Single-Cell Dissection of BCAA Metabolism Unveils ACAT1-Dependent CS Acetylation as a Metabolic Checkpoint for Immunosuppression in Prostate Cancer

Single-Cell Dissection of BCAA Metabolism Unveils ACAT1-Dependent CS Acetylation as a Metabolic Checkpoint for Immunosuppression in Prostate 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 Single-Cell Dissection of BCAA Metabolism Unveils ACAT1-Dependent CS Acetylation as a Metabolic Checkpoint for Immunosuppression in Prostate Cancer Fuxin Huang, Mingzhao Li, Xiaokun Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9592453/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The catabolism of branched-chain amino acids (BCAA) usually drives the growth of cancer cells, but the role and mechanism of BCAA in the progression of prostate cancer and the formation of the immunosuppressive microenvironment remain unclear. Methods In this study, single-cell sequencing technology was used to analyze the compositional differences in the tumor immune microenvironment of different BCAA catabolism levels (LOW/Med/HIGH). Evaluate the functional states of T cells in different BCAA strata using single - cell gene scores. In vivo and in vitro experiments were conducted to verify the regulation of BCAA treatment on the growth of prostate cancer xenografts and cancer cells. In addition, the Cancer Genome Atlas (TCGA) database was used to determine the clinical feature correlation of the key gene ACAT1 and to study its crosstalk in BCAA metabolism and prostate cancer immune regulation. Results It was found that BCAA-HIGH inhibited the infiltration, cytotoxicity, and proliferation of CD8 T cells, which impaired the anti-tumor immune response of T cells. Mechanistically, this study identified that ACAT1, in response to BCAA, not only promoted the malignant proliferation of prostate cancer cells but also promoted the acetylation modification of citrate synthase, leading to increased citrate synthase activity and citrate production, which promoted the formation of an immunosuppressive microenvironment and further led to the malignant progression of prostate cancer. Conclusions In summary, the exploration of the BCAA-ACAT1-CS acetylation axis expands our understanding of the role of BCAA in prostate cancer, identifies ACAT1 as a target with dual roles in metabolism and post-translational modification (PTM), and it may become a new target for metabolic-immunotherapy. This study provides new therapeutic targets and theoretical support for prostate cancer treatment by targeting BCAA-ACAT1-CS acetylation axis. Prostate cancer Branched-chain amino acid metabolism Tumor immune microenvironment ACAT1 Acetylation Citric acid Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Prostate cancer (PCa) is one of the most common malignancies among men globally and accounts for a significant proportion of cancer-related deaths worldwide. Over the past five years, there has been a significant shift in the treatment landscape, leading to better patient outcomes. Systemic treatment is crucial for advanced and metastatic, cases and currently, there are multiple treatment options available, including androgen deprivation therapy,Novel hormonal therapy,chemotherapy, and emerging targeted drugs. Further research is needed to determine the most effective treatment sequences and combinations for prostate cancer(1). The tumor microenvironment (TME) is composed of tumor cells and other components, which can interact with each other and jointly affect the occurrence and development of tumors(2). It is a key factor in promoting the drug resistance of multiple malignant tumors to immunotherapy(3). Prostate cancer (PCa) tissue contains multiple subsets of T lymphocytes, and each subset has different roles and functions in the PCa microenvironment. Helper T (Th) cells mainly include two types, Th1 and Th2. An increase in Th1 cells in the microenvironment is closely associated with a good prognosis. Th2 cells drive tumor progression, treatment resistance, and castration resistance by producing transforming growth factor-β (TGF-β) or prostaglandin E2 (PGE2), leading to a poor prognosis for PCa patients(4). Regulatory T (Treg) cells in the PCa microenvironment suppress the inflammatory response and control autoimmunity, thereby promoting tumor progression. It has been reported that Treg cells secrete IL-2 to regulate the homeostasis and function of natural killer (NK) cells(5, 6). Macrophages have also been well studied in the occurrence and development of cancer due to their roles in antigen presentation and the regulation of inflammation through polarization(7). Patients with advanced metastatic castration-resistant prostate cancer (mCRPC) do not respond to immune checkpoint inhibitors (ICIs), partly because of the presence of immunosuppressive myeloid cells in the tumor(8, 9). For example, SPP1hi-TAMs play a key role in suppressing anti-tumor activity and may serve as a biomarker for predicting treatment efficacy by activating adenosine signaling in prostate cancer(10). Epigenetic regulation is a complex process, including factors that post - translationally modify DNA or histones, which determines whether a certain gene is turned on or off(11, 12). Lysine acetylation is a process of transferring an acetyl group from acetyl-CoA to a lysine residue catalyzed by acetyltransferases. This is the earliest discovered and most extensively studied protein acylation process. Lysine acetyltransferases (KATs) and lysine deacetylases (KDAC) are responsible for catalysis respectively(13). Acetyl-CoA is a key metabolic intermediate and also a precursor of many biological macromolecules. As a metabolite of sugars, lipids, and amino acids, acetyl-CoA has a wide range of functions and participates in protein acetylation. For example, cancer stem cells (CSCs) promote the transfer of the metabolite acetyl-CoA to interacting T cells through an exosome-dependent pathway. The acetylation of B lymphocyte-induced maturation protein 1 (Blimp-1), a key transcription factor controlling the differentiation of CD103 T cells, is a key factor in impairing CD103 T cells(14). The acetyltransferase KAT2B can promote the acetylation of ACSL4 at lysine residues K500, K571, and K692. This post-translational modification acts as a molecular switch, significantly enhancing the affinity between ACSL4 and the CMA-recognizing chaperone protein HSPA8, thereby promoting the efficient targeting and degradation of ACSL4 through the CMA pathway(15). In recent years, research in metabolomics and tumor biology has revealed the crucial role of amino acid metabolism in cancer development, progression, and treatment response(16). Amino acids are not only the building blocks of proteins but also serve as key precursors for energy metabolism and biosynthetic pathways. Dysregulated amino acid metabolism is closely associated tumor with cell proliferation, invasion, metastasis, and immune escape(17). Branched-chain amino acids (BCAAs)-leucine, isoleucine, and valine-are essential and cannot be newly synthesized in humans. In addition to serving as essential substrates for protein synthesis and muscle maintenance, they promote nitrogen balance and metabolic homeostasis by supporting anabolic signaling and attenuating proteolysis(18). Specifically, leucine catabolism generates acetyl-CoA and acetoacetate, valine yields propionyl-CoA, and isoleucine produces both acetyl-CoA and propionyl-CoA. Propionyl-CoA is subsequently converted to succinyl-CoA(19). These intermediates are ultimately converted into acetyl-CoA or succinyl-CoA, which enter the tricarboxylic acid (TCA) cycle to support adenosine triphosphate (ATP) production(20). BCAA-derived acetyl-CoA promotes histone acetylation and affects gene expression. For example, leucine-derived acetyl-CoA promotes tumor growth, while lysine-derived acetyl-CoA supports the self-renewal of colorectal cancer cells(21). Strategies targeting branched-chain amino acid transaminases (BCATs), including enzyme inhibitors, dietary BCAA restriction, and combination therapies, have been shown to have the potential to overcome drug resistance and improve treatment outcomes(22). In this study, the BCAA metabolic activity of prostate cancer samples was estimated through single-cell sequencing technology, and the samples were grouped. The differences in the of the composition tumor immune microenvironment among different BCAA metabolic levels (LOW/Med/HIGH) were compared. It was found that BCAA-HIGH inhibited the infiltration, cytotoxicity, and proliferation of CD8 T cells, which impaired the anti-tumor immune response of T cells. In the analysis of genes related to branched-chain amino acid metabolism, we identified that ACAT1 not only had a relatively high hazard ratio (HR) but also showed an expression pattern significantly associated with poor prognosis, and was significantly expressed in ERG+ cancer cells and LE-type cells. Mechanistically, this study identified that ACAT1 responded to BCAA, which not only promoted the malignant proliferation of prostate cancer cells but also promoted the acetylation modification of citrate synthase, increased the activity of citrate synthase and the production of citrate, and promoted the formation of an immunosuppressive microenvironment, further leading to the malignant progression of prostate cancer. Materials and methods Databases and software The expression raw matrix of single-cell RNA sequencing (scRNA-seq) of localized prostate cancer biopsy samples was obtained from the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/), with the accession number GSE274229. Level 3 data of prostate cancer patients were downloaded from The Cancer Genome Atlas (TCGA, https://tcga-data.nci.nih.gov/tcga/). The dataset contains relevant clinical data and survival information. The Limma R and ClusterProfiler software packages were used for differential analysis and functional enrichment of the data. P < 0.05 was considered statistically significant. The University of Alabama at the Birmingham Cancer Data Analysis Portal (UALCAN, http://ualcan.path.uab.edu) was used for transcriptome, proteome, and clinical correlation analyses. Cell culture, Lentiviral vectors and transduction The cell lines used in this study include human prostate cancer cell lines (DU145 and LNCAP), mouse prostate cancer cell line RM-1, and 293T (Wuhan, China) cell line. All these cell lines were tested and verified to ensure the absence of mycoplasma contamination. The cells were cultured in DMEM/1640 medium (Gibco, New York, USA) containing 10% fetal bovine serum (FBS, Invitrogen) and placed in a humidified incubator at 37℃ with 5% CO 2 . When the of density 293T cells approached 70%, the packaging operation of equine lentivirus was carried out. The corresponding overexpression plasmids and packaging vectors were co-transfected into 293T cells using Lipofectamine 2000 transfection reagent (Invitrogen, 11668-019) to produce lentiviral particles. The virus supernatant was collected 48 hours after transfection and used to infect target cells. Mouse subcutaneous tumor model Four-week-old male C57BL/6 mice were selected, and 3*10^6 RM-1 cells were injected subcutaneously. After 21 days, tumors were collected, and single-cell suspensions were prepared for subsequent experiments. The cell suspensions were stained and labeled, and immune cell infiltration, T cell activation and exhaustion, macrophage antigen presentation, as well as neutrophil antigen presentation and NETs, were measured using flow cytometry. After 21 days, collect the tumors and prepare single-cell suspensions or fix them with 4% paraformaldehyde for subsequent experiments. Perform flow staining and labeling on the cell suspensions, and use flow cytometry to detect immune cell infiltration, T cell activation and exhaustion, macrophage and neutrophil antigen presentation. For the animal study, all the experiments were performed in accordance with the guidelines of the ethics committee of the Third Affiliated Hospital of Sun Yat-sen University. The mice were housed at Guangzhou LingFu TopBiotech Co, Ltd. at a temperature of 22–26℃ and humidity of 40–60% under a 12-hour light/12-hour dark cycle. Antibodies and chemicals. Antibodies(chemicals) Source Identifier FITC-conjugated anti-mouse CD8a antibody Biolegend 100706 PE-conjugated anti-mouse CD69 antibody Biolegend 104507 PE-conjugated anti-mouse GZMB antibody Biolegend 372207 PE-conjugated anti-mouse IFNR antibody Biolegend 505807 PE anti-mouse/human GL7 Antigen Biolegend 144607 PerCP/Cy5.5-conjugated anti-mouse CD45 antibody Biolegend 157207 APC-conjugated anti-mouse CD45 antibody Biolegend 103111 PerCP/Cy5.5-conjugated anti-mouse F4/80 antibody Biolegend 157317 FITC anti-mouse CD11b Biolegend 101205 PE anti-mouse I-A/I-E Biolegend 107607 PE anti-mouse H2Kb Biolegend 116507 PE anti-mouse I-Ab Biolegend 116407 Lipofectamine 2000 Invitrogen L3000015 CD8a Monoclonal antibody Proteintech 66868-1-Ig Anti-Granzyme B antibody Abcam ab255598 DAPI Bioqiandu B0011 Antifade Mounting Medium Bioqiandu B0007 Normal Goat Serum Boster Bio AR1009 L-leucine Sigma 4330 L-Isoleucine Sigma 4160 L-valine Sigma 94619 ACAT1 Polyclonal antibody Proteintech 16215-1-AP Citrate synthase Polyclonal antibody Proteintech 16131-1-AP HRP-conjugated GAPDH Monoclonal antibody Proteintech HRP-60004 Pan Acetylation Monoclonal antibody Proteintech 66289-1-Ig Detection of acetyl-coenzyme A content Malate dehydrogenase can catalyze the reaction between malate and NAD⁺ to produce oxaloacetate and NADH. Citrate synthase can catalyze the reaction between acetyl-CoA and oxaloacetate to produce citrate and CoA. Using the coupling reaction of these two enzymes, the content of acetyl-CoA is proportional to the formation rate of NADH, and the increase rate of the absorbance at 340 nm can reflect the level of acetyl-CoA content. After the cells are properly cultured in the cell culture flask, collect the cells. According to the operation steps provided by the reagent supplier, add 0.99 mL of buffer1 and 0.01 mL of buffer2. Disrupt the cells by ultrasonication in an ice bath, centrifuge at 8000 g for 10 minutes, and take the supernatant to measure the absorbance at 340 nm. Calculate the content of acetyl-CoA according to the operation steps of the kit. Detection of citric acid content Citric acid can reduce Cr⁶⁺ to Cr³⁺ under acidic conditions and form a chromium citrate chelate. The product has a characteristic absorption peak at 545 nm. The content of citric acid can be quantitatively detected through the change in absorbance. After the cells are properly cultured in a cell culture flask, the cells are collected by centrifugation. According to the operation steps of the reagent manufacturer, 1 mL of buffer 1 is added to treat the samples. The cells are disrupted by ultrasonic treatment in an ice-bath. Centrifuge at 11,000 g for 10 min at 4℃. Take the supernatant and place it on ice to measure the absorbance at 545 nm. Establish a standard curve according to the operation steps of the kit and calculate the content of citric acid. Detection of citrate synthase activity Citrate synthase catalyzes the reaction between acetyl-CoA and oxaloacetate to form citryl-CoA, which is further hydrolyzed to produce citric acid. During this process, DTNB is converted to yellow TNB. The product has a characteristic absorption peak at 412 nm, and the activity of citrate synthase can be characterized by the change in absorbance. After the cells are appropriately cultured in cell culture flasks, the cells are collected by centrifugation. According to the operation steps provided by the reagent supplier, 1 mL of extraction solution and 10µL of Reagent 2 are added. Use a homogenizer or mortar to grind the cells in an ice-bath until a homogeneous slurry is obtained. Centrifuge the homogenate at 600 g for 5 min at 4℃, discard the precipitate, and collect the supernatant. Then centrifuge the supernatant at 12000 g for 10 min at 4℃. Add 200µL of Reagent 1 and 2µL of Reagent 2 to the precipitate, and repeatedly pipette to mix thoroughly. This is the crude enzyme solution, which is used for the determination of citrate synthase activity. Detect the absorbance at 412 nm according to the operation steps of the kit, and calculate the citrate content. Western blotting and Co-immunoprecipitation Cells were harvested with lysis buffer containing 250 mM NaCl, 50 mM Tris (pH 7.4), 1 mM Na 3 VO 4 , 5 mM EDTA, 1% Nonidet™ P40 (NP40), 50 mM NaF, 0.02% NaN 3 , 2 µg/ml protease cocktail inhibitor and 1mM PMSF by dropwise addition to the plates and kept on ice for few minutes. After sample preparation, mix the samples with protein loading buffer and heat them to 100°C in a water bath for 10 minutes to denature the proteins. Then, use Vazyme's rapid PAGE gel kit to separate the samples by SDS-PAGE electrophoresis, and subsequently transfer them to a PVDF membrane in an ice bath. After the membrane transfer is completed, block the PVDF membrane with 5% BSA at room temperature for one hour. Then add the primary antibody and gently shake it at 4℃ overnight. The next day, wash the membrane 3 times with 1×TBST (Tris-buffered saline, 0.1% Tween-20) on a platform shaker, 10 minutes each time. Then incubate the membrane with a secondary antibody conjugated to horseradish peroxidase (HRP) at room temperature for one hour. After that, wash the membrane 3 more times with 1×TBST, 10 minutes each time. Subsequently, use a high-sensitivity ECL chemiluminescence detection kit to visualize the protein bands through a gel imaging system. Immunofluorescence For tissue immunofluorescence detection, according to the operating protocol of the Seville technique, dewax the sections, and then perform antigen retrieval using citrate buffer (pH 6.0). Subsequently, block the sections with 10% goat serum at room temperature for 1 hour. Next, perform primary and secondary antibody staining, and then counterstain the cell nuclei with DAPI. Finally, mount the sections using an anti-fade mounting medium. Conduct imaging analysis using a laser scanning confocal fluorescence microscope. Evaluate the BCAA-score by AUcell AUcell (AUCell) is an R package specifically designed for calculating Gene Set Enrichment Scores. It evaluates the activity level of specific gene sets in each cell based on the AUC (Area Under the Curve) method. Load single-cell data as an expression matrix, create a gene set containing genes related to BCAA metabolism, with the genes sourced from the GSEA database. Run the AUcell algorithm to calculate scores, integrate the scores into the single-cell object, and conduct further analysis. Statistical analysis R language and GraphPad Prism 9.0 were used to collect, summarize, statistically analyze, and graph the data. Experiments were performed at least in triplicate and error bars were presented as the mean ± standard deviation (SD). Student’s test was used to evaluate statistical significance, defined as *p < 0.05, **p < 0.01, ***p < 0.001. A P value less than 0.05 was considered statistically significant. Results The stratification of branched-chain amino acids shows an imbalance between humoral immunity (CD8 T cells) and innate immunity (γδ T cells). To elucidate the branched-chain amino acid (BCAA) metabolic profile in prostate cancer (PCa), we analyzed 13 primary tumors (localized) using single-cell RNA sequencing (scRNA-seq)(10, 23). A total of 28 cell subsets were identified by t-distributed stochastic neighbor embedding (tSNE) visualization (Figure 1A) . By annotating with consistent cell-specific markers and stratifying BCAA metabolism using AUCell, we obtained three BCAA subgroups: BCAA-HIGH, BCAA-Med, and BCAA-LOW (Figure 1B, C; Supplementary Figure 1A) . Moreover, we found that with the increase of BCAA stratification, the proportion of humoral immunity-related CD8+ T cells decreased, while that of innate immunity-related γδ T cells increased, suggesting that BCAA metabolism may mediate the imbalance between humoral and innate immunity (Figure 1D) . Cell–cell communication analysis showed that the high-BCAA subgroup exhibited reduced intercellular communication, including γδ–CD8 communication (Figure 1E–H) . Furthermore, we found that the high-BCAA subgroup had stronger communication axes related to innate immunity and cancer phenotypes, such as SPP1, TNF, WNT, VEGF, KIT, and TGF-β, but weaker immune-related chemokine interaction axes, such as CXCL and CCL (Figure 1I) . In addition, we observed the presence of antigen-presenting MHC I and II (Figure 1J–K) . Here, the key functions of CD8+ T cells, including cytotoxicity and proliferation, were significantly reduced in the high-BCAA subgroup (Figure 1L) . Moreover, we observed high expression of multiple BCAA metabolic genes in ILC3, γδ T cells, and epithelial cancer cells (Supplementary Figure 1B) . Additionally, multiple cancer progression-related pathways were upregulated in the BCAA-HIGH subgroup, while the corresponding immune-related regulatory pathways were suppressed (Supplementary Figure 1C) . To determine the potential reasons for these differences among different BCAA subgroups (Figure 1M) , univariate and multivariate analyses identified ACAT1 as a key prognostic factor (Figure 1N–O) . Further analysis of ACAT1 expression differences among different molecular signatures and patient Gleason scores showed an increasing trend (Figure 1P–Q) . We further analyzed the heterogeneity of ACAT1 expression in different cancer cell subsets (Figure 1S) . Expression profiling indicated that ACAT1 was highly expressed in ERG+ cancer cells and luminal epithelial (LE)-type cancer cells (Figure 1T) . Cell proportion analysis showed a significant increase in the proportion of ERG+ cancer cells in samples with active BCAA metabolism (Figure 1U) . In summary, by analyzing cell proportions and cell–cell communication in different BCAA subgroups, we observed an imbalance between humoral and innate immunity caused by BCAA metabolic imbalance, with CD8+ T cells and γδ T cells being the most representative. Highly stratified BCAA mediates T cell dysfunction and promotes tumor growth in mice by altering cell-cell interactions. To further analyze the mechanism by which BCAA affects the imbalance between humoral immunity and innate immunity, we isolated CD4, CD8, γδT, and ILC3 cells, performed tSNE clustering again, and annotated the clusters based on existing literature reports and marker genes of each cell subset (Figure 2A-B, Supplementary Figure 2A) . We identified subsets of CD8 T cells (including CD8-SLC7A5, CD8-CD69, CD8-GZMA, CD8-GZMK, CD8-IFNR, CD8-GZMB) and γδ T cell subsets (including γδ-CXCL8, γδ-FKBP1B, γδ-FASN, γδ-IGFBP2, γδ-ADIRF, γδ-APOD, γδ-HAVCR2) (Figure 2C, Supplementary Figure 2B) . The results showed that as the BCAA stratification increased, the CD4 and CD8 T cells related to humoral immunity gradually decreased, while the γδ T cells related to innate immunity gradually increased, indicating an imbalance between humoral immunity and innate immunity (Figure 2D-F) . Subset analysis showed that the proportion of activated T cells, such as CD69⁺ T cells, gradually decreased, while the proportion of CXCL8⁺γδ T, PFKBP1B⁺ γδ T, and FASN⁺ γδ T cells increased (Figure 2D-F, Supplementary Figure 2A) . Cell-communication analysis revealed that with the increase in BCAA stratification, the communication of T cells related to humoral immunity was weakened, which often suggested the possibility of immune impairment (Figure 2G-H) . Further analysis of input and output signals showed that high BCAA stratification was associated with enhanced output signals of γδ T cells and weakened output signals of CD8 cells. Similar conclusions were drawn from the analysis of input signals (Figure 2I-J) . Next, by comparing the overall information flow of each signaling pathway, the analysis showed that high BCAA stratification was associated with higher information flow related to angiogenesis (ANGPTL and VEGF) and multiple cancer-progression-related information axes (WNT, PDGF, EGF, and KIT) (Figure 2K) . In addition, the increase in BCAA stratification was associated with weakened expression of molecules related to the LCK, TNF, ITGB2, and CD99 signaling axes (Figure 2L-P) . Functional enrichment analysis showed that the increase in BCAA stratification was associated with weakened T-cell proliferation, migration, chemotaxis, immune regulation, and NK T-cell function (Figure 2Q, Supplementary Figure 2C) . Analysis of the expression characteristics of ACAT1 showed that ACAT1 was highly expressed mainly in FKBP1B+ γδT and APOD+ γδT cells (Figure 2R) . MIF plays an important role in signal transduction such as inflammation and cell proliferation, participates in the occurrence of various diseases and cancers, and is an important biomarker and drug target. Cell-communication analysis of FKBP1B+ γδT and APOD+ γδT cells showed that the MIF cell-communication axis exhibited prominent characteristics (Figure 2S-T) . These results suggest that the mechanism of the imbalance between humoral immunity and innate immunity may involve the regulation of cell migration and inflammatory regulatory responses by the MIF cell-communication axis. To verify whether the elevated BCAA metabolism affects tumor growth in vivo, we established a subcutaneous tumor model in mice. By providing additional dietary BCAA, we constructed a high-BCAA-metabolism model (Figure 3A) . Tumor measurements showed that the tumor proliferation rate was faster significantly and the tumor volume was significantly larger in the high-BCAA diet group (Figure 3A) . Flow cytometry analysis of cells extracted from subcutaneous tumor tissues showed that the proportion of CD45+ CD8⁺+ T cells infiltrating the tumors was significantly lower in the high-BCAA diet group (Figure 3B-I) . Moreover, the expression levels of markers of T-cell activation, such as CD69, GZNB, IFNR, and GL7, were significantly lower in the high-BCAA diet group, indicating that high-BCAA diet metabolism inhibited the anti-tumor immune function of T cells (Figure 3B-I) . Multiplex immunofluorescence staining also showed that the high-BCAA diet group exhibited lower infiltration of CD8 T cells and activation of GZMB (Figure 3J) . Elevated BCAA metabolism promotes tumor growth in mice by inhibiting antigen presentation of macrophages and neutrophils. To analyze the effects of BCAA stratification on other components in the tumor microenvironment, we performed further subgroup analysis of myeloid cells (Figure 4A-B) . Through the annotation of marker genes of specific cell populations and combined with cell proportion analysis, we found that the high-BCAA-stratified group had a higher proportion of APOE+ macrophages, G0S2+ macrophages and a lower proportion of IL1B+ macrophages (Figure 4C) . By comparing the cell numbers, we found that the number of G0S2+ macrophages/neutrophils increased with the increase of BCAA stratification, which indicated that the extracellular trap network of macrophages/neutrophils might be activated in response to BCAA. Cell communication analysis showed that the high-BCAA-stratified group exhibited weaker interactions among PPIA+ macrophages, G0S2+ macrophages, IL1B+ macrophages and G0S2+ neutrophils (Figure 4D-E) . By comparing the input and output signal flows, we found that the cell-communication axes related to antigen presentation of macrophages and neutrophils dominated in almost all cell types (Figure 4F) . Further analysis of the expression characteristics of molecules in the MHC-I and MHC-II communication axes revealed that the BCAA-HIGH group showed significantly lower expression of HLA molecules in several cell subgroups such as G0S2+ macrophages, PPIA+ macrophages and G0S2+ neutrophils (Figure 4G-H) . To analyze whether such phenotypes are also applicable to cell communication, we conducted cell-communication analysis, and the BCAA-HIGH group showed weaker MHC-I and MHC-II communication (Figure 4I) . Further analysis of specific interaction axes visualized all the communication axes that showed weaker interactions in the BCAA-HIGH group. These results suggested that elevated BCAA metabolism led to the inhibition of the antigen-presentation function of myeloid cells. To verify whether elevated BCAA metabolism affects the anti-tumor effect of myeloid cells in the in-vivo tumor microenvironment, we established a subcutaneous tumor model in mice. By dietary supplementation of BCAA, we constructed a high-BCAA-metabolism model. Based on the previous results, we tested the antigen-presentation function of myeloid cells. After dietary BCAA supplementation, we found that BCAA supplementation decreased the expression of antigen-presentation molecules H2KB, I-A/B and I-A/E in macrophages (F4/80+), indicating that the antigen-presentation function of macrophages was impaired (Figure 4L, Supplementary Figure 3A-C) . Similarly, there have been more and more studies on the antigen-presentation function of neutrophils in recent years. Through detection, we found that BCAA supplementation decreased the expression of antigen-presentation molecules H2KB, I-A/B and I-A/E in neutrophils (LY6G+), indicating that the antigen-presentation function of neutrophils was impaired (Figure 4M, Supplementary Figure-3D F) . The above results indicated that elevated BCAA metabolism inhibited anti-tumor immunity by suppressing the antigen-presentation function of myeloid cells and activated the extracellular trap network of neutrophils to inhibit T-cell infiltration. High expression of the key BCAA metabolic enzyme ACAT1 promotes the clonal formation of prostate cancer cells, inhibits T-cell activation, and promotes tumor growth in mice. In previous studies, through the analysis of cancer cell subsets, we found that ACAT1 was significantly highly expressed in ERG + cancer cells, and ERG + cancer cells were significantly aggregated in the BCAA-HIGH group. As a transcription factor, the high expression of ERG in prostate cancer cells is usually driven by gene fusion events, the most common of which is the TMPRSS2-ERG gene fusion. This is one of the most common genetic alterations in prostate cancer. The abnormal activation of the ERG gene drives a series of downstream signaling pathways, promotes the proliferation of tumor cells, inhibits apoptosis, and may enhance their invasion and metastasis abilities. We over-expressed ACAT1 in murine prostate cancer cells. Through subcutaneous tumorigenesis in C57 mice, we observed that the high expression of ACAT1 significantly promoted tumor progression (Figure 5A) . Through clone formation experiments, we over-expressed and knocked down ACAT1 in murine and human prostate cancer cell lines. The experimental results showed that the high expression of ACAT1 significantly increased the clonal formation ability of prostate cancer cells, while the knockdown of ACAT1 significantly reduced this ability (Figure 5B-C) . In addition, by adding BCAA to the culture medium, the experiment showed that the addition of different concentrations of BCAA increased the clonal formation ability of prostate cancer cells (Figure 5D) . Further experiments found that knocking down ACAT1 under the condition that BCAA promotes the clonal formation ability of prostate cancer cells reduced the malignant proliferation mediated by high levels of BCAA (Figure 5E) . The above experiments suggest that the ability of BCAA to promote the clonal formation of prostate cancer cells may depend on the expression of ACAT1. Immunofluorescence detection also showed that tumors with high ACAT1 expression had lower infiltration of CD8 T cells and activation of GZMB (Figure 5F) . ACAT1 mediates an increase in citrate synthesis by mediating the acetylation of CS, thereby inhibiting anti-tumor immunity. ACAT1 is a metabolic enzyme that participates in the reversible conversion of acetoacetyl-CoA into two molecules of acetyl-CoA(24). In recent years, many studies have also been conducted on the non-metabolic regulatory role of ACAT1. Some studies have pointed out that in colorectal cancer (CRC), nuclear ACAT1 directly acetylates lysine 146 of p50 (NFKB1), weakening its DNA-binding and transcriptional repression activities, thereby increasing the expression of immune-related factors, and further promoting the recruitment and activation of NK cells to inhibit the growth of CRC(24). This means that in addition to acting as a metabolic enzyme, ACAT1 may also act as an acetylation regulatory factor to regulate the acetylation modification of target proteins. Through the STRING database, we analyzed the protein-interaction network in which ACAT1 participates and identified several key targets (Figure 6A) . Using the GPS-PAIL software, we analyzed the possible potential acetylation of the targets. We identified citrate synthase CS, and K459 is the main acetylation site of CS (Figure 6B) . To analyze the dual-sided regulatory role (metabolism and PTM) of ACAT1 in prostate cancer (Figure 6C) , first, we treated prostate cancer cell lines with different concentrations of BCAA. We detected that the protein level of ACAT1 increased in response to BCAA treatment, and the content of the BCAA metabolite acetyl-CoA also increased in a concentration-dependent manner (Figure 6D-E) . To study whether BCAA treatment affects the acetylation modification level of CS, after treating prostate cancer cell lines with different concentrations of BCAA, we detected the acetylation level of CS through immunoprecipitation and pan-acetylation modification antibodies. The experimental results showed that BCAA treatment significantly increased the acetylation modification of CS (Figure 6F) . Similarly, we also observed that after treating prostate cancer cell lines with different concentrations of BCAA, the cellular citrate content and citrate synthase activity increased significantly (Figure 6G-H) . This indicates that BCAA treatment promotes the acetylation modification of CS and the activity of citrate synthase, thereby promoting an increase in citrate production. Acetyl-CoA is often considered the source of acetylation. Therefore, by exogenously adding acetyl-CoA, the experiment detected that exogenously added acetyl-CoA had a similar effect to BCAA addition, and the cellular citrate content and citrate synthase activity increased significantly (Figure 6I-J) . Next, we overexpressed ACAT1 in prostate cancer cells. The experimental results showed that the overexpression of ACAT1 promoted the acetylation modification of CS, while its background protein level did not change significantly. The simultaneous treatment with BCAA and ACAT1 overexpression further promoted the acetylation modification of CS (Figure 6K) . Correspondingly, we observed that the overexpression of ACAT1 also significantly increased the cellular citrate content and citrate synthase activity (Figure 6L-M) . In addition, we also observed that directly overexpressing CS or ACAT1 alone could increase the cellular citrate content and citrate synthase activity, and the effect was more obvious when both were overexpressed simultaneously. Among them, the mutation of the acetylation modification site of CS significantly weakened this effect. Therefore, the acetylation modification site of CS plays an important role in the regulation of its citrate synthase activity. Discussion In this study, by combining single-cell sequencing data with in vivo and in vitro experiments, it was systematically revealed that the active metabolism of branched-chain amino acids (BCAAs) affects the acetylation modification of citrate synthase (CS) through the dual-regulatory role of ACAT1. Our results show that the increased activity of BCAA metabolism leads to an imbalance between T-cell humoral immunity and innate immunity and has an inhibitory effect on the antigen presentation of myeloid cells. Among them, ACAT1 responds to the active BCAA metabolism, promotes the acetylation modification of citrate synthase, resulting in increased citrate synthase activity and thus increased citrate production, forming an immunosuppressive microenvironment (Figure 6P) . Our study expands the understanding of the role of BCAA metabolism in prostate cancer and further investigates the dual-regulatory role of ACAT1. These works deepen our understanding of metabolism and modification and provide new targets for the treatment of prostate cancer. In this study, by combining single-cell sequencing data with in vivo and in vitro experiments, we systematically revealed that the active metabolism of branched-chain amino acids (BCAAs) affects the acetylation modification of citrate synthase (CS) through the dual regulatory role of ACAT1. Our results showed that the increased active metabolism of BCAAs led to an imbalance between humoral and innate immunity in T cells and had an inhibitory effect on antigen presentation by myeloid cells. Specifically, ACAT1, in response to active BCAA metabolism, promotes the acetylation of citrate synthase, leading to an increase in its activity and thus the production of more citrate, creating an immunosuppressive microenvironment. Our study broadens the understanding of the role of BCAA metabolism in prostate cancer and further explores the dual regulatory role of ACAT1. These findings deepen our understanding of metabolism and modification and provide new targets for the treatment of prostate cancer. All along, mitochondrial acetyl-CoA acetyltransferase (ACAT1) is the earliest purified mitochondrial matrix thiolase. It participates in isoleucine degradation, ketone body production/decomposition, and fatty acid oxidation through catalyzing the reversible reaction of condensing two molecules of acetyl-CoA into acetoacetyl-CoA(24, 25). In recent years, continuous research has pointed out that ACAT1 regulates the acetylation modification of target proteins through its acetyltransferase activity(26, 27). In prostate cancer, a previous study on persistent organic pollutants and prostate cancer aggressiveness indicated that ACAT1 mediates the effect of dioxin (a persistent organic pollutant) on cell migration(28). In a prospective study on prostate cancer, a prognostic validation set including the ACAT1 gene was obtained for the PCa risk group(29). However, as a part of branched-chain amino acid metabolism, the response of ACAT1 to BCAA and its regulation in the formation of the immunosuppressive microenvironment of prostate cancer are still unknown. BCAA, serving as fuel for cancer growth, undergoes changes in many solid tumors, including breast cancer, liver cancer, and pancreatic cancer(30). In breast cancer, the expression of the BCAA metabolic enzyme BCAT1 is elevated, and it promotes mitochondrial biogenesis in an mTOR-dependent manner to support cell proliferation(31). In pancreatic cancer, leucine supplementation promotes tumor growth in mice through diet-dependent effects(32). In liver cancer mouse and human models, the blood levels of BCAA are also elevated and are associated with overactivation of mTOR(33). A previous comprehensive proteomic analysis of high-risk prostate cancer samples identified three subtypes (S-I/II/III) through proteomic clustering. Among them, S-III has the highest degree of malignancy, with high expression of metabolism/proliferation-related proteins, which directly drive rapid tumor growth, invasion, and metastasis, and are often associated with treatment resistance and poor prognosis. Pathway enrichment analysis shows that the S-III subtype is enriched in proteins related to oxidative phosphorylation, valine-leucine-isoleucine degradation, and the tricarboxylic acid cycle(34). The S-I subtype has a low tumor burden, high stromal content, and slow proliferation rate, and has the best prognosis, also exhibiting features of immune activation, suggesting that the good prognosis of patients with the S-I subtype may be related to enhanced immune surveillance activity. However, current research on the role of abnormal BCAA metabolism in cancer cells in the establishment of the tumor immunosuppressive microenvironment is still in its infancy. In this study, by integrating single-cell sequencing data of prostate cancer, we classified prostate cancer patients into BCAA (LOW/Med/HIGH) groups using the AuCell software algorithm. The study shows that the BCAA-HIGH group exhibits a decrease in CD8 T cells and attenuation of T-cell cytotoxicity and proliferation. In the subgroup analysis of epithelial/cancer cells, we identified significantly high expression of ACAT1 in ERG+ tumors enriched in the BCAA-HIGH group, which is often associated with the proliferative, invasive, and metastatic abilities of cancer cells. Mechanistically, this study constructed a protein-interaction network of ACAT1 and demonstrated that ACAT1 responds to BCAA metabolism to promote acetylation modification of citrate synthase, mediating an up-regulation of citrate synthase activity and an increase in citrate production. Through this mechanism, not only does it provide fuel for cancer cells through the metabolic axis, but the increase in the citrate metabolic axis also promotes the formation of an immunosuppressive microenvironment. Although this study used multiple methods to confirm the role of the BCAA-ACAT1-CS axis in the prostate cancer immune microenvironment, there are still some limitations in this study. First, the study initially relied on public databases (GEO and TCGA), which means that the sample size and patient population were relatively limited, and there was a lack of large-scale and multi-cohort studies to deepen its clinical translation value. In addition, although we constructed a subcutaneous tumor model using prostate cancer cells and conducted experiments, using genetically engineered mice could provide a more specific and accurate understanding of the regulatory mechanism of the BCAA-ACAT1-CS axis and its impact on tumor growth and immune regulation. In addition, the imbalance of CD8 T cells and γδ T cells in T cell subsets has not been fully studied in this study. The next plan should be to study the biological effects of BCAA-ACAT1 using genetically engineered mice as a model. Conclusion In general, this study analyzed the composition of the tumor immune microenvironment at different levels of BCAA metabolism. It was analyzed and proposed that BCAA-HIGH led to a decrease in CD8 T cells, a weakening of cytotoxicity and proliferation, suggesting impaired anti-tumor functions of T cells. Through in vitro/in vivo experiments, the results of single-cell data analysis were verified. The dual functions (metabolism and PTM) of ACAT1 in prostate cancer were identified, and the molecular mechanism of ACAT1 regulating the acetylation of citrate synthase was analyzed, opening up new directions for exploring novel treatment strategies. This study provides new strategies for future metabolic/immunotherapy of prostate cancer, that is, by targeting ACAT1-CS acetylation to regulate citrate production and its metabolism, the immune response can be effectively initiated and activated. More experimental validations and clinical trials are still needed in the future to confirm the practical application value of these preliminary conclusions. Abbreviations Pca:Prostate cance BCAA:Branched-chain amino acid TME:Tumor microenvironment ACAT1:Acetyl-CoA acetyltransferase PTM:Posttranslational modification GD T cell:γδ T cell HLA:Human Leukocyte Antigen ERG:Transcriptional regulator ERG STRING:Known and Predicted Protein-Protein Interactions GPS-PAIL:prediction of lysine acetyltransferase-specific modification sites from protein sequences CS:Citrate synthase GEO: Gene Expression Omnibus TCGA:The Cancer Genome Atlas TSNE:T-distributed stochastic neighbor embedding UALCAN:The University Of Alabama At Birmingham Cancer Data Analysis Portal GSVA:Gene Set Variation Analysis Declarations Ethics approval and consent to participate Ethical approval was obtained from the laboratory animal management and use committee of shenzhen TOPBIOTECH Co., Ltd.TOP-2PZ-GM260317. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) repositories under the following URLs: (GEO, http://www.ncbi.nlm.nih.gov/geo/) and (TCGA, https://tcga-data.nci.nih.gov/tcga/). Competing interests The authors declare that they have no competing interests. Funding No financial support. Author contributions XM conceived and led this study. FH,ML,XMdesigned the experiments and wrote the manuscript. FH, ML, and XM performed the experiments and data analysis. All authors edited and approved the final version. FH and ML contributed asco-authors.XM was corresponding author. All authors read and approved the final manuscript. Acknowledgements We thank Central Laboratory of the Third Affiliated Hospital of Sun Yat-sen University (guangzhou, China) for their contribution to the provide an experimental platform. References Valérie F, Alison T, Elena C, Karim T, Jochen W. Prostate cancer. Lancet. 2026;407:10528. Fuxin H, Cheukfai L, Yue C, Jianmin L, Jike F, Zhongyan Z, et al. Succinate dehydrogenase B palmitoylation promotes T cell exhaustion through the H3K27ac-PD1 axis in pancreatic cancer. Cancer Lett. 2026;642:218277. Mikhail B, Edward W R, Kelly K, Vincent C, Douglas F F, Miriam M, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24:541-550. Erin G S, Haleema Yoosuf A, Masood K, Graham A P, Stephanie E M. Novel Combinatorial Approaches to Tackle the Immunosuppressive Microenvironment of Prostate Cancer. Cancers (Basel). 2021;13:1145. Line B A, Maibritt N, Martin R, Jacob F, Michael B, Benedicte P U, et al. Immune cell analyses of the tumor microenvironment in prostate cancer highlight infiltrating regulatory T cells and macrophages as adverse prognostic factors. J Pathol. 2021;255:155-165. Qintao G, Zhijie Z, Xiao L, Feixiang Y, Meng Z, Zongyao H, et al. Deciphering the suppressive immune microenvironment of prostate cancer based on CD4+ regulatory T cells: Implications for prognosis and therapy prediction. Clin Transl Med. 2024;14:e1552. Fuxin H, Zhongyan Z, Jike F, Yue C, Jinhui W, Quanzhang L, et al. Integrated scRNA-seq and transcriptome analyses uncover the effects of UBE2H on the immune microenvironment regulation in pancreatic cancer. Cancer Cell Int. 2025;25:422. Thomas P, Kobe C Y, Silke G, Edward E 3rd K, Dana R, Nobuaki M, et al. Atezolizumab with enzalutamide versus enzalutamide alone in metastatic castration-resistant prostate cancer: a randomized phase 3 trial. Nat Med. 2022;28:144-153. Filippos K, Anastasia X, Evangelia C, Vassiliki L, Chrissovalantis A, Athanasios K. Myeloid-Derived Suppressor Cells in Prostate Cancer: Present Knowledge and Future Perspectives. Cells. 2022;11:20. Aram L, Zenghua F, Matthew C, Averey L, Diamond L, Ali S, et al. Evolution of myeloid-mediated immunotherapy resistance in prostate cancer. Nature. 2025;637:1207-1217. Peter A J, Stephen B B. The epigenomics of cancer. Cell. 2007;128:683-692. Anbarasu K, Katherine R WL, Thomas C W, Joel A Y, Shuang G Z, Christopher P E, et al. Recent Advances in Epigenetic Biomarkers and Epigenetic Targeting in Prostate Cancer. Eur Urol. 2021;80:71-81. Haiqing J, Lei J, Xiaoyu S, Huinan Y, Xinguang L, Liwei Z, et al. Post-translational modifications of cancer immune checkpoints: mechanisms and therapeutic strategies. Mol Cancer. 2025;24:193. Jiaxin L, Huiyan J, Jing G, Mengdi L, Danhua S, Yiran Z, et al. Cancer Stem Cells Shift Metabolite Acetyl-Coenzyme A to Abrogate the Differentiation of CD103(+) T Cells. Adv Sci (Weinh). 2026;13:e13535. Zhouwei W, Zhichen J, Chenglong H, Shu Y, Shuqing J, Chenyu W, et al. Acetylation Regulates ACSL4 Degradation Through Chaperone-Mediated Autophagy to Alleviate Intervertebral Disc Degeneration. Adv Sci (Weinh). 2026;13:e16015. Lucie S, Katerina H, Julia S. Targeting amino acid metabolism in cancer. Int Rev Cell Mol Biol. 2022;373:37-79. Xiaoli S, Xinyi W, Wentao Y, Dongmin S, Xihuan S, Zhengqing L, et al. Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives. Signal Transduct Target Ther. 2024;9:192. Tomoki B, Junichi F. Primary Roles of Branched Chain Amino Acids (BCAAs) and Their Metabolism in Physiology and Metabolic Disorders. Molecules. 2025;30:56. Gagandeep M, Stephen M, Glory M, Olasunkanmi A J A. Branched-chain Amino Acids: Catabolism in Skeletal Muscle and Implications for Muscle and Whole-body Metabolism. Front Physiol. 2021;12:702826. Chuang D, Wen-Jie L, Jing Y, Shan-Shan Z, Hui-Xin L. The Role of Branched-Chain Amino Acids and Branched-Chain α-Keto Acid Dehydrogenase Kinase in Metabolic Disorders. Front Nutr. 2022;9:932670. Sofia LV, Carlos S. Metabolic pathways regulating colorectal cancer initiation and progression. Semin Cell Dev Biol. 2019;98:63-70. Weiran Z, Jie S, Xuanyin D, Hele L, Xu W, Dan F. Branched-chain amino acid transaminases as promising targets in tumor therapy. Front Cell Dev Biol. 2026;14:1712076. Ru M W, Jessica C S, G Edward W M, Zenghua F, Aram L, Fernando Jose GM, et al. Sialylated glycoproteins suppress immune cell killing by binding to Siglec-7 and Siglec-9 in prostate cancer. J Clin Invest. 2024;134:e180282. Chen W, Kun L, Hao-Jie C, Zi-Xuan X, Qi M, Ze-Kun L, et al. Nuclear mitochondrial acetyl-CoA acetyltransferase 1 orchestrates natural killer cell-dependent antitumor immunity in colorectal cancer. Signal Transduct Target Ther. 2025;10:138. Antti M H, Gitte M, Päivi L P, Naomi K, Toshiyuki F, Rik K W. Crystallographic and kinetic studies of human mitochondrial acetoacetyl-CoA thiolase: the importance of potassium and chloride ions for its structure and function. Biochemistry. 2007;46:4305-4321. Jun F, Changliang S, Hee-Bum K, Shannon E, Jianxin X, Meghan T, et al. Tyr phosphorylation of PDP1 toggles recruitment between ACAT1 and SIRT3 to regulate the pyruvate dehydrogenase complex. Mol Cell. 2014;53:534-548. Cuimiao Z, Hao T, Gang N, Xi H, Jingyi L, Siqi C, et al. ACAT1-Mediated ME2 Acetylation Drives Chemoresistance in Ovarian Cancer by Linking Glutaminolysis to Lactate Production. Adv Sci (Weinh). 2025;12:e2416467. Julio B, Myriam K, Christelle D-S, Angélique DH, Jean-Paul S, Amalia T, et al. Persistent organic pollutants promote aggressiveness in prostate cancer. Oncogene. 2023;42:2854-2867. Heba A, Robert M, Ewan H, Matthew S, Aroul R, Benjamin Matthew S, et al. Chromatin conformation changes in peripheral blood can detect prostate cancer and stratify disease risk groups. J Transl Med. 2021;19:46. Sharanya S, Matthew G VH. Emerging Roles for Branched-Chain Amino Acid Metabolism in Cancer. Cancer Cell. 2020;37:147-156. Ling Z, Junqing H. Branched-chain amino acid transaminase 1 (BCAT1) promotes the growth of breast cancer cells through improving mTOR-mediated mitochondrial biogenesis and function. Biochem Biophys Res Commun. 2017;486:224-231. Kristyn A L, Laura M L, Audrey J R, Stephen D H. Leucine supplementation differentially enhances pancreatic cancer growth in lean and overweight mice. Cancer Metab. 2014;2:6. Russell E E, Siew Lan L, Eoin M, Wai Ho S, Maya V, Phillip J W, et al. Loss of BCAA Catabolism during Carcinogenesis Enhances mTORC1 Activity and Promotes Tumor Development and Progression. Cell Metab. 2019;29:1151-1165. Baijun D, Jun-Yu X, Yuqi H, Jiacheng G, Qun D, Yanqing W, et al. Integrative proteogenomic profiling of high-risk prostate cancer samples from Chinese patients indicates metabolic vulnerabilities and diagnostic biomarkers. Nat Cancer. 2024;5:1427-1447. Additional Declarations No competing interests reported. Supplementary Files supplementaryfile1.docx supplementaryfile2.pptx Cite Share Download PDF Status: Posted Version 1 posted 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-9592453","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637126830,"identity":"9724b2a6-6050-494f-804a-6806eb300b67","order_by":0,"name":"Fuxin Huang","email":"","orcid":"","institution":"South China University of Technology School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fuxin","middleName":"","lastName":"Huang","suffix":""},{"id":637126831,"identity":"961cac3c-07d7-44ed-88a2-847cc7652808","order_by":1,"name":"Mingzhao Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingzhao","middleName":"","lastName":"Li","suffix":""},{"id":637126832,"identity":"314c614d-75e9-44df-a0c5-f79f9024e3e5","order_by":2,"name":"Xiaokun Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYJCCAwwMCXIMDIyNB2AiEsRoMQZqaSBeCxAkJDZA9BKhRbf9jOGBHxVp6WvbDwNt+XPY3uAA88HbPAx2ebi0mJ1JSzjYcyYnd9uZxIYDjG2HEzccYEu25mFILsap5UDygQO8bRW52w6AtDQcTjA4wGMmzcNwAOxUrFrOP2w4+LetIh3EgDqM/xt+LTeSDxzmbctJMLsBtIWB7TDjhgM8bAS0PEs4LHMmzXDbDaAtiW3piTMPsxlbzjFIxuOwHOOPbyqS5c3Opz988OGPtT3f8eaHN95U2OHUggoSGJoZGJhBLAOi1INBHfFKR8EoGAWjYMQAACOZZZSA6FqhAAAAAElFTkSuQmCC","orcid":"","institution":"The Third Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":true,"prefix":"","firstName":"Xiaokun","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2026-05-02 09:24:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9592453/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9592453/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109406643,"identity":"a182c6ee-8212-45cf-a8c9-bd192d3a1147","added_by":"auto","created_at":"2026-05-17 13:29:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1375998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of ACAT1 as a prognostic marker in high-grade BCAA-induced immunosuppressive microenvironment and ERG+ tumors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: TSNE illustrating 28 cell populations across more than 80,000 single-cell transcriptomes from PCa. Cell types are annotated by unique colors. B: Bubble chart shows the classic markers of each cell type. C: TSNE of the identified cell populations in BCAA-LOW, BCAA-Med and BCAA-HIGH PCa groups. D: The bar chart shows the abundance changes of different sub-populations in different BCAA levels. E: Differences in the quantity and intensity of cell communication in different levers of BCAA. F: The bar chart statistically shows the differences in the quantity and intensity of cell communication among different levers of BCAA. G: Heat map statistics of the differences in the number and intensity of cell-cell communications among different levers of BCAA. H: Comparison of outgoing signaling in cell communication between BCAA-HIGH and BCAA-LOW. I: Compare the overall information flow of each signaling pathway between BCAA-HIGH and BCAA-LOW. J-K: Communication analysis of MHC-I and MHC-II among different cell subsets. L: Evaluation of T cell cytotoxicity and proliferative activity in different BCAA levels. M: Schematic diagram of the process of identifying ACAT1 through the branched-chain amino acid metabolism gene set. N: The survival curve of ACAT1 in TCGA database. O: Expression differences of ACAT1 in the normal and cancer groups in the TCGA database. P: Expression of ACAT1 in PCa based on molecular signature. Q: Expression of ACAT1 in PCa based on patient’s gleason score. R: Bubble chart shows the classic markers of Epi/Cancer cell type. S: TSNE of the identified cell populations. T: The density plot shows the specific expression of ACAT1 in cancer cell subpopulations. U: The bar chart shows the abundance changes of different cancer cell sub-populations in different BCAA levels.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/d9392fec1e71a06325ae6a50.png"},{"id":109406127,"identity":"ff7de269-d8a0-4e77-ae59-20f42d4eb105","added_by":"auto","created_at":"2026-05-17 13:25:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":570842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHighly BCAA mediates T cell dysfunction and promotes tumor growth.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: TSNE clustering of T cell subsets yielded 34 subsets. B: Bubble chart shows the classic markers of each cell type. C-D: TSNE of the identified cell populations in BCAA-LOW, BCAA-Med and BCAA-HIGH PCa groups. E-F: The bar chart shows the abundance changes of different sub-populations in different BCAA levels. G: Differences in the quantity and intensity of cell communication in different levers of BCAA. H: The bar chart statistically shows the differences in the quantity and intensity of cell communication among BCAA-LOW and BCAA-HIGH PCa groups. I: Comparison of outgoing signaling in cell communication between BCAA-HIGH and BCAA-LOW. J: Comparison of incoming signaling in cell communication between BCAA-HIGH and BCAA-LOW. K: Compare the overall information flow of each signaling pathway between BCAA-HIGH and BCAA-LOW. L-P: Comparison of key genes in the cell communication axis of LCK, TNF, ITGB2, CD99 and MHC-I. Q: Analysis of T cell function in different BCAA levels. R: Expression of ACAT1 in different T cell subsets. S: Cell communication axes related to FKBP1B+GDT cells. T: Cell communication axes related to APOD+GDT cells.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/ccb6b2afe01d1a0cc8e0fb34.jpg"},{"id":109405930,"identity":"1844d8a6-8efc-4872-b553-8bbe111d5ef7","added_by":"auto","created_at":"2026-05-17 13:22:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":668905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDietary supplementation with BCAA promotes the progression of prostate cancer and suppresses T-cell function.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Dietary supplementation of BCAA detects the growth of subcutaneous tumorigenesis in prostate cancer. B: Statistical analysis of the regulation of the activation ratio of CD8/CD69 T cells by BCAA supplementation. C: Flow scatter plot of the regulation of BCAA supplementation on the activation ratio of CD8/CD69 T cells. D: Statistical analysis of the regulation of the activation ratio of CD8/GZMB T cells by BCAA supplementation. E: Flow scatter plot of the regulation of BCAA supplementation on the activation ratio of CD8/GZMB T cells. F: Statistical analysis of the regulation of the activation ratio of CD8/IFNR T cells by BCAA supplementation. G: Flow scatter plot of the regulation of BCAA supplementation on the activation ratio of CD8/IFNR T cells. H: Statistical analysis of the regulation of the activation ratio of CD8/GL7 T cells by BCAA supplementation. I: Flow scatter plot of the regulation of BCAA supplementation on the activation ratio of CD8/GL7 T cells. J: Fluorescence colocalization analysis of CD8 (green) and GZMB (red) in infiltrating cells in tumor tissues.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/c34efc30a9d9b1bf14c54b1b.jpg"},{"id":109406128,"identity":"18e70ba8-08a1-43db-817a-f4be8c5b9d90","added_by":"auto","created_at":"2026-05-17 13:25:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":689886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHIGH BCAA inhibits antigen presentation by macrophages and neutrophils.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: TSNE clustering of myeloid cell subsets yielded 19 subsets. B: TSNE of the identified cell populations in BCAA-LOW, BCAA-Med and BCAA-HIGH PCa groups. C: The bar chart shows the abundance changes of different sub-populations in different BCAA levels. D: Differences in the quantity and intensity of cell communication in different levers of BCAA. E: The bar chart statistically shows the differences in the quantity and intensity of cell communication among BCAA-LOW and BCAA-HIGH PCa groups. F: Comparison of outgoing and incoming signaling in cell communication between BCAA-HIGH and BCAA-LOW. G-H: Comparison of key genes in the cell communication axis of MHC-II and MHC-I. I-K: Comparison of the intensity in the MHC-II and MHC-I cell communication axes between the BCAA-LOW and BCAA-HIGH groups. L: Statistical analysis of the regulation of the activation ratio of H2Kb/I-Ab/I-A/E macrophage cells by BCAA supplementation. M: Statistical analysis of the regulation of the activation ratio of H2Kb/I-Ab/I-A/E neutrophil cells by BCAA supplementation.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/12f9537f35d678720712bdf3.jpg"},{"id":109405950,"identity":"faa62766-c0c5-4e68-967e-d25399687baa","added_by":"auto","created_at":"2026-05-17 13:22:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":477089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh expression of ACAT1 promotes the malignant proliferation of prostate cancer and suppresses T cell function.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: High expression of ACAT1 promotes the progression of subcutaneous prostate cancer. B: High expression of ACAT1 promotes the clonogenic ability of prostate cancer cells. C: Knockdown of ACAT1 inhibits the clonogenic ability of prostate cancer cells. D: Supplementation with different concentrations of BCAA promotes the clonogenic ability of prostate cancer cells. E: BCAA supplementation promotes the clonogenic ability of prostate cancer cells, and knockdown of ACAT1 attenuates the effect of BCAA supplementation. F：Fluorescence colocalization analysis of CD8 (green) and GZMB (red) in infiltrating cells in tumor tissues.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/dad8042b0cf12f1e23a88473.jpg"},{"id":109405933,"identity":"6e39e32d-47ad-4db5-8753-b180aa764d3b","added_by":"auto","created_at":"2026-05-17 13:22:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":699367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eACAT1 mediates the increase of citrate synthesis by mediating CS acetylation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Construction of the protein-protein interaction network analysis of ACAT1. B: Prediction of the acetylation sites of citrate synthase (CS). C: Diagram of the metabolic-PTM dual-function hypothesis of ACAT1. D: Effects of different concentrations of BCAA on the protein level of ACAT1 in prostate cancer cells. E: Effects of different concentrations of BCAA on the acetyl-CoA content in prostate cancer cells. F: Effects of different concentrations of BCAA on the acetylation modification of CS in prostate cancer cells. G: Effects of different concentrations of BCAA on the citrate content in prostate cancer cells. H: Effects of different concentrations of BCAA on the activity of citrate synthase in prostate cancer cells. I: Effects of different concentrations of acetyl-CoA on the citrate content in prostate cancer cells. J: Effects of different concentrations of acetyl-CoA on the activity of citrate synthase in prostate cancer cells. K: Effects of high expression of ACAT1 and BCAA on the acetylation modification of CS in prostate cancer cells. L: Effects of high expression of ACAT1 and BCAA on the citrate content in prostate cancer cells. M: Effects of high expression of ACAT1 and BCAA on the activity of citrate synthase in prostate cancer cells. N: Effects of high expression of ACAT1 and mutation of CS acetylation sites on the citrate content in prostate cancer cells. OEffects of high expression of ACAT1 and mutation of CS acetylation sites on the activity of citrate synthase in prostate cancer cells. P: The diagram illustrates the role of BCAA-ACAT1-CS in prostate cancer cells. High expression of ACAT1 can regulate BCAA metabolism and promote the citrate metabolism axis through increasing the acetylation of citrate synthase, inhibiting T-cell activation and forming an immunosuppressive microenvironment.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/5e81a50e981ea2931785958d.png"},{"id":109497383,"identity":"e3530e2c-5e16-4912-a368-f172887fcfa4","added_by":"auto","created_at":"2026-05-18 20:24:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4716497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/e775ee6d-0540-4389-93cc-6be6b08da304.pdf"},{"id":109405932,"identity":"7fdc4949-bc23-4b58-b70b-2cc7717811f0","added_by":"auto","created_at":"2026-05-17 13:22:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2822130,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/fe613952c5de8fd01e512fc1.docx"},{"id":109405931,"identity":"0b05dc7e-f5ac-4ec5-9f0e-ccad2fb7e624","added_by":"auto","created_at":"2026-05-17 13:22:34","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":327530903,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile2.pptx","url":"https://assets-eu.researchsquare.com/files/rs-9592453/v1/204d2a32430aee8373e6be81.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-Cell Dissection of BCAA Metabolism Unveils ACAT1-Dependent CS Acetylation as a Metabolic Checkpoint for Immunosuppression in Prostate Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) is one of the most common malignancies among men globally and accounts for a significant proportion of cancer-related deaths worldwide. Over the past five years, there has been a significant shift in the treatment landscape, leading to better patient outcomes. Systemic treatment is crucial for advanced and metastatic, cases and currently, there are multiple treatment options available, including androgen deprivation therapy,Novel hormonal therapy,chemotherapy, and emerging targeted drugs. Further research is needed to determine the most effective treatment sequences and combinations for prostate cancer(1).\u003c/p\u003e\n\u003cp\u003eThe tumor microenvironment (TME) is composed of tumor cells and other components, which can interact with each other and jointly affect the occurrence and development of tumors(2). It is a key factor in promoting the drug resistance of multiple malignant tumors to immunotherapy(3). Prostate cancer (PCa) tissue contains multiple subsets of T lymphocytes, and each subset has different roles and functions in the PCa microenvironment. Helper T (Th) cells mainly include two types, Th1 and Th2. An increase in Th1 cells in the microenvironment is closely associated with a good prognosis. Th2 cells drive tumor progression, treatment resistance, and castration resistance by producing transforming growth factor-\u0026beta; (TGF-\u0026beta;) or prostaglandin E2 (PGE2), leading to a poor prognosis for PCa patients(4). Regulatory T (Treg) cells in the PCa microenvironment suppress the inflammatory response and control autoimmunity, thereby promoting tumor progression. It has been reported that Treg cells secrete IL-2 to regulate the homeostasis and function of natural killer (NK) cells(5, 6). Macrophages have also been well studied in the occurrence and development of cancer due to their roles in antigen presentation and the regulation of inflammation through polarization(7). Patients with advanced metastatic castration-resistant prostate cancer (mCRPC) do not respond to immune checkpoint inhibitors (ICIs), partly because of the presence of immunosuppressive myeloid cells in the tumor(8, 9). For example, SPP1hi-TAMs play a key role in suppressing anti-tumor activity and may serve as a biomarker for predicting treatment efficacy by activating adenosine signaling in prostate cancer(10).\u003c/p\u003e\n\u003cp\u003eEpigenetic regulation is a complex process, including factors that post - translationally modify DNA or histones, which determines whether a certain gene is turned on or off(11, 12). Lysine acetylation is a process of transferring an acetyl group from acetyl-CoA to a lysine residue catalyzed by acetyltransferases. This is the earliest discovered and most extensively studied protein acylation process. Lysine acetyltransferases (KATs) and lysine deacetylases (KDAC) are responsible for catalysis respectively(13). Acetyl-CoA is a key metabolic intermediate and also a precursor of many biological macromolecules. As a metabolite of sugars, lipids, and amino acids, acetyl-CoA has a wide range of functions and participates in protein acetylation. For example, cancer stem cells (CSCs) promote the transfer of the metabolite acetyl-CoA to interacting T cells through an exosome-dependent pathway. The acetylation of B lymphocyte-induced maturation protein 1 (Blimp-1), a key transcription factor controlling the differentiation of CD103 T cells, is a key factor in impairing CD103 T cells(14). The acetyltransferase KAT2B can promote the acetylation of ACSL4 at lysine residues K500, K571, and K692. This post-translational modification acts as a molecular switch, significantly enhancing the affinity between ACSL4 and the CMA-recognizing chaperone protein HSPA8, thereby promoting the efficient targeting and degradation of ACSL4 through the CMA pathway(15).\u003c/p\u003e\n\u003cp\u003eIn recent years, research in metabolomics and tumor biology has revealed the crucial role of amino acid metabolism in cancer development, progression, and treatment response(16). Amino acids are not only the building blocks of proteins but also serve as key precursors for energy metabolism and biosynthetic pathways. Dysregulated amino acid metabolism is closely associated tumor with cell proliferation, invasion, metastasis, and immune escape(17). Branched-chain amino acids (BCAAs)-leucine, isoleucine, and valine-are essential and cannot be newly synthesized in humans. In addition to serving as essential substrates for protein synthesis and muscle maintenance, they promote nitrogen balance and metabolic homeostasis by supporting anabolic signaling and attenuating proteolysis(18). Specifically, leucine catabolism generates acetyl-CoA and acetoacetate, valine yields propionyl-CoA, and isoleucine produces both acetyl-CoA and propionyl-CoA. Propionyl-CoA is subsequently converted to succinyl-CoA(19). These intermediates are ultimately converted into acetyl-CoA or succinyl-CoA, which enter the tricarboxylic acid (TCA) cycle to support adenosine triphosphate (ATP) production(20). BCAA-derived acetyl-CoA promotes histone acetylation and affects gene expression. For example, leucine-derived acetyl-CoA promotes tumor growth, while lysine-derived acetyl-CoA supports the self-renewal of colorectal cancer cells(21). Strategies targeting branched-chain amino acid transaminases (BCATs), including enzyme inhibitors, dietary BCAA restriction, and combination therapies, have been shown to have the potential to overcome drug resistance and improve treatment outcomes(22).\u003c/p\u003e\n\u003cp\u003eIn this study, the BCAA metabolic activity of prostate cancer samples was estimated through single-cell sequencing technology, and the samples were grouped. The differences in the of the composition tumor immune microenvironment among different BCAA metabolic levels (LOW/Med/HIGH) were compared. It was found that BCAA-HIGH inhibited the infiltration, cytotoxicity, and proliferation of CD8 T cells, which impaired the anti-tumor immune response of T cells. In the analysis of genes related to branched-chain amino acid metabolism, we identified that ACAT1 not only had a relatively high hazard ratio (HR) but also showed an expression pattern significantly associated with poor prognosis, and was significantly expressed in ERG+ cancer cells and LE-type cells. Mechanistically, this study identified that ACAT1 responded to BCAA, which not only promoted the malignant proliferation of prostate cancer cells but also promoted the acetylation modification of citrate synthase, increased the activity of citrate synthase and the production of citrate, and promoted the formation of an immunosuppressive microenvironment, further leading to the malignant progression of prostate cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eDatabases and software\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression raw matrix of single-cell RNA sequencing (scRNA-seq) of localized prostate cancer biopsy samples was obtained from the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/), with the accession number GSE274229. Level 3 data of prostate cancer patients were downloaded from The Cancer Genome Atlas (TCGA, https://tcga-data.nci.nih.gov/tcga/). The dataset contains relevant clinical data and survival information. The Limma R and ClusterProfiler software packages were used for differential analysis and functional enrichment of the data.\u003cem\u003e\u0026nbsp;\u003c/em\u003eP \u0026lt; 0.05 was considered statistically significant. The University of Alabama at the Birmingham Cancer Data Analysis Portal (UALCAN, http://ualcan.path.uab.edu) was used for transcriptome, proteome, and clinical correlation analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture,\u003c/strong\u003e \u003cstrong\u003eLentiviral vectors and transduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cell lines used in this study include human prostate cancer cell lines (DU145 and LNCAP), mouse prostate cancer cell line RM-1, and 293T (Wuhan, China) cell line. All these cell lines were tested and verified to ensure the absence of mycoplasma contamination. The cells were cultured in DMEM/1640 medium (Gibco, New York, USA) containing 10% fetal bovine serum (FBS, Invitrogen) and placed in a humidified incubator at 37℃\u0026nbsp;with 5% CO\u003csub\u003e2\u003c/sub\u003e. When the of density 293T cells approached 70%, the packaging operation of equine lentivirus was carried out. The corresponding overexpression plasmids and packaging vectors were co-transfected into 293T cells using Lipofectamine 2000 transfection reagent (Invitrogen, 11668-019) to produce lentiviral particles. The virus supernatant was collected 48 hours after transfection and used to infect target cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse subcutaneous tumor model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour-week-old male C57BL/6 mice were selected, and 3*10^6 RM-1 cells were injected subcutaneously.\u0026nbsp;After 21 days, tumors were collected, and single-cell suspensions were prepared for subsequent experiments. The cell suspensions were stained and labeled, and immune cell infiltration, T cell activation and exhaustion, macrophage antigen presentation, as well as neutrophil antigen presentation and NETs, were measured using flow cytometry. After 21 days, collect the tumors and prepare single-cell suspensions or fix them with 4% paraformaldehyde for subsequent experiments. Perform flow staining and labeling on the cell suspensions, and use flow cytometry to detect immune cell infiltration, T cell activation and exhaustion, macrophage and neutrophil antigen presentation. For the animal study, all the experiments were performed in accordance with the guidelines of the ethics committee of the Third Affiliated Hospital of Sun Yat-sen University. The mice were housed at Guangzhou LingFu TopBiotech Co, Ltd. at a temperature of 22\u0026ndash;26℃ and humidity of 40\u0026ndash;60% under a 12-hour light/12-hour dark cycle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntibodies and chemicals.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibodies(chemicals)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdentifier\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eFITC-conjugated anti-mouse CD8a antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e100706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePE-conjugated anti-mouse CD69 antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e104507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePE-conjugated anti-mouse GZMB antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e372207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePE-conjugated anti-mouse IFNR antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e505807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePE anti-mouse/human GL7 Antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e144607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePerCP/Cy5.5-conjugated anti-mouse CD45 antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e157207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eAPC-conjugated anti-mouse CD45 antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e103111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePerCP/Cy5.5-conjugated anti-mouse F4/80 antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e157317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eFITC anti-mouse CD11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e101205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePE anti-mouse I-A/I-E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e107607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePE anti-mouse H2Kb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e116507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePE anti-mouse I-Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e116407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eLipofectamine 2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eInvitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eL3000015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eCD8a Monoclonal antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eProteintech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e66868-1-Ig\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eAnti-Granzyme B antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eAbcam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eab255598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eDAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBioqiandu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eB0011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eAntifade Mounting Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBioqiandu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eB0007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNormal Goat Serum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBoster Bio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eAR1009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eL-leucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eL-Isoleucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eL-valine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e94619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eACAT1 Polyclonal antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eProteintech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e16215-1-AP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eCitrate synthase Polyclonal antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eProteintech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e16131-1-AP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eHRP-conjugated GAPDH Monoclonal antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eProteintech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eHRP-60004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePan Acetylation Monoclonal antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eProteintech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e66289-1-Ig\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of acetyl-coenzyme A content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMalate dehydrogenase can catalyze the reaction between malate and NAD⁺ to produce oxaloacetate and NADH. Citrate synthase can catalyze the reaction between acetyl-CoA and oxaloacetate to produce citrate and CoA. Using the coupling reaction of these two enzymes, the content of acetyl-CoA is proportional to the formation rate of NADH, and the increase rate of the absorbance at 340 nm can reflect the level of acetyl-CoA content. After the cells are properly cultured in the cell culture flask, collect the cells. According to the operation steps provided by the reagent supplier, add 0.99 mL of buffer1 and 0.01 mL of buffer2. Disrupt the cells by ultrasonication in an ice bath, centrifuge at 8000 g for 10 minutes, and take the supernatant to measure the absorbance at 340 nm. Calculate the content of acetyl-CoA according to the operation steps of the kit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of citric acid content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCitric acid can reduce Cr⁶⁺ to Cr\u0026sup3;⁺ under acidic conditions and form a chromium citrate chelate. The product has a characteristic absorption peak at 545 nm. The content of citric acid can be quantitatively detected through the change in absorbance. After the cells are properly cultured in a cell culture flask, the cells are collected by centrifugation. According to the operation steps of the reagent manufacturer, 1 mL of buffer 1 is added to treat the samples. The cells are disrupted by ultrasonic treatment in an ice-bath. Centrifuge at 11,000 g for 10 min at 4℃. Take the supernatant and place it on ice to measure the absorbance at 545 nm. Establish a standard curve according to the operation steps of the kit and calculate the content of citric acid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of citrate synthase activity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCitrate synthase catalyzes the reaction between acetyl-CoA and oxaloacetate to form citryl-CoA, which is further hydrolyzed to produce citric acid. During this process, DTNB is converted to yellow TNB. The product has a characteristic absorption peak at 412 nm, and the activity of citrate synthase can be characterized by the change in absorbance. After the cells are appropriately cultured in cell culture flasks, the cells are collected by centrifugation. According to the operation steps provided by the reagent supplier, 1 mL of extraction solution and 10\u0026micro;L of Reagent 2 are added. Use a homogenizer or mortar to grind the cells in an ice-bath until a homogeneous slurry is obtained. Centrifuge the homogenate at 600 g for 5 min at 4℃, discard the precipitate, and collect the supernatant. Then centrifuge the supernatant at 12000 g for 10 min at 4℃. Add 200\u0026micro;L of Reagent 1 and 2\u0026micro;L of Reagent 2 to the precipitate, and repeatedly pipette to mix thoroughly. This is the crude enzyme solution, which is used for the determination of citrate synthase activity. Detect the absorbance at 412 nm according to the operation steps of the kit, and calculate the citrate content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blotting and Co-immunoprecipitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were harvested with lysis buffer containing 250 mM NaCl, 50 mM Tris (pH 7.4), 1 mM Na\u003csub\u003e3\u003c/sub\u003eVO\u003csub\u003e4\u003c/sub\u003e, 5 mM EDTA, 1% Nonidet\u0026trade;\u0026nbsp;P40 (NP40), 50 mM NaF, 0.02% NaN\u003csub\u003e3\u003c/sub\u003e, 2 \u0026micro;g/ml protease cocktail inhibitor and 1mM PMSF by dropwise addition to the plates and kept on ice for few minutes. After sample preparation, mix the samples with protein loading buffer and heat them to 100\u0026deg;C in a water bath for 10 minutes to denature the proteins. Then, use Vazyme\u0026apos;s rapid PAGE gel kit to separate the samples by SDS-PAGE electrophoresis, and subsequently transfer them to a PVDF membrane in an ice bath. After the membrane transfer is completed, block the PVDF membrane with 5% BSA at room temperature for one hour. Then add the primary antibody and gently shake it at 4℃\u0026nbsp;overnight. The next day, wash the membrane 3 times with 1\u0026times;TBST (Tris-buffered saline, 0.1% Tween-20) on a platform shaker, 10 minutes each time. Then incubate the membrane with a secondary antibody conjugated to horseradish peroxidase (HRP) at room temperature for one hour. After that, wash the membrane 3 more times with 1\u0026times;TBST, 10 minutes each time. Subsequently, use a high-sensitivity ECL chemiluminescence detection kit to visualize the protein bands through a gel imaging system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor tissue immunofluorescence detection, according to the operating protocol of the Seville technique, dewax the sections, and then perform antigen retrieval using citrate buffer (pH 6.0). Subsequently, block the sections with 10% goat serum at room temperature for 1 hour. Next, perform primary and secondary antibody staining, and then counterstain the cell nuclei with DAPI. Finally, mount the sections using an anti-fade mounting medium. Conduct imaging analysis using a laser scanning confocal fluorescence microscope.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluate the BCAA-score by AUcell\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAUcell (AUCell) is an R package specifically designed for calculating Gene Set Enrichment Scores. It evaluates the activity level of specific gene sets in each cell based on the AUC (Area Under the Curve) method. Load single-cell data as an expression matrix, create a gene set containing genes related to BCAA metabolism, with the genes sourced from the GSEA database. Run the AUcell algorithm to calculate scores, integrate the scores into the single-cell object, and conduct further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR language and GraphPad Prism 9.0 were used to collect, summarize, statistically analyze, and graph the data. Experiments were performed at least in triplicate and error bars were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Student\u0026rsquo;s test was used to evaluate statistical significance, defined as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. A P value less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe stratification of branched-chain amino acids shows an imbalance between humoral immunity (CD8 T cells) and innate immunity (\u0026gamma;\u0026delta; T cells).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the branched-chain amino acid (BCAA) metabolic profile in prostate cancer (PCa), we analyzed 13 primary tumors (localized) using single-cell RNA sequencing (scRNA-seq)(10, 23). A total of 28 cell subsets were identified by t-distributed stochastic neighbor embedding (tSNE) visualization \u003cstrong\u003e(Figure 1A)\u003c/strong\u003e. By annotating with consistent cell-specific markers and stratifying BCAA metabolism using AUCell, we obtained three BCAA subgroups: BCAA-HIGH, BCAA-Med, and BCAA-LOW \u003cstrong\u003e(Figure 1B, C; Supplementary Figure 1A)\u003c/strong\u003e. Moreover, we found that with the increase of BCAA stratification, the proportion of humoral immunity-related CD8+ T cells decreased, while that of innate immunity-related \u0026gamma;\u0026delta; T cells increased, suggesting that BCAA metabolism may mediate the imbalance between humoral and innate immunity \u003cstrong\u003e(Figure 1D)\u003c/strong\u003e. Cell\u0026ndash;cell communication analysis showed that the high-BCAA subgroup exhibited reduced intercellular communication, including \u0026gamma;\u0026delta;\u0026ndash;CD8 communication \u003cstrong\u003e(Figure 1E\u0026ndash;H)\u003c/strong\u003e. Furthermore, we found that the high-BCAA subgroup had stronger communication axes related to innate immunity and cancer phenotypes, such as SPP1, TNF, WNT, VEGF, KIT, and TGF-\u0026beta;, but weaker immune-related chemokine interaction axes, such as CXCL and CCL \u003cstrong\u003e(Figure 1I)\u003c/strong\u003e. In addition, we observed the presence of antigen-presenting MHC I and II \u003cstrong\u003e(Figure 1J\u0026ndash;K)\u003c/strong\u003e. Here, the key functions of CD8+ T cells, including cytotoxicity and proliferation, were significantly reduced in the high-BCAA subgroup \u003cstrong\u003e(Figure 1L)\u003c/strong\u003e. Moreover, we observed high expression of multiple BCAA metabolic genes in ILC3, \u0026gamma;\u0026delta; T cells, and epithelial cancer cells\u003cstrong\u003e\u0026nbsp;(Supplementary Figure 1B)\u003c/strong\u003e. Additionally, multiple cancer progression-related pathways were upregulated in the BCAA-HIGH subgroup, while the corresponding immune-related regulatory pathways were suppressed \u003cstrong\u003e(Supplementary Figure 1C)\u003c/strong\u003e. To determine the potential reasons for these differences among different BCAA subgroups \u003cstrong\u003e(Figure 1M)\u003c/strong\u003e, univariate and multivariate analyses identified ACAT1 as a key prognostic factor \u003cstrong\u003e(Figure 1N\u0026ndash;O)\u003c/strong\u003e. Further analysis of ACAT1 expression differences among different molecular signatures and patient Gleason scores showed an increasing trend \u003cstrong\u003e(Figure 1P\u0026ndash;Q)\u003c/strong\u003e. We further analyzed the heterogeneity of ACAT1 expression in different cancer cell subsets \u003cstrong\u003e(Figure 1S)\u003c/strong\u003e. Expression profiling indicated that ACAT1 was highly expressed in ERG+ cancer cells and luminal epithelial (LE)-type cancer cells \u003cstrong\u003e(Figure 1T)\u003c/strong\u003e. Cell proportion analysis showed a significant increase in the proportion of ERG+ cancer cells in samples with active BCAA metabolism \u003cstrong\u003e(Figure 1U)\u003c/strong\u003e. In summary, by analyzing cell proportions and cell\u0026ndash;cell communication in different BCAA subgroups, we observed an imbalance between humoral and innate immunity caused by BCAA metabolic imbalance, with CD8+ T cells and \u0026gamma;\u0026delta; T cells being the most representative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHighly stratified BCAA mediates T cell dysfunction and promotes tumor growth in mice by altering cell-cell interactions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further analyze the mechanism by which BCAA affects the imbalance between humoral immunity and innate immunity, we isolated CD4, CD8,\u0026nbsp;\u0026gamma;\u0026delta;T, and ILC3 cells, performed tSNE clustering again, and annotated the clusters based on existing literature reports and marker genes of each cell subset \u003cstrong\u003e(Figure 2A-B, Supplementary Figure 2A)\u003c/strong\u003e. We identified subsets of CD8 T cells (including CD8-SLC7A5, CD8-CD69, CD8-GZMA, CD8-GZMK, CD8-IFNR, CD8-GZMB) and\u0026nbsp;\u0026gamma;\u0026delta;\u0026nbsp;T cell subsets (including\u0026nbsp;\u0026gamma;\u0026delta;-CXCL8,\u0026nbsp;\u0026gamma;\u0026delta;-FKBP1B,\u0026nbsp;\u0026gamma;\u0026delta;-FASN,\u0026nbsp;\u0026gamma;\u0026delta;-IGFBP2,\u0026nbsp;\u0026gamma;\u0026delta;-ADIRF,\u0026nbsp;\u0026gamma;\u0026delta;-APOD,\u0026nbsp;\u0026gamma;\u0026delta;-HAVCR2) \u003cstrong\u003e(Figure 2C, Supplementary Figure 2B)\u003c/strong\u003e. The results showed that as the BCAA stratification increased, the CD4 and CD8 T cells related to humoral immunity gradually decreased, while the\u0026nbsp;\u0026gamma;\u0026delta;\u0026nbsp;T cells related to innate immunity gradually increased, indicating an imbalance between humoral immunity and innate immunity \u003cstrong\u003e(Figure 2D-F)\u003c/strong\u003e. Subset analysis showed that the proportion of activated T cells, such as CD69⁺ T cells, gradually decreased, while the proportion of CXCL8⁺\u0026gamma;\u0026delta; T, PFKBP1B⁺ \u0026gamma;\u0026delta; T, and FASN⁺ \u0026gamma;\u0026delta; T cells increased \u003cstrong\u003e(Figure 2D-F, Supplementary Figure 2A)\u003c/strong\u003e. Cell-communication analysis revealed that with the increase in BCAA stratification, the communication of T cells related to humoral immunity was weakened, which often suggested the possibility of immune impairment \u003cstrong\u003e(Figure 2G-H)\u003c/strong\u003e. Further analysis of input and output signals showed that high BCAA stratification was associated with enhanced output signals of\u0026nbsp;\u0026gamma;\u0026delta;\u0026nbsp;T cells and weakened output signals of CD8 cells. Similar conclusions were drawn from the analysis of input signals \u003cstrong\u003e(Figure 2I-J)\u003c/strong\u003e. Next, by comparing the overall information flow of each signaling pathway, the analysis showed that high BCAA stratification was associated with higher information flow related to angiogenesis (ANGPTL and VEGF) and multiple cancer-progression-related information axes (WNT, PDGF, EGF, and KIT) \u003cstrong\u003e(Figure 2K)\u003c/strong\u003e. In addition, the increase in BCAA stratification was associated with weakened expression of molecules related to the LCK, TNF, ITGB2, and CD99 signaling axes \u003cstrong\u003e(Figure 2L-P)\u003c/strong\u003e. Functional enrichment analysis showed that the increase in BCAA stratification was associated with weakened T-cell proliferation, migration, chemotaxis, immune regulation, and NK T-cell function \u003cstrong\u003e(Figure 2Q, Supplementary Figure 2C)\u003c/strong\u003e. Analysis of the expression characteristics of ACAT1 showed that ACAT1 was highly expressed mainly in FKBP1B+ \u0026gamma;\u0026delta;T and APOD+ \u0026gamma;\u0026delta;T cells \u003cstrong\u003e(Figure 2R)\u003c/strong\u003e. MIF plays an important role in signal transduction such as inflammation and cell proliferation, participates in the occurrence of various diseases and cancers, and is an important biomarker and drug target. Cell-communication analysis of FKBP1B+ \u0026gamma;\u0026delta;T and APOD+ \u0026gamma;\u0026delta;T cells showed that the MIF cell-communication axis exhibited prominent characteristics \u003cstrong\u003e(Figure 2S-T)\u003c/strong\u003e. These results suggest that the mechanism of the imbalance between humoral immunity and innate immunity may involve the regulation of cell migration and inflammatory regulatory responses by the MIF cell-communication axis.\u003c/p\u003e\n\u003cp\u003eTo verify whether the elevated BCAA metabolism affects tumor growth in vivo, we established a subcutaneous tumor model in mice. By providing additional dietary BCAA, we constructed a high-BCAA-metabolism model \u003cstrong\u003e(Figure 3A)\u003c/strong\u003e. Tumor measurements showed that the tumor proliferation rate was faster significantly and the tumor volume was significantly larger in the high-BCAA diet group \u003cstrong\u003e(Figure 3A)\u003c/strong\u003e. Flow cytometry analysis of cells extracted from subcutaneous tumor tissues showed that the proportion of CD45+ CD8⁺+ T cells infiltrating the tumors was significantly lower in the high-BCAA diet group \u003cstrong\u003e(Figure 3B-I)\u003c/strong\u003e. Moreover, the expression levels of markers of T-cell activation, such as CD69, GZNB, IFNR, and GL7, were significantly lower in the high-BCAA diet group, indicating that high-BCAA diet metabolism inhibited the anti-tumor immune function of T cells \u003cstrong\u003e(Figure 3B-I)\u003c/strong\u003e. Multiplex immunofluorescence staining also showed that the high-BCAA diet group exhibited lower infiltration of CD8 T cells and activation of GZMB \u003cstrong\u003e(Figure 3J)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElevated BCAA metabolism promotes tumor growth in mice by inhibiting antigen presentation of macrophages and neutrophils.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze the effects of BCAA stratification on other components in the tumor microenvironment, we performed further subgroup analysis of myeloid cells \u003cstrong\u003e(Figure 4A-B)\u003c/strong\u003e. Through the annotation of marker genes of specific cell populations and combined with cell proportion analysis, we found that the high-BCAA-stratified group had a higher proportion of APOE+ macrophages, G0S2+ macrophages and a lower proportion of IL1B+ macrophages \u003cstrong\u003e(Figure 4C)\u003c/strong\u003e. By comparing the cell numbers, we found that the number of G0S2+ macrophages/neutrophils increased with the increase of BCAA stratification, which indicated that the extracellular trap network of macrophages/neutrophils might be activated in response to BCAA. Cell communication analysis showed that the high-BCAA-stratified group exhibited weaker interactions among PPIA+ macrophages, G0S2+ macrophages, IL1B+ macrophages and G0S2+ neutrophils \u003cstrong\u003e(Figure 4D-E)\u003c/strong\u003e. By comparing the input and output signal flows, we found that the cell-communication axes related to antigen presentation of macrophages and neutrophils dominated in almost all cell types \u003cstrong\u003e(Figure 4F)\u003c/strong\u003e. Further analysis of the expression characteristics of molecules in the MHC-I and MHC-II communication axes revealed that the BCAA-HIGH group showed significantly lower expression of HLA molecules in several cell subgroups such as G0S2+ macrophages, PPIA+ macrophages and G0S2+ neutrophils \u003cstrong\u003e(Figure 4G-H)\u003c/strong\u003e. To analyze whether such phenotypes are also applicable to cell communication, we conducted cell-communication analysis, and the BCAA-HIGH group showed weaker MHC-I and MHC-II communication \u003cstrong\u003e(Figure 4I)\u003c/strong\u003e. Further analysis of specific interaction axes visualized all the communication axes that showed weaker interactions in the BCAA-HIGH group. These results suggested that elevated BCAA metabolism led to the inhibition of the antigen-presentation function of myeloid cells.\u003c/p\u003e\n\u003cp\u003eTo verify whether elevated BCAA metabolism affects the anti-tumor effect of myeloid cells in the in-vivo tumor microenvironment, we established a subcutaneous tumor model in mice. By dietary supplementation of BCAA, we constructed a high-BCAA-metabolism model. Based on the previous results, we tested the antigen-presentation function of myeloid cells. After dietary BCAA supplementation, we found that BCAA supplementation decreased the expression of antigen-presentation molecules H2KB, I-A/B and I-A/E in macrophages (F4/80+), indicating that the antigen-presentation function of macrophages was impaired \u003cstrong\u003e(Figure 4L, Supplementary Figure 3A-C)\u003c/strong\u003e. Similarly, there have been more and more studies on the antigen-presentation function of neutrophils in recent years. Through detection, we found that BCAA supplementation decreased the expression of antigen-presentation molecules H2KB, I-A/B and I-A/E in neutrophils (LY6G+), indicating that the antigen-presentation function of neutrophils was impaired \u003cstrong\u003e(Figure 4M, Supplementary Figure-3D F)\u003c/strong\u003e. The above results indicated that elevated BCAA metabolism inhibited anti-tumor immunity by suppressing the antigen-presentation function of myeloid cells and activated the extracellular trap network of neutrophils to inhibit T-cell infiltration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh expression of the key BCAA metabolic enzyme ACAT1 promotes the clonal formation of prostate cancer cells, inhibits T-cell activation, and promotes tumor growth in mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn previous studies, through the analysis of cancer cell subsets, we found that ACAT1 was significantly highly expressed in ERG + cancer cells, and ERG + cancer cells were significantly aggregated in the BCAA-HIGH group. As a transcription factor, the high expression of ERG in prostate cancer cells is usually driven by gene fusion events, the most common of which is the TMPRSS2-ERG gene fusion. This is one of the most common genetic alterations in prostate cancer. The abnormal activation of the ERG gene drives a series of downstream signaling pathways, promotes the proliferation of tumor cells, inhibits apoptosis, and may enhance their invasion and metastasis abilities.\u003c/p\u003e\n\u003cp\u003eWe over-expressed ACAT1 in murine prostate cancer cells. Through subcutaneous tumorigenesis in C57 mice, we observed that the high expression of ACAT1 significantly promoted tumor progression \u003cstrong\u003e(Figure 5A)\u003c/strong\u003e. Through clone formation experiments, we over-expressed and knocked down ACAT1 in murine and human prostate cancer cell lines. The experimental results showed that the high expression of ACAT1 significantly increased the clonal formation ability of prostate cancer cells, while the knockdown of ACAT1 significantly reduced this ability \u003cstrong\u003e(Figure 5B-C)\u003c/strong\u003e. In addition, by adding BCAA to the culture medium, the experiment showed that the addition of different concentrations of BCAA increased the clonal formation ability of prostate cancer cells \u003cstrong\u003e(Figure 5D)\u003c/strong\u003e. Further experiments found that knocking down ACAT1 under the condition that BCAA promotes the clonal formation ability of prostate cancer cells reduced the malignant proliferation mediated by high levels of BCAA \u003cstrong\u003e(Figure 5E)\u003c/strong\u003e. The above experiments suggest that the ability of BCAA to promote the clonal formation of prostate cancer cells may depend on the expression of ACAT1. Immunofluorescence detection also showed that tumors with high ACAT1 expression had lower infiltration of CD8 T cells and activation of GZMB \u003cstrong\u003e(Figure 5F)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACAT1 mediates an increase in citrate synthesis by mediating the acetylation of CS, thereby inhibiting anti-tumor immunity.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eACAT1 is a metabolic enzyme that participates in the reversible conversion of acetoacetyl-CoA into two molecules of acetyl-CoA(24). In recent years, many studies have also been conducted on the non-metabolic regulatory role of ACAT1. Some studies have pointed out that in colorectal cancer (CRC), nuclear ACAT1 directly acetylates lysine 146 of p50 (NFKB1), weakening its DNA-binding and transcriptional repression activities, thereby increasing the expression of immune-related factors, and further promoting the recruitment and activation of NK cells to inhibit the growth of CRC(24). This means that in addition to acting as a metabolic enzyme, ACAT1 may also act as an acetylation regulatory factor to regulate the acetylation modification of target proteins.\u003c/p\u003e\n\u003cp\u003eThrough the STRING database, we analyzed the protein-interaction network in which ACAT1 participates and identified several key targets \u003cstrong\u003e(Figure 6A)\u003c/strong\u003e. Using the GPS-PAIL software, we analyzed the possible potential acetylation of the targets. We identified citrate synthase CS, and K459 is the main acetylation site of CS \u003cstrong\u003e(Figure 6B)\u003c/strong\u003e. To analyze the dual-sided regulatory role (metabolism and PTM) of ACAT1 in prostate cancer \u003cstrong\u003e(Figure 6C)\u003c/strong\u003e, first, we treated prostate cancer cell lines with different concentrations of BCAA. We detected that the protein level of ACAT1 increased in response to BCAA treatment, and the content of the BCAA metabolite acetyl-CoA also increased in a concentration-dependent manner \u003cstrong\u003e(Figure 6D-E)\u003c/strong\u003e. To study whether BCAA treatment affects the acetylation modification level of CS, after treating prostate cancer cell lines with different concentrations of BCAA, we detected the acetylation level of CS through immunoprecipitation and pan-acetylation modification antibodies. The experimental results showed that BCAA treatment significantly increased the acetylation modification of CS \u003cstrong\u003e(Figure 6F)\u003c/strong\u003e. Similarly, we also observed that after treating prostate cancer cell lines with different concentrations of BCAA, the cellular citrate content and citrate synthase activity increased significantly \u003cstrong\u003e(Figure 6G-H)\u003c/strong\u003e. This indicates that BCAA treatment promotes the acetylation modification of CS and the activity of citrate synthase, thereby promoting an increase in citrate production. Acetyl-CoA is often considered the source of acetylation. Therefore, by exogenously adding acetyl-CoA, the experiment detected that exogenously added acetyl-CoA had a similar effect to BCAA addition, and the cellular citrate content and citrate synthase activity increased significantly \u003cstrong\u003e(Figure 6I-J)\u003c/strong\u003e. Next, we overexpressed ACAT1 in prostate cancer cells. The experimental results showed that the overexpression of ACAT1 promoted the acetylation modification of CS, while its background protein level did not change significantly. The simultaneous treatment with BCAA and ACAT1 overexpression further promoted the acetylation modification of CS \u003cstrong\u003e(Figure 6K)\u003c/strong\u003e. Correspondingly, we observed that the overexpression of ACAT1 also significantly increased the cellular citrate content and citrate synthase activity \u003cstrong\u003e(Figure 6L-M)\u003c/strong\u003e. In addition, we also observed that directly overexpressing CS or ACAT1 alone could increase the cellular citrate content and citrate synthase activity, and the effect was more obvious when both were overexpressed simultaneously. Among them, the mutation of the acetylation modification site of CS significantly weakened this effect. Therefore, the acetylation modification site of CS plays an important role in the regulation of its citrate synthase activity.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, by combining single-cell sequencing data with in vivo and in vitro experiments, it was systematically revealed that the active metabolism of branched-chain amino acids (BCAAs) affects the acetylation modification of citrate synthase (CS) through the dual-regulatory role of ACAT1. Our results show that the increased activity of BCAA metabolism leads to an imbalance between T-cell humoral immunity and innate immunity and has an inhibitory effect on the antigen presentation of myeloid cells. Among them, ACAT1 responds to the active BCAA metabolism, promotes the acetylation modification of citrate synthase, resulting in increased citrate synthase activity and thus increased citrate production, forming an immunosuppressive microenvironment \u003cstrong\u003e(Figure 6P)\u003c/strong\u003e. Our study expands the understanding of the role of BCAA metabolism in prostate cancer and further investigates the dual-regulatory role of ACAT1. These works deepen our understanding of metabolism and modification and provide new targets for the treatment of prostate cancer.\u003c/p\u003e\n\u003cp\u003eIn this study, by combining single-cell sequencing data with in vivo and in vitro experiments, we systematically revealed that the active metabolism of branched-chain amino acids (BCAAs) affects the acetylation modification of citrate synthase (CS) through the dual regulatory role of ACAT1. Our results showed that the increased active metabolism of BCAAs led to an imbalance between humoral and innate immunity in T cells and had an inhibitory effect on antigen presentation by myeloid cells. Specifically, ACAT1, in response to active BCAA metabolism, promotes the acetylation of citrate synthase, leading to an increase in its activity and thus the production of more citrate, creating an immunosuppressive microenvironment. Our study broadens the understanding of the role of BCAA metabolism in prostate cancer and further explores the dual regulatory role of ACAT1. These findings deepen our understanding of metabolism and modification and provide new targets for the treatment of prostate cancer.\u003c/p\u003e\n\u003cp\u003eAll along, mitochondrial acetyl-CoA acetyltransferase (ACAT1) is the earliest purified mitochondrial matrix thiolase. It participates in isoleucine degradation, ketone body production/decomposition, and fatty acid oxidation through catalyzing the reversible reaction of condensing two molecules of acetyl-CoA into acetoacetyl-CoA(24, 25). In recent years, continuous research has pointed out that ACAT1 regulates the acetylation modification of target proteins through its acetyltransferase activity(26, 27). In prostate cancer, a previous study on persistent organic pollutants and prostate cancer aggressiveness indicated that ACAT1 mediates the effect of dioxin (a persistent organic pollutant) on cell migration(28). In a prospective study on prostate cancer, a prognostic validation set including the ACAT1 gene was obtained for the PCa risk group(29). However, as a part of branched-chain amino acid metabolism, the response of ACAT1 to BCAA and its regulation in the formation of the immunosuppressive microenvironment of prostate cancer are still unknown.\u003c/p\u003e\n\u003cp\u003eBCAA, serving as fuel for cancer growth, undergoes changes in many solid tumors, including breast cancer, liver cancer, and pancreatic cancer(30). In breast cancer, the expression of the BCAA metabolic enzyme BCAT1 is elevated, and it promotes mitochondrial biogenesis in an mTOR-dependent manner to support cell proliferation(31). In pancreatic cancer, leucine supplementation promotes tumor growth in mice through diet-dependent effects(32). In liver cancer mouse and human models, the blood levels of BCAA are also elevated and are associated with overactivation of mTOR(33). A previous comprehensive proteomic analysis of high-risk prostate cancer samples identified three subtypes (S-I/II/III) through proteomic clustering. Among them, S-III has the highest degree of malignancy, with high expression of metabolism/proliferation-related proteins, which directly drive rapid tumor growth, invasion, and metastasis, and are often associated with treatment resistance and poor prognosis. Pathway enrichment analysis shows that the S-III subtype is enriched in proteins related to oxidative phosphorylation, valine-leucine-isoleucine degradation, and the tricarboxylic acid cycle(34). The S-I subtype has a low tumor burden, high stromal content, and slow proliferation rate, and has the best prognosis, also exhibiting features of immune activation, suggesting that the good prognosis of patients with the S-I subtype may be related to enhanced immune surveillance activity. However, current research on the role of abnormal BCAA metabolism in cancer cells in the establishment of the tumor immunosuppressive microenvironment is still in its infancy. In this study, by integrating single-cell sequencing data of prostate cancer, we classified prostate cancer patients into BCAA (LOW/Med/HIGH) groups using the AuCell software algorithm. The study shows that the BCAA-HIGH group exhibits a decrease in CD8 T cells and attenuation of T-cell cytotoxicity and proliferation. In the subgroup analysis of epithelial/cancer cells, we identified significantly high expression of ACAT1 in ERG+ tumors enriched in the BCAA-HIGH group, which is often associated with the proliferative, invasive, and metastatic abilities of cancer cells. Mechanistically, this study constructed a protein-interaction network of ACAT1 and demonstrated that ACAT1 responds to BCAA metabolism to promote acetylation modification of citrate synthase, mediating an up-regulation of citrate synthase activity and an increase in citrate production. Through this mechanism, not only does it provide fuel for cancer cells through the metabolic axis, but the increase in the citrate metabolic axis also promotes the formation of an immunosuppressive microenvironment.\u003c/p\u003e\n\u003cp\u003eAlthough this study used multiple methods to confirm the role of the BCAA-ACAT1-CS axis in the prostate cancer immune microenvironment, there are still some limitations in this study. First, the study initially relied on public databases (GEO and TCGA), which means that the sample size and patient population were relatively limited, and there was a lack of large-scale and multi-cohort studies to deepen its clinical translation value. In addition, although we constructed a subcutaneous tumor model using prostate cancer cells and conducted experiments, using genetically engineered mice could provide a more specific and accurate understanding of the regulatory mechanism of the BCAA-ACAT1-CS axis and its impact on tumor growth and immune regulation. In addition, the imbalance of CD8 T cells and \u0026gamma;\u0026delta; T cells in T cell subsets has not been fully studied in this study. The next plan should be to study the biological effects of BCAA-ACAT1 using genetically engineered mice as a model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn general, this study analyzed the composition of the tumor immune microenvironment at different levels of BCAA metabolism. It was analyzed and proposed that BCAA-HIGH led to a decrease in CD8 T cells, a weakening of cytotoxicity and proliferation, suggesting impaired anti-tumor functions of T cells. Through in vitro/in vivo experiments, the results of single-cell data analysis were verified. The dual functions (metabolism and PTM) of ACAT1 in prostate cancer were identified, and the molecular mechanism of ACAT1 regulating the acetylation of citrate synthase was analyzed, opening up new directions for exploring novel treatment strategies. This study provides new strategies for future metabolic/immunotherapy of prostate cancer, that is, by targeting ACAT1-CS acetylation to regulate citrate production and its metabolism, the immune response can be effectively initiated and activated. More experimental validations and clinical trials are still needed in the future to confirm the practical application value of these preliminary conclusions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePca:Prostate cance\u003c/p\u003e\n\u003cp\u003eBCAA:Branched-chain amino acid\u003c/p\u003e\n\u003cp\u003eTME:Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eACAT1:Acetyl-CoA acetyltransferase\u003c/p\u003e\n\u003cp\u003ePTM:Posttranslational modification\u003c/p\u003e\n\u003cp\u003eGD T cell:\u0026gamma;\u0026delta; T cell\u003c/p\u003e\n\u003cp\u003eHLA:Human Leukocyte Antigen\u003c/p\u003e\n\u003cp\u003eERG:Transcriptional regulator ERG\u003c/p\u003e\n\u003cp\u003eSTRING:Known and Predicted Protein-Protein Interactions\u003c/p\u003e\n\u003cp\u003eGPS-PAIL:prediction of lysine acetyltransferase-specific modification sites from protein sequences\u003c/p\u003e\n\u003cp\u003eCS:Citrate synthase\u003c/p\u003e\n\u003cp\u003eGEO: Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eTCGA:The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eTSNE:T-distributed stochastic neighbor embedding\u003c/p\u003e\n\u003cp\u003eUALCAN:The University Of Alabama At Birmingham Cancer Data Analysis Portal\u003c/p\u003e\n\u003cp\u003eGSVA:Gene Set Variation Analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the laboratory animal management and use committee of shenzhen TOPBIOTECH Co., Ltd.TOP-2PZ-GM260317.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) repositories under the following URLs: (GEO, http://www.ncbi.nlm.nih.gov/geo/) and (TCGA, https://tcga-data.nci.nih.gov/tcga/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXM conceived and led this study. FH,ML,XMdesigned the experiments and wrote the manuscript. FH, ML, and XM performed the experiments and data analysis. All authors edited and approved the final version. FH and ML contributed asco-authors.XM was corresponding author. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Central Laboratory of the Third Affiliated Hospital of Sun Yat-sen University (guangzhou, China) for their contribution to the provide an experimental platform.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVal\u0026eacute;rie F, Alison T, Elena C, Karim T, Jochen W. Prostate cancer. Lancet. 2026;407:10528.\u003c/li\u003e\n\u003cli\u003eFuxin H, Cheukfai L, Yue C, Jianmin L, Jike F, Zhongyan Z, et al. Succinate dehydrogenase B palmitoylation promotes T cell exhaustion through the H3K27ac-PD1 axis in pancreatic cancer. Cancer Lett. 2026;642:218277.\u003c/li\u003e\n\u003cli\u003eMikhail B, Edward W R, Kelly K, Vincent C, Douglas F F, Miriam M, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24:541-550.\u003c/li\u003e\n\u003cli\u003eErin G S, Haleema Yoosuf A, Masood K, Graham A P, Stephanie E M. Novel Combinatorial Approaches to Tackle the Immunosuppressive Microenvironment of Prostate Cancer. Cancers (Basel). 2021;13:1145.\u003c/li\u003e\n\u003cli\u003eLine B A, Maibritt N, Martin R, Jacob F, Michael B, Benedicte P U, et al. Immune cell analyses of the tumor microenvironment in prostate cancer highlight infiltrating regulatory T cells and macrophages as adverse prognostic factors. J Pathol. 2021;255:155-165.\u003c/li\u003e\n\u003cli\u003eQintao G, Zhijie Z, Xiao L, Feixiang Y, Meng Z, Zongyao H, et al. Deciphering the suppressive immune microenvironment of prostate cancer based on CD4+ regulatory T cells: Implications for prognosis and therapy prediction. Clin Transl Med. 2024;14:e1552.\u003c/li\u003e\n\u003cli\u003eFuxin H, Zhongyan Z, Jike F, Yue C, Jinhui W, Quanzhang L, et al. Integrated scRNA-seq and transcriptome analyses uncover the effects of UBE2H on the immune microenvironment regulation in pancreatic cancer. Cancer Cell Int. 2025;25:422.\u003c/li\u003e\n\u003cli\u003eThomas P, Kobe C Y, Silke G, Edward E 3rd K, Dana R, Nobuaki M, et al. Atezolizumab with enzalutamide versus enzalutamide alone in metastatic castration-resistant prostate cancer: a randomized phase 3 trial. Nat Med. 2022;28:144-153.\u003c/li\u003e\n\u003cli\u003eFilippos K, Anastasia X, Evangelia C, Vassiliki L, Chrissovalantis A, Athanasios K. Myeloid-Derived Suppressor Cells in Prostate Cancer: Present Knowledge and Future Perspectives. Cells. 2022;11:20.\u003c/li\u003e\n\u003cli\u003eAram L, Zenghua F, Matthew C, Averey L, Diamond L, Ali S, et al. Evolution of myeloid-mediated immunotherapy resistance in prostate cancer. Nature. 2025;637:1207-1217.\u003c/li\u003e\n\u003cli\u003ePeter A J, Stephen B B. The epigenomics of cancer. Cell. 2007;128:683-692.\u003c/li\u003e\n\u003cli\u003eAnbarasu K, Katherine R WL, Thomas C W, Joel A Y, Shuang G Z, Christopher P E, et al. Recent Advances in Epigenetic Biomarkers and Epigenetic Targeting in Prostate Cancer. Eur Urol. 2021;80:71-81.\u003c/li\u003e\n\u003cli\u003eHaiqing J, Lei J, Xiaoyu S, Huinan Y, Xinguang L, Liwei Z, et al. Post-translational modifications of cancer immune checkpoints: mechanisms and therapeutic strategies. Mol Cancer. 2025;24:193.\u003c/li\u003e\n\u003cli\u003eJiaxin L, Huiyan J, Jing G, Mengdi L, Danhua S, Yiran Z, et al. Cancer Stem Cells Shift Metabolite Acetyl-Coenzyme A to Abrogate the Differentiation of CD103(+) T Cells. Adv Sci (Weinh). 2026;13:e13535.\u003c/li\u003e\n\u003cli\u003eZhouwei W, Zhichen J, Chenglong H, Shu Y, Shuqing J, Chenyu W, et al. Acetylation Regulates ACSL4 Degradation Through Chaperone-Mediated Autophagy to Alleviate Intervertebral Disc Degeneration. Adv Sci (Weinh). 2026;13:e16015.\u003c/li\u003e\n\u003cli\u003eLucie S, Katerina H, Julia S. Targeting amino acid metabolism in cancer. Int Rev Cell Mol Biol. 2022;373:37-79.\u003c/li\u003e\n\u003cli\u003eXiaoli S, Xinyi W, Wentao Y, Dongmin S, Xihuan S, Zhengqing L, et al. Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives. Signal Transduct Target Ther. 2024;9:192.\u003c/li\u003e\n\u003cli\u003eTomoki B, Junichi F. Primary Roles of Branched Chain Amino Acids (BCAAs) and Their Metabolism in Physiology and Metabolic Disorders. Molecules. 2025;30:56.\u003c/li\u003e\n\u003cli\u003eGagandeep M, Stephen M, Glory M, Olasunkanmi A J A. Branched-chain Amino Acids: Catabolism in Skeletal Muscle and Implications for Muscle and Whole-body Metabolism. Front Physiol. 2021;12:702826.\u003c/li\u003e\n\u003cli\u003eChuang D, Wen-Jie L, Jing Y, Shan-Shan Z, Hui-Xin L. The Role of Branched-Chain Amino Acids and Branched-Chain \u0026alpha;-Keto Acid Dehydrogenase Kinase in Metabolic Disorders. Front Nutr. 2022;9:932670.\u003c/li\u003e\n\u003cli\u003eSofia LV, Carlos S. Metabolic pathways regulating colorectal cancer initiation and progression. Semin Cell Dev Biol. 2019;98:63-70.\u003c/li\u003e\n\u003cli\u003eWeiran Z, Jie S, Xuanyin D, Hele L, Xu W, Dan F. Branched-chain amino acid transaminases as promising targets in tumor therapy. Front Cell Dev Biol. 2026;14:1712076.\u003c/li\u003e\n\u003cli\u003eRu M W, Jessica C S, G Edward W M, Zenghua F, Aram L, Fernando Jose GM, et al. Sialylated glycoproteins suppress immune cell killing by binding to Siglec-7 and Siglec-9 in prostate cancer. J Clin Invest. 2024;134:e180282.\u003c/li\u003e\n\u003cli\u003eChen W, Kun L, Hao-Jie C, Zi-Xuan X, Qi M, Ze-Kun L, et al. Nuclear mitochondrial acetyl-CoA acetyltransferase 1 orchestrates natural killer cell-dependent antitumor immunity in colorectal cancer. Signal Transduct Target Ther. 2025;10:138.\u003c/li\u003e\n\u003cli\u003eAntti M H, Gitte M, P\u0026auml;ivi L P, Naomi K, Toshiyuki F, Rik K W. Crystallographic and kinetic studies of human mitochondrial acetoacetyl-CoA thiolase: the importance of potassium and chloride ions for its structure and function. Biochemistry. 2007;46:4305-4321.\u003c/li\u003e\n\u003cli\u003eJun F, Changliang S, Hee-Bum K, Shannon E, Jianxin X, Meghan T, et al. Tyr phosphorylation of PDP1 toggles recruitment between ACAT1 and SIRT3 to regulate the pyruvate dehydrogenase complex. Mol Cell. 2014;53:534-548.\u003c/li\u003e\n\u003cli\u003eCuimiao Z, Hao T, Gang N, Xi H, Jingyi L, Siqi C, et al. ACAT1-Mediated ME2 Acetylation Drives Chemoresistance in Ovarian Cancer by Linking Glutaminolysis to Lactate Production. Adv Sci (Weinh). 2025;12:e2416467.\u003c/li\u003e\n\u003cli\u003eJulio B, Myriam K, Christelle D-S, Ang\u0026eacute;lique DH, Jean-Paul S, Amalia T, et al. Persistent organic pollutants promote aggressiveness in prostate cancer. Oncogene. 2023;42:2854-2867.\u003c/li\u003e\n\u003cli\u003eHeba A, Robert M, Ewan H, Matthew S, Aroul R, Benjamin Matthew S, et al. Chromatin conformation changes in peripheral blood can detect prostate cancer and stratify disease risk groups. J Transl Med. 2021;19:46.\u003c/li\u003e\n\u003cli\u003eSharanya S, Matthew G VH. Emerging Roles for Branched-Chain Amino Acid Metabolism in Cancer. Cancer Cell. 2020;37:147-156.\u003c/li\u003e\n\u003cli\u003eLing Z, Junqing H. Branched-chain amino acid transaminase 1 (BCAT1) promotes the growth of breast cancer cells through improving mTOR-mediated mitochondrial biogenesis and function. Biochem Biophys Res Commun. 2017;486:224-231.\u003c/li\u003e\n\u003cli\u003eKristyn A L, Laura M L, Audrey J R, Stephen D H. Leucine supplementation differentially enhances pancreatic cancer growth in lean and overweight mice. Cancer Metab. 2014;2:6.\u003c/li\u003e\n\u003cli\u003eRussell E E, Siew Lan L, Eoin M, Wai Ho S, Maya V, Phillip J W, et al. Loss of BCAA Catabolism during Carcinogenesis Enhances mTORC1 Activity and Promotes Tumor Development and Progression. Cell Metab. 2019;29:1151-1165.\u003c/li\u003e\n\u003cli\u003eBaijun D, Jun-Yu X, Yuqi H, Jiacheng G, Qun D, Yanqing W, et al. Integrative proteogenomic profiling of high-risk prostate cancer samples from Chinese patients indicates metabolic vulnerabilities and diagnostic biomarkers. Nat Cancer. 2024;5:1427-1447.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, Branched-chain amino acid metabolism, Tumor immune microenvironment, ACAT1, Acetylation, Citric acid","lastPublishedDoi":"10.21203/rs.3.rs-9592453/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9592453/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eThe catabolism of branched-chain amino acids (BCAA) usually drives the growth of cancer cells, but the role and mechanism of BCAA in the progression of prostate cancer and the formation of the immunosuppressive microenvironment remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eIn this study, single-cell sequencing technology was used to analyze the compositional differences in the tumor immune microenvironment of different BCAA catabolism levels (LOW/Med/HIGH). Evaluate the functional states of T cells in different BCAA strata using single - cell gene scores. In vivo and in vitro experiments were conducted to verify the regulation of BCAA treatment on the growth of prostate cancer xenografts and cancer cells. In addition, the Cancer Genome Atlas (TCGA) database was used to determine the clinical feature correlation of the key gene ACAT1 and to study its crosstalk in BCAA metabolism and prostate cancer immune regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eIt was found that BCAA-HIGH inhibited the infiltration, cytotoxicity, and proliferation of CD8 T cells, which impaired the anti-tumor immune response of T cells. Mechanistically, this study identified that ACAT1, in response to BCAA, not only promoted the malignant proliferation of prostate cancer cells but also promoted the acetylation modification of citrate synthase, leading to increased citrate synthase activity and citrate production, which promoted the formation of an immunosuppressive microenvironment and further led to the malignant progression of prostate cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eIn summary, the exploration of the BCAA-ACAT1-CS acetylation axis expands our understanding of the role of BCAA in prostate cancer, identifies ACAT1 as a target with dual roles in metabolism and post-translational modification (PTM), and it may become a new target for metabolic-immunotherapy. This study provides new therapeutic targets and theoretical support for prostate cancer treatment by targeting BCAA-ACAT1-CS acetylation axis.\u003c/p\u003e","manuscriptTitle":"Single-Cell Dissection of BCAA Metabolism Unveils ACAT1-Dependent CS Acetylation as a Metabolic Checkpoint for Immunosuppression in Prostate Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 18:31:24","doi":"10.21203/rs.3.rs-9592453/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22369490-b48f-4ba2-8efc-9a804bfa2c9c","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T20:13:41+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"20","date":"2026-05-06T13:02:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-06T13:00:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-06T08:41:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biology Direct","date":"2026-05-02T09:19:41+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T20:24:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 18:31:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9592453","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9592453","identity":"rs-9592453","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}