Abstract In this study, comprehensive and systematic nontargeted metabolomics analysis was performed with the metabolites of Zangju peel (Citrus reticulata cv. Manau Gan, CRZP, which has been cultivated for over 400 years in Derong County, China.) at four different mature stages. A total of 1878 metabolites were identified, among which flavonoids were the most abundant (62.04 %), and identified 62 key differential metabolites significantly affected by maturity. Based on biological activity measurements, CRZP showed better antioxidant activity, lipase inhibition ability, inhibition of adipogenic differentiation in 3TT-L1 cells and promotion of lipid metabolism, with the biological activity of CRZP at different maturity stages being associated with key differential metabolite. Thus, CRZP is natural antioxidants and possess anti-obesity potential, and industrial production needs to consider the Maturity stage of its collection. 1. Introduction Citrus fruits, with their unique flavor and nutritional value, are among the most consumed fruits worldwide and are grown in over 140 countries and regions globally (FAO Statistics, 2021; https://www.fao.org/statistics/en/). Previous studies have shown that modern citrus is derived from two hybrids (e.g., sweet orange, C. Unshiu) or three hybrids (lemon) of its ancestors (C. reticulata, C. maxima, C. medica), followed by various locally cultivated varieties (e.g., C. reticulata ‘ChaChi’, C. reticulata ‘ponkan’) due to human intervention ([39]Wu et al., 2018), which are important in industrial production. In addition to being consumed as fruits, citrus is also processed and used to produce other commodities, such as juice/juice drinks, preserved fruits, and canned fruits. However, the production of citrus peel waste after processing is approximately 50–70 % w/w of citrus fruits, depending on the technology used and the variety of fruit cultivation, and its annual global production may be close to 10 million Mg (Mg is the SI unit equivalent to tons) ([40]Matsuo et al., 2022, [41]Zema et al., 2018). The medicinal use of citrus peel can be traced back to the 10th century, and its bioactive components have only recently been characterized. Citrus peel is rich in polyphenolic compounds, including flavonoids, limonoids, terpenoids, lignans, coumarins, phenolic acids, and other compounds. The peel exhibits anti-inflammatory, antioxidant, anticancer, and lipid-lowering effects and has been used as a source of functional active ingredients in food, medicine, spices, and other fields ([42]Gomez-Mejia et al., 2019, [43]Rafiq et al., 2018, [44]Tian et al., 2018). However, research on the peel of different citrus varieties is limited, especially in locally cultivated varieties, leading to the waste of industrial byproduct resources. More research and knowledge are needed to fully utilize the peel of different citrus varieties. In China, local varieties of C. reticulata are abundant, such as C. Unshiu, C. reticulata ‘Chachi’, C. reticulata ‘Ponkan’ and Zangju (C. reticulata cv. Manau Gan). Their dried fruit peels (CP) are often used as medicinal, health food, or food seasoning agents (e.g., ‘Preserved Mandarin Peel’- preserved fruit, ‘Ganpu Tea’ - using C. reticulata ‘Chachi’ and Pu'er tea as raw materials, Tangerine Powder) ([45]Lv et al., 2020). However, the peel byproducts of many local varieties of citrus are not comprehensively utilized. Among them, Zangju peel (CRZP) is used by local residents as a cooking season due to its unique aroma and flavor and is often added to beef and mutton stews to reduce the greasy taste. Zangju is known locally (in the Tibetan region) as “Jia Xu”, symbolizing “fruit of longevity”; this name may be inspired by the oldest Zangju tree in the area, which has been growing for a hundred years. Zangju fruits are popular among people because they generate a unique flavor and are easy to peel. The fruit is mainly distributed in Derong, Muli and other counties in the agricultural and pastoral areas of the middle and lower reaches of the Sanjiang River Valley (subtropical valley climate) in the southern section of the Hengduan Mountains, China ([46]Song, 2016). The fruit has been cultivated for over 400 years, with a planting area of over 11,600 ha and an annual output of approximately 130,000 tons (according to local government statistics, https://www.nongjixie.org/Library). Although there have been numerous studies on the peel of C. reticulata local varieties ([47]Costanzo et al., 2022, [48]Luo et al., 2017, [49]Wang et al., 2022), no research has been performed on the CRZP. Thus, the potential of CRZP is an attractive topic to investigate in detail, especially its metabolic characteristics and changes in biological activity during maturation, as this variety represents a typical resource in many underdeveloped citrus local varieties in China. In our previous research, we identified the volatile compounds of CRZP and their changes at different ripening stages, including alcohols and aldehydes that produce a green fruit aroma, as well as monoterpenes, ketones, and esters with a mature fruit aroma. We also screened potential flavor markers to distinguish different stages ([50]Wang et al., 2023, [51]Wang et al., 2023). As shown by previous studies, in addition to using citrus peel as an essential oil extraction and feed source ([52]Bampidis and Robinson, 2006, [53]Calabrò et al., 2016), citrus peel could provide a source of phytochemicals as a supplement in the human diet; thus, there is enormous development potential and application prospects for citrus industrial byproducts ([54]Deng et al., 2012, [55]Elkhatim et al., 2018, [56]Manthey and Grohmann, 2001), and maturity should be an important parameter to evaluate the application potential of citrus peel ([57]Costanzo et al., 2022, [58]Moulehi et al., 2012). During the ripening process of citrus fruits, the color of the citrus peel changes from green to orange red or bright red, and the corresponding content of polyphenols, carotenoids, flavonoids, and other compounds changes ([59]Maduwanthi and Marapana, 2019, [60]Multari et al., 2020). In addition to maturity, different varieties, cultivation methods, climate conditions, etc., also affect the final characteristics of citrus fruits ([61]Moulehi et al., 2012). Therefore, evaluating the metabolic characteristics and changes in biological activity of CRZP during the maturation process is crucial for using CRZP as a functional food and gaining a deeper understanding of its chemical composition characteristics and functional activity. In recent studies on food ingredients, nontargeted and targeted metabolomics analysis based on liquid chromatography–mass spectrometry (LC–MS/MS) has been widely applied ([62]Cheng et al., 2020, [63]Lyu et al., 2021, [64]Miao et al., 2023). Compared to traditional liquid chromatography (HPLC), nontargeted metabolomics can separate components and utilize MS characterization to identify as many metabolites as possible ([65]Bach et al., 2018, [66]Song et al., 2019). In particular, MS/MS can provide structural information from rich fragment ions, and retention time can provide further evidence. Generally, the method can cover common metabolites with moderate polarity in plants and be used to explore the chemical characteristics of unknown objects. However, quantitative analysis of the main active ingredients is lacking in nontargeted metabolomics, and in this case, selecting targeted metabolomics is a suitable strategy. For example, when the active components in experimental materials are known, HPLC can be used to perform quantitative analysis with the main flavonoid compounds naringin, melididin, and neoepicitrin in Shatianyu (Citrus grandis L.) ([67]Deng et al., 2023). Overall, adopting a nontargeted metabolomic research strategy can more effectively characterize the metabolic characteristics of foods with unknown components. The aim of this study was to evaluate the potential and possibility of utilizing CRZP as a natural functional food source, as well as the impact of maturity on the metabolic characteristics and biological activity of CRZP. Therefore, the experimental design of this study was performed as follows: (1) nontargeted metabolomics based on UPLC–MS/MS was used to characterize the chemical characteristics of CRZP and analyze the differential metabolites of Tibetan orange peel at different maturity stages; (2) differences in the biological activity of CRZP collected at different maturity stages were analyzed; and (3) the correlation between key differential metabolites and biological activity were analyzed and the changes in chemical composition and biological activity during the maturation process were revealed. 2. Materials and methods 2.1. Plant material The samples (C. reticulata cv. Manau Gan, Zangju) were all obtained from Derong County, China (99° 16′ 37′′ E; 28° 32′ 32′′ N; 2225.15 m); 4 adjacent Zangju trees with high yield and stable quality were randomly selected, numbered and listed, and 6 fruits of similar size and normal growth were randomly selected from each tree. Samples once a month (from October 2022 to February 2023, the same growth conditions) were numbered ZGP, ZIP, ZJP, ZKP, and ZLP, as shown in [68]Fig. 1. We divided the 5 harvest stages into 4 mature stages based on their appearance and color characteristics. The immature stage (October) has a dark green peel, the immature stage has a yellow mixed with green peel (November), the mature stage has an orange peel (December), and the fully mature stage has an orange peel (January and February of the following year). The collected samples were washed, the skin was removed manually, and the samples were dried with hot air at a constant temperature of 50 ℃ for 18 h. After preparation, all samples were stored in a dry place at room temperature. The processing and storage of samples after collection are carried out in the science and technology building of the department of Pharmacy, Chengdu University of Traditional Chinese Medicine. Fig. 1. [69]Fig. 1 [70]Open in a new tab Zangju (Citrus reticulata cv. Manau Gan) samples from each stage. 2.2. Chemicals and reagents 3T3-L1 preadipocytes, dulbecco’s modified eagle’s medium (DMEM, high glucose), Fetal bovine serum (FBS) were purchased from Procell Life Science&Technology Co.,Ltd.. Methanol, acetonitrile (Fisher Chemical, USA). Formic acid (MREDA Technology Inc., USA), all of which were LC–MS grade. Reactive oxygen species (ROS) assay kit (Beyotime Biotechnology Co., Ltd), Cell Counting Kit-8 (CCK-8, BOSTER Biological Technology co.ltd), Total antioxidant capacity (T-AOC) assay kit (Ferric ion reducing antioxidant power, FRAP), Superoxide dismutase (SOD) assay kit, Malondialdehyde (MDA) assay kit, Glutathione peroxidase (GSH) assay kit, nonestesterified fatty acid (NEFA), glycerol assay kit, Oil Red O and BCA protein assay were purchased from Nanjing Jiancheng Biotechnology Research Institute, China. Pig pancreatic lipase (30,000 u/g, Shanghai Yuanye Biotechnology Co., Ltd., China). Triiodothyronine (T3), dexamethasone, isobutylmethylxanthine (sigma), indomethacin and rosiglitazone were purchased from Shanghai Aladdin Biochemical Technology Co.,Ltd.. Insulin, 2,2-Diphenyl-1-Picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) were both purchased from Shanghai Macklin Biochemical Technology Co., Ltd. China, 4-nitrophenyl laurate (purity ≥ 98 %, Beijing Jingming Biotechnology Co., Ltd. China), Tris base, Servicebio, anhydrous sodium acetate and hydrochloric acid (purity ≥ 98 %, Chengdu Kelon Chemical Co., Ltd., China) were both AR grade. 2.3. Sample preparation and extraction The dried peels of oranges from different ripening periods were ground with a grinder (MM 400, Retsch) at 30 Hz for 1.5 min until they were a powder. Fifty milligrams of powder was accurately weighed and added to 1.2 mL of a precooled 70 % methanol solution at −20 ℃. The mixture was vortexed once every 30 min for 30 s, for a total of 6 times. After centrifugation (at 12,000 rpm for 3 min), the supernatant was extracted, a microporous filter membrane (0.22 μ Filter the sample) was used, and the filtrate was stored in the injection bottle for UPLC–MS/MS analysis. Quality control samples (QC) were made by mixing five sets of sample extracts. 2.4. UPLC–MS/MS conditions The UPLC–MS/MS system for analysis mainly includes ultra-performance liquid chromatography (UPLC) (ExionLC ™ AD, https://sciex.com.cn/) tandem mass spectrometry (MS/MS, SCIEX Triple Quad ^TM 6500+). Chromatographic conditions: (1) Chromatographic column: Agilent SB-C[18]1.8 µm, 2.1 mm * 100 mm; (2) Mobile phase: A phase was ultrapure water, B phase was acetonitrile, both of which were added with 0.1 % formic acid; (3) Elution gradient: The B phase ratio was 5 % at 0.00 min, and linearly increased to 95 % within 9.00 min and maintained at 95 % for 1 min, 10.00–11.10 min, and the B phase ratio decreased to 5 % and equilibrated at 5 % to 14 min; (4) Flow rate 0.35 mL/min; Column temperature 40 ℃; Injection Volume 2 μL. Mass profile conditions: electric spray ion source (ESI) temperature, 500 °C; ion spray voltage (IS), 5500 V (positive ion mode)/−4500 V (negative ion mode). The ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were set to 50, 60, and 25 psi, respectively, and the collision-induced ionization parameters were set to high. QQ scanning used MRM mode and the collision gas (nitrogen) was set to medium. By further optimizing the clustering potential (DP) and collision energy (CE), the DP and CE of each MRM ion pair were completed. A specific set of MRM ion pairs were monitored at each period based on the metabolites eluted during each period. 2.5. Identification and quantification of metabolites Analyst 1.6.3 software was used to process mass spectrometry data and perform qualitative and quantitative analysis of metabolites as described by Sun et al. ([71]Sun et al., 2022). Qualitative analysis was conducted by comparing accurate precursor ion (Q1) and yield (Q3) values and retention time (RT) and matching them with the self-built database MWDB (MetWare Biological Co., Ltd., Wuhan, China). Quantitative analysis of metabolites was conducted through multiple reaction monitoring (MRM) analysis of QQ using a mass spectrometer to obtain mass spectrometry data of metabolites in different samples. Then, the peak areas of the mass spectra peaks of all metabolites were integrated. Multi quantum (AB SCIEXFramingham, MA, USA) software was used to integrate and calibrate chromatographic peaks and calculate the relative concentration of the corresponding substance in the peak area of each chromatographic peak ([72]Fig. S1). 2.6. In vitro biological activity evaluation Sample extraction. A total of 0.1 g of ground sample powder was added to 10 mL of 70 % methanol, extracted with ultrasound assistance for 1 h, and centrifuged at 12,000 r/min for 2 min, and the supernatant was collected to obtain the sample extraction solution, which were stored at 4 ℃. The extract was diluted 8 times for antioxidant experiments, and the original solution was used for lipase inhibition rate experiments. All results are the average of three parallel experiments. 2.6.1. Antioxidant activity DPPH. DPPH was determined according to a method by Senouwa et al. ([73]Dossou et al., 2022), with modifications. Diluted extract (100 μL) and DPPH solution (0.0404 mg/mL) (100 μL) were added to the 96-well plate. The solutions were reacted under room temperature conditions in a dark environment for 1 h, and the absorbance value was measured at 517 nm using a microplate reader. In the control group, 70 % methanol was used instead of DPPH solution. The blank group consisted of 70 % methanol. The DPPH clearance rate was calculated as follows: DPPH clearance rate = [1 − (sample absorbance − reference absorbance) ÷ blank absorbance] × 100. ABTS. Refer to the Senouwa et al. method for ABTS determination, which was followed with slight modifications ([74]Dossou et al., 2022). The ABTS solution (6.94 mmol/L) was mixed with K[2]S[2]O[8] solution (2.6 mmol/L) and reacted thoroughly in a cool place for 12–16 h. Then, the mixed solution were diluted 8 times with 70 % methanol (with an absorbance of 7.00 ± 0.02 at 734 nm) to obtain the ABTS working solution. Then, 25 μL of diluted extract was added to the 96-well plate, 175 μL of the working solution of ABTS was mixed well and reacted in the dark for 40 min, and the absorbance value was measured at 734 nm using a microplate reader. In the control group, 70 % methanol was used instead of DPPH solution. The blank group consisted of 70 % methanol. The ABTS clearance rate was calculated as follows: ABTS clearance rate = [1- (sample absorbance - reference absorbance) ÷ blank absorbance] × 100. FRAP. The FRAP clearance rate was determined using a total antioxidant assay kit (Nanjing Jiancheng Bioengineering Research Institute, China). In short, the standard curves for the determination of FeSO[4] solutions were prepared with concentrations of 0.15, 0.3, 0.6, 0.9, 1.2, and 1.5 mmol/L. Then, 180 μL of FRAP working fluid was added to the 96-well plate, 5 μL of FeSO[4] solutions of different concentrations and 5 μL of diluted extracts each. In the blank group, the sample extraction solution was replaced with distilled water. The mixed solution were incubated at 37 °C for 3–5 min, and the absorbance value was measured at 593 nm using a microplate reader. The standard curve is: y = 0.3144x + 0.0032 (R^2 = 0.9994). 2.6.2. Pancreatic lipase (PL) inhibition rate The inhibitory activity of PL was measured using a colorimetric assay according to Hu et al.'s method ([75]Hu et al., 2015) with slight modifications. Pancreatic lipase was dissolved in 0.1 mol/L Tris-HCl buffer (pH 8.2) and prepared as an enzyme solution of 0.01 g/L. The reaction substrate was 4-nitrophenyl laurate (0.8 mg/mL), which was prepared using a sodium acetate buffer solution (5 mmol/L, pH 5.0). Then, 200 μL of sample extract, 200 μL of pancreatic lipase solution and 500 μL of mixed Tris-HCl buffer solution were added and incubated at 37 °C for 10 min. After reacting with substrate (600 μL) at 37 °C for 20 min, the reaction was terminated by boiling water for 10 min. The blank group contained 700 μL Tris-HCl buffer, 200 μL pancreatic lipase solution and 600 μL substrate. The blank control group and sample control group were treated with buffer solution instead of pancreatic lipase solution, respectively, and orlistat (ORL) was used as a positive control. After the reaction, the sample was centrifuged for 3 min (12,000 r/min), the supernatant was collected, and the absorbance was measured at a wavelength of 405 nm. The formula for calculating the inhibition rate is as follows: inhibition rate/%=[1- (Asample - Asample control)/(Ablank - Ablank control)] × 100 %. 2.6.3. Cultivation and induction differentiation of 3T3L1 pre-adipocytes According to Wei et al.'s method ([76]Wei et al., 2021), 3 T3-L1 cells were cultured in growth medium (DMEM (high sugar), 10 % FBS, 1 % P/S). For adipocyte differentiation, cells were cultured to confluence and incubated with DMEM (high glucose) plus 10 % FBS, 0.125 mM indomethacin, 1 nMT3, 20 nM insulin, 5 mM dexamethasone, 1 mM rosiglitazone and 0.5 mM Sigma for 2 days. The cells were then maintained in growth medium containing 1 nMT3 and 20 nM insulin for an additional 4 days, was added and replaced every 2 days. By day 6, the mature adipocytes were switched to growth medium and treated with inducer-containing samples at different concentrations for 24 h before harvesting. Model group (MOD), cells induced with inducer only; positive group (ORL), cells incubated with inducer-containing orlistat; experimental group, cells incubated with inducer-containing samples at different maturity stages. 2.6.4. Analysis of 3T3-L1 preadipocyte viability The effect of CRZP extract on cell viability of 3T3-L1 preadipocytes was determined by CCK8 method ([77]Zheng et al., 2023). The 100 μL 1.5 × 10^5 cells/mL 3T3-L1 cells were inoculated for 24 h. CRZP extract was prepared at different concentrations (0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5 and 4 mg/mL), added to 3T3-L1 cells separately, and incubated for 24 h. 3T3-L1 cell was washed with 1 × PBS once, added 100 μL serum-free medium and 10 μL CCK8 solution, incubated for 1.5 h, and then measured the absorbance at 450 nm. Cell viability was calculated as: Cell viability (%) = [ (A[1]-A[0])/(A[2]-A[0])] × 100 %. Where A[1] represents absorbances of cells treated with CRZP, CCK8, and serum-free medium, A[2] represents absorbances of cells without CRZP treated, but CCK8, and serum-free medium, and A[0] represents absorbances of CCK8, and serum-free medium, but no cells. 2.6.5. Oil red O staining and observation Induce differentiation of 3T3-L1 cells using the method described in 2.6.3 and simultaneously add CRZP extract. After cell induction, 1 mL of 4 % paraformaldehyde solution was used to immobilise the cells for 15 min. Then, the cells were washed with PBS twice. The staining process was performed according to the Oil Red O staining kits method, and the lipid droplets condition in the cells and teh cells morphology was recorded under an inverted microscope. 2.6.6. Determination of glycerol and FFA in adipocytes Evaluate lipolytic activity by detecting intracellular glycerol and Free Fatty Acid (FFA) release ([78]Wei et al., 2021). The fully differentiated adipocytes were cultured in serum-free DMEM for 3 h in a 96-well plate, with or without CRZP. The culture medium from each well was collected for detection glycerol and FFA levels using glycerol assay kit and NEFA assay kit, respectively. Glycerol and FFA levels were normalized to total cellular protein amount using the BCA protein assay kit. 2.6.7. Intracellular ROS, MDA, SOD and GSH content analysis Treat differentiated cells with CRZP extract for 24 h, then collect the cells and add protease inhibitors in 1:100 ratio to 1 × PBS buffer. Crush cells in an ice water bath to prepare a cell suspension. The protein concentration of cells was detected using a BCA kit, while the ROS, MDA, SOD and GSH content of each group was detected using a ROS assay kit, MDA assay kit, SOD assay kit and GSH assay kit, respectively. 2.7. Statistical analysis All measurements were conducted in triplicates and the results were presented as the mean ± SD. To ensure that the experimental results were accurate, a quality control sample was inserted into every 2 detection and analysis samples, and the total ion flow diagrams of different quality control samples for mass spectrometry detection and analysis were overlapped and displayed for analysis to monitor the repeatability of the analysis process. The statistical function prcomp in R software was used for principal component analysis (https://www.r-project.org/), and the ComplexHetmap package in R software was used for hierarchical cluster analysis (HCA) and to draw heatmaps. The MetaboAnalystR package in R software calculates VIP values, permutations, and rating maps in OPLS-DA and screens differential metabolites using criteria with VIP > 1 and P < 0.05. GraphPad Prism 8.4.3 (GraphPad Software, USA) was used for statistical analysis, plotting bar charts and ANOVA analysis (p < 0.05 indicated statistical significance). Pearson correlation was used to analyze the correlation between variables. In addition, the metabolites obtained will be annotated through the KEGG database (compounds, https://www.kegg.jp/kegg/compound/; Pathways, http://www.kegg.jp/kegg/pathway.html). 3. Results and discussion 3.1. Identification of metabolites from CRZP at different maturation stages Citrus peel is a major byproduct of the citrus industry, accounting for approximately half of the total weight of citrus fruits. Due to processes such as microbial decay, citrus peel may become an economic and environmental issue ([79]Mahato et al., 2018); as a result, the development and utilization of citrus peel based on chemical composition research is particularly important. To detect the variability of metabolites in CRZP at different maturity stages, we used a nontargeted metabolomics method based on UPLC–MS/MS and conducted metabolomics analysis on CRZP from five harvesting periods with four maturity stages (the immature stage, the incomplete maturity stage, the commercial ripening stage, and the fully matured stage). The samples were analyzed in negative and positive spray ionization (ESI) modes, which is helpful for detecting more metabolites ([80]Farag et al., 2019). The repeatability and reliability of the method have been confirmed by the quality control (QC) sample results ([81]Farag et al., 2019, [82]Senouwa et al., 2022). Total ion chromatography (TIC) shows the differences in chromatographic specifications ([83]Fig. S2), as well as the repeatability and reliability of the results. The MRM results are shown in [84]Fig. S3, with each peak of different colors representing the metabolites detected in the sample. A total of 1,878 metabolites were identified ([85]Table S1, [86]Fig. 2a, 2b), including 737 flavonoids, 364 phenolic acids, 276 alkaloids, 203 lignans and coumarins, 120 terpenoids, 10 quinones, 6 tannins, 1 steroid, and 161 other types, mainly flavonoids (62.04 %), followed by phenolic acids (11.97 %) and alkaloids (6.11 %). The main active components in CRZP are phenolic acids and flavonoids, which is similar to most citrus varieties, such as C. reticulata Blanco. Thus, CRZP exhibits potential antioxidant, anti-inflammatory, and lipid-lowering activities ([87]Deng et al., 2023, [88]Singh et al., 2020). Fig. 2. [89]Fig. 2 [90]Open in a new tab Statistics and differences of metabolites in CRZP at different maturation stages. (a) Classification pie chart of identified metabolites. (b) The distribution of 1,878 metabolites in different ripening stages of Tibetan orange peel. (c) Principal Component Analysis Results Chart. (For interpretation of the references to color