Microbial biotransformation of Pericarpium Citri Reticulatae (PCR) by Aspergillus niger and effects on antioxidant activity

Abstract Pericarpium Citri Reticulatae (PCR), the mature fruit peel of Citrus reticulata Blanco and its different cultivars, is an important citrus by‐product with beneficial health and nutritive properties. However, due to the lack of value‐added methods for its development and utilization, a large amount of PCR is discarded or wasted. To explore a possibly more effective method to utilize PCR, we compared the chemical and biological differences before (CK) and after (CP) microbial transformation of PCR by Aspergillus niger. UPLC‐ESI‐MS/MS, HPLC, and LC‐MS methods were used to compare the chemical profiles of CK and CP. The results demonstrated that microbial biotransformation by A. niger could transform flavonoid compounds by utilizing the carbohydrate and amino acid nutrients in PCR. This could also promote the accumulation of polyhydroxyflavones compounds in CP. The antioxidant assay demonstrated that CP had significantly greater free radical‐scavenging activity than CK. The higher antioxidant activity of CP may result from the high level of flavonoids with associated phenolic hydroxyl groups. Microbial biotransformation is an effective method for improving the antioxidant capacity of PCR and may be effective and useful in other natural product situations.

Aspergillus niger has also been used in the biotransformation of flavonoids such as flavone (Parshikov and Sutherland, 2015), flavonol (Kostrzewa-Susłow et al., 2014), flavonoid glycoside (Cao et al., 2015), flavanone (Kostrzewa-Susłow and Janeczko, 2012), polymethoxy flavonoids (Sanchez- Gonzalez & Rosazza, 2004 ), chalcone, and isoflavanone (Abdella et al., 2018). The biotransformation mechanism may involve the formation of transformation products through hydroxylation, methylation, dehydrogenation, and other processes with the participation of enzymes produced by A. niger (Caspani et al., 2019;Bianchini et al., 2015). However, little is known about the specific enzymes involved in the biotransformation. The same A. niger strain may produce different conversion products in response to different flavonoids, indicating that the microbial transformation can be highly specific (Das & Rosazza, 2006). Present research involves screening the metabolic enzymes of A. niger and the biotransformation of chemical components.
Pericarpium Citri Reticulatae contains large amounts of flavonoids, with hesperidin was identified as the major compound. To date, only a small number of constituents, such as naringenin , rutin (You et al., 2010), tangeretin (Mahmoud et al., 2008), and nobiletin (Okuno & Miyazawa, 2004), have been reported to be transformed by A. niger. No studies have analyzed the chemical profiles and the biological differences before and after microbial biotransformation of PCR by A. niger.
We selected PCR as the experimental material and transformed it with a strain of A. niger. We compared the chemical and biological differences before (CK) and after (CP) microbial transformation by A. niger. UPLC-ESI-MS/MS techniques were used to compare the chemical profiles of CP and CK. HPLC coupled with diode array detector (HPLC-DAD) method and LC-MS were used to determine the major constituents in CP and CK. Three different methods (DPPH, FRAP, and ABTS) were used to evaluate the antioxidant activity. The objective of this study was to provide a practical method for the additional development and use of PCR.

Microbial transformation
The strain of A. niger (3.13901) was isolated from soil and preserved in the China General Microbiological Culture Collection Center.
The methods refer to the reported literature (Stankov-Jovanović et al., 2015). The details are as follows: an 8 g sample was weighed and spread in a petri dish. It was sterilized by ultraviolet irradiation on an ultra-clean workbench for 30 min, then turned over, and sterilized by irradiation for an additional 30 min. The sterilized samples were divided into the reverse inoculation group (CP) and the control group (CK). The spore suspension was obtained by eluting A. niger culture dish with sterile normal saline and filtered with absorbent cotton; then, 1 ml spore suspension was diluted 1,000 times with normal saline to obtain the standard spore suspension. CP: 1 ml standard A. niger spore suspension (10 6 cfu/ml) was added to each petri dish (n = 6). CK: We added 1 ml of sterile water per petri dish (n = 6). The two groups of samples were cultured in an artificial climate chamber at 30°C with 95% RH. Samples were removed for detection after 5 days.

Metabolites extraction
A 50 mg sample was added to an EP tube and 1,000 μl of extraction solution (acetonitrile: methanol: water = 2:2: 1) containing internal standard (L-2-chlorophenylalanine, 2 μg/ml) was added. After a 30 s vortex, the samples were homogenized at 35 Hz for 4 min and sonicated for 5 min in an ice-water bath. The homogenization and sonication cycle was repeated two times. Then the samples were incubated at −40°C for 1 hr and centrifuged at 11180 g for 15 min at 4°C. A 250 μl sample of the supernatant was transferred to a fresh tube and dried in a vacuum concentrator at 37°C. The dried samples were reconstituted in 400 μl of 50% acetonitrile by sonication on ice for 10 min. The solution was then centrifuged at 18894.2 g for 15 min at 4°C, and 75 μl of the supernatant was transferred to a fresh glass vial for LC/MS analysis. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants from all of the samples (Wu, Jiao, et al., 2018).
The TripleTOF 6,600 mass spectrometry (AB Sciex) was used for its ability to acquire MS/MS spectra on an information-dependent basis (IDA) during an LC/MS experiment. In this mode, the acquisition software (Analyst TF 1.7, AB Sciex) continuously evaluates the full scan survey MS data as it collects and triggers the acquisition of MS/MS spectra depending on preselected criteria. In each cycle, the most intensive 12 precursor ions with intensity above 100 were chosen for MS/MS at collision energy (CE) of 30 eV. The cycle time was 0.56 s. ESI source conditions were set as following: Gas 1 at 60 psi, Gas 2 at 60 psi, Curtain Gas at 35 psi, Source Temperature at 600°C, Declustering potential at 60 V, and Ion Spray Voltage Floating (ISVF) at 5,000 V in positive mode (Shimizu et al., 2018).

Data preprocessing and annotation
MS raw data (.wiff) files were converted to the mzXML format by ProteoWizard and processed by R package XCMS (version 3.2). The process included peak deconvolution, alignment, and integration.
Minfrac and cutoff were set as 0.5 and 0.3, respectively. In-house MS2 database was used for metabolite identification.

Determination of total antioxidant capacity
Approximately 2.0 g of citrus peels from each sample were freezedried using a vacuum freeze dryer and then ground into powder using a mortar. The antioxidant activities of citrus peels were evaluated by the DPPH radical scavenging activity assay. Briefly, 0.02 g of citrus peels was mixed with 180 µl of a DPPH working solution.
The mixture was incubated at room temperature for 30 min in darkness. The absorbance was measured at 517 nm with a microplate reader (Sirivibulkovit et al., 2018). The ABTS radical scavenging capacities of citrus peels were conducted with a Total Antioxidant Capacity Assay Kit with ABTS method (Beyotime Biotechnology Co., Ltd.). Trolox was used as a standard compound. A calibration curve was prepared with different concentrations of Trolox in solution, and the results were expressed as mmol TEAC/L of citrus peels where TEAC is defined as the Trolox equivalent antioxidant capacity (Polak & Bartoszik, 2018). The reducing abilities of citrus peels were measured by a Total Antioxidant Capacity Assay Kit with the FRAP method (Beyotime Biotechnology Co., Ltd., Shanghai, China).
The standard curve was constructed using FeSO4 solution, and the results were expressed as l M Fe(II)/g dry weight of the citrus peels (Mozaffari et al., 2018).

Targeted verification of quentin
Quentin was analyzed by HPLC, as described previously with some modifications (Xiao et al., 2020). The mobile phase, a mixture of buffer (0.4% phosphoric acid), and methanol (50:50 v/v) were filtered through a 0.45 µm membrane filter and degassed by sonication.
HPLC analysis was performed at 30°C with a flow rate of 0.5 ml/min, and the samples were injected into an ODS C18 (4.6 mm × 250 mm) column (Beckman Coulter Inc.). The column effluent was monitored at 360 nm. Quantification was performed by comparing the peak areas obtained from the samples with those of standards.

Targeted verification of narirutin and naringenin
Narirutin and naringenin were determined according to the method described by Wang . We used 0.2 g powder and added 25 ml of methanol. The mixture was refluxed for 1 hr in a 75°C water bath. Following centrifugation at 10,000 × g for

Metabolic profiling
The metabolites of the citrus peels from CK and CP were investigated based on UPLC-ESI-MS/MS and relevant databases. In this study, 2,136 metabolites were detected (Table S1), and 767 of the metabolites were identified (Table S2) This showed that the metabolic transformation caused by A. niger had substantial effects on the metabolic components in the citrus peels. Figure 1b shows that the correlation coefficient R 2 within the group were all greater than 0.9, indicating good repeatability between samples. This finding was demonstrated by clustering analysis of the two samples and showed that they could be clearly distinguished from each other.

PCA and OPLS-DA analyses of differential metabolites
Principal component analysis is an unsupervised pattern recognition method used for analyzing, classifying, and reducing the dimensionality of numerical datasets in multivariate problems (Ardila et al., 2015). This approach has been widely used for quality control of herbal medicines. Similarly, OPLS-DA analysis maximizes the variations between groups and is commonly used to screen differential metabolites (Triba et al., 2015). In this study, PCA was carried out to provide additional insight into the chemical differences between CK and CP. As shown in Figure 2a, the cumulative contribution rate of PC1 and PC2 was 88.64%, with 82.80% attributed to PC1 and 5.84% attributed to PC2 (Figure 2a,b). The classification results of PCA show noticeable differences between the CP and CK. Of the differential metabolites, they were used to establish an OPLS-DA model. The parameters of log2FC, p-value, and VIP values are shown in Table S3. The results presented in Figure 2c demonstrate that the R 2 X, R 2 Y, and Q 2 values determined using this model are 0.809, 0.999, and 0.986, respectively. Considering that Q 2 exceeds 0.9 and the red and green dot did not exceed the corresponding line (Figure 2d), the OPLS-DA model is stable and reliable and it can be used to identify the differential metabolites.

Differential metabolite screening analysis
The differential metabolites of CK and CP were screened based on the fold change and variable importance in project (VIP) values of the OPLS-DA model. Specifically, the metabolites having fold change values ≥2 or ≤0.5 and VIP values ≥1 were identified as differential ( Table   S3). The fold change of metabolites of the two samples was compared and analyzed, and the metabolites with greater changes are shown in  Figure 3c shows that 244 F I G U R E 1 (a) Clustering heat-map of metabolites between Pericarpium Citri Reticulatae (CK) and Pericarpium Citri Reticulatae (CP). The upregulated and downregulated metabolites were expressed with different shade colors of red and green, respectively. With the increase in the abundance value, the color of the bar changed from green to red. When the abundance value was 0, the color of bar was white, as shown in the bar at the upper right. (b) Correlation between Pericarpium Citri Reticulatae (CK) and Pericarpium Citri Reticulatae (CP). The correlation analysis between samples was used to estimate the biological duplication among samples within a group. The closer R 2 is to 1, the stronger the correlation between the two repeated samples In addition, we analyze the enrichment of the differential metabolites and interestingly found that some primary metabolites such as amino acids and sugars changed greatly (Figure 4), which suggest

F I G U R E 2 (a) Differential metabolites analysis on the basis of principal component analysis (PCA). (b) PCA 3D plots. (c) Orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA). (d) Validation plots of OPLS-DA model. The horizontal lines correspond
to R 2 and Q 2 of the original model, and the blue and red dots represent R 2 ′ and Q 2 ′ of the model after Y substitution, respectively. If R 2 ′ and Q 2 ′ are both smaller than R 2 and Q 2 of the original model, (the corresponding points do not exceed the corresponding lines), it indicates that the model is of significance. Differential metabolites can be analyzed and screened according to Variable Importance in the Projection (VIP) that the mechanism of metabolite transformation by A. niger may also be closely related to the changes of energy substances, such as amino acids or sugars.

Targeted quantitative analysis
To verify the absolute content of upregulated flavonoids, five upregulated differential flavonoids-quercetin, naringenin, hesperetin, F I G U R E 3 Differential metabolites analysis. (a) Volcano plots of different metabolites. The green dots in the figure represent downregulated differential metabolites, the red dots represent upregulated differential metabolites, and the black dots represent metabolites detected but that are not significantly different. (b) Histogram of fold change. The green pillars in the figure represent downregulated differential metabolites, the red pillars represent upregulated differential metabolites. (c) Number of different types of detected flavonoids metabolites. (d) Number of different types of differential flavonoids metabolites. Red bars represent upregulated differential flavonoids metabolites; green bars represent downregulated differential flavonoids metabolites vitex, and narirutin-were quantitatively analyzed by HPLC or LC-MS. The contents of quercetin, naringenin, hesperetin, vitex, and narirutin in CP were 15.11 ± 3.23 μg/g, 14.83 ± 2.02 μg/g, 47.13 ± 2.11 μg/g, 48.80 ± 1.47 ng/g, and 2.26 ± 1.33 mg/g, respectively ( Table 2). The contents of quercetin, naringenin, hesperetin, vitex, and narirutin in CK were 12.11 ± 2.66 μg/g, 12.33 ± 2.36 μg/g, 21.33 ± 2.01 μg/g, 29.33 ± 1.22 ng/g, and 1.33 ± 1.25 mg/g, respectively. CP exhibited higher contents than CK (Figure 5b-d).
The absolute quantitative results were consistent with the relative quantitative results. Previous studies have reported a linear positive correlation between flavonoid compounds rich in phenolic hydroxyl groups with their antioxidant capacities (Wang et al., 2017).
Therefore, microbial transformation of PCR by A. niger can change the composition of metabolites, transform flavonoids, and promote the accumulation of phenolic hydroxyl compounds. The higher antioxidant activity of CP may be attributed to the presence of a higher level of flavonoids with rich phenolic hydroxyl groups.

DISCUSS ION
Bioconversion technology has attracted attention because of its potential for producing novel active chemicals (Hidalgo et al., 2018).
A. niger is a eukaryote that is considered safe in biomicrobial transformation (Schuster et al., 2002). The metabolites of A. niger contain abundant metabolic enzymes, which can efficiently transform different types of compounds (Cao et al., 2015;Kumar & Pandey, 2013).
A. niger transformation of monomer compounds has been a focus because of its great potential for producing novel active components. However, the conversion of complex mixtures has rarely been TA B L E 1 A list of upregulated differential flavonoids metabolites between Pericarpium Citri Reticulatae (CK) and Pericarpium Citri Reticulatae (CP) No.

Molecular weight (Da)
Compounds Class Hydroxyl group and its position reported. The purpose of this study is to provide a practical method for the additional development and use of PCR using microbial transformation.
Previously, the UPLC-ESI-MS/MS-based widely targeted metabolomics has been widely used in the differential analysis of samples . HPLC and LC-MS methods have also been used to quantitative analysis of sample compositions (Suresh et al., 2018).
In this study, microbial biotransformation of PCR by A. niger was an-   (Savinova, et al., 2018). Another A. niger JMU-TS528 could transform rutin to isoquercitrin (Li et al., 2020). Aspergillus niger NRRL 3,122 could promote the hydrolysis of soy flour isoflavone glycosides to their aglycone. All these results are attributed to the specific enzymes produced by A. niger (Abdella et al., 2018).

CON CLUS ION
We analyzed the changes of metabolites before (CK) and after ( F I G U R E 5 (a) Total antioxidant activity (TAA) in Pericarpium Citri Reticulatae (CK) and Pericarpium Citri Reticulatae (CP). All values are means ± SD (n = 6); significant differences were evaluated using oneway ANOVA. *indicates significant level 0.01 < p < .05 and **indicates a significance level p < .01. (b) Content of upregulated differential metabolite hesperetin. (c) Content of upregulated differential metabolite vitex. (d) Content of upregulated differential metabolite narirutin

ACK N OWLED G M ENTS
This work was supported by the National Natural Science Foundation of China (81973436).

CO N FLI C T O F I NTE R E S T
All the authors declare that there are no conflicts of interest.

E TH I C A L A PPROVA L
This study does not involve any human or animal testing.