Skip to main content
Download PDF

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India

BMC Plant Biology volume 24, Article number: 402 (2024) Cite this article

Abstract

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

Peer Review reports

Background

Rice (Oryza sativa L) is a primary dietary component for more than half of the global population and ranks as the second largest cultivated cereal crop across the globe [1, 2]. SDG 2 specifically addresses food, aiming to “end hunger, achieve food security and im-proved nutrition and promote sustainable agriculture.” However, a number of other goals also address issues facing the food system.

The realization has emerged in recent years that traditional rice varieties constitute a valuable gene pool for features that may support modern rice varieties ability to adapt to climate change, particularly in light of the phenomenon of climate change. After COVID 19 Pandemic, the eating behaviour of the Indian people especially the Tamilians were changed drastically. This mainly highlighted the importance of rebuilding the immune system through nutrition rich diet balances [3].

Traditional rice can be categorized into two types: pigmented and non-pigmented. Non-pigmented rice is consumed by approximately 85% of the world’s population, whereas pigmented rice has traditionally been enjoyed primarily in China, Japan, and Korea due to its distinct flavor and perceived health benefits [4].

The rice grain contains metabolites that exhibit protective properties against human diseases when consumed through the diet. These metabolites also contribute positively to the immune system. In recent years, there has been a growing interest in pigmented rice varieties, with red rice in particular garnering attention due to its bioactive compounds. These compounds have been found to possess superior antioxidant, anti-inflammatory, antitumor, and hypoglycemic effects as supported by various studies [5,6,7,8,9]. In addition to its various health advantages, including other benefits as mentioned by [10, 11], it is noteworthy that rice with darker shades holds properties beyond those found in light-colored varieties.

The presence of distinct polyphenol subgroups in whole grain rice of different colors, which have the potential to positively influence human well-being [11, 12]. The primary phenolics found in red rice varieties include ferulic acid, p-coumaric acid, and vanillic acid [12] which is also reported in this study. Studies have suggested that p-coumaric acid and vanillic acid may play a crucial role in the antioxidant activity of red rice [13]. Furthermore, existing research indicates a notable positive correlation between phenolic components and antioxidation [12, 14,15,16].

The predominant focus of research on pigmented rice has been on the relationship between anthocyanins and antioxidants, as well as its nutritional properties. This is because rice is widely acknowledged as a functional food in various Asian countries, known for its numerous reported health advantages [17]. Conversely, Red rice (another type of pigmented rice) boasts elevated levels of proanthocyanidins and other phenolics [18, 19]. Additionally, it has been observed that pigmented rice exhibits a higher level of antioxidant activity compared to non-pigmented rice. The degree of pigmentation also plays a role, with darker pigmentation indicating a greater presence of flavonoids and thus stronger antioxidant properties [12, 20, 21].

These cultivars have rich and varied nutrient profiles including antioxidants, which greatly contribute to global food security and nutrition. Thereby, traditional rice is very compatible with SDG 2’s objective of guaranteeing that everyone has access to safe and nourishing food because of their ability to prevent malnutrition and address certain nutrient deficits. Their production also promotes resilience to climate change, sustainable farming methods, and the preservation of agricultural biodiversity.

Extensive research has explored the different metabolites found in both types of rice around the world. However, until now, there have been few published studies on the metabolomics of pigmented and non-pigmented traditional rice varieties of Tamil Nadu. Hence, it is important to highlight the absence of comprehensive metabolic profiling that encompasses primary and secondary metabolites across various cultivars of pigmented and non-pigmented traditional rice. Additionally, there is a need to comprehend the metabolic networks that connect extensive datasets from metabolite profiling with metabolic pathways.

The present study employed a gas chromatography-mass spectrometry (GC-MS/MS) metabolomics technique, specifically using triple quadrupole mass spectrometry. This technique was utilized to explore and quantify primary and secondary metabolites in pigmented and non-pigmented traditional rice varieties, namely Kullakar and Milagu Samba. The metabolite profiling of Kullakar and Milagu Samba is the first study of its kind. The analysis involved chemometric methods like principle component analysis (PCA) and partial least square discrimination analysis (PLS-DA) to effectively classify the samples based on their diversity. To categorize the functional metabolites that offer health benefits from traditional rice varieties, we conducted hierarchical cluster analysis and metabolic pathway identification. The results of this study will provide a valuable theoretical foundation for developing functional foods using traditional rice.

Materials and methods

Two rice cultivars were chosen based on their pericarp colour: Red rice (Kullakar) and white rice (Milagu Samba) (Fig. 1; Table 1). Prior to collecting the plant material, we have obtained legal permission from the Director of Research at Tamil Nadu Agricultural University in Coimbatore, Tamil Nadu, India. The collection process adheres to all institutional, national and international guidelines and legislation. The samples included in this study were exclusively collected from the farmers. The mentioned traditional varieties were cultivated by the farmers of Thanjavur district during the Samba season (July-December, 2021) by adopting the required agronomic practices. Following cultivation, the grains were sun-dried to achieve a water content of approximately 10–11%, and subsequently stored in darkness at a temperature of 4 °C until further use. Prior to analysis, each sample underwent manual dehulling to obtain brown rice, while any broken grains were discarded. To ensure accuracy and reliability of results, three biological replicates were conducted for each rice cultivar.

Fig. 1
figure 1

Photographs illustrating the pigmented and non-pigmented traditional rice

Full size image
Table 1 Detailed agronomic characteristics of the selected traditional rice
Full size table

Sample preparation and extraction

To begin the analysis, a 20 ml centrifuge tube was utilized. Inside the tube, one gram of finely ground rice sample was carefully placed. To create a suitable solution, 10 ml of HPLC grade ethanol was added to the tube. The mixture in the tube underwent vortexing using a LABOID (International instrument from Himachal Pradesh, India) at a speed of 2000 rpm for a duration of ten minutes. After vortexing, the mixture was subjected to centrifugation for 20 min at a speed of 5000 rpm [22]. Following centrifugation, the supernatant obtained from the process underwent concentration using a rotary evaporator and subsequently filtered utilizing a PVDF syringe filter with a pore size of 0. 2 μm. The resulting filtrate was then stored in an air-tight glass vial at temperatures of 4 °C in preparation for further chromatographic analysis.

Chromatography Condition and Analysis

The ethanolic extract that had undergone filtration was analyzed for metabolites using a Thermo Fisher ISQ triple quadrupole gas chromatograph - mass spectrometer, specifically the Thermo Fisher TSQ 8000 Duo Triple Quadrupole GC-MS/MS. The gas chromatograph (GC) was equipped with a fused silica capillary column DB-5 measuring 30 m in length and 0. 25 mm in internal diameter, with a film thickness of 0. 25 μm. Helium gas was utilized as the carrier gas at a flow rate of 1.0 ml/min. Subsequently, a volume of 1 ml from the specimen was preserved in a screw-top vial of 2 ml capacity, which was then loaded into an auto-injector. A minute quantity of 1 µl from the sample was injected in split mode (ratio: 1:10). The temperature of both the detector and injector were maintained at 250◦C throughout the process. The oven temperature was programmed to increase gradually, starting at 70◦C for a duration of 15 min, followed by a rapid rise to 280◦C at a rate of 30◦C per minute. Afterward, it remained constant for another ten minutes be-fore being lowered to 250◦C at a rate of ten degrees per minute. The mass spectrometer set-tings consisted of operating in full scan mode with electron impact spectra at an energy level of 70 eV. Additionally, the ion source temperature was set at 260◦C while the trans-mission line temperature remained steady at 280◦C. The range of the mass scan, measured in mass-to-charge ratio (m/z), was set between 50 and 650 atomic mass units (amu). A solvent delay of 3 min was implemented [23]. The identification of bioactive molecules involved comparing their mass spectra with the NIST 08 Mass Spectra Library, which is maintained by the National Institute of Standards and Technology. The name, molecular weight, and structure of the identified molecules were determined using data-bases such as NIST, Pub Chem and HMDB.

Statistical analysis

The experiments were conducted in triplicate and the metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the human metabolome database (HMDB). To analyze the metabolites, both univariate and multivariate analysis techniques were employed using the R package-based Metabo Analyst 5. 0 [24]. Specifically, an OPLS-DA model was utilized to compare the metabolic characteristics of pigmented and non-pigmented rice varieties. Prior to analysis, the metabolite data underwent normalization and auto scaling procedures. The criteria for screening differential metabolites involved setting the variable importance in the projection (VIP) value to be greater than or equal to 1 in the OPLS-DA model, and also requiring an absolute Log2FC (fold change) value of at least 1. VIP metabolites were subjected to Debiased Sparse Partial Correlation algorithm (DSPC) and pathway map network in Cytoscape software. Venn diagrams were utilized to visually represent the count of these differential metabolites. Furthermore, pathways containing metabolites that exhibited significant regulation were subjected to a metabolite sets enrichment analysis (MSEA). The significance of these pathways was evaluated using p-values derived from hypergeometric tests.

Results

Metabolite detection

The metabolite profiles of samples were systematically analyzed pigmented (Kullakar) and non-pigmented (Milagu Samba) rice for the first time in this research work. A comprehensive non targeted metabolite analysis of pigmented and non-pigmented rice was conducted using GC-MS/MS revealed total of 168 metabolites (Table S1, Fig. S1 & S2). Precisely, Kullakar exhibited 114 metabolites whereas non-pigmented Milagu Samba dis-closed with 103 metabolites with 49 metabolites being shared commonly between 2 varieties. Identified 168 metabolites includes 11 Benzene and substituted derivatives, 13 each under Prenol lipids and Saturated hydrocarbons, 19 separately by Organooxygen compounds and Steroids and steroid derivatives and 62 Fatty Acyls. Biological classes namely Acyl halides, Carboxylic acids and derivatives, Dihydrofurans, Phenols, Pyridines and derivatives and Unsaturated hydrocarbons consists of 2 metabolites under each category. The remaining 19 classes shares one metabolite individually.

Identified metabolites were categorized into 32 significant biological classes. These covered of Benzene and substituted derivatives (6.63%), Prenol lipids (7.83%), Saturated hydrocarbons (7.83%), Organooxygen compounds (11.40%), Steroids and steroid derivatives (11.40%) and Fatty Acyls (37.3%) (Fig. 2). Classes such as Acyl halides, Carboxylic acids and derivatives, Dihydrofurans, Phenols, Pyridines and derivatives and Unsaturated hydrocarbons shares 1.20% each.

A Venn diagram was constructed to exemplify the overlapping and differed metabolites between Kullakar and Milagu Samba (Fig. 3; Table S2). In the pairwise comparison, 49 metabolites were found to be overlapped that includes majorly fatty acids, steroids, sugars, terpenes and alkanes. Venn diagram exhibited 65 metabolites were particular to Kullakar and 54 to Milagu samba.

Fig. 2
figure 2

The provided pie chart illustrates the distribution of identified metabolites across various biological classes, as per the classifications of the Human Metabolome Database (HMDB)

Full size image
Fig. 3
figure 3

Venn diagram can be employed to visually represent the shared and distinct metabolites between pigmented and non-pigmented varieties of rice

Full size image

Principal component analysis

A complete multivariate statistical analysis called PCA was performed on two different coloured traditional rice to explicate the dissimilarities in their metabolite composition. The principal components (PCs) that exhibited eigen value of greater than one was held in the study. Selected PCs in the score diagram (Fig. 4) clearly indicates the metabolite loadings of Kullakar on the negative and the non-pigmented on positive side which undoubtedly demonstrates the pigmentation based discernible variation between the samples. The unsupervised classification extracted two PCs explaining 98.30% of total variation (Table S3; Fig. S3).

Fig. 4
figure 4

Score plot of PCA

Full size image

The first PC alone described 96.4% variation that is correlated to 21 metabolites such as (3beta,4alpha,5alpha,9beta)-4,14-Dimethyl-9,19-cycloergost-24-en-3-ol, (9Z,11 S,16 S)-1-Acetoxy-9,17-octadecadiene-12,14-diyne-11,16-diol, 6-Octadecenoic acid, Acetylhydrazine, alpha-Sitosterol, Benzothiazole, Bovinic acid, Cholest-5-ene, Choles-tan-3-ol, cis-Vaccenic acid, Clionasterol, Dihydrobrassicasterol, Isofucosterol 3-O-[6-O-(9,12-Octadecadienoyl)-b-D-glucopyranoside], Lignocerane, Linoleic acid, Me-thyl hexadecanoic acid, Palmitic acid, Paullinic acid, Pentadecanoic acid, Stigmasterol and trans-12-Octadecenoic acid. The second PC shared 1.4% to the total variation was mainly contributed by 1-Hexadecanol, 1-hydroxylycopene, 2,6,6-Trimethylcyclohex-2-en-1-one, 3-Palmitoyl-sn-glycerol, 6-Octadecenoic acid, Ascorbic acid, Cholest-5-ene, Clionasterol, cis-Vaccenic acid, Dihydrobrassicasterol, Geranyl-geranyl-PP, Heptadecanoic acid, Isofucosterol 3-O-[6-O-(9,12-Octadecadienoyl)-b-D-glucopyranoside], Lignocerane, Oleic acid, Palmitic acid, Palmitoyl chloride, Paullinic acid, petroselinate and trans-12-Octadecenoic acid.

Screening of differential metabolites

An OPLS-DA model was constructed to precisely recognize the distinct metabolites among the 2 rice varieties. The values of R2Y and Q2 were adjacent to 1 (R2X = 0.687, R2Y = 0.999, Q2 = 0.977), that validates the reliability and stability of OPLS-DA model for identifying the differential metabolites between the sample groups (Fig. S4, Table S4). The score graph of OPLS-DA (Fig. 5) markedly separated the Kullakar from Milagu samba, indicating the ascertain variation in the metabolite phenotypes of pigmented and non-pigmented rice varieties. The score plot of OPLS-DA model (Fig. 5) efficiently discriminated the metabolite distribution within and between the sample groups and the organisation of samples were exactly matched the PCA score plot (Fig. 4). The PLS-DA showed similar variance like PCA, and showed 68.7% and 9.5% variance along latent variables 1 and latent variables 2, respectively (Fig. 5).

Differential metabolites were chosen between the 2 rice groups according to the OPLS-DA model (Fig. 5), variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. The screened 144 metabolites (80 up regulated and 64 down regulated) are visually depicted through volcano plots (Fig. 6) (Table S5) that were significantly different among Kullakar and Milagu Samba.

Fig. 5
figure 5

Score plot of OPLS-DA for differential metabolites analysis of pigmented rice compared to non-pigmented rice

Full size image
Fig. 6
figure 6

The volcano plot diagram depicts the expression levels of differential metabolites (with a fold change greater than 2) between pigmented and non-pigmented rice varieties

Full size image

Variable important projection (VIP)

The PLS-DA model was used to calculate each metabolite’s Variable Importance in the Projection (VIP) ratings. The most significant biomarkers were found to be metabolites with a VIP score higher than 1.0. These particular metabolites have the ability to distinguish between pigmented and non-pigmented rice samples. The top 30 metabolite features having VIP value of more than one was identified from the pigmented and non-pigmented rice (Fig. 7). Most discriminated list of metabolites included Stearic acid, Paullinic acid, Cholestan-3-ol, Linoleic acid, Dihydrobrassicosterol, Lignocerane, Palmitic acid, Bovinic acid, Palmitoleic acid, Ribavirin and petroselinate, alpha-Sitosterol and Stigmasterol.

Fig. 7
figure 7

VIP scores for the selected differential metabolites

Full size image

Correlation network analysis

To further elucidate the importance and association of identified top 30 metabolite features correlation network was constructed using a regularization technique called the DSPC was created to handle high-dimensional metabolomics data derived from mass spectrometry [25] in Cytoscape software. A total of 30 metabolites were identified as candidate biomarkers based on OPLS-DA analysis through VIP Score among pigmented and non-pigmented varieties. A visualization of the correlation networks is presented in Fig. 8. All these 30 high influential metabolites were belonged to fatty acids, steroids, alkanes and organooxygen compounds. High positive correlation among the fatty acids were found between (9Z,11 S,16 S)-1-Acetoxy-9,17-octadecadiene-12,14-diyne-11,16-diol and 9Z,12E-Octadecadienoic acid. An organooxygen metabolite 2,6,6-Trimethylcyclohex-2-en-1-one was positively correlated with 9Z,12Z-octadecadienoyl-CoA and Methyl hexadecanoic acid. Among the steroids 24-Methylenecycloartan-3-ol was positively correlated with the fatty acid compounds like (9Z,11 S,16 S)-1-Acetoxy-9,17-octadecadiene-12,14-diyne-11,16-diol and 9Z,12E-Octadecadienoic acid. Alkanes namely Lignocerane and Heneicosane are having high negative correlation with 6 Octadecenoic acid. There is no negative correlation found among the fatty acids. Steroids have strong negative correlation with fatty acids.

Fig. 8
figure 8

The correlation network among the VIP metabolites is visualized. Positive correlations are represented by the orange color, while negative correlations are depicted in violet. The size of the network lines reflects the strength of the associations between the metabolites

Full size image

Pathway enrichment analysis

Enrichment analysis was conducted with the purpose of revealing the vital biological role, which can greatly contribute to our understanding of the primary molecular function. The differential metabolites (144) identified through the Volcano analysis and Fold change were subjected to enrichment analysis based on the Overall representative analysis and p-values of the biological pathways that involved 27 metabolic pathways (Fig. 9; Table S6). Pathways namely Fatty Acid Biosynthesis, Beta Oxidation of Very Long Chain Fatty Acids and Plasmalogen Synthesis were enriched significantly (p-value < 0.05) in the Kullakar and Milagu Samba comparison.

Discussion

Rice holds great significance both in terms of human impact and economic value on a global scale. Enhancing grain yield and disease resistance are the main objectives when it comes to improving crop genetics [26]. Additionally, the focus on enhancing the nutritional quality of rice has gained considerable attention in recent years [27]. Wild rice and landraces, with their diverse genetic makeup, have become crucial resources for advancing rice genetics and developing new cultivars [28]. Landraces have garnered attention for their ability to adapt to local environments, tolerate abiotic stress, and possess specific metabolic components [29]. Through ongoing domestication and improvement processes, landraces have exhibited distinct characteristics compared to cultivated rice [30].

Pigmented rice is increasingly popular due to its bioactive compounds like phenols, flavonoids, nutritional benefits, minerals, vitamins and plant sterols [11, 31, 32] amidst the COVID pandemic. Untargeted metabolomic approach was used to produce a comprehensive report on metabolomic profile of pigmented and non-pigmented rice. The study identified 168 metabolites comprised of fatty acids, sugars, steroids and benzene com-pounds which has significant role in nutritional as well as the pharmacological sectors (Table 2 & Table S1).

Table 2 Biological significance of metabolites identified from the study
Full size table

Significant differences were observed for the total levels of covered fatty alcohols be-tween pigmented and non-pigmented rice. Moreover, pigmented rice had lower fatty acid levels than non-pigmented rice as demonstrated by [33]. Besides, both saturated and un-saturated fatty acids were also identified in both types of rice. Markedly, the pigmented rice exhibited a comparatively higher abundance of monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), suggesting its efficacy in reducing cholesterol levels by raising high density lipoprotein (HDL) [34] and potentially enhancing consumer preference. Edible oils are commonly relied upon as a source of PUFAs in the diet, nevertheless the presence of polyunsaturated fatty acids (PUFAs) in traditional rice offers a valuable and cost-effective alternative for individuals who may find it challenging to afford high-value edible oils. This not only addresses nutritional needs but also contributes to dietary diversity, emphasizing the significance of locally available and affordable food sources (traditional varieties) and helps to achieve SDG 2. Moreover, the fatty acid composition of rice is closely tied to its palatability. Studies [35] report that rice cultivars with higher fatty acid content generally exhibit superior eating quality.

Differences in the composition and contents of phenolic acids among various rice varieties have been observed, potentially contributing to their bioactivities. Specifically, this study found that Kullakar had a relatively higher content of 2-Methoxy-4-vinylphenol compared to Milagu Samba. Consequently, variations in the antioxidant activities of these two varieties may be associated with discrepancies in phenolic compound levels as reported in the previous finding [11]. These findings suggest that consumer preferences during the pandemic situation could be influenced by such differences.

Vitamins and phytosterols, including campesterol, β-sitosterol, cholesterol, and stigmasterol, Dihydrobrassicasterol exhibited higher abundance in Kullakar compared to non-pigmented Milagu Samba agreeing the findings of Kim et al. [36]. Notably, these metabolites played a pivotal role in distinguishing Kullakar from Milagu Samba samples in both PCA and OPLS-DA, forming a distinct cluster separate from other metabolites. Nevertheless, levels of plant sterols are exemplary found in both types specifies the need for its consumption irrespective of pigmentation.

Content of terpenoids were higher in Kullakar except for Geranylgeranyl-PP, plays a crucial role in treating a diverse range of diseases. Numerous in vitro and in vivo studies have explored its potential applications as anticancer agents, antimicrobial agents, anti-inflammatory agents, antioxidants, antiallergic agents, neuroprotective agents, anti-aggregators, anti-coagulants, sedatives and analgesics. These therapeutic effects are attributed to the activities of monoterpenes, sesquiterpenes, diterpenes, triterpenes, tetraterpenes and glycoside compounds as outlined in the previous research [37].

In the present study, it was found that α-Tocopherol was the most prevalent form of the tocopherols analyzed. This finding is consistent with previous targeted analyses conducted on both coloured and non-coloured rice [38, 39]. According to a previous study [40], α-tocopherol levels are highest in Black rice. However, in the present study, pigmented rice contained less β-tocopherol than non-pigmented white rice.

Among the varieties studied, there were significant variations in the levels of α-tocopherol, resulting in a wide range for this compound. It is worth noting that Milagu Samba exhibited particular benzothiazoles, carbothioic S-acids, and carboxylic acid derivatives not observed in other varieties.

The PCA analysis clearly separated the Kullakar and Milagu Samba in two different clusters (Fig. 4). The PC1 and PC2 contributed to 96.4% and 1.9% of the variance, respectively. The results demonstrated that PC1 separated pigmented (Kullakar) and non-pigmented rice (Milagu Samba), indicating that genetic variation strongly influenced the metabolite profiles of pigmented and non-pigmented rice varieties, align with the previous finding [41]. The present study revealed a distinct separation based on the colour of rice is in accordance with the earlier research [39]. From the study, it is clearly evident that the due to the colour discrimination between the 2 varieties, lycopene and carotene were specific only to pigmented variety.

Furthermore, OPLS-DA was used to find potentially significant biomarker metabolites that are connected to the pigmentation of rice. It is a supervised technique, primarily utilized for sample classification and biomarker metabolite selection [42]. The interaction between two rice groups and variable compounds was established using an effective statistical tool called OPLS-DA (43). R2X and R2Y represents the percentage of the fitting equation explanations for X and Y matrices, respectively, while Q2 shows the fitting equations predictive ability. The coefficients of R2 and Q2 surpassing 0.5 and closer to 1.0 can be considered as accurate findings as suggested by [44]. In the present investigation (Fig. S4), established predictive model fitted the differential compounds between the pigmented and non-pigmented traditional rice varieties with predictive ability Q2 (cum) = 0.977, goodness-of-fit parameter R2X (cum) = 0.687 and variance explanatory ability R2Y (cum) = 0.999. The OPLS-DA score plot (Fig. 5) indicated the clear separation of these 2 varieties which corroborates with the previous findings [11].

The screened 144 metabolites are visually depicted through volcano plots (Fig. 6) (Table S5) that were significantly different among Kullakar and Milagu Samba. Eighty compounds were up regulated in Kullakar over Milagu Samba that includes Glycerolipids, Lactones, Oxocins, Phenol esters, Phenols and Thiols. The down regulated (64 compounds) metabolites comprised of Acyl halides, Aspidospermatan-type alkaloids, Benzothiazoles, Carbothioic S-acids, Carboxylic acids and derivatives, Heteroaromatic compounds, Naphthalenes, Pyridines and derivatives and Triazole ribonucleosides. Findings of this clearly indicates the discernible variations in metabolite composition of pigmented and non-pigmented rice.

The score plot of OPLS-DA model (Fig. 5) efficiently discriminated the metabolite distribution within and between the sample groups and the organisation of samples were exactly matched the PCA score plot (Fig. 4). The PLS-DA showed similar variance like PCA, and showed 68.7% and 9.5% variance along latent variables 1 and latent variables 2, respectively (Fig. 5). Same results were also reported by [45, 46].

The differential metabolites identified through OPLS-DA model was 144 (64 down regulated and 85 up regulated) and these significant differences observed between Kullakar and Milagu Samba amounted to 59 per cent. Hence, these significant values validate the notion that the influence of colour variations among rice genotypes on their metabolite profiles extend beyond environmental factors and confirm the findings of previous investigation [39].

Biomarker metabolites with a larger influence on the separation of pigmentation were chosen based on their VIP values (VIP > 1.0) in order to decrease the overall number of metabolites discovered. According to the OPLS-DA score plot, these biomarker metabolites were in charge of the clustering and separation of the pigmentation. The top 30 metabolite features having VIP value of more than one was identified from the pigmented and non-pigmented rice (Fig. 7) was aligns with the previous study [47]. Fatty acids prove to be good biomarkers for differentiating two rice groups. Most discriminated list of metabolites included Stearic acid, Paullinic acid, Cholestan-3-ol, Linoleic acid, Dihydrobras sicosterol, Lignocerane, Palmitic acid, Bovinic acid, Palmitoleic acid, Ribavirin and petroselinate, alpha-Sitosterol and Stigmasterol.

Two alkanes that show a notable negative connection with 6-Octadecenoic acid are lignocerane and heniceicosane. This implies a novel link where 6-Octadecenoic acid levels are inversely correlated with the presence or concentration of alkanes in the context of lignocerane and henocersane. Interestingly, though, there were no unfavourable associations found between the different fatty acids, suggests that each member of this class of chemicals exhibits unique and autonomous behavior.

The Kyoto Encyclopedia of Genes and Genomes (KEGG) database holds a prominent and esteemed position as the foremost public pathway database. It is highly regarded for its exceptional utility in conducting research on signal transduction pathways and the accumulation of metabolites [48]. In our study, we took great care to meticulously annotate and enhance the differential metabolites for each comparison group. These metabolites were then systematically organized into specific KEGG pathways, allowing for a more comprehensive understanding of their roles and functions. The purpose of this enrichment analysis is to identify crucial biological pathways that impact specific biological processes, thus revealing the fundamental molecular mechanisms that underlie these processes (Fig. 9). The differential metabolites (144) were subjected to enrichment analysis and identified 27 metabolic pathways (Fig. 9; Table S6). Pathways namely Fatty Acid Biosynthesis, Beta Oxidation of Very Long Chain Fatty Acids and Plasmalogen Synthesis were enriched significantly (p-value < 0.05) in the Kullakar and Milagu Samba comparison. This indicates the significant difference in the concentration of MUFAs and other differential metabolites. The red colour bubble of this pathway indicates that it has high confidence (low p values) and less chance of error. On another side, Alpha Linolenic Acid and Linoleic Acid Metabolic pathway exhibited higher bubble size but low enrichment factor, accompanied by low p values, indicating the less chance of error. Nearly 6 numbers of metabolites have been involved in this pathway.

The heatmap presented top 40 differential metabolites are belonging to the biological classes of amino acids, sugars, sterols and alkanes. Metabolites are visually represented in a colour gradient from green to red, indicating their content in decreasing order. Cells rep-resented in red indicate a higher relative abundance of the metabolites in the rice samples, while green designate lower concentration. Interestingly, over 50% of the highlighted metabolites are found in samples with high relative abundances. The results of heatmap associated with the data resultant from PCA, confirming the evenness of the results. Ac-cording to the present study heatmap (Fig. 9), Stearic acid, Paullinic acid, Palmitic acid, Bovinic acid, Palmitoleic acid, Ribavirin and (+)-Aspidospermidine had lower relative abundance among the Kullakar replications compared to Milagu Samba. These variations revealed that the metabolite composition is primarily influenced by the pigmentation of the variety. Present study confirms the findings of [49] (Fig. 10).

Fig. 9
figure 9

The KEGG pathway analysis was performed on distinct metabolites for pigmented and non-pigmented rice. In the visual representation, each bubble represents a metabolic pathway, with the horizontal axis indicating the extent of associated factors (larger bubbles indicating more significant impacts). The colour of the bubbles corresponds to the p-value obtained from the enrichment analysis, with lighter colors indicating lower levels of enrichment

Full size image
Fig. 10
figure 10

Heat map analysis showing the abundance of OPLS-DA based differential metabolites between the grains of Kullakar and Milagu Samba. Cells represented in red indicate a higher relative abundance of the metabolites in the rice samples, while green designate lower concentration. The cluster was constructed based on the fold-change heat-map, utilizing similarity measured by Euclidean distance and linkage rule by Ward’s method. K1-3 indicates Kullakar and MS1-3 denotes Milagu Samba

Full size image

In order to meet Sustainable Development Goal 2 (SDG 2) of the UN, it is imperative to cultivate nutritious traditional rice varieties. Due to their rich and varied nutrient pro-files, these cultivars greatly improve nutrition and global food security. Traditional rice varieties are strongly aligned with SDG 2’s commitment to ensuring that everyone has ac-cess to safe and nutritious food, as they have the potential to prevent malnutrition and correct specific nutrient deficiencies. Their production also promotes resilient agriculture, climate change adaptation, and the preservation of agricultural biodiversity. In addition to empowering communities and protecting cultural heritage, states can support SDG 2’s larger goals of fostering a more resilient, diverse, and sustainable global food system by encouraging the production and consumption of these varieties.

Conclusions

This study presents a comprehensive examination of the range of metabolites found in Kullakar and Milagu samba, which has not been done before. By utilizing relevant sta-tistical methods, researchers were able to analyze and evaluate the variations in metabolism between pigmented and non-pigmented rice. With the progression of the social economy and improvements in living standards, the occurrence of a pandemic situation adds a complex dynamic. To fulfil the aim of achieving Sustainable Development Goal 2 (SDG 2) for zero hunger and proper nutrition, there is an escalating demand for higher quality rice among consumers. Consequently, there has been an enhanced interest in consuming traditional rice varieties. The future trajectory of seed industry development will likely prioritize the breeding of rice varieties with superior eating quality. These findings not only provide valuable insights into the metabolite composition of pigmented and non-pigmented rice grains but also lay the groundwork to expedite the breeding of rice varieties with superior functional and nutritional significance.

Data availability

The datasets generated and analyzed in the current study are available from the corresponding author on reasonable request. All data generated or analyzed during this study are included in this published article and its Supplementary information files.

Abbreviations

SDG 2:

Sustainable Development Goal - Zero Hunger

PCA:

Principal Component Analysis

OPLS-DA:

Orthogonal Partial Least Square Discrimination Analysis

KEGG:

Kyoto Encyclopedia of Genes and Genomes

GC-MS/MS:

Gas Chromatography-Mass Spectrometry

PVDF:

Polyvinylidene fluoride

RPM:

Rotation Per Minute

HMDB:

Human Metabolome Database

NIST:

National Institute of Standards and Technology

MSEA:

Metabolite Sets Enrichment Analysis

HPLC:

High-performance liquid chromatography

VIP:

variable importance in the projection

DSPC:

Debiased Sparse Partial Correlation algorithm

FC:

Fold Change

MUFAs:

Monounsaturated Fatty Acids

PUFAs:

Polyunsaturated Fatty Acids

HDL:

High Density Lipoprotein

References

  1. Hussain M, Farooq M, Lee DJ. Evaluating the role of seed priming in improving drought tolerance of pigmented and non-pigmented rice. J Agron Crop Sci. 2017;203:269–76. https://doi.org/10.1111/jac.12195.

    Article  CAS  Google Scholar 

  2. Kim TJ, Kim SY, Park YJ, Lim SH, Ha SH, Park SU, Kim JK. Metabolite profiling reveals distinct modulation of complex metabolic networks in non-pigmented, black, and red rice (Oryza sativa L.) cultivars. Metabolites. 2021;11:367. https://doi.org/10.3390/metabo11060367.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Rawat D, Gulati A, Singh N, Vikram NK, Kumar A, Sharma A. Holistic approach during a pandemic for healthy well-being. Indian J Nutr Diet. 2020;57:329–40. https://doi.org/10.21048/IJND.2020.57.3.25459.

    Article  Google Scholar 

  4. Deng GF, Xu XR, Zhang Y, Li D, Gan RY, Li HB. Phenolic compounds and bioactivities of pigmented rice. Crit Rev Food Sci. 2013;53:296–306. https://doi.org/10.1080/10408398.2010.529624.

    Article  CAS  Google Scholar 

  5. Kim JK, Park SY, Lim SH, Yeo Y, Cho HS, Ha SH. Comparative metabolic profiling of pigmented rice (Oryza sativa L.) cultivars reveals primary metabolites are correlated with secondary metabolites. J Cereal Sci. 2013;57:14–20. https://doi.org/10.1016/j.jcs.2012.09.012.

    Article  CAS  Google Scholar 

  6. Nam SH, Choi SP, Kang MY, Koh HJ, Kozukue N, Friedman M. Antioxidative activities of bran extracts from twenty one pigmented rice cultivars. Food Chem. 2006;94:613–20. https://doi.org/10.1016/j.foodchem.2004.12.010.

    Article  CAS  Google Scholar 

  7. Tsuda T, Horio F, Osawa T. Cyanidin 3-O-beta-D-glucoside suppresses nitric oxide production during a zymosan treatment in rats. J Nutr Sci Vitaminol. 2002;48:305–10. https://doi.org/10.3177/jnsv.48.305.

    Article  CAS  PubMed  Google Scholar 

  8. Chang W, Mi MT, Ling WH. Effects of anthocyanin-rich extract from black rice alone or combined with chemotherapeutic agents on proliferation of different cancer cells. Acta Acad Med Mlitaris. 2007;29:1943–6.

    CAS  Google Scholar 

  9. Guo HH, Ling WH, Wang Q, Liu C, Hu Y, Xia M, Feng X, Xia XD. Effect of anthocyanin-rich extract from Black Rice (Oryza sativa L. indica) on hyperlipidemia and insulin resistance in Fructose-Fed rats. Plant Food Hum Nutr. 2007;62:1–6. https://doi.org/10.1007/s11130-006-0031-7.

    Article  CAS  Google Scholar 

  10. Alves GH, Ferreira CD, Vivian PG, Monks JL, Elias MC, Vanier NL, de Oliveira M. The revisited levels of free and bound phenolics in rice: effects of the extraction procedure. Food Chem. 2016;208:116–23. https://doi.org/10.1016/j.foodchem.2016.03.107.

    Article  CAS  PubMed  Google Scholar 

  11. Di Zhang Lina X, Ma B, Han L, Han. Comparative analysis of rice reveals insights into the mechanism of colored rice via widely targeted metabolomics. Food Chem. 2023;399:133926. https://doi.org/10.1016/j.foodchem.2022.133926.

    Article  CAS  PubMed  Google Scholar 

  12. Pang Y, Ahmed S, Xu Y, Beta T, Zhu Z, Shao Y, Bao J. Bound phenolic compounds and antioxidant properties of whole grain and bran of white, red and black rice. Food Chem. 2018;240:212–21. https://doi.org/10.1016/j.foodchem.2017.07.095.

    Article  CAS  PubMed  Google Scholar 

  13. Niu Y, Gao B, Slavin M, Zhang X, Yang F, Bao J, Yu L. Phytochemical compositions, and antioxidant and an-ti-inflammatory properties of twenty-two red rice samples grown in Zhejiang. LWT - Food Sci Technol. 2013;54:521–7. https://doi.org/10.1016/j.lwt.2013.06.018.

    Article  CAS  Google Scholar 

  14. Ge X, Jing L, Zhao K, Su C, Zhang B, Zhang Q, Li W. The phenolic compounds profile, quantitative analysis and anti-oxidant activity of four naked barley grains with different color. Food Chem. 2021;335:127655. https://doi.org/10.1016/j.foodchem.2020.127655.

    Article  CAS  PubMed  Google Scholar 

  15. Min B, Gu L, McClung AM, Bergman CJ, Chen MH. Free and bound total phenolic concentrations, antioxidant capacities, and profiles of proanthocyanidins and anthocyanins in whole grain rice (Oryza sativa L.) of different bran colours. Food Chem. 2012;133:715–22. https://doi.org/10.1016/j.foodchem.2012.01.079.

    Article  CAS  Google Scholar 

  16. Paiva FF, Vanier NL, Berrios JDJ, Pinto VZ, Wood D, Williams T, Elias MC. Polishing and parboiling effect on the nutritional and technological properties of pigmented rice. Food Chem. 2016;191:105–12. https://doi.org/10.1016/j.foodchem.2015.02.047.

    Article  CAS  PubMed  Google Scholar 

  17. Sukhonthara S, Theerakulkait C, Miyazawa M. Characterization of volatile aroma compounds from red and black rice bran. J Oleo Sci. 2009;58:155–61. https://doi.org/10.5650/jos.58.155.

    Article  CAS  PubMed  Google Scholar 

  18. Gunaratne A, Wu K, Li D, Bentota A, Corke H, Cai YZ. Antioxidant activity and nutritional quality of traditional red-grained rice varieties containing proanthocyanidins. Food Chem. 2013;138:1153–61. https://doi.org/10.1016/j.foodchem.2012.11.129.

    Article  CAS  PubMed  Google Scholar 

  19. Srisawat U, Panunto W, Kaendee N, Tanuchit S, Itharat A, Lerdvuthisopon N, Hansakul P. Determination of phenolic compounds, flavonoids, and antioxidant activities in water extracts of Thai red and white rice cultivars. J Med Assoc Thai. 2010;93:S83–91. https://doi.org/10.1055/s-0030-1264431.

    Article  PubMed  Google Scholar 

  20. Shao Y, Hu Z, Yu Y, Mou R, Zhu Z, Beta T. Phenolic acids, anthocyanins, proanthocyanidins, antioxidant activity, minerals and their correlations in non-pigmented, red, and black rice. Food Chem. 2018;239:733–41. https://doi.org/10.1016/j.foodchem.2017.07.009.

    Article  CAS  PubMed  Google Scholar 

  21. Shao Y, Xu F, Sun X, Bao J, Beta T. Identification and quantification of phenolic acids and anthocyanins as antioxidants in bran, embryo and endosperm of white, red and black rice kernels (Oryza sativa L). J Cereal Sci. 2014;59:211–8. https://doi.org/10.1016/j.jcs.2014.01.004.

    Article  CAS  Google Scholar 

  22. Krishnanunni K, Senthilvel P, Ramaiah S, Anbarasu A. Study of chemical composition and volatile compounds along with in-vitro assay of antioxidant activity of two medicinal rice varieties: Karungkuravai and Mappilai samba. J Food Sci Technol. 2015;52:2572–84. https://doi.org/10.1007/s13197-014-1292-z.

    Article  CAS  PubMed  Google Scholar 

  23. Ashokkumar K, Govindaraj M, Vellaikumar S, Shobhana VG, Karthikeyan A, Akilan M. Comparative profiling of volatile compounds in popular south Indian traditional and modern rice varieties by gas chromatographymass spectrometry analysis. Front Nutr. 2020;7:599119. https://doi.org/10.3389/fnut.2020.599119.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M. Metabo Analyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021;49:388–96. https://doi.org/10.1093/nar/gkab382.

    Article  CAS  Google Scholar 

  25. De Perez L, Alseekh S, Brotman Y, Fernie AR. Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Rev Proteomics. 2020;17:243–55. https://doi.org/10.1080/14789450.2020.1766975.

    Article  CAS  Google Scholar 

  26. Wambugu PW, Ndjiondjop MN, Henry R. Genetics and genomics of African rice (Oryza glaberrima Steud) domestication. Rice. 2021;14:6. https://doi.org/10.1186/s12284-020-00449-6.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Mbanjo EGN, Kretzschmar T, Jones H, Ereful N, Blanchard C, Boyd LA, Sreenivasulu N. The genetic basis and nutritional benefits of pigmented rice grain. Front Genet. 2020;11:229. https://doi.org/10.3389/fgene.2020.00229.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Ray A, Deb D, Ray R, Chattopadhayay B. Phenotypic characters of rice landraces reveal independent lineages of short-grain aromatic indica rice. AOB Plants. 2013;5:32. https://doi.org/10.1093/aobpla/plt032.

    Article  Google Scholar 

  29. Fukuoka S, Suu TD, Ebana K, Trinh LN, Nagamine T, Okuno K. Diversity in phenotypic profiles in landrace populations of Vietnamese rice: a case study of agronomic characters for conserving crop genetic diversity on farm. Genetic Re-sources Crop Evol. 2006;53:753–61. https://doi.org/10.1007/s10722-004-4635-1.

    Article  Google Scholar 

  30. Luong NH, Linh LH, Shim KC, Adeva C, Lee HS, Ahn SN. Genetic structure and geographical differentiation of traditional rice (Oryza sativa L.) from Northern Vietnam. Plants. 2021;10:2094. https://doi.org/10.3390/plants10102094.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Ito VC, Lacerda LG. Black rice (Oryza sativa L.): a review of its historical aspects, chemical composition, nutritional and functional properties, and applications and processing technologies. Food Chem. 2019;301:125304. https://doi.org/10.1016/j.foodchem.2019.125304.

    Article  CAS  PubMed  Google Scholar 

  32. Mau JL, Lee CC, Chen YP, Lin SD. Physicochemical, antioxidant and sensory characteristics of chiffon cake prepared with black rice as replacement for wheat flour. LWT - Food Sci Technol. 2017;75:434–9. https://doi.org/10.1016/j.lwt.2016.09.019.

    Article  CAS  Google Scholar 

  33. Kim TJ, Kim SY, Park YJ, Lim SH, Ha SH, Park SU, Lee B, Kim JK. Metabolite Profiling reveals distinct modulation of Complex Metabolic Networks in Non-pigmented, Black, and Red Rice (Oryza sativa L.) cultivars. Metabolites. 2021;11:367. https://doi.org/10.3390/metabo11060367.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Lunn J, Theobald HE. The health effects of dietary unsaturated fatty acids. Nutr Bull. 2006;31:178–224. https://doi.org/10.1111/j.1467-3010.2006.00571.x.

    Article  Google Scholar 

  35. Yoon MR, Rico CW, Koh HJ, Kang MY. A study on the lipid components of rice in relation to palatability and storage. J Korean Soc Appl Biol Chem. 2012;55:515–21. https://doi.org/10.1007/s13765-012-2050-4.

    Article  CAS  Google Scholar 

  36. Kim JK, Lee SY, Chu SM, Lim SH, Suh SC, Lee YT, Cho HS, Ha SH. Variation and correlation analysis of fla-vonoids and carotenoids in Korean pigmented rice (Oryza sativa L.) cultivars. J Agric Food Chem. 2010;58:12804–9. https://doi.org/10.1021/jf103277g.

    Article  CAS  PubMed  Google Scholar 

  37. Zhao DD, Jiang LL, Li HY, Yan PF, Zhang YL. Chemical components and pharmacological activities of terpene natural products from the genus Paeonia. Molecules. 2016;21:1362. https://doi.org/10.3390/molecules21101362.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Yoshida H, Tomiyama Y, Mizushina Y. Lipid components, fatty acids and triacylglycerol molecular species of black and red rices. Food Chem. 2010;123:210–5. https://doi.org/10.1016/j.foodchem.2010.04.010.

    Article  CAS  Google Scholar 

  39. Frank T, Reichardt B, Shu Q, Engel KH. Metabolite profiling of colored rice (Oryza sativa L.) grains. J Cereal Sci. 2012;55:112–9. https://doi.org/10.1016/j.jcs.2011.09.009.

    Article  CAS  Google Scholar 

  40. Minatel IO, Han SI, Aldini G, Colzani M, Matthan NR, Correa CR, Fecchio D, Yeum KJ. Fat-soluble bioactive components in colored rice varieties. J Med Food. 2014;17:1134–41. https://doi.org/10.1089/jmf.2014.3146.

    Article  CAS  PubMed  Google Scholar 

  41. Daygon VD, Prakash S, Calingacion M, Riedel A, Ovenden B, Snell P, Fitzgerald M. Understanding the Jasmine phenotype of rice through metabolite profiling and sensory evaluation. Metabolomics. 2016;12:1–15. https://doi.org/10.1007/s11306-016-0989-6.

    Article  CAS  Google Scholar 

  42. Szymanska E, Saccenti E, Smilde AK, Westerhuis JA. Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics. 2012;8:3–16. https://doi.org/10.1007/s11306-011-0330-3.

    Article  CAS  PubMed  Google Scholar 

  43. Wang X, Rogers KM, Li Y, Yang S, Chen L, Zhou J. Untargeted and targeted discrimination of honey collected by Apis cerana and Apis mellifera based on volatiles using HS-GC-IMS and HS-SPME-GC-MS. J Agric Food Chem. 2019;67:12144–52. https://doi.org/10.1021/acs.jafc.9b04438.

    Article  CAS  PubMed  Google Scholar 

  44. Dou X, Zhang L, Yang R, Wang X, Yu L, Yue X. Adulteration detection of essence in sesame oil based on headspace gas chromatography-ion mobility spectrometry. Food Chem. 2022;370:131373. https://doi.org/10.1016/j.foodchem.2021.131373.

    Article  CAS  PubMed  Google Scholar 

  45. Tian Y, Abdallah MF, De Boevre M, Audenaert K, Wang C, De Saeger S, Wu A. Deciphering Alternaria metabolic responses in microbial confrontation via an integrated mass spectrometric targeted and non-targeted strategy. Food Chem. 2023;404:134694. https://doi.org/10.1016/j.foodchem.2022.134694.

    Article  CAS  PubMed  Google Scholar 

  46. Jin W, Jiang L, Hu S, Zhu A. Metabolite features of serum and intestinal microbiota response of largemouth bass (Micropterus salmoides) after Aeromonas hydrophila challenge. Comp Biochem Physiol C: Toxicol Pharmacol. 2023;263:109496. https://doi.org/10.1016/j.cbpc.2022.109496.

    Article  CAS  PubMed  Google Scholar 

  47. Zhang YC, Lin QB, Zhong HN, Zeng Y. Identification and source analysis of volatile flavor compounds in paper pack-aged yogurt by headspace solid-phase microextraction-gas chromatography-mass spectrometry. Food Packaging Shelf Life. 2022;34:100947. https://doi.org/10.1016/j.fpsl.2022.100947.

    Article  CAS  Google Scholar 

  48. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of genes and genomes. Nucleic Acids Res. 2000;27–30. https://doi.org/10.1093/nar/28.1.27.

  49. Rocchetti G, Bocchi S, Senizza B, Giuberti G, Trevisan M, Lucini L. Metabolomic insights into the phytochemical profile of cooked pigmented rice varieties following in vitro gastrointestinal digestion. J Food Compos Anal. 2022;106:104293. https://doi.org/10.1016/j.jfca.2021.104293.

    Article  CAS  Google Scholar 

  50. Fattore E, Fanelli R. Palm oil and palmitic acid: a review on cardiovascular effects and carcinogenicity. Int J Food Sci Nutr. 2013;64:648–59. https://doi.org/10.3109/09637486.2013.768213.

    Article  CAS  PubMed  Google Scholar 

  51. Tanabe E, Kitayosh M, Fujii K, Ohmori H, Luo Y, Kadochi Y, Shiori Mori R, Fujiwara Y, Nishiguchi. Taka-Mitsu Sasaki, Kuniyasu H. Fatty acids inhibit anticancer effects of 5-fluorouracil in mouse cancer cell lines. Oncol Lett. 2017;14:681–6. https://doi.org/10.3892/ol.2017.6190.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Priore P, Gnoni A, Natali F, Testini M, Gnoni GV, Siculella L, Damiano F. Oleic acid and hydroxytyrosol inhibit cholesterol and fatty acid synthesis in C6 glioma cells. Oxidative Medicine and Cellular Longevity. 2017; 2017: 1–10. https://doi.org/10.1155/2017/9076052.

  53. Khan AA, Alanazi AM, Jabeen M, Pervez K, Wahab R, Abdelhameed AS, Chauhan A. Biophysical interactions of novel oleic acid conjugate and its anticancer potential in HeLa cells. J Fluoresc. 2015;25:519–25. https://doi.org/10.1007/s10895-015-1512-6.

    Article  CAS  PubMed  Google Scholar 

  54. Roopashree PG, Shetty SS, Kumari NS. Effect of medium chain fatty acid in human health and disease. J Funct Foods. 2021;87:104724. https://doi.org/10.1016/j.jff.2021.104724.

    Article  CAS  Google Scholar 

  55. Tallima H, El Ridi R. Arachidonic acid: physiological roles and potential health benefits–a review. J Adv re-search. 2018;11:33–41. https://doi.org/10.1016/j.jare.2017.11.004.

    Article  CAS  Google Scholar 

  56. Verruck S, Balthazar CF, Rocha RS, Silva R, Esmerino EA, Pimentel TC, Freitas MQ, Silva MC. Adriano Gomes Da Cruz, Prudencio ES. Dairy foods and positive impact on the consumer’s health. Adv Food Nutr Res. 2019;89:95–164. https://doi.org/10.1016/bs.afnr.2019.03.002.

    Article  CAS  PubMed  Google Scholar 

  57. Cater NB, Denke MA. Behenic acid is a cholesterol-raising saturated fatty acid in humans. Am J Clin Nutr. 2001;73:41–4.

    Article  CAS  PubMed  Google Scholar 

  58. Tripathy S, Jump DB. Elovl5 regulates the mTORC2-Akt-FOXO1 pathway by controlling hepatic cis-vaccenic acid synthesis in diet-induced obese mice [S]. J Lipid Res. 2013;54:71–84. https://doi.org/10.1194/jlr.M028787.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  59. Li H, Huang J, Wang Y, Wang X, Ren Y, Yue T, Zhouli Wang, Gao Z. Study on the nutritional characteristics and anti-oxidant activity of dealcoholized sequentially fermented apple juice with Saccharomyces cerevisiae and Lactobacillus plantarum fermentation. Food Chem. 2021;363:130351. https://doi.org/10.1016/j.foodchem.2021.130351.

    Article  CAS  PubMed  Google Scholar 

  60. Pandohee J. Alpha-linolenic acid. Nutraceuticals and Health Care. Academic; 2022. pp. 279–88.

  61. Darmstadt GL, MaoQiang M, Chi E, Saha SK, Ziboh VA, Black RE, Santosham M, Elias PM. Impact of topical oils on the skin barrier: possible implications for neonatal health in developing countries. Acta Paediatr. 2002;91:546–54. https://doi.org/10.1111/j.1651-2227.2002.tb03275.x.

    Article  CAS  PubMed  Google Scholar 

  62. Varsha KK, Devendra L, Shilpa G, Priya S, Pandey A, Nampoothiri KM. 2, 4-Di-tert-butyl phenol as the antifungal, antioxidant bioactive purified from a newly isolated Lactococcus Sp. Int J Food Microbiol. 2015;211:44–50. https://doi.org/10.1016/j.ijfoodmicro.2015.06.025.

    Article  CAS  PubMed  Google Scholar 

  63. Cho S, Choi CW, Lee DH, Won CH, Kim SM, Lee S, Lee MJ, Chung JH. High-dose squalene ingestion increases type I procollagen and decreases ultraviolet‐induced DNA damage in human skin in vivo but is associated with transient adverse effects. Clin Exp Dermatol. 2009;34:500–8. https://doi.org/10.1111/j.1365-2230.2008.03133.x.

    Article  CAS  PubMed  Google Scholar 

  64. Sesso HD, Buring JE, Christen WG, Kurth T, Belanger C, MacFadyen J, Gaziano JM. Vitamins E and C in the prevention of cardiovascular disease in men: the Physicians’ Health Study II randomized controlled trial. JAMA. 2008;300:2123–33. https://doi.org/10.1001/jama.2008.600.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  65. Muhammad Imran F, Ghorat H, Ur-Rehman F, Aslam M, Heydari MA, Shariati Ele-onora, Okuskhanova. Zhanibek Yessimbekov, Muthu Thiruvengadam, Mohammad Hashem Hashempur, Maksim Rebe-zov, Maksim Rebezov. Lycopene as a natural antioxidant used to prevent human health disorders. Antioxidants. 2020;9:706. https://doi.org/10.3390/antiox9080706.

    Article  CAS  PubMed  Google Scholar 

  66. Tanumihardjo JP, Morganstern E, Gunter KE, Martinez A, Altschuler S, Towns C, Eric Schwartz MD, Kathleen Hopkins MBA, Jessica Burnett BS, Coiel Ricks-Stephen BS, Ricks-Stephen MPHMHL. Community health collaborative facilitates health system and community change to address unmet medical and social needs in New Jersey. J Gen Intern Med. 2023;38:65–9. https://doi.org/10.1007/s11606-022-07927-1.

    Article  PubMed Central  PubMed  Google Scholar 

  67. Yogeswari S, Ramalakshmi S, Neelavathy R, Muthumary JY. Identification and comparative studies of different volatile fractions from Monochaetia kansensis by GCMS. Global J Pharmacol. 2012;6:65–71.

    Google Scholar 

  68. Sanclemente T, Marques-Lopes I, Fajo-Pascual M, Cofan M, Jarauta E, Ros E, Puzo J, Garcia Otin AL. Naturally occurring phytosterols in the usual diet influence cholesterol metabolism in healthy subjects. Nutr Metabolism Cardiovasc Dis. 2012;22:849–55. https://doi.org/10.1016/j.numecd.2011.01.010.

    Article  CAS  Google Scholar 

  69. Yao L, Mo Y, Chen D, Feng T, Song S, Wang H, Sun M. Characterization of key aroma compounds in Xinjiang dried figs (Ficus carica L.) by GC–MS, GC olfactometry, odor activity values, and sensory analyses. Lwt. 2021;150:111982. https://doi.org/10.1016/j.lwt.2021.111982.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors are indebted to the Centre of Excellence in Sustaining Soil Health, Anbil Dharmalingam Agricultural College & Research Institute, Tamil Nadu Agricultural University, and Trichy for providing necessary laboratory and instrumentation facilities to support the study.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Directorate of Research, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641 003, India

    Venkatesan Subramanian, Senthilraja Kandasamy, Sriram Nagappan & Raveendran Muthurajan

  2. Centre of Excellence in sustaining Soil Health, Anbil Dharmalingam Agricultural College & Research Institute, Trichy, Tamil Nadu, 620 027, India

    Udhaya Nandhini Dhandayuthapani

  3. Amrita School of Agricultural Sciences, Tamil Nadu, Coimbatore, 642 109, India

    Jidhu Vaishnavi Sivaprakasam

  4. Department of Renewable Energy Engineering, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641 003, India

    Prabha Balasubramaniam

  5. Agro-Climatic Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641 003, India

    Mohan Kumar Shanmugam & Geethalakshmi Vellingiri

  6. Krishi Vigyan Kendra, Madurai, Tamil Nadu, 625 104, India

    Subramanian Elangovan

  7. Office of the Vice Chancellor, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641 003, India

    Umesh Kanna Subramani

  8. Agribusiness Development, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641 003, India

    Kumaresan Palaniyappan

Authors
  1. Venkatesan Subramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Udhaya Nandhini Dhandayuthapani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Senthilraja Kandasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jidhu Vaishnavi Sivaprakasam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Prabha Balasubramaniam
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohan Kumar Shanmugam
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sriram Nagappan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Subramanian Elangovan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Umesh Kanna Subramani
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kumaresan Palaniyappan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Geethalakshmi Vellingiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Raveendran Muthurajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.V., D.U.N. and K.S. designed the experiments. N.S., E.S., S.U.K., V.G. and M.R. supervised the project. S.V., D.U.N., K.S., S.J.V., B.P., P.K. and S.M.K. performed the experiments and analysed the data. All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Geethalakshmi Vellingiri or Raveendran Muthurajan.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Subramanian, V., Dhandayuthapani, U.N., Kandasamy, S. et al. Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India. BMC Plant Biol 24, 402 (2024). https://doi.org/10.1186/s12870-024-05123-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-024-05123-3

Keywords

BMC Plant Biology

ISSN: 1471-2229

Contact us