003e (I)\u003c/strong\u003e Tumor weights measured at the end of the treatment period. \u003cstrong\u003e(J)\u003c/strong\u003e Relative glutamine concentration in SGC7901/DDP cells transfected with GLUL overexpression (oeGLUL) or vector control, with or without peptide 1 treatment for 48 hours, as measured by a glutamine assay kit. \u003cstrong\u003e(K) \u003c/strong\u003eFlow cytometry analysis of apoptosis in vector and oeGLUL tumor tissues treated with vehicle or Peptide 1, showing Annexin V-FITC and PI staining. \u003cstrong\u003e(L) \u003c/strong\u003eQuantification of apoptosis percentage in different groups, as determined by flow cytometry. * \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/549ca7eed801eab39b9347be.jpeg"},{"id":101691349,"identity":"9f0e1f1d-0690-4de9-88d3-e10b3a971c0f","added_by":"auto","created_at":"2026-02-02 16:13:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3145691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7691539/v1/d77eaa65-dd34-4f50-922a-0a7303c3e62c.pdf"}],"financialInterests":"","formattedTitle":"RHOJ Derived Peptide Promotes Chemosensitivity by Inhibiting Glutamine Metabolism in Gastric Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) remains a significant global health burden, with substantial variability in its incidence and mortality rates worldwide. Based on the latest epidemiological data, there were over 968,000 new cases of GC in 2022 and close to 660,000 deaths, ranking the disease as fifth in terms of both incidence and mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While incidence rates have declined in many high-income countries over the last half century, GC continues to pose a considerable public health challenge, particularly in regions of Eastern Asia, Central and South America, Eastern Europe, and parts of Africa [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Helicobacter pylori infection remains a major risk factor for GC. Furthermore, genetic predisposition, chronic gastritis, and certain occupational exposures have been implicated in gastric carcinogenesis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite advancements in diagnosis and treatment modalities, prognosis for GC remains guarded, particularly for patients diagnosed at advanced stages. The treatment of GC typically involves a multimodal approach, incorporating surgery, chemotherapy, immunotherapy and targeted therapies. Chemotherapy remains the first-line treatment approach in advanced or metastatic GC. However, chemotherapy resistance remains a significant obstacle in the management of GC, contributing to disease progression, metastasis, and poor clinical outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, understanding the mechanisms underlying chemotherapy resistance is crucial for developing effective therapeutic strategies and improving patient survival. Chemotherapy resistance can arise through various mechanisms, including alterations in drug transporters, activation of DNA repair mechanisms, dysregulation of apoptosis pathways, and emergence of cancer stem cells with enhanced self-renewal [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, emerging evidence suggests that alterations in glutamine metabolism may play a critical role in the development of chemotherapy resistance, highlighting the importance of understanding the interplay between metabolic reprogramming and drug resistance mechanisms in GC.\u003c/p\u003e\u003cp\u003eGlutamine is an amino acid that serves as a crucial nutrient for cancer cell growth and proliferation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition to its role as a building block for protein synthesis, glutamine fuels various metabolic pathways, including the tricarboxylic acid (TCA) cycle, nucleotide synthesis, and antioxidant defense mechanisms[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Dysregulation of glutamine metabolism has been implicated in cancer progression and resistance to chemotherapy agents, suggesting that targeting glutamine-dependent pathways may represent a promising therapeutic strategy for overcoming drug resistance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Several studies have indicated a close association between glutamine metabolism and drug resistance in cancers. A study on breast cancer revealed that the tumor-associated fibroblasts in tumor microenvironment (TME) can produce and secrete glutamine, which promotes the energy metabolism of cancer cells and thus leading to tamoxifen resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Glutamine metabolism in colorectal cancer cells can also affect signal pathway transduction to promote metformin resistance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Meanwhile, glutamine played a significant role on metabolic enzymes such as glutaminase 1 (GLS1) and glutamate dehydrogenase (GDH) to promote drug resistance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another study also demonstrated that that tumor cells secrete glutaminase 1 (GLS1) to promote glutamine metabolism thus contributing to acquired trastuzumab resistance in HER2-positive GC. Anti-glutamine metabolism therapy may provide a new insight into reversing trastuzumab resistance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Targeting glutamine metabolism using small molecule inhibitors or metabolic modulators may sensitize chemoresistant tumors to standard chemotherapy agents [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], offering new avenues for improving treatment outcomes and overcoming therapeutic resistance in cancer patients. Small molecule peptide compounds have become one of the hotspots in cancer treatment research due to their advantages such as small molecular weight, strong targeting ability, high bioactivity, low toxicity, and easy transmembrane absorption [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Researchers have confirmed that various peptide compounds can act on targets within tumor cells. For instance, a specific RAGE-binding peptide RP7 could induce apoptosis and inhibit epithelial-mesenchymal transition (EMT) in TNBC cells through blocking of Erk1/2/NF-κB pathway [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Pep5-based antitumor peptides also exhibit remarkable antitumor activity towards tumor cells (HepG2, A549) and animal models through promoting the apoptosis and necrosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, they can also exert anti-tumor effects by targeting neovascularization and immune cells in the TME [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The above studies provided compelling evidence and valuable insights into the development of peptide-based antitumor agents. Their unique structures and mechanisms of action making them a potential choice for cancer therapy. Despite the increasing attention on peptide-based anticancer research, the role of peptide in GC drug resistance remains unclear, and there is limited reporting on the role of endogenous peptides in sensitizing chemotherapy. Therefore, exploring the role and mechanisms of peptides in GC drug resistance has become a key focus of our research attention.\u003c/p\u003e\u003cp\u003eIn this study, we first performed proteomic analysis to identify proteins that were highly expressed in cisplatin-sensitive GC cells. Peptides derived from RHOJ were then synthesized and subjected to functional screening to assess their potential role in reversing cisplatin resistance. We screened and identified six peptides form RHOJ, among which peptide 1 (AVFDEAILTIFHPK) exhibited antitumor effects and significantly enhanced the sensitivity of GC resistant cells to chemotherapy drugs. Subsequently, non-targeted metabolomics revealed differences in glutamine metabolism between GC resistant and sensitive cells, which hints us that this could potentially serve as a mechanism for peptide 1 to enhance chemosensitivity. Through \u003cem\u003ein vivo\u003c/em\u003e, \u003cem\u003ein vitro\u003c/em\u003e experiments and molecular simulations, we found that peptide 1 could inhibit glutamine metabolism by binding to the key enzyme GLUL, further inducing intracellular oxidative stress imbalance, thereby promoting apoptosis and DNA damage in resistant cells to enhance their sensitivity to chemotherapy.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Screening and Validation of Chemotherapy-Sensitizing Peptides in gastric cancer.\u003c/h2\u003e\n \u003cp\u003eTo explore the molecular mechanisms underlying cisplatin resistance in GC, we performed proteomic profiling of cisplatin-sensitive (SGC7901, HGC27) and cisplatin-resistant (SGC7901/DDP, HGC27/DDP) cells. The analysis revealed a significant upregulation of 17 proteins in the cisplatin-sensitive cell lines, with Ras homolog family member J (RHOJ) identified as one of elevated proteins compared to the resistant cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA and B). As a member of the Rho GTPase family, RHOJ is known to participate in cytoskeletal regulation and cell motility, which may be linked to chemotherapy resistance. Its higher expression in cisplatin-sensitive cells prompted us to explore its potential involvement in the regulation of chemosensitivity. Based on the identification of RHOJ as a candidate protein associated with cisplatin response, we sought to evaluate the functional relevance of RHOJ-derived peptides in modulating cisplatin sensitivity. To this end, six peptide fragments were selected from the RHOJ protein sequence using a random selection strategy. These peptides (Peptide 1\u0026ndash;6) were synthesized based on different functional domains of RHOJ and tested for their potential to enhance cisplatin sensitivity in SGC7901/DDP cells (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e The sequences of RHOJ derived peptides and corresponding PeptideRanker Scores\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\u003cstrong\u003ePeptide ID\u003c/strong\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence (14 aa)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptideRanker Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePeptide 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAVFDEAILTIFHPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.421969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePeptide 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eLSGGAGGGGGGSRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.500732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePeptide 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eQFFVDHPGAVPITT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.532915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePeptide 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eDLKQFFVDHPGAVP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.434701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePeptide 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAALSGGAGGGGGGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.474199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003ePeptide 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eGKTCLLISYTTNQF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.423952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eFirst of all, in order to evaluate the cytotoxicity of these peptides on normal human gastric epithelial cells, we treated GES-1 cells with each peptide under the treatment of cisplatin. The apoptosis rate was assessed using Annexin V-FITC/PI staining followed by flow cytometric analysis. The results revealed that treatment with peptides 3 and 5 induced a significantly higher apoptosis rate in GES-1 cells compared to the control group, whereas the other peptides exhibited no significant cytotoxic effects (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). These findings indicate that peptides 3 and 5 may exert toxicity on normal gastric epithelial cells, limiting their potential as therapeutic candidates. Next, we investigated whether these six peptides could enhance cisplatin sensitivity in cisplatin-resistant GC cells (SGC7901/DDP). Using the CCK-8 assay, we assessed cell viability following peptide treatment in combination with cisplatin. The results showed that peptides P2 to P6 had no significant effect on the IC50 of cisplatin in resistant cells compared to the untreated control. In contrast, peptide P1 significantly decreased the IC50 in SGC7901/DDP cells, rendering their sensitivity to cisplatin comparable to that of parental SGC7901 cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Based on these results, peptide 1 was identified as a potential candidate for overcoming cisplatin resistance in gastric cancer and was selected for further investigation in subsequent studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Glutamine metabolism was enhanced in the cisplatin resistant gastric cancer cells\u003c/h2\u003e\n \u003cp\u003eTo explore the underlying mechanism by which peptide 1 reverses cisplatin resistance, we investigated its impact on glutamine metabolism, a pathway previously implicated in chemoresistance[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. To investigate the relationship between metabolic reprogramming and cisplatin resistance in GC, untargeted metabolomics was carried out on both cisplatin-sensitive and cisplatin-resistant GC cell lines. A heat map was created to display the metabolic differences between cisplatin resistant and sensitive GC cells, illustrating the variance in metabolite levels between the two cell types. As it shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, the disparity in glutamine levels was found to be significant between the cisplatin sensitive and cisplatin resistant GC cell lines. Notably, glutamine abundance was significantly elevated in the resistant cells compared to their sensitive counterparts (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). This finding suggests a potential metabolic reprogramming toward enhanced glutamine utilization or synthesis in the development of chemoresistance. In addition, KEGG pathway analysis showed that amino acid metabolism pathways were among the most significantly enriched in cisplatin-resistant cells compared to sensitive cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC), aligning with findings from earlier studies [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Given the critical role of glutamine in supporting tumor cell proliferation, redox balance, and survival under stress conditions[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], we hypothesized that glutamine metabolism may contribute to the maintenance of cisplatin resistance in GC. To further validate the impact of glutamine on chemotherapy resistance, we measured the cell viability via CCK8 assay with or without the deprivation of glutamine both in sensitive and resistant cells under cisplatin treatment. The results revealed that there was no significant difference in cell viability in cisplatin sensitive cells line following glutamine deprivation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). However, a significant reduction in cell viability in the resistant cells was observed with the absence of glutamine, indicating a more pronounced dependency on glutamine in the resistant cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). Given the distinct differences in glutamine metabolism between resistant and sensitive cells, and the previously identified peptide 1 could reduce the IC50 of cisplatin in resistant cells, we hypothesized that peptide 1 might enhance chemosensitivity by affecting cellular glutamine metabolism. We first assessed the glutamine levels in both SGC7901 and SGC7901/DDP, and discovered that the glutamine content was significantly higher in the resistant cells compared to the sensitive ones, which was consistent with our metabolomic sequencing results. We then measured the glutamine levels after peptide1 treatment in SGC7901/DDP. GLN assay revealed that the glutamine levels in the resistant cells significantly decreased following peptide 1 treatment when compared to the non-treatment group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). The above data suggested that glutamine metabolism played a significant role in GC cisplatin resistant, and peptide1 treatment could reduce the glutamine level in resistant cells, but the specific mechanism need further elucidation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Peptide 1 treatment increased the sensitivity of resistant cells to chemotherapy in gastric cancer.\u003c/h2\u003e\n \u003cp\u003ePrevious data has demonstrated that heightened glutamine metabolism is characteristic of chemotherapy resistance in GC. It has also been observed that peptides derived from chemo-sensitive cells are capable of reducing intracellular glutamine levels. In pursuit of further clarification on whether peptide 1 could enhance the efficacy of cisplatin (DDP) treatment, we first try to confirm that whether peptide 1 is a transmembrane peptide or not. By synthesizing the peptide tagged with FITC and treating both resistant and sensitive cells, immunofluorescence assays confirmed the efficient cellular uptake of peptide 1(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, in order to evaluate the peptide\u0026apos;s role in enhancing the efficacy of platinum-based drugs, we examined the impact of peptide 1 on cell viability in the presence of four generations of platinum drugs. Our findings revealed that peptide 1 significantly increased the cytotoxic effects of these drugs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Apoptosis assays further confirmed that peptide 1 could enhance the sensitivity of GC cells to the platinum drug DDP (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Additionally, cell apoptosis typically involves alterations in apoptosis-related proteins. To delineate the specific molecular mechanism by which peptide 1 promotes apoptosis in resistant cells, we conducted Western blot analyses focusing on key proteins involved in the apoptotic process including cleaved Caspase-3, a critical executor of apoptosis, alongside Bcl-2, an anti-apoptotic protein, and Bax, a pro-apoptotic protein. The results showed that treatment with DDP led to apoptosis in resistant cells, which was evidenced by a marked upregulation of Bax and a downregulation of Bcl-2. The expression pattern of Cleaved Caspase-3 followed the same trend as Bax. Notably, the addition of peptide 1 to the treatment regimen further amplified the expression trends of these proteins, resulting in a higher apoptosis rate compared to treatment with cisplatin alone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). In conclusion, these experiments demonstrated that peptide 1 function as a transmembrane peptide and can significantly enhance the sensitivity of chemo-resistant cells to DDP by activating apoptotic signaling pathways.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2.4. Peptide 1 restores chemosensitivity by inhibiting enzymes within the glutamine metabolism pathway and inducing reactive oxygen species (ROS) production in gastric cancer both in vitro and in vivo.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMetabolic reprogramming is identified as one of the hallmark features of cancers [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the aspect of amino acid metabolism, an elevated uptake of nutrients such as glutamine in tumor cells was observed. Researches indicated that the consumption rate of glutamine in tumor cells is 5 to 10 times that of normal cells, underscoring their dependency on glutamine. Glutamine metabolism supports the excessively activated glycolysis and oxidative phosphorylation in tumor cells by providing essential raw materials. Additionally, it directly contributes to chemotherapy resistance by impacting the homeostatic balance of glucose, lipid, and protein metabolism. The glutamine metabolic pathway plays a role in tumor cell resistance through several mechanisms: dynamic changes in glutamine transporter activity directly affect intracellular glutamine levels and impact cell resistance; metabolites in the tumor microenvironment mediate resistance through immune responses; changes in the expression and activity of key enzymes in the glutamine metabolic pathway are also crucial for the development of tumor cell resistance [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eGiven the role of peptide 1 in altering glutamine levels in resistant cells and its potential to enhance the efficacy of DDP in GC treatment, we conducted further investigations to elucidate the effect of peptide 1 on enzymes within the glutamine metabolic pathway. We began by assessing the enzyme changes via RT-qPCR for GLUL, SLC1A5, GGT1, and CPS1, which had significant role in glutamine metabolism. Our findings revealed that DDP or peptide 1 treatment alone significantly inhibited the mRNA levels of these enzymes, and this inhibitory trend was further enhanced when both treatments were combined (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The Western blot results for these proteins were consistent with the PCR findings, as it shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB and C. Furthermore, we then measured the glutamine changes caused by the expression of the above proteins. Glutamine assays demonstrated that treatment with either DDP or peptide 1 alone significantly reduced the glutamine content within cells, with a further decrease observed when used in combination (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). One of the roles of glutamine metabolism is to provide important carbon and nitrogen sources for cells; due to the activation of glycolysis, the surge in demand for glutamine by tumor cells is crucial for providing essential nutrients and is significant for the survival of tumor cells. Another important role of glutamine metabolism is to maintain the homeostasis of ROS within cells. Glutamine is a precursor for the antioxidant glutathione (GSH), a potent intracellular antioxidant that effectively scavenges redundant ROS within cells. Preliminary data suggest that peptide 1 could induce a reduction in intracellular glutamine levels by inhibiting enzymes involved in the glutamine metabolic pathway. To further confirm whether changes in glutamine levels influenced ROS levels within resistant cells, we utilized flow cytometry to assess ROS levels across different treatment groups. Experimental results showed that treatments with either DDP or peptide 1 alone led to a significant increase in ROS levels in resistant cells, with their combination further destabilizing the ROS balance evidenced by the highest ROS level among different groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE and F). According to previous studies, elevated intracellular ROS can directly damage cellular DNA, leading to DNA breaks. When DNA damage reaches a certain level that cannot be repaired, the apoptotic processes will initiate, which aligns with our observations. Next, we assessed in vivo cooperativity between peptide 1 and DDP in nude mice bearing SGC7901/DDP xenografts (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG and H). Compared to the control group, treatment with peptide 1 or DDP resulted in a reduction in tumor weight and volume in mice. However, treatment of SGC7901/DDP-bearing mice with DDP plus P1 led to a marked reduction in tumor weight and growth when compared with treatment with DDP or P1 alone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eI and J). Of note, western blot analysis shown that compare to control group, the levels of GLUL, SLC1A5, GGT1, CPS1 were significantly lower expressed in xenograft tumor tissues with peptide 1 or DDP treatment, and the expression of the above proteins showed a further decrease by combined treatment with peptide 1 and DDP (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eK and L). In summary, our research demonstrates that peptide 1 can inhibit the glutamine metabolic pathway, resulting in decreased glutamine synthesis and further promoting an imbalance in cellular ROS homeostasis. This triggers ROS accumulation and ultimately initiates cell apoptosis, thereby enhancing the sensitivity of resistant cells to platinum-based chemotherapy drugs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Identification of GLUL as the molecular target of peptide 1.\u003c/h2\u003e\n \u003cp\u003eGlutamine synthetase (GLUL), also known as glutamate-ammonia ligase, plays a pivotal role in glutamine metabolism. It is a key enzyme responsible for catalyzing the combination of ammonia and glutamate to form glutamine [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. This process is an ATP-dependent synthetic reaction and crucial for regulating intracellular glutamine levels. To explore the specific regulatory mechanisms of peptide 1 on the glutamine metabolic pathway, we first examined the expression differences of GLUL in sensitive and resistant cells. Western blot analysis revealed that the expression level of GLUL was significantly higher in resistant cells compared to sensitive SGC7901 cells, correlating with the increased need for glutamine in these cells to maintain drug resistance. Subsequently, we employed molecular docking and microscale thermophoresis (MST) experiments to investigate the potential interaction between peptide 1 and GLUL. Molecular docking is a computer simulation technique used to predict the mode of interaction between a molecule and a target molecule, playing a significant role in drug design and discovery, as well as molecular biology research. In our study, we initially retrieved the structural data of GLUL from the Protein Data Bank (PDB) and constructed the three-dimensional structure of peptide 1 using GaussView software, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA. Molecular docking results indicated that peptide P1 binds to an active site on GLUL, and the structure of the resultant complex is depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB. This docked complex structure was then used for subsequent molecular dynamics simulations. The Root Mean Square Deviation (RMSD) results suggest rapid increase in RMSD values within 0\u0026ndash;10 ns, indicating significant conformational changes in the complex systems, which stabilized after 100 ns indicating minor main chain atomic displacements and suggesting that the peptide-protein complex can stably exist in solution (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). The root mean square fluctuation (RMSF) was calculated to assess the flexibility of Peptide 1 and individual amino acid residues in the GLUL protein throughout the molecular docking simulation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD and E). Additionally, we further utilized surface plasmon resonance (SPR) to study biomolecular interactions. SPR results indicated that with the introduction of different concentrations of peptide P1, the response unit (RU) values began to increase, and upon cessation of peptide injection, the RU values started to decline. These experimental findings suggest that peptide 1 binds to GLUL protein immobilized on the chip, with a dissociation constant (K_D) of 5.310E-5, demonstrating high affinity between peptide P1 and GLUL protein (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF and G). Furthermore, to validate the results of molecular docking, differential scanning fluorimetry (DSF) was employed, a technique used to assess the thermal stability and folding state of proteins by detecting changes in fluorescence signals as the temperature increases. This technique determined the thermal denaturation temperature of proteins, thus assessing the corresponding stability. In our study, this technique was utilized to evaluate changes in the Tm of the protein before and after the addition of peptide P1. DSF results showed that the Tm of GLUL protein changed by more than 0.4\u0026deg;C after the addition of peptide P1, indicating binding between peptide 1 and GLUL (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eH). Collectively, these findings provide converging evidence that peptide 1 directly binds to GLUL. This interaction was consistently supported by multiple complementary approaches. The results confirm that peptide 1 interacts with GLUL in a stable and specific manner, laying a mechanistic foundation for its role in modulating glutamine metabolism and chemosensitivity in gastric cancer cells.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e2.6. The peptide 1 increases the sensitivity of cisplatin-resistant gastric cancer cells to cisplatin by targeting GLUL-mediated glutamine metabolism.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eIn order to identify whether peptide 1 influences DDP sensitivity through GLUL-mediated glutamine metabolism, we utilized the cisplatin-resistant cell line SGC7901/DDP to construct cell lines with stable overexpression and knockdown of GLUL (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B and C). CCK8 assays revealed that, compared to the control group, cell viability decreased upon addition of peptide 1. However, the proliferative capacity of the resistant cells significantly increased when GLUL was overexpressed; this trend of increased cell viability was reversed under peptide 1 treatment, with the most notable reduction in cell viability occurred when peptide 1 was combined with shRNA-mediated GLUL suppression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). ROS assay showed that peptide 1 led to an accumulation of cellular ROS. Overexpression of GLUL reduced ROS levels, but their levels increased upon treatment with peptide 1. Further elevation in ROS levels was observed when GLUL was suppressed with shRNA and combined with peptide 1 treatment (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE). Our results demonstrated that treatment with peptide 1 significantly reduced intracellular glutamine levels in cisplatin-resistant GC cells. In contrast, GLUL overexpression led to a marked increase in glutamine abundance. Notably, co-treatment with peptide 1 and GLUL overexpression partially reversed the p1-induced glutamine depletion. Furthermore, knockdown of GLUL combined with peptide 1 treatment resulted in a further reduction in glutamine levels (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF). Flow cytometry analysis of cell apoptosis demonstrated that peptide 1 significantly promoted cell apoptosis. Compared to the control group, the apoptosis rate significantly decreased after GLUL overexpression, but this trend was reversed following the addition of peptide 1. Combining shRNA-mediated GLUL suppression with peptide 1 significantly increased the apoptosis rate (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eG and H). Subsequent analysis of apoptosis-related proteins confirmed these levels of apoptosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eI). Furthermore, Western blot analysis examined the changes in key enzymes of the glutamine metabolic pathway after overexpression or suppression of GLUL. Overexpression of GLUL promoted the expression of enzymes in the metabolic pathway, whereas the addition of peptide 1 significantly inhibited their expression. Suppression of GLUL further enhanced this trend of inhibited expression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA and B). Although CPS1 and GGT1 do not directly participate in DNA repair mechanisms, their roles in maintaining cellular environmental stability and antioxidative defenses indirectly affect the processes of DNA damage and repair. \u0026gamma;-H2AX is a key component of the cellular mechanism to recognize and respond to DNA damage. It is often served as an early marker for the presence of double-strand breaks. We then assessed the levels of DNA damage in cells treated with peptide 1 combined with shGLUL or oeGLUL via immunofluorescence, finding that peptide 1 treatment could promote DNA damage. Overexpression of GLUL significantly mitigated DNA damage, while suppression of GLUL significantly increased DNA damage (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC and D).\u003c/p\u003e\n \u003cp\u003eTo further determine whether the effect of Peptide 1 on cisplatin sensitivity was mediated through GLUL, we evaluated tumor growth and metabolic changes in xenograft models with GLUL knockdown or overexpression. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, GLUL knockdown significantly inhibited tumor growth compared with control tumors, while the combination of GLUL knockdown and Peptide 1 treatment further suppressed tumor progression, leading to the smallest tumor volumes and weights (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA, B and C). Consistently, glutamine levels were markedly reduced in GLUL-silenced tumors, and Peptide 1 treatment enhanced this effect (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD). Flow cytometry analysis further revealed that combined GLUL knockdown and Peptide 1 treatment resulted in a significant increase in apoptosis compared with either intervention alone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eE and F). Conversely, GLUL overexpression promoted tumor growth, increased glutamine abundance, and reduced apoptosis in resistant cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eG, H and I). Importantly, Peptide 1 treatment partially abrogated the pro-tumorigenic effects of GLUL overexpression, as evidenced by decreased tumor volume, reduced glutamine levels, and enhanced apoptosis in oeGLUL\u0026thinsp;+\u0026thinsp;P1 tumors compared with oeGLUL alone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eJ, K and L).\u003c/p\u003e\n \u003cp\u003eThese findings provide compelling evidence that Peptide 1 exerts its chemosensitizing effect by targeting GLUL-mediated glutamine metabolism. By lowering intracellular glutamine levels, Peptide 1 disrupts metabolic homeostasis, increases oxidative stress, and enhances apoptosis in cisplatin-resistant GC cells. Moreover, the ability of Peptide 1 to counteract the effects of GLUL overexpression highlights its potential as a metabolic vulnerability\u0026ndash;targeting therapeutic strategy. Together, these results support the concept that inhibition of GLUL is central to overcoming cisplatin resistance and that RHOJ-derived Peptide 1 functions as an effective GLUL-targeting sensitizer in vivo.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Materials and methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Cell lines and cell culture\u003c/h2\u003e\u003cp\u003eSGC7901, HGC27 and corresponding cisplatin-resistant SGC7901/DDP and HGC27/DDP cells were purchased from Cell Bank, Chinese Academy of Sciences (Shanghai, China). Cells were maintained in RPMI 1640 (Gibco, Grand Island, USA) supplemented with 10% fetal bovine serum (FBS) in a humidified atmosphere at 37\u0026deg;C with 5% CO2. Cisplatin-resistant SGC7901/DDP and HGC27/DDP cells were cultured in medium supplemented with cisplatin at a final concentration of 4 \u0026micro;M, to maintain drug resistance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Peptide selection and synthesis\u003c/h2\u003e\u003cp\u003eBased on our proteomic analysis, RHOJ was identified as a significantly downregulated protein in cisplatin-resistant gastric cancer cells. To investigate whether endogenous peptides derived from RHOJ could influence chemosensitivity, we designed a set of peptide fragments based on the full-length amino acid sequence of human RHOJ (UniProt ID: Q9H4E5). Using a sliding window approach (14 amino acids in length), multiple overlapping peptide fragments were generated across the RHOJ sequence. The selection of peptide segments was based on (i) uniform coverage across the RHOJ protein sequence, (ii) predicted biological activity (via PeptideRanker), and (iii) suitability for synthesis and solubility. From these, six peptide fragments were randomly selected for synthesis and functional screening. These peptides, designated as peptide 1 to peptide 6, were synthesized at \u0026gt;\u0026thinsp;95% purity (GL Biochem, Shanghai) and used in subsequent in vitro assays to evaluate their effect on cisplatin sensitivity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. CCK-8 assay\u003c/h2\u003e\u003cp\u003eLogarithmic phase GC cells were seeded at a density of 4,000 cells per well in a 96-well cell culture plate with 200 \u0026micro;L medium per well and cultured overnight. Cells were treated according to experimental requirements (3 parallel wells per group, independently repeated 3 times), with an equal volume of PBS added as a control. After incubation in the cell culture incubator for 48 hours, the culture medium was removed, and 100 \u0026micro;L of pre-prepared 10% CCK-8 solution was added to each well. After incubating for 2 hours, the absorbance of each well was measured at a wavelength of 450 nm.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Apoptosis\u003c/h2\u003e\u003cp\u003eA total of 6 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well were seeded in 6-well plates, with each well containing 2 mL of growth medium. The cells were allowed to adhere overnight to achieve approximately 70\u0026ndash;80% confluence. After achieving the desired confluence, the cells were subjected to experimental treatments. Various concentrations of DDP were added to the respective wells. Control wells received equivalent volumes of vehicle or PBS. Each treatment condition was performed in triplicate wells. Following treatment, the cells were incubated at 37\u0026deg;C in a CO2 incubator for 48 hours. After 48 hours incubation, apoptosis was assessed using Annexin V-FITC/propidium iodide (PI) staining. Cells were harvested, washed with cold PBS, and stained with Annexin V-FITC and PI according to the manufacturer's instructions. Stained cells were then analyzed using flow cytometry to quantify the percentage of apoptotic cells. Data acquisition and analysis were performed using Flowjo.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Western blot\u003c/h2\u003e\u003cp\u003eProteins were extracted from cells using lysis buffer to disrupt cell membranes and release proteins. The concentration of extracted proteins was measured using protein quantification methods BCA assay (P0009, Beyotime, Shanghai). 30 \u0026micro;g of protein samples were loaded onto sodium dodecyl sulfate-polyacrylamide gels (SDS-PAGE) and separated by electrophoresis. The separated proteins were then transferred onto a polyvinylidene difluoride (Merck Millipore, Boston, MA, USA) using semi-dry method. Membranes were blocked by 5% non-fat milk for 2 h and incubated with the indicated primary antibody at 4\u0026deg;C overnight. Membranes were then washed with buffer to remove unbound primary antibody and followed by HRP-conjugated secondary antibody (A0208 and A0192, Beyotime, Shanghai) incubation for 2 h at room temperature. Mixed enhanced chemiluminescence (ECL) was added to produce a visible signal when it reacts with the protein-antibody-secondary antibody complex after the membrane was washed again to remove unbound secondary antibody. A chemiluminescence detection system (Tanon, Shanghai, China) was used to visualize the image of the membrane and the signals were quantified using Image J. Antibodies for WB were list as follows: anti-GLUL (1:1,000; Cat#80636S), anti-ASCT2 or SLC1A5 (1:1,000; Cat#8057S), anti-bax (1:1,000; Cat#5023S), anti-bcl2 (1:1,000; Cat#3498S), anti-C-capase3 (1:1,000; Cat# 9661T) were obtained from Cell Signaling Technology (Beverly, MA, USA); anti-GGT1 (1:1,000; Cat#ab55138), anti-CPS1 (1:1,000; Cat#ab129076), were obtained from Abcam (Cambridge, England); anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH; 1:5,000; 60004-1-Ig) were obtained from Proteintech (Rosemont, IL, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Quantitative real-time PCR (qRT-PCR)\u003c/h2\u003e\u003cp\u003eTotal RNA was isolated from cells using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. The extracted RNA was reverse transcribed into complementary DNA (cDNA) using the HiScript II Q RT SuperMix for qPCR (Vazyme, Nanjing, Jiangsu, China) reverse transcription kit. qRT-PCR was performed on a QuantStudio 6 Pro Real-Time PCR System (Thermo Fisher). The amplification program consisted of an initial denaturation step at 95\u0026deg;C for 3 minutes, followed by 39 cycles of denaturation at 95\u0026deg;C for 10 seconds and annealing/extension at 55\u0026deg;C for 30 seconds. Relative gene expression levels were determined using the 2^\u0026ndash;ΔΔCt method, with GAPDH expression serving as the internal control for normalization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Glutamine (Gln) assay\u003c/h2\u003e\u003cp\u003eGlutamine levels were measured using a commercially available assay kit (BC5305 Solabio, Beijing) following the manufacturer's instructions. Briefly, cells were collected into centrifuge tubes, discard the supernatant after centrifugation, resuspend cells in 1 mL of extraction buffer per 10^4 cells, and then disrupt the cells by ultrasonication. Centrifuge at 12,000 \u0026times; g for 5 minutes at 4\u0026deg;C, transfer the supernatant to a new tube, add 500 \u0026micro;L of extraction buffer two, vigorously vortex for 5 minutes, centrifuge at 12,000 \u0026times; g for 5 minutes at 4\u0026deg;C, and collect the upper layer of liquid for further analysis on ice. Glutamine concentrations were determined by a spectrophotometer comparing to a standard curve generated using known concentrations of glutamine standards provided in the kit. Data analysis was performed using Graphpad prism.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.8. ROS detection\u003c/h2\u003e\u003cp\u003eCultivate cells in complete medium until reaching desired confluence. After treated cells with the experimental conditions, harvest treated cells and wash them with phosphate-buffered saline (PBS). Incubate cells with a ROS-sensitive fluorescent dye, 2',7'-dichlorofluorescein diacetate (DCF-DA), at a final concentration of 10 \u0026micro;M in serum-free medium for 20 minutes at 37\u0026deg;C in the dark. After staining, wash the cells with PBS to remove excess dye. Analyze the fluorescence intensity of stained cells using a flow cytometer. Set the appropriate compensation controls using unstained and single-stained cells to correct for spectral overlap between channels. Gate the cell population of interest based on forward scatter (FSC) and side scatter (SSC) to exclude debris and aggregates. Acquire at least 10000 cells per sample to ensure statistical robustness. Analyze flow cytometry data using dedicated software to quantify the mean fluorescence intensity (MFI) of ROS-stained cells. Compare the MFI of treated cells with untreated control cells to assess changes in ROS levels. Perform statistical analyses to determine the significance of observed differences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.9. Nano differential scanning fluorimetry assay (NanoDSF)\u003c/h2\u003e\u003cp\u003ePrometheusTM Series NT.48 was used to determine start temperatures, melt temperature, and end temperature for GLUL and the mixture of GLUL and peptide 1. Samples were filled in NanoDSF Standard-grade capillaries and transferred into the device. Thermal unfolding was tested during in a linear temperature slope (7\u0026deg;C/min; 20\u0026ndash;95\u0026deg;C). Experiments were conducted in triplicates. If the change of temperature was great than 0.4\u0026deg;C or 3 times of stander error, the protein conformation is considered to have changed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.10. Surface plasmon resonance (SPR)\u003c/h2\u003e\u003cp\u003eGLUL was immobilized to the series S sensor chip CM5. Protein fixation requires specific acidic conditions (pH values of 4.0, 4.5, and 5.0 were tested). Ligand proteins were diluted to 20 \u0026micro;g/ml using 10 mM sodium acetate at different pH levels. Samples were manually injected in sequence in manual mode, and the pH environment yielding the highest signal was selected. Protein fixation parameters were set as follows: concentration of 30\u0026ndash;50 \u0026micro;g/ml, pH environment of 4.0, flow rate of 10 \u0026micro;l/min, and temperature of 25\u0026deg;C. The solution required for fixation includes 100 \u0026micro;l NHS, 100 \u0026micro;l EDC, and 150 \u0026micro;l Ethamolamine. The instrument automatically activates the chip surface by mixing EDC and NHS. After protein fixation, the reference channel was subjected to blocking treatment (using EDC, NHS, and Ethamolamine). Peptide 1 at 3.125 \u0026micro;M to 200 \u0026micro;M in running buffer (1 x PBS with 0.05% Tween 20 ) were introduced and allowed to contact for 120 s and disassociate for 300 s. Biacore T200 system was used to perform SPR experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.11. Molecular docking\u003c/h2\u003e\u003cp\u003eStructure of GLUL protein (PDB ID 2OJW) was obtained from the Protein Data Bank (PDB), which comprises 14 subunits. Each subunit's conformation is extracted individually to represent the structure of the GLUL protein. The peptides sequence is AVFDEAILTIFHPK. The structural model of peptide was predicted by Robetta Web protein structure prediction server. Using deep learning methods, the peptides sequence undergoes fragment assembly or homology modeling to obtain initial peptide conformations. The initial peptide structure undergoes energy optimization of side chain and backbone positions to find the lowest energy conformation and score it. Rosetta program suite was used to perform molecular docking of peptides and CLNA, and Monte Carlo algorithm-based global docking system was employed to explore the relative conformational space of peptides and protein. In this system, Rosetta centers proteins as spheres and uniformly places peptides on 26 initial positions on the sphere's surface, enabling parallel docking simulations. The docking approach is semi-flexible, utilizing empirical energy functions within Rosetta, which include van der Waals interactions, electrostatic interactions, hydrogen bonds, and entropy contributions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.12. Molecular dynamics simulations\u003c/h2\u003e\u003cp\u003eThe system established was a fully atomic explicit solvent model of GLUL using the GROMACS-2022 software. The parameters of the system were set as follows: temperature, 310 K; simulation time, 100 ns; force field, AMBER99SB; water model, TIP3P. The C-terminus and N-terminus were modeled as COO- and NH3+, respectively. The minimum protein-box distance was 1 nm, and the system was contained within a rectangular box with dimensions of 5.979 \u0026times; 6.599 \u0026times; 7.250 nm\u0026sup3;. The GLNA protein system comprised a total of 49,260 atoms, including two sodium ions. All simulations utilized periodic boundary conditions. Prior to molecular dynamics simulations, energy minimization was performed using 50,000 steps of steepest descent to alleviate spatial conflicts within the system. Subsequently, simulations were conducted under NVT and NPT (1 bar) ensembles for 0.1 ns each to equilibrate the system. The Parrinello-Rahman method was employed to maintain pressure at 1 bar, while temperature was controlled at 310 K using separate temperature coupling for protein and non-protein moieties via velocity rescaling. The Verlet cutoff scheme updated the neighbor list every 10 steps with a cutoff distance of 1.0 nm. Bond lengths were constrained using the LINCS method (protein) and SETTLE algorithm (water molecules) allowing for a time step of 2 fs. Van der Waals interactions were truncated at 1.0 nm, and electrostatic interactions were computed using the particle mesh Ewald (PME) method with a real-space cutoff of 1.0 nm. The structural and conformational properties of the GLUL and peptide were characterized using all-atom RMSD and RMSF parameters based on residues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.13. Proteomic profiling of cisplatin-sensitive and -resistant Gastric Cancer Cells\u003c/h2\u003e\u003cp\u003eTo identify differentially expressed proteins associated with cisplatin resistance, proteomic analysis was performed on cisplatin-sensitive gastric cancer cells (SGC7901, HGC27) and their cisplatin-resistant counterpart (SGC7901/DDP, HGC27/DDP). Cells were cultured under standard conditions, harvested at 70\u0026ndash;80% confluence, and washed twice with cold PBS. Total proteins were extracted using RIPA lysis buffer containing protease and phosphatase inhibitors. Lysates were sonicated on ice and centrifuged at 12,000 \u0026times; g for 15 minutes at 4\u0026deg;C to remove debris. Protein concentration was determined using a BCA protein assay kit. Equal amounts of protein from each sample were reduced with dithiothreitol (DTT), alkylated with iodoacetamide (IAA), and digested with trypsin overnight at 37\u0026deg;C. The resulting peptides were desalted and fractionated using high-pH reverse-phase liquid chromatography to improve proteome coverage. Each peptide fraction was analyzed by liquid chromatography\u0026ndash;tandem mass spectrometry (LC-MS/MS) using a high-resolution Orbitrap mass spectrometer coupled to an ultra-performance liquid chromatography (UPLC) system. The mass spectrometer was operated in data-dependent acquisition (DDA) mode. MS/MS spectra were searched against the human UniProt database using the Sequest search engine in Proteome Discoverer (Thermo Fisher Scientific). Protein identification and quantification were performed with a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;1%. Label-free quantification (LFQ) was used to compare protein abundance across samples. Differentially expressed proteins between cisplatin-sensitive and -resistant cells were identified based on fold change and statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Selected proteins with consistent upregulation were further analyzed and used for downstream peptide design.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.14. Non-target metabolomics\u003c/h2\u003e\u003cp\u003eCells were harvested and washed with phosphate-buffered saline (PBS) to remove extracellular metabolites. Metabolites were extracted using a methanol: water (4:1) solvent mixture. Samples were vortexed and incubated on ice for 10 minutes to ensure efficient extraction. Subsequently, samples were centrifuged at 14,000 rpm for 10 minutes at 4\u0026deg;C, and the supernatants were collected for analysis. Metabolite profiling was performed using a high-performance liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) system. Chromatographic separation was achieved using a reverse-phase C18 column with a binary solvent system consisting of water with 0.1% formic acid (solvent A) and acetonitrile with 0.1% formic acid (solvent B). Gradient elution was employed at a flow rate of 0.3 mL/min. Mass spectrometric detection was performed in both positive and negative ionization modes over a predefined mass range. Raw LC-MS data were acquired and processed using dedicated software platforms. Peak detection, retention time alignment, and peak integration were performed to generate a list of detected metabolites. Peak areas were normalized to internal standards to correct for variations in sample preparation and instrument response. Subsequently, statistical analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), were conducted to identify metabolite features that distinguish between experimental groups. Metabolite features were annotated by comparing retention times and mass spectra with reference standards available in public databases such as METLIN and HMDB. Putative metabolite identifications were further validated using tandem mass spectrometry fragmentation patterns and isotopic patterns. Metabolite identities were confirmed by comparing experimental data with authentic standards or by elucidating chemical structures using additional analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.15. Virus production, infection, and selection\u003c/h2\u003e\u003cp\u003eViral particles were produced using a lentiviral packaging system. Briefly, HEK293T cells were seeded in 10 cm culture dishes at a density of 5\u0026times;10^6 cells per dish. The following day, cells were transfected with the desired lentiviral vector along with the packaging plasmids psPax2 and pMD2.G (Addgene plasmids #12260 and #12259) using a calcium phosphate transfection method. Twenty-four hours post-transfection, the culture medium was replaced with fresh medium, and supernatants containing viral particles were harvested 48 hours later. Supernatants were then filtered through a 0.45 \u0026micro;m filter to remove cellular debris. Target cells were seeded in six-well plates at a density sufficient to reach 50% confluence at the time of infection. Viral particles were added to the cells at a concentration of 40 \u0026micro;l/ml supplemented with 5 mg/ml polybrene to enhance infection efficiency. After 24 hours of viral exposure, the medium was replaced with fresh growth medium. Following viral infection, cells were subjected to selection with puromycin to enrich for stably transduced cells. Puromycin selection was initiated 24 hours post-infection at a concentration of 2 \u0026micro;g/ml and continued for at least one week. Surviving cells were considered successfully infected and selected for RT-qPCR and WB validation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.16. Fluorescence detection\u003c/h2\u003e\u003cp\u003eCells were fixed with 4% paraformaldehyde for 15 minutes at room temperature and permeabilized with 0.1% Triton X-100 for 10 minutes. After blocking with 5% bovine serum albumin (BSA) for 1 hour, cells were incubated with primary antibodies overnight at 4\u0026deg;C. Primary antibodies against the target protein were diluted according to manufacturer recommendations. The following day, cells were washed and incubated with fluorophore-conjugated secondary antibodies for 1 hour at room temperature. Nuclei were counterstained with 4',6-diamidino-2-phenylindole (DAPI). Images were acquired using a fluorescence microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.17. Subcutaneous tumor model establishment\u003c/h2\u003e\u003cp\u003eTo establish the subcutaneous tumor model, male BALB/c nude mice aged 6\u0026ndash;8 weeks were obtained from the experimental animal center, school of pharmacy, Fudan university. Animals were housed in a controlled environment with a 12-hour light/dark cycle and provided with standard rodent chow and water ad libitum. Prior to tumor inoculation, mice were acclimatized to the laboratory conditions for seven days. SGC7901/DDP-shNC, SGC7901/DDP-shGLUL, SGC7901/DDP-Vector, SGC7901/DDP-oeGlUL were cultured RPMI 1640 complete medium at 37\u0026deg;C in a humidified atmosphere with 5% CO2 until reaching logarithmic growth phase. Cells were subcutaneously injected into the left flank of each mouse using a sterile syringe with a 27-gauge needle. Tumor growth was monitored every 3 days using digital calipers, and tumor volume was calculated using the formula: tumor volume\u0026thinsp;=\u0026thinsp;0.5 \u0026times; L \u0026times; W\u003csup\u003e2\u003c/sup\u003e, where V is the tumor volume, L is the longest diameter, and W is the shortest diameter. Eleven days after tumor implantation, the mice were randomly separated into eight experimental groups (5 mice per group): SGC7901/DDP-shNC, SGC7901/DDP-shGLUL, SGC7901/DDP-NC, SGC7901/DDP-shGLUL; SGC7901/DDP-Vector, SGC7901/DDP-oeGLUL, SGC7901/DDP-Vector, SGC7901/DDP oeGLUL, the following 3-week treatments were given as one or as a combination of the following: PBS, 100 \u0026micro;L per mice, intraperitoneal injection, once a week; cisplatin, 5 mg/kg, intraperitoneal injection, once a week; peptide 1, 10 mg/kg, intraperitoneal injection, once every two days. Mice were euthanized and tumors were excised for further analysis after three weeks later.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.18. Statistical analysis\u003c/h2\u003e\u003cp\u003eThe quantitative data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). GraphPad Prism version10.0.0 software (GraphPad Software, Boston, Massachusetts, USA) was used for statistical analysis. The statistical differences between experimental groups were analyzed by one-way analysis of variance (ANOVA) or Student's t-test. Differences were considered statistically significant if P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn recent years, peptide drugs have become a research hotspot due to their advantages such as low molecular weight, high sensitivity, low resistance, and ease of modification. Currently, there are approximately 80 peptide drugs available on the global market, and over 150 peptides in clinical development and another 400\u0026ndash;600 peptides in preclinical studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The discovery of novel peptides has become a significant focus in biomedical research due to their potential therapeutic applications. Currently, the discovery of novel peptides primarily relies on the following methods: Phage Display Technology, Computational Design and Modeling, Natural Product Screening, Mass Spectrometry-Based Proteomics. Although many anti-tumor peptides have been discovered, and numerous studies have elucidated their potential molecular mechanisms in cancer biology, the role of peptides in the context of drug resistance remains limited. Based on the advantages of peptides, this class of small molecules could provide new insights into solving clinical problems of tumor drug resistance. Therefore, in order to discover novel peptides that might played a significant role in reverse GC chemoresistance. We initially employed proteomic analysis to compare the protein expression profiles of cisplatin-sensitive and -resistant GC cells, identifying candidate proteins for subsequent peptide design and functional screening. By focusing on peptides that were upregulated in sensitive cells, we hypothesized that these molecules might play a role in mediating sensitivity to chemotherapeutic agents. Subsequently, we conducted experiments to verify the role of these six peptides in sensitizing gastric cancer cells to chemotherapy and found one of these peptides significantly reduced the IC50 of cisplatin in the resistant cells.\u003c/p\u003e\u003cp\u003eAt present, studies investigating the mechanisms of chemotherapy resistance in GC primarily concentrate on molecular changes, epigenetic modifications, metabolic reprogramming, microenvironment influences. Metabolic reprogramming allows cancer cells to adapt to and survive the harsh conditions imposed by factors such as anti-tumor agents. Glucose metabolism and lipid metabolism have been reported closely linked to chemotherapy resistance [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and targeted metabolism can increase trastuzumab sensitivity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, an elevated glutamine metabolism was observed trastuzumab resistance in gastric cancer, and modulation of glutamine metabolism can reverse trastuzumab resistance both in vitro and in vivo [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In this study, metabolomic analysis of cisplatin-sensitive and cisplatin‐resistant cell lines showed that glutamine metabolism was markedly enhanced in the cisplatin-resistant group. As a critical nutrient for cancer cells, glutamine supports both their energetic and biosynthetic needs. In drug-resistant GC cells, we also observed an upregulated glutamine metabolism, which is consistent with previous studies that have highlighted the role of glutamine in promoting resistance to chemotherapy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Interestingly, when treated the cisplatin-resistant cells with peptide1, the glutamine content in resistant cells was significantly reduced compared to non-treatment group, suggesting that peptide 1 can inhibit glutamine synthesis. Moreover, the combination of peptide 1 and DDP enhances apoptosis in resistant cells, thereby restoring chemotherapy sensitivity. Our finding suggests that the peptides not only directly impact cell survival but also potentiate the effects of chemotherapeutic drugs.\u003c/p\u003e\u003cp\u003eThis provides a mechanistic direction for studying how peptide1 promotes chemotherapy sensitivity. Therefore, we first conducted PCR and WB analyses of key enzymes in the glutamine metabolism pathway, the results revealed that the combination of peptide 1 and DDP significantly downregulated the expression of these enzymes. Consistent results were obtained from glutamine content measurements across different treatment groups. Glutamine not only provides additional nutrients to tumor cells but also plays a crucial role in maintaining intracellular ROS balance. Glutamine is an important precursor for the synthesis of glutathione, a potent intracellular antioxidant capable of scavenging excessive ROS. Through the conversion of glutamine, cells can maintain adequate glutathione levels, thereby regulating and neutralizing ROS. Therefore, we measured ROS levels in resistant cells treated with peptide 1 using flow cytometry. The results showed that ROS levels increased after peptide 1 treatment, further leading to apoptosis in resistant cells. Similar results were observed in animal experiments. ROS are known to induce oxidative stress and damage cellular components, including lipids, proteins, and DNA, which can lead to cell death if not adequately managed. The inhibition of glutamine metabolism by anti-tumor peptides led to a significant accumulation of ROS within the gastric cancer cells. The accumulation of ROS in the context of inhibited glutamine metabolism creates a state of metabolic stress that drug-resistant cells are unable to compensate for, making them susceptible to chemotherapy agents. Our experiments showed that treatment with anti-tumor peptides significantly reduced the viability of drug-resistant gastric cancer cells when combined with conventional chemotherapeutics. Our experiments showed that increased glutamine metabolism is associated with cisplatin resistance in gastric cancer, and targeting glutamine metabolism by anti-tumor peptide can effectively reverse chemoresistance in vitro and in vivo.\u003c/p\u003e\u003cp\u003eGLUL plays a crucial role in glutamine metabolism, and its dysregulation has been implicated in various cancers, including liver cancer, lung cancer, breast cancer, pancreatic cancer, ovarian cancer, colon cancer, and gastric cancer [\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37 CR38 CR39 CR40\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, its role in drug-resistant remains unclear. In this study, we found that GLUL is highly expressed in cisplatin-resistant cell lines, which indicates more glutamine is needed in drug resistant cells. We therefore hypothesized that GLUL might be a target of peptide 1 and conducted subsequent studies to investigate the binding of peptide 1 and GLUL. Our study demonstrates that the small molecule peptide 1 interacts specifically with the target protein GLUL, inhibiting its activity and thereby modulating downstream signaling pathways. Peptide 1 was shown to bind to the active site of GLUL with high affinity, as evidenced by our molecular docking and MST experiments, which revealed significant binding interactions. In our cell experiments, with the presence of DPP, peptide 1 treatment could decrease the GLN level and increase the level of ROS, apoptosis and DNA damage. The overexpression of GLUL by lentivirus could alleviate the above tendency. peptide 1 treatment with oeGLUL further decreased the level of ROS, apoptosis and DNA damage. Dual inhibition of GLUL by peptide 1 and shRNA significantly lead to an increased the level of ROS, apoptosis and DNA damage. The above data suggested that GLUL is a critical regulator of glutamine metabolism, and played a significant role in GC drug resistance. In summary, peptide 1 enhances the sensitivity of GC cells to DDP by inhibiting GLUL-mediated glutamine metabolism, which subsequently contributes to ROS imbalance and DNA damage and eventually induces the apoptosis of cisplatin-resistant cells.\u003c/p\u003e\u003cp\u003eAn increasing number of studies have shown that glutamine metabolism plays a crucial role in tumor resistance. Both basic and clinical trials have demonstrated that targeting tumor-dependent glutamine metabolism can effectively inhibit tumor growth, overcome tumor immune evasion and reverse drug resistance [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Inhibitors targeting glutamine metabolism are gradually being developed, with the latest generation, CB839, having passed Phase I/II clinical trials, potentially bringing new hope and breakthroughs in future cancer treatments [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In this study, we explored the potential of anti-tumor peptides to enhance the chemosensitivity of drug-resistant gastric cancer cells by inhibiting glutamine metabolism pathways, ultimately leading to the accumulation of ROS and DNA damage within the cells. Our findings provide new insights into the mechanisms underlying the reversal of chemoresistance and suggest novel therapeutic approaches for treating gastric cancer. In summary, the peptides studied in this research, by targeting glutamine metabolism, have broad prospects for clinical application in overcoming chemotherapy resistance. They have the potential to be used as part of combination therapies to overcome resistance to platinum-based drugs, making them a promising first-line treatment for advanced gastric cancer, especially those patients who have developed resistance to standard chemotherapy regimens.\u003c/p\u003e\u003cp\u003eOur study had some limitations. Firstly, the differences in peptidomes and glutamine metabolism characteristics between chemotherapy-sensitive and resistant patients need to be clarified. Due to limitations in current experimental conditions and resources, this study only discusses these aspects based on cell lines. Secondly, further investigation is needed to clarify the differences in GLUL expression at the tissue and serum levels between sensitive and resistant patients. This is crucial for future precise treatments of gastric cancer based on GLUL expression to distinguish between chemotherapy resistance and sensitivity patients. Lastly, we only constructed CDX model in this study. In future research, obtaining gastric cancer tissues from resistant and sensitive patients and constructing PDX models would greatly provide more solid evidence to this study.\u003c/p\u003e\u003cp\u003eIn conclusion, our study highlights the potential of anti-tumor peptides to inhibit glutamine metabolism, induce ROS accumulation and DNA damage, thus enhancing the chemosensitivity of drug-resistant gastric cancer cells. This approach not only sheds light on novel therapeutic targets but also offers a new strategy to improve the efficacy of chemotherapy in gastric cancer treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, Xiaohong Zhang, Jun Jiang and Li Feng; Data curation, Jian Li, Fangzhou Ye, Jiayi Wang and Jun Jiang; Formal analysis, Jian Li, Huanqing Li, Fangzhou Ye, Jiayi Wang and Jun Jiang; Funding acquisition, Huanqing Li and Li Feng; Investigation, Jian Li, Huanqing Li, Fangzhou Ye and Jun Jiang; Methodology, Jian Li, Huanqing Li, Fangzhou Ye, Jiayi Wang, Songhua Bei and Jun Jiang; Project administration, Xiaohong Zhang, Jun Jiang and Li Feng; Resources, Jiayi Wang, Xiaohong Zhang, Jun Jiang and Li Feng; Software, Huanqing Li, Jiayi Wang and Songhua Bei; Supervision, Xiaohong Zhang, Jun Jiang and Li Feng; Validation, Huanqing Li and Fangzhou Ye; Visualization, Fan Li and Songhua Bei; Writing\u0026ndash;original draft, Jian Li; Writing\u0026nbsp;\u0026ndash;review \u0026amp; editing, Jian Li, Fan Li, Songhua Bei, Xiaohong Zhang and Li Feng.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by the National Nature Science Foundation of China (Grant number: 8217100675 and 82503570), Major Discipline Construction of Minhang District, Shanghai (Grant number: 2020MWDXK03), High-Level Specialist Physician Training Program under the Minhang District Integrated Medical-Education-Research Health Service System (Grant number: 2024MZYS16), and the APC was funded by National Nature Science Foundation of China (Grant number: 8217100675).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e This study was approved by the Ethics Committee of Minhang hospital, Fudan University, and all animal experiments were approved by Laboratory Animal Center, Fudan University and performed in accordance with the guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data generated from this study are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe thank Jun Jiang for assisting with peptide selection and preparation for subsequent functional experiments. We are also grateful for the research equipment provided by the school of Pharmacy at Fudan University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., et al., \u003cem\u003eGlobal cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.\u003c/em\u003e CA Cancer J Clin, 2024. \u003cstrong\u003e74\u003c/strong\u003e(3): p. 229-263.\u003c/li\u003e\n\u003cli\u003eThrift, A.P. and H.B. El-Serag, \u003cem\u003eBurden of Gastric Cancer.\u003c/em\u003e Clin Gastroenterol Hepatol, 2020. \u003cstrong\u003e18\u003c/strong\u003e(3): p. 534-542.\u003c/li\u003e\n\u003cli\u003eThrift, A.P., T.N. Wenker, and H.B. 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Recent studies have highlighted that dysregulation of glutamine metabolism plays a pivotal role in promoting chemoresistance. While small molecule inhibitors targeting glutamine metabolism have been investigated, peptide-based compounds have gained increasing attention due to their high specificity and low toxicity. Endogenous or rationally designed peptides have shown potential in inducing apoptosis, disrupting cancer-related signaling pathways, and overcoming drug resistance in various cancers. However, the potential of functional peptides to target glutamine metabolism and reverse drug resistance in GC has not been thoroughly explored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe performed proteomic profiling to identify proteins upregulated in cisplatin-sensitive GC cells, from which peptides were derived for functional screening. A RHOJ-derived peptide (peptide 1) was identified and validated as a candidate chemosensitizer. Untargeted metabolomics, flow cytometry, molecular docking, molecular dynamics simulations, fluorescence imaging, and a subcutaneous xenograft model were employed to investigate the mechanism by which peptide 1 modulates GLUL-mediated glutamine metabolism and reverses cisplatin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eIn this study, we found glutamine metabolism was enhanced in the cisplatin resistant GC cells, and identified a peptide which derived from RHOJ (named peptide 1) could increase the sensitivity of resistant cells to chemotherapy in GC. Molecular docking analysis revealed that this peptide could bind to the key enzyme glutamine synthetase in glutamine metabolism pathway. Mechanistically, peptide 1 inhibited glutamine production, increased ROS levels, induced DNA damage, and promoted apoptosis in resistant cells, ultimately restoring cisplatin sensitivity both in vitro and in vivo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eOur study demonstrated that glutamine metabolism plays a vital role in chemoresistance of GC, and RHOJ-derived peptide 1 enhances the chemosensitivity of drug-resistant GC cells through targeting GLUL, depleting glutamine, inducing ROS accumulation, and promoting DNA damage. This mechanism ultimately restores chemosensitivity in drug-resistant cells and highlights peptide 1 as a promising therapeutic strategy for overcoming chemoresistance in GC.\u003c/p\u003e","manuscriptTitle":"RHOJ Derived Peptide Promotes Chemosensitivity by Inhibiting Glutamine Metabolism in Gastric Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 08:37:20","doi":"10.21203/rs.3.rs-7691539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-10-29T17:41:31+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-10-08T04:25:57+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-07T08:04:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T15:24:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-09-28T03:50:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"397f62a2-4dff-4f81-a7f1-19fc91474d69","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:08:30+00:00","versionOfRecord":{"articleIdentity":"rs-7691539","link":"https://doi.org/10.1186/s12967-026-07731-z","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2026-01-28 15:58:59","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-10-20 08:37:20","video":"","vorDoi":"10.1186/s12967-026-07731-z","vorDoiUrl":"https://doi.org/10.1186/s12967-026-07731-z","workflowStages":[]},"version":"v1","identity":"rs-7691539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7691539","identity":"rs-7691539","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}