Metabolome analysis elucidates the mechanism of radish taproot hollowness during the storageABSTRACT:
The hollowness phenomenon of radish (Raphanus sativus L.) root is very easy to occur during the storage, which seriously affects its quality. “Weixian radish” was used as the test material, and metabolomics technology and multivariate statistical analysis were employed to explore the underlying mechanism. The results revealed 384 differential metabolites in VL0 vs VL1 (174 up-regulated, 210 down-regulated), 440 differential metabolites in VL0 vs VL4 (279 up-regulated, 161 down-regulated), and 298 differential metabolites in VL1 vs VL4 (226 up-regulated, 72 down-regulated). The KEGG enrichment analysis indicated that a total of four metabolic pathways were significantly enriched in the process of radish hollowness. Notably, only cysteine and methionine metabolism was significantly enriched in the comparisons of VL0 vs VL1 and VL0 vs VL4 positive ions. Therefore, the cysteine and methionine metabolism could be the most critical metabolic pathway involved in the response of radish taproots to the hollowness phenomenon. The study suggested that a decrease in homocysteine content may lead to reducing glutathione levels, impacting the antioxidant capacity of radish taproots. In conclusion, hollowness may indirectly influence the anabolism of glucosinolates in radish taproots by affecting the metabolism of cysteine and methionine, as well as the biosynthetic pathways of phenylalanine, tyrosine, and tryptophan, thereby impacting the antioxidant capacity of radish taproots.
Key words:
Raphanus sativus L.; hollowness; metabolite; antioxidant capacity.
RESUMO:
O fenômeno da cavidade da raiz de rabanete (Raphanus sativus L.) é muito fácil de ocorrer durante o armazenamento, o que afeta seriamente sua qualidade. “Rabanete Weixian” foi usado como material de teste, e tecnologia metabolômica e análise estatística multivariada foram empregadas para explorar o mecanismo subjacente. Os resultados revelaram 384 metabolitos diferenciais em VL0 vs VL1 (174 para cima, 210 para baixo), 440 metabolitos diferenciais em VL0 vs VL4 (279 para cima, 161 para baixo) e 298 metabolitos diferenciais em VL1 vs VL4 (226 para cima, 72 para baixo). A análise de enriquecimento com KEGG indicou que um total de quatro vias metabólicas foram significativamente enriquecidas no processo de oco do rabanete. Notavelmente, apenas o metabolismo da cisteína e metionina foi significativamente enriquecido nas comparações dos íons VL0 vs VL1 e VL0 vs VL4 positivos. Portanto, o metabolismo da cisteína e da metionina poderia ser a via metabólica mais crítica envolvida na resposta das raízes de rabanete ao fenômeno da cavidade. O estudo sugeriu que uma diminuição no teor de homocisteína pode levar à redução dos níveis de glutationa, impactando na capacidade antioxidante das raízes de rabanete. Em conclusão, a cavidade pode influenciar indiretamente o anabolismo dos glucosinolatos nas raízes de rabanete afetando o metabolismo da cisteína e metionina, bem como as vias biossintéticas da fenilalanina, tirosina e triptofano, impactando assim a capacidade antioxidante das raízes de rabanete.
Palavras-chave:
rabanete; vazio; metabolismo; capacidade antioxidante
INTRODUCTION
Radish (Raphanus sativus L.) is a root vegetable belonging to the cruciferous family. It has a wide variety and is widely cultivated around the world. The taproots of radish are rich in carbohydrates, vitamin C, plant protein, minerals, amino acids, glucosinolates, polyphenols, flavonoids, and other nutrients. These components are known to effectively enhance human immunity, so radish is known as ‘little ginseng’ (GAMBA et al., 2021; PARK et al., 2022). However, during the storage, the taproots of radish are susceptible to water loss, hollowness, and rot, which significantly impacts its edible quality and commercial value (TSOUVALTZIS & BRECHT, 2014; LIM et al., 2015; DUBININA et al., 2018).
Hollowness is a physiological disease during the growth and development of radish, which occurs during both the growth and storage periods of radish. Studies have shown that hollowness can reduce the radish taproots weight, water content, and nutrients (such as vitamins, sugar, starch, and protein), which seriously affects the storage, processing, and edible properties of radish (SHANG et al., 2015; RAN et al., 2022).
The research on the effects of hollowness on the quality of radish is limited to the nutrients such as soluble sugar, soluble protein, cellulose, lignin and vitamin C. However, there is still a lack of research on the effects of hollowness on other small molecule metabolites such as nucleotides, amino acids, and organic acids. Metabolomics is a scientific discipline that focuses on the qualitative and quantitative analysis of small molecule metabolites. It utilizes high-throughput detection technology to identify key metabolites. In recent years, metabolomics technology has gained significant popularity and has been extensively employed in the analysis of metabolites in various plants, such as cucumber, melon, tomato, and maize (ZHANG et al., 2021; LIU et al., 2023; SHUANG et al., 2023; ZHAN et al., 2023).
In this study, non-targeted metabolomics was applied to analyze the variations in small molecule metabolites in the taproots of radish during the hollowness process. Subsequently, multivariate statistical analysis methods such as principal component analysis and orthogonal partial least squares discriminant analysis were employed to identify significantly altered differential metabolites. This research established a theoretical foundation for enhancing the storage quality and edible value of radish.
MATERIALS AND METHODS
Plant materials
The experiment was conducted at the Key Laboratory of Modern Agriculture, Weifang University, from 2022 to 2023. The test material used was ‘Weixian radish’. After the radishes were harvested, radishes with uniform size, neat shape, and no pests, diseases, or mechanical damage were selected. Their tassels were removed, and remaining parts were placed into polyethylene fresh-keeping bags, with 25 radishes per bag and a total of 2 bags. These bags were stored in an ordinary room with a daytime temperature of 15 ºC ± 2 ºC and a night temperature of 10 ºC ± 2 ºC, simulating household normal temperature storage. Samples were collected at three different time points: 0 days (VL0), 7 days (VL1), and 28 days (VL4) of storage. Samples were taken around 9:00 am. Ten radishes were collected each time, with five randomly selected from each bag. The samples were mixed, frozen in liquid nitrogen, and stored in -80 ºC refrigerator for subsequent analysis.
Metabolite extraction
Tissues (100 mg) ground by liquid nitrogen were placed in EP tubes, and then were resuspended with prechilled 80% methanol and 0.1% formic acid by well vortex. The samples were incubated on ice for 5 min, and then were centrifuged at 15,000 g, 4 ºC for 20 min. Some of supernatant was diluted to final concentration containing 53% methanol by LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube and then were centrifuged at 15000 g, 4 ºC for 20 min. Finally, the supernatant was injected into the LC-MS/MS system analysis. Six biological replicates were set for each sample.
To ensure quality control, an equal volume of samples from each experimental sample was taken and mixed together, which was called the QC sample. Process and detect this QC sample using the same method as the analytical sample, repeating the process three times.
UHPLC-MS/MS analysis
UHPLC-MS/MS analyses were performed using a Vanquish UHPLC system (ThermoFisher, Germany) coupled with an Orbitrap Q ExactiveTM HF mass spectrometer (Thermo Fisher, Germany) in Novogene Co., Ltd. (Beijing, China). Samples were injected onto a Hypesil Goldcolumn (100 × 2.1 mm, 1.9μm) using a 17-min linear gradient at a flow rate of 0.2 mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA in Water) and eluent B (Methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH 9.0) and eluent B (Methanol).The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min; 100-2% B, 14.1 min; 2% B, 17 min. Q ExactiveTM HF mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320 ºC, sheath gas flow rate of 40 arb and aux gas flow rate of 10 arb.
Data processing and metabolite identification
The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, ThermoFisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 minutes; actual mass tolerance, 5ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity, et al. After that, peak intensities were normalized to the total spectral intensity. The normalized data was used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. And then peaks were matched with the mzCloud <https://www.mzcloud.org/>, mzVault and MassList database to obtain the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version) and CentOS (CentOS release 6.6), When data were not normally distributed, normal transformations were attempted using area normalization method.
Metabolite data analysis
Excel 2021 software was used to perform statistical analysis on the data. These metabolites were annotated using the KEGG database <https://www.genome.jp/kegg/pathway.html>. Principal components analysis (PCA) and Partial least squares discriminant analysis (PLS-DA) were performed at metaX (WEN et al., 2017). (a flexible and comprehensive software for processing metabolomics data). Univariate analysis was applied (t-test) to calculate the statistical significance (P-value). The metabolites with the Variable Importance in Projection (VIP) > 1 and P-value < 0.05 and fold change ≥ 2 or FC ≤ 0.5 were differential metabolites. Volcano plots were used to filter metabolites of interest, which was based on log2(FoldChange) and -log10(P-value) of metabolites by ggplot2 in R language. The correlation between differential metabolites were analyzed by cor () in R language (method = pearson). Statistically significant correlation between differential metabolites were calculated by cor.mtest() in R language. P-value < 0.05 was considered as statistically significant and correlation plots were ploted by corrplot package in R language. The functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathway enrichment of differential metabolites was performed, when ratio was satisfied by x/n > y/N, metabolic pathway was considered as enrichment, when P-value of metabolic pathway < 0.05, metabolic pathway was considered as statistically significant enrichment.
RESULTS
Changes in the phenotype of radish taproot during the storage
By observing the phenotype of the ‘Weixian radish’ taproots, it was found that the hollowness phenomenon began to appear when they were stored at room temperature for 7 days. This phenomenon gradually increased in severity as the storage time extended. When the taproots were stored for 28 days, the almost 4/5 of the taproots showed severe hollowness (Figure 1).
Overall metabolite analysis
The analysis of metabolome was conducted on radish taproots at different stages of the kernel process using non-targeted LC-MS/MS to explore their kernel condition. A total of 789 metabolites were identified in the positive ion mode, with 273 compounds having clear classification information and the remaining 516 compounds lacking clear classification. In the negative ion mode, 336 metabolites were identified, with 201 compounds having clear classification and 135 compounds lacking clear classification (Appendix 1 and 2: meta_intensity_{pos,neg}.xls).
Metabolites were annotated separately in positive and negative ions using the KEGG database. In the positive ion mode, compounds were categorized into three main groups, namely environmental information processing (12, 4%), genetic information processing (8, 3%), and metabolism (253, 93%). In the negative ion mode, compounds were also divided into three categories including environmental information processing (9, 4%), genetic information processing (8, 4%), and metabolism (184, 92%) (Figure 2).
The KEGG annotation information of positive and negative ion. Note: KEGG = Kyoto Encyclopedia of Genes and Genomes.
Data quality
The correlation coefficients between the four replicates of the QC samples in this experiment were all above 0.99, indicating good repeatability. The sequencing data was reliable (Figure 3). Principal component analysis of VL0, VL1, VL4, and QC samples demonstrated effective separation, highlighting the significantly different metabolic profiles of VL0, VL1, and VL4 samples (Figure 4).
PCA+QC chart of positive and negative ions. Note: PCA= Principal Components Analysis, QC = Quality Control.
Differential metabolite screening
The results of the OPLS-DA analysis conducted on metabolites from samples collected at various time points during the hollowness process revealed significant differences in metabolites between sample pairs at different time periods (VL0 vs VL1, VL0 vs VL4, VL1 vs VL4). These differences allowed for clear segregation of samples into two distinct groups along the horizontal axis (Figure 5). Additionally, the sample arrangement test conducted on both positive and negative ion modes indicated that in the positive ion mode, the smallest R2 value observed was 0.84, with a corresponding Q2 value of -0.59; In the negative ion mode, the smallest R2 value was 0.68, and the smallest Q2 value was -0.72 (Figure 6). Importantly, none of the results indicated overfitting, demonstrating the prediction ability of the model was good and the reliability of the model was high.
PLS-DA analysis of samples in positive ion and negative ion modes. Note: PLS-DA= Partial Least Squares Discriminant Analysis.
Based on the OPLS-DA results, the Variable Importance in Projection (VIP) of the multivariate analysis model could be used to identify metabolites that differed between treatments. This could be further refined by considering the fold change values and applying screening criteria of fold change ≥ 2 or ≤ 0.5, along with VIP ≥ 1. For VL0 vs VL1, 244 differential metabolites (94 up-regulated and 150 down-regulated) were identified in positive ion mode, and 140 differential metabolites (80 up-regulated, 60 down-regulated) were screened in negative ions. For VL0 vs VL4 positive ions, 281 differential metabolites (152 up-regulated, 129 down-regulated) were identified, along with 159 differential metabolites in negative ions (127 up-regulated, 32 down-regulated). For VL1 vs VL4, 198 metabolites (146 up-regulated, 52 down-regulated) were detected in positive ion mode, and 100 (80 up-regulated, 20 down-regulated) were detected in negative ion mode. (Table 1, Figure 7).
Venn diagram volcano diagram of differential metabolites between comparison groups at different hollowness stages.
Wayne’s analysis revealed that in the positive ions, the comparison groups VL0 vs VL1 and VL0 vs VL4 had a total of 115 differential metabolites, while VL0 vs VL1 and VL1 vs VL4 had 60 differential metabolites. Moreover, there were 91 differential metabolites in the comparison groups VL0 vs VL4 and VL1 vs VL4. Specifically, 69 metabolites were unique to VL0 vs VL1, 75 were unique to VL0 vs VL4, and 47 were unique to VL1 vs VL4. Interestingly, there were no common differential metabolites among the three contrast groups. In negative ions, there were 83 differential metabolites in VL0 vs VL1 and VL0 vs VL4, and 34 in VL0 vs VL1 and VL1 vs VL4. Additionally, 34 differential metabolites were identified in VL0 vs VL4 and VL1 vs VL4. Out of these, 49 metabolites were common, with 23 unique to VL0 vs VL1, 27 unique to VL0 vs VL4, and 17 unique to VL1 vs VL4. Similar to the positive ions, no common differential metabolites were found among the three comparison groups (Figure 8).
Venn diagram of differential metabolites between comparison groups at different hollowness stages.
Differential metabolite enrichment analysis
The KEGG database is a valuable resource to inferring the functions and interactions of genes, proteins, and metabolites in biological systems, such as cells and tissues. Conducting metabolic pathway enrichment analysis of differential metabolites using the KEGG database can aid in understanding metabolic changes in different samples. In the comparison between VL0 and VL1 in the positive ion mode, differential metabolites were notably enriched in pathways like cysteine and methionine metabolism, as well as phenylalanine, tyrosine, and tryptophan biosynthesis. At the same time, there were more differential metabolites enriched in metabolic pathways. In negative ion mode, pathways related to amino acid biosynthesis and aminoacyl-tRNA biosynthesis exhibited high enrichment. Additionally, there was a significant enrichment of differential metabolites in metabolic pathways and biosynthesis of secondary metabolites. For VL0 vs VL4, in positive ion mode, differential metabolites showed greater enrichment in pathways, such as zeatin biosynthesis, cysteine and methionine metabolism. Additionally, there were more differential metabolites enriched in metabolic pathways. Conversely, in negative ion mode, differential metabolites were notably enriched in pathways like lysine degradation and the pentose phosphate pathway. Moreover, there was a significant presence of differential metabolites in metabolic pathways and the biosynthesis of secondary metabolites. In VL1 vs VL4, positive ion mode analysis revealed that differential metabolites were rich in metabolic pathways, including lysine degradation, nicotinic acid and nicotinamide metabolism, phenylalanine metabolism, and β-alanine metabolism, with a notable enrichment in amino acid biosynthesis. On the other hand, negative ion mode analysis showed that differential metabolites were rich in metabolic pathways such as α-linolenic acid metabolism, plant hormone signal transduction, stilbene, diarylheptane, and gingerol biosynthesis, tryptophan metabolism, and purine metabolism, with a significant enrichment in the biosynthesis of secondary metabolites (Figure 9). Among these, differential metabolites were significantly enriched in the four metabolic pathways with P < 0.05, namely cysteine and methionine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, metabolic pathways, and zeatin biosynthesis.
KEGG analysis of differential metabolites between comparison groups at different hollowness stages.
DISCUSSION
Metabolites serve as the ultimate products of plant cell biological regulation, acting as a crucial link between plant genotype and phenotype. Variations in both the type and quantity of metabolites directly or indirectly indicate the plants’ response to environmental stimuli. In this study, a total of 244 differential metabolites were identified in the positive ions of VL0 vs VL1, along with 140 in the negative ions. Similarly, 281 differential metabolites were found in the positive ions of VL0 vs VL4, and 159 were included in the negative ions. Additionally, 198 differential metabolites were detected in VL1 vs VL4 positive ions, and 100 in negative ions. Combined with KEGG pathway analysis, it was found that these differential metabolites were primarily involved in metabolic processes, including cysteine and methionine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, metabolic pathways, and zeatin biosynthesis. Notably, only cysteine and methionine metabolism were the common metabolic processe in the comparisons of VL0 vs VL1 and VL0 vs VL4 positive ions.
Cysteine and methionine metabolism are key pathways in radish fleshy taproots responding to the hollowness phenomenon. Cysteine is a vital sulfur-containing amino acid with significant roles in protein structure, function, metabolism, and redox signaling (ALVAREZ et al., 2012; GARRIDO RUIZ et al., 2022; MAN et al., 2024). Moreover, cysteine and glutamic acid combine to form γ-glutamylcysteine with the help of glutamylcysteine ligase. Subsequently, with the assistance of Glutathionesynthase, γ-glutamylcysteine and glycine react to produce glutathione (MARQUEZ-GARCIA et al., 2013). Glutathione, a tripeptide with the full name of γ-L-glutamyl-L-cysteinylglycine, is the most important, widely distributed, and abundant endogenous non-enzymatic antioxidant substance. It serves as the most abundant non-protein thiol, providing protection against oxidative stress (NOCTOR et al., 2011). Additionally, glutathione is a crucial factor in redox signaling and contributes significantly to stress resistance (KANG et al., 2023).
Studies have shown that the transsulfurization pathway of methionine is crucial for providing cysteine for glutathione synthesis (MUDD et al., 2007). Methionine is converted to homocysteine by methionine adenosyltransferase and S-adenosyl homocysteine hydrolase, which then reacts with serine catalyzed by cysteine β-synthase to form cystathionine. Cystathionine is further broken down into cysteine, α-ketobutyrate, and ammonia by cysteine lyase (POLONI et al., 2015). Therefore, supplementing methionine can elevate cysteine levels, enhance glutathione synthesis, and ultimately boost antioxidant capacity. Moreover, methionine, as a sulfur-containing amino acid, possesses intrinsic antioxidant properties that effectively combat reactive oxygen species accumulation and alleviate oxidative stress (LI et al., 2019).
In this study, six metabolites were predominantly enriched in the cysteine and methionine metabolic pathways. Specifically, L-aspartic acid and S-adenosyl-L-homocysteine showed significant up-regulation in response to hollowness in radish taproot. Conversely, the levels of L-homocysteine, S-methyl-5’-Thioadenosine, homocysteine, and S-adenosylmethionine were all notably down-regulated. These six substances could be crucial in influencing the response of radish taproot to the hollowness. Specifically, homocysteine plays a key role as an intermediate substance in the conversion process of methionine to cysteine. A decrease in homocysteine content can directly result in reduced cysteine levels. Since cysteine is essential for glutathione synthesis, a decrease in its content ultimately leads to lower levels of glutathione, consequently reducing antioxidant capacity. Therefore, this study suggested that the hollowness may impact glutathione levels by influencing homocysteine content in the metabolism of cysteine and methionine, subsequently affecting the overall antioxidant capacity of radish taproots.
Research has shown that the human body primarily receives sulfur from cysteine and methionine, which are organic sulfides (LI et al., 2020). Common sources of sulfur in diets include alliums containing allyl sulfide and cruciferous vegetables containing glucosinolates. Glucosinolates are secondary metabolites derived from amino acids. Based on the structure of the amino acid side chain, glucosinolates can be categorized into aliphatic glucosinolates originating from methionine (Met), indole glucosinolates originating from tryptophan (Trp), and aromatic glucosinolates originating from phenylalanine (HANSEN et al., 2005). The primary glucosinolate found in the taproots of radish is 4-methylthio-3-butenyl glucosinolate (an aliphatic glucosinolate) (LI et al., 2008). Isothiocyanate, the breakdown product of 4-methylthio-3-butenylglucosinolate, is commonly found in cruciferous vegetables in the form of glucosinolates (FAHEY et al., 2001; CLARKE, 2010). These glucosinolates can be enzymatically converted by myrosinase into sulforaphane, a compound known for its cancer-preventive properties (JANCZEWSKI, 2022). Research has demonstrated that the addition of methionine and tryptophan externally can enhance the levels of glucosinolates and sulforaphane in cruciferous vegetables, thereby boosting their antioxidant capacity (JIANG et al., 2009; LI et al., 2024). This study suggested that hollowness may impact the metabolism of cysteine and methionine; subsequently, influencing the synthesis of glucosinolates in radish taproots and indirectly affecting their antioxidant capacity.
This study found that the biosynthetic pathway of phenylalanine, tyrosine, and tryptophan was significantly enriched in the positive ions of VL0 vs VL1, and L-phenylalanine, L-tryptophan, and L-tyrosine were all significantly upregulated. Additionally, the glucosinolate biosynthetic pathway in the positive ions of VL0 vs VL1 and VL0 vs VL4 also predominantly contained these three metabolites (L-phenylalanine, L-tryptophan, and L-tyrosine), and all of which were significantly upregulated. This suggested a possible correlation between these metabolites and the metabolic response of radish taproots to hollowness. Notably, previous studies have demonstrated the positive impact of increased L-tryptophan content on enhancing crop resistance (MARIA et al., 2022; LI et al., 2024). This study revealed that the substantial increase in the levels of L-phenylalanine, L-tryptophan, and L-tyrosine may enhance the glucosinolate content in radish taproots; consequently, improving their resistance. The specific mechanisms underlying this relationship warrant further investigation.
CONCLUSION
A total of 384 differential metabolites were identified in the comparisons of VL0 vs VL1 (174 up-regulated, 210 down-regulated), VL0 vs VL4 (279 up-regulated, 161 down-regulated), and VL1 vs VL4 (226 up-regulated, 72 down-regulated). Cysteine and methionine metabolism represented the primary metabolic pathway in radish taproots in response to the hollowness phenomenon. In addition, the significant decrease in homocysteine, a metabolite of cysteine and methionine, may result in a reduced glutathione content in the radish taproots, thereby diminishing their antioxidant capacity. This study showed that the hollowness may indirectly influence the anabolism of glucosinolates in radish taproots by affecting the cysteine and methionine metabolism, as well as the biosynthetic pathways of phenylalanine, tyrosine, and tryptophan, ultimately impacting the antioxidant capacity of radish taproots.
ACKNOWLEDGMENTS
Thanks for financial support: Key R&D Program of Shandong Province, China (2022LZGCQY013) ; Weifang Science and Technology Development Plan Project (2021GX003, 2021GX008); Weifang University Doctoral Research Start-up Fund Project (2019BS11).
REFERENCES
-
ALVAREZ, C. et al. Cysteine homeostasis plays an essential role in plant immunity. New Phytol, v.193, n.01, p.165-177, 2012. Available from: <Available from: https://doi.org/10.1111/j.1469-8137.2011.03889.x >. Accessed: Oct. 11, 2024. doi: 10.1111/j.1469-8137.2011.03889.x.
» https://doi.org/10.1111/j.1469-8137.2011.03889.x.» https://doi.org/10.1111/j.1469-8137.2011.03889.x -
CLARKE, D. B. Glucosinolates, structures and analysis in food. Analytical methods, v.2, n.4, p.310-325, 2010. Available from: <Available from: https://doi.org/10.1039/b9ay00280 >. Accessed: Jan. 01, 2024. doi: 10.1039/b9ay00280.
» https://doi.org/10.1039/b9ay00280.» https://doi.org/10.1039/b9ay00280 -
DUBININA, A. et al. Changes in the nutritive value of the radish of different varieties depending on the storage method. Food Science and Technology, v.12, n.2, p.46-53, 2018. Available from: <Available from: https://doi.org/10.15673/fst.v12i2.942 >. Accessed: Apr. 16, 2024. doi: 10.15673/fst.v12i2.942.
» https://doi.org/10.15673/fst.v12i2.942.» https://doi.org/10.15673/fst.v12i2.942 -
FAHEY, J. W. et al. The chemical diversity and distribution of glucosinolates and isothiocyanates among plants. Phytochemistry, v.56, n.1, p.5-51, 2001. Available from: <Available from: https://doi.org/10.1016/s0031-9422(00)00316-2 >. Accessed: Jan. 01, 2024. doi: 10.1016/s0031-9422(00)00316-2.
» https://doi.org/10.1016/s0031-9422(00)00316-2.» https://doi.org/10.1016/s0031-9422(00)00316-2 -
GAMBA, M. et al. Nutritional and phytochemical characterization of radish (Raphanus sativus): A systematic review. Trends in Food Science and Technology, v.113, p.205-218, 2021. Available from: <Available from: https://doi.org/10.1016/j.tifs.2021.04.045 >. Accessed: May, 06, 2024. doi: 10.1016/j.tifs.2021.04.045.
» https://doi.org/10.1016/j.tifs.2021.04.045.» https://doi.org/10.1016/j.tifs.2021.04.045 -
GARRIDO RUIZ, D. et al. Cysteine oxidation in proteins: structure, biophysics, and simulation. Biochemistry, v.61, n.20, p.2165-2176, 2022. Available from: <Available from: https://doi.org/10.1021/acs.biochem.2c00349 >. Accessed: Sept. 26, 2024. doi: 10.1021/acs.biochem.2c00349.
» https://doi.org/10.1021/acs.biochem.2c00349.» https://doi.org/10.1021/acs.biochem.2c00349 -
HANSEN, B. G.; HALKIER, B, A. New insight into the biosynthesis and regulation of indole compounds in Arabidopsis thaliana. Planta, v.221, n.5, p.603-606, 2005. Available from: <Available from: https://doi.org/10.1007/s00425-005-1553-1 >. Accessed: Jun. 02, 2024. doi: 10.1007/s00425-005-1553-1.
» https://doi.org/10.1007/s00425-005-1553-1.» https://doi.org/10.1007/s00425-005-1553-1 -
JANCZEWSKI, Ł. Sulforaphane and its bifunctional analogs: Synthesis and biological activity. Molecules, v.27, n.5, p.1-55, 2022. Available from: <Available from: https://doi.org/10.3390/molecules27051750 >. Accessed: Mar. 07, 2024. doi: 10.3390/molecules27051750.
» https://doi.org/10.3390/molecules27051750.» https://doi.org/10.3390/molecules27051750 -
JIANG, X. L. et al. Application of methionine and methyl jasmonate to affect glucosinolates of chinese kale. Acta Agric. Boreali-Sin, v.24, n.01, p.31-35, 2009. Available from: <Available from: https://doi.org/10.7668/hbnxb.2009.01.008 >. Accessed: Jan. 01, 2024. doi: 10.7668/hbnxb.2009.01.008.
» https://doi.org/10.7668/hbnxb.2009.01.008.» https://doi.org/10.7668/hbnxb.2009.01.008 -
KANG, Y. et al. Exogenous glutathione enhances tolerance of the potato (Solanum tuberosum L.) to cadmium stress by regulating the biosynthesis of phenylpropanoid and the signal transduction of plant hormones. Chemical and Biological Technologies in Agriculture, v.10, n.1, p.1-13, 2023. Available from: <Available from: https://doi.org/10.1186/s40538-023-00400-z >. Accessed: Mar. 16, 2024. doi: 10.1186/s40538-023-00400-z.
» https://doi.org/10.1186/s40538-023-00400-z» https://doi.org/10.1186/s40538-023-00400-z -
LI, H. et al.Methionine augments antioxidant activity of rice protein during gastrointestinal digestion. International Journal of Molecular Sciences, v.20, n.4, p.868-880, 2019. Available from: <Available from: https://doi.org/10.3390/ijms20040868 >. Accessed: Feb. 17, 2024. doi: 10.3390/ijms20040868.
» https://doi.org/10.3390/ijms20040868.» https://doi.org/10.3390/ijms20040868 -
LI, Q. Y. et al. Analysis of glucosinolate composition and content in radish. Acta Horticulturae Sinica, v.35, n.8, p.1205-1208, 2008. Available from: <Available from: https://doi.org/10.16420/j.issn.0513-353x.2008.08.020 >. Accessed: Aug. 19, 2024. doi: 10.16420/j.issn.0513-353x.2008.08.020.
» https://doi.org/10.16420/j.issn.0513-353x.2008.08.020.» https://doi.org/10.16420/j.issn.0513-353x.2008.08.020 -
LI, Q. et al. “Sulfur homeostasis in plants.”.International Journal of Molecular Sciences, v.21, n.23, p.1-16, 2020. Available from: <Available from: https://doi.org/10.3390/ijms21238926 >. Accessed: Nov. 25, 2024. doi: 10.3390/ijms21238926.
» https://doi.org/10.3390/ijms21238926.» https://doi.org/10.3390/ijms21238926 -
LI, R. et al. Application of tryptophan and methionine in broccoli seedlings enhances formation of anticancer compounds sulforaphane and indole-3-carbinol and promotes growth. Foods, v.13, n.5, p.1-15, 2024. Available from: <Available from: https://doi.org/10.3390/foods13050696 >. Accessed: Feb. 24, 2024. doi: 10.3390/foods13050696.
» https://doi.org/10.3390/foods13050696.» https://doi.org/10.3390/foods13050696 -
LIM, S. et al. Decreased sulforaphene concentration and reduced myrosinase activity of radish (Raphanus sativus L.) root during cold storage. Postharvest Biology and Technology, v.100, p.219-225, 2015. Available from: <Available from: https://doi.org/10.1016/j.postharvbio.2014.10.007 >. Accessed: Nov. 01, 2024. doi: 10.1016/j.postharvbio.2014.10.007.
» https://doi.org/10.1016/j.postharvbio.2014.10.007.» https://doi.org/10.1016/j.postharvbio.2014.10.007 -
LIU, W. et al. Comparative Transcriptome and widely targeted metabolome analysis reveals the molecular mechanism of powdery mildew resistance in tomato. International Journal of Molecular Sciences, v.24, n.9, p.8236-8256, 2023. Available from: <Available from: https://doi.org/10.3390/ijms24098236 >. Accessed: May, 04, 2024. doi: 10.3390/ijms24098236.
» https://doi.org/10.3390/ijms24098236.» https://doi.org/10.3390/ijms24098236 -
MAN, Y. et al. New insights into mehg accumulation in rice (Oryza sativa L.): evidence from cysteine. Environmental science technology, v.58, n.13, p.5942-5951, 2024. Available from: <Available from: https://doi.org/10.1021/acs.est.3c08385 >. Accessed: Mar. 20, 2024. doi: 10.1021/acs.est.3c08385.
» https://doi.org/10.1021/acs.est.3c08385.» https://doi.org/10.1021/acs.est.3c08385 -
MARIA, G. et al. Alleviation of salt stress in solanum tuberosum l. by exogenous application of indoleacetic acid and l-tryptophan. Journal of Plant Growth Regulation, v.42, n.5, p.3257-3273, 2022. Available from: <Available from: https://doi.org/10.1007/s00344-022-10788-x >. Accessed: Sept. 16, 2024. doi: 10.1007/s00344-022-10788-x.
» https://doi.org/10.1007/s00344-022-10788-x.» https://doi.org/10.1007/s00344-022-10788-x -
MARQUEZ-GARCIA, B. et al. A new role for glutathione in the regulation of root architecture linked to strigolactones. Plant Cell Environ, v.37, n.2, p.488-498, 2013. Available from: <Available from: https://doi.org/10.1111/pce.12172 >. Accessed: Aug. 22, 2024. doi: 10.1111/pce.12172.
» https://doi.org/10.1111/pce.12172.» https://doi.org/10.1111/pce.12172 -
MUDD, S. H. et al. Methyl balance and transmethylation fluxes in humans. American Journal of Clinical Nutrition, v.85, n.1, p.19-25, 2007. Available from: <Available from: https://doi.org/10.1093/ajcn/85.1.19 >. Accessed: Jan. 01, 2024. doi:10.1093/ajcn/85.1.19.
» https://doi.org/10.1093/ajcn/85.1.19» https://doi.org/10.1093/ajcn/85.1.19 -
NOCTOR, G. et al. Glutathione in plants: an integrated overview. Plant Cell Environ, v.35, n.2, p.454-484, 2011. Available from: <Available from: https://doi.org/10.1111/j.1365-3040.2011.02400.x >. Accessed: Aug. 30, 2024. doi: 10.1111/j.1365-3040.2011.02400.x.
» https://doi.org/10.1111/j.1365-3040.2011.02400.x.» https://doi.org/10.1111/j.1365-3040.2011.02400.x -
PARK, C. H. et al. Metabolic profiling of white and green radish cultivars (Raphanus sativus). Horticulturae, v.8, n.4, p.310-310, 2022. Available from: <Available from: https://doi.org/10.3390/horticulturae8040310 >. Accessed: Apr. 07, 2022. doi: 10.3390/horticulturae8040310.
» https://doi.org/10.3390/horticulturae8040310.» https://doi.org/10.3390/horticulturae8040310 -
POLONI, S.et al. Stearoyl-CoA desaturase-1: is it the link between sulfur amino acids and lipid metabolism. Biology (Basel), v.4, n.2, p.383-396, 2015. Available from: <Available from: https://doi.org/10.3390/biology4020383 >. Accessed: Jun. 03, 2024. doi: 10.3390/biology4020383.
» https://doi.org/10.3390/biology4020383.» https://doi.org/10.3390/biology4020383 -
RAN, M. L. et al. Changes of physiological and biochemical indexes of hollowing radish fleshy root. Acta Botanica Boreali-Occidentalia Sinica, v.42, n.04, p.637-645, 2022. Available from: <Available from: https://doi.org/10.7606/j.issn.1000-4025.2022.04.0637 >. Accessed: Apr. 30, 2024. doi: 10.7606/j.issn.1000-4025.2022.04.0637.
» https://doi.org/10.7606/j.issn.1000-4025.2022.04.0637.» https://doi.org/10.7606/j.issn.1000-4025.2022.04.0637 -
SHANG, H. T. et al. Advance in hollowing mechanism and postharvest control technologies of fruits and vegetables. Farm Prod. Process, n.2, p.61-64, 2015. Available from: <Available from: https://doi.org/10.3969/jissn.1671-9646(X).2015.01.049 >. Accessed: Jan. 30, 2024. doi: 10.3969/jissn.1671-9646(X).2015.01.049.
» https://doi.org/10.3969/jissn.1671-9646(X).2015.01.049.» https://doi.org/10.3969/jissn.1671-9646(X).2015.01.049 -
SHUANG, H. H. et al. Insights into flavonoid biosynthesis during cucumber fruit peel coloration based on metabolite profiling and transcriptome analyses. Horticultural Plant Journal, v.9, n.4, p.763-776, 2023. Available from: <Available from: https://doi.org/10.1016/j.hpj.2023.02.002 >. Accessed: Aug. 01, 2023. doi: 10.1016/j.hpj.2023.02.002.
» https://doi.org/10.1016/j.hpj.2023.02.002.» https://doi.org/10.1016/j.hpj.2023.02.002 -
TSOUVALTZIS, P.; BRECHT, J. K. Changes in quality and antioxidant enzyme activities of bunched and topped radish (Raphanus sativus L.) plants during storage at 5 or 10 degrees C. Journal of Food Quality, v.37, n.3, p.157-167, 2014. Available from: <Available from: https://doi.org/10.1111/jfq.12082 >. Accessed: Mar. 20 2014. doi: 10.1111/jfq.12082.
» https://doi.org/10.1111/jfq.12082.» https://doi.org/10.1111/jfq.12082 -
WEN, B. et al. MetaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics, v.18, n.1, p.1-14, 2017. Available from: <Available from: https://doi.org/10.1186/s12859-017-1579-y >. Accessed: Mar. 21, 2024. doi: 10.1186/s12859-017-1579-y.
» https://doi.org/10.1186/s12859-017-1579-y.» https://doi.org/10.1186/s12859-017-1579-y -
ZHAN, W. M. et al. Combined transcriptome and metabolome analysis reveals the effects of light quality on maize hybrids. BMC Plant Biology, v.23, n.1, p.1-28, 2023. Available from: <Available from: https://doi.org/10.1186/s12870-023-04059-4 >. Accessed: Jan. 18, 2024. doi: 10.1186/s12870-023-04059-4.
» https://doi.org/10.1186/s12870-023-04059-4.» https://doi.org/10.1186/s12870-023-04059-4 -
ZHANG, A. et al. Comprehensive analysis of transcriptome and metabolome reveals the flavonoid metabolic pathway is associated with fruit peel coloration of melon. Molecules, v.26, n.9, p.2830-2845, 2021. Available from: <Available from: https://doi.org/10.3390/molecules26092830 >. Accessed: May, 10, 2024. doi: 10.3390/molecules26092830.
» https://doi.org/10.3390/molecules26092830.» https://doi.org/10.3390/molecules26092830
Edited by
-
Editors
Alessandro Dal’Col Lúcio (0000-0003-0761-4200) José Luís Trevizan Chiomento (0000-0003-4819-2761)
Publication Dates
-
Publication in this collection
26 May 2025 -
Date of issue
2025
History
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Received
08 June 2024 -
Accepted
11 Nov 2024 -
Reviewed
17 Feb 2025
