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Article

Large-Scale Non-Targeted Metabolomics Reveals Antioxidant, Nutraceutical and Therapeutic Potentials of Sorghum

by
Ajay Prasanth Ramalingam
1,
Williams Mohanavel
1,
Ameena Premnath
1,
Raveendran Muthurajan
1,*,
P. V. Vara Prasad
2 and
Ramasamy Perumal
3,*
1
Department of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
2
Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
3
Agricultural Research Center, Kansas State University, Hays, KS 67601, USA
*
Authors to whom correspondence should be addressed.
Antioxidants 2021, 10(10), 1511; https://doi.org/10.3390/antiox10101511
Submission received: 30 August 2021 / Revised: 17 September 2021 / Accepted: 18 September 2021 / Published: 23 September 2021
(This article belongs to the Special Issue Phenolics as Antioxidant Agents)
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Abstract

:
Sorghum is one of the most important food and feed cereal crops and has been gaining industrial importance in recent years for its biofuel, nutraceutical and antioxidant values. A genetic profile variation study was undertaken for the accumulation of phytochemicals in 61 diverse sorghum accessions differing in their growth habitat and grain color through non-targeted Gas Chromatography–Mass Spectrometry (GC-MS/MS) analysis. Mass Spectrometry–Data Independent AnaLysis (MS-DIAL) and MetaboAnalyst identified 221 metabolites belonging to 27 different phytochemicals. Tropical and temperate sorghums were distinct in their metabolic profiles with minimum overlaps, and 51 different metabolites were crucial in differentiating the two groups. Temperate sorghums had the ability to accumulate more of phenolic acids, phytosterols, flavonoids, carotenoids, and tropical sorghums for stress-related amino acids, sugars and fatty acids. Grain-color-based Partial Least Square–Discriminant Analysis (PLS-DA) analysis identified 94 Variable Importance in Projections (VIP) metabolites containing majority of flavonoids, phenylpropanoids and phytosterols. This study identified two sorghum lines (IS 7748 and IS 14861) with rich amounts of antioxidants (catechins and epicatechins) belonging to the group of condensed tannins that otherwise do not accumulate commonly in sorghum. Out of 13 metabolic pathways identified, flavonoid biosynthesis showed the highest expression. This study provided new opportunities for developing biofortified sorghum with enhanced nutraceutical and therapeutics through molecular breeding and metabolic engineering.

1. Introduction

Sorghum (Sorghum bicolor (L.) Moench) is the fifth important cereal crop globally after rice (Oryza sativa L.), wheat (Triticum aestivum L.), maize (Zea mays L.) and barley (Hordeum vulgare L.), with 40.1 million ha under cultivation and 57.9 million metric tonnes of grain production [1,2]. Sorghum is highly adapted to marginal and stressful environments and remains the crop of choice in semi-arid regions [2]. Sorghum has diverse applications, such as grain as food and feed, fodder and forage sorghum for pasture and hay in livestock feed, broomcorn sorghum for making brooms and sweet sorghum for biofuel and syrup production. Sorghum is used for industrial applications such as biscuit industries, therapeutics, synthesis of organic compounds, and utility items. A declining trend in sorghum consumption in recent years is attributed to increasing urbanization and a lack of diversified value-added products in sorghum. Under a changing climatic scenario, sorghum can be considered as a better alternative to rice, wheat or maize due to its high adaptability to marginal environments [3,4].
Changing human lifestyles and increased frequency due to the occurrence of new diseases, namely, obesity, diabetes and cardio vascular diseases, requires a change in the human diet [5]. In this context, sorghum has been rated as a valuable and economically functional food due to the possession of unique bioactive compounds, namely, flavonoids, phytosterols and polyphenols, exhibiting immense health benefits, including anti-cancer and antioxidant potentials [6,7,8,9,10,11]. Sorghum bran is a rich source of various phytochemicals and antioxidants [12,13]. Antioxidant activities can be attributed to their phenolic compounds [6,14,15,16,17].
Sorghum grains are used to produce functional snacks [18,19,20,21], beverages [22,23,24], food colorants [25], meat preservatives [26] and animal feed [27]. Therapeutic potentials of sorghum indicated its suitability to patients with diabetes and cardiovascular problems [28,29]. Encapsulated sorghum tannins rich in antioxidants were found to reduce the rate of gastric digestion and found to exhibit anti-hyperglycaemic effects [30,31]. Non-targeted metabolomics in the kernels of diverse rice and maize genotypes using GC-MS and UHPLC-MS/MS revealed the genotypic differences in the accumulation of bioactive compounds [32,33]. Tugizimana et al. (2019) [34] conducted metabolomic analysis of disease responsiveness in three different sorghum accessions and identified key pathways reprogrammed during disease progression. Turner et al. (2016) [35] analyzed the metabolome profiles of 11 sorghum lines and revealed that accumulation of primary and secondary metabolites are tightly related to photosynthesis and biomass accumulation. In another study, 217 metabolites differentiating white, red and purple sorghum grains were identified [36]. Brewing sorghum accessions exhibited higher levels of antioxidant properties due to the possession of phenolic acids and proanthocyanidins [8]. Normally, most sorghums contain common antioxidants, namely, phenoloic acids, flavonoids and anthocyanins, but accumulation of effective antioxidant “condensed tannins” is rarely reported [37]. In the present study, a large-scale non-targeted metabolomic analysis was carried out in a set of 61 sorghum accessions differing in grain color and geographical origin. Results of this study unravelled the metabolome complexity and nutraceutical, therapeutic and antioxidant potential of sorghum grains. Outcomes of this study provide a pathway for dissecting the genetic control of metabolite accumulation in sorghum, which will in turn accelerate the development of biofortified sorghum varieties through metabolic engineering and molecular breeding.

2. Materials and Methods

2.1. Genetic Materials

Present study was carried out in a subset of 61 sorghum accessions exhibiting wider geographic and genetic diversity with varied grain color obtained from National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India (Supplementary Table S1 and Figure 1). The study materials were representative of five different grain color categories: black (2), brown (21), red (17), white (20) and yellow (1). All the accessions (10 plants each accession) were evaluated in a randomized block design during Rabi season (October to March) 2019 at Agricultural Research Station, Kovilpatti, India (Latitude 9.17′ N, Longitude 77.88′ E) [38] under dryland condition. Three panicles were selfed prior to flowering to avoid outcrossing. Seeds from the selfed panicles were used for color grading and metabolomics studies.

2.2. Grading of Grain Color

Grains of all the accessions were graded using Royal Horticulture Society (RHS) color chart [39]. Grain color, namely, black, brown, yellow, red or white, was assigned by matching with the sorghum descriptor color grade following the standards of the International Board for Plant Genetic Resources, Rome, Italy [40] (Figure 1).

2.3. Extraction of Secondary Metabolites and Gas Chromatography-Mass Spectrometry Analysis

Secondary metabolites were extracted from the grains of 61 accessions using Soxhlet extraction procedure [41]. Ten grams of grain samples were powdered using mixer grinder, packed with countryman filter paper and kept in the extraction chamber. Boiling flask with 300 mL sonicated 100% methanol (High Performance Liquid Chromatography, HPLC) grade was attached with the extraction chamber provided with a condenser above it. Extraction was started by heating the flask to an initial temperature of 40 °C for 10 min followed by heating at 60 °C for 10 min and finally increased to 80 °C till the completion of five cycles. The collected methanol extract was collected in a conical flask and air-dried until the volume of extract reached 5 mL. Gas chromatography-MS/MS analysis was performed using Perkin Elmer 680 GC (Perkin Elmer Inc, Akron, OH, USA) instrument coupled with AxION iQT’s MASS IQ software for data acquisition with DB-5 MS Capillary Standard non-polar column (30 Mts, ID: 0.25 mm, Film: 0.25 IM, (Perkin Elmer Inc, Akron, OH, USA)). One mL of methanolic extract of the sample was injected into the column using helium as the carrier gas. GC-MS/MS analysis was performed with mass range scan of 50–1000 m/z, 70 eV was applied for fragmentation and precursor ions were isolated with an isolation window of 10 m/z. Raw mass spectra obtained were converted to .abf format using ABF converter <www.reifycs.com/AbfConverter/> (accessed on 19 November 2020) for further analysis.

2.4. Data Processing and Peak Annotation

MS-DIAL (mass spectrometry-data independent analysis) was used for processing the data in .abf format [42], from which MS/MS was performed in data-dependent mode (Supplementary Figure S1). Based on the grain color, samples were grouped into four, namely, brown (21), red (17), white (20) and others (black (2); yellow (1). The processed mass spectra data consisting of peak masses and its area intensities were generated using default MS-DIAL parameters. This included MS1 and MS2 being centroid; ion mode-positive; mass range 0–1000 m/z; retention time range 0–30 min; mass tolerance of 0.25 m/z; retention time tolerance (5 s); minimum peak width and height 5 and 1000; deconvolution parameters (sigma value—0.5); data filtering (inter-quantile range); normalization (normalization by sum); data transformation (log); and data scaling (mean centering). Annotation was done on MS-DIAL using publicly available libraries in “.msp format” from MassBank of North America (MoNA), including Massbank and HMDB <http://www.hmdb.ca/> (accessed on 19 November 2020), by comparing the processed mass spectra data against the libraries with 80% identification score cut off [43].

2.5. Statistical Analysis

The processed data were used to perform statistical analysis using the web platform ‘MetaboAnalyst 5.0’ www.metaboanalyst.ca (accessed on 19 November 2020) [44] with the missing values being replaced by 1/5 of minimum positive values of their corresponding variables. Analyses, including univariate model like one way analysis of variance (ANOVA), multivariate models such as principal component analysis (PCA), partial least squares discriminant analyses (PLS-DA) and hierarchical clustering were performed for understanding metabolite variation and identifying significant metabolites. Fold change analysis was performed by keeping a threshold value of two for determining up-regulated and down-regulated metabolites between colored and white grain groups, followed by mapping the regulated metabolites in sorghum secondary metabolism pathway using MapMan <https://mapman.gabipd.org/> (accessed on 19 November 2020) [45].

2.6. Pathway Mapping

Significant metabolites exhibiting contrasting differences between different color groups were mapped onto metabolic pathways using MetaboAnalyst 5.0 [44,46]. Mummichog algorithm was used to predict the pathway analysis of the processed data using Kyoto Encyclopaedia of Genes and Genomes (KEGG) database <www.genome.jp/kegg/pathway.html> (accessed on 19 November 2020) [47]. False discovery ratio for the pathway analysis was set to ≤0.05.

3. Results

3.1. Genetic Variation for Grain Color

Based on the descriptors and color chart of Royal Horticulture Society, 61 sorghum accessions were classified into black, brown, red, yellow and white grains (Supplementary Table S1; Figure 1). Adequate care was taken to include sorghum accessions representing all the major grain color groups, namely, black (203A; 2 accessions), brown (164A and 164B; 21 accessions), red (165A, 165B, 166A and 166B; 17 accessions), white (155A, 155B, 155C and 155D; 20 accessions) and yellow (6D; 1 accession).

3.2. Metabolic Profile

Mass spectrometry data analysis identified a total of 221 known compounds (Supplementary Figure S2; Supplementary Table S2), including varied classes of primary and secondary metabolites (Figure 2) mapping onto 27 sub-pathways listed in KEGG database (Table 1). These 221 metabolites belonged to diverse categories of metabolism (Figure 2). Among the metabolites, predominant ones were in the order of carboxylic acids (50), flavonoids (35), amino acids (25), phenylpropanoids (21) and sesquiterpenoids (10).
Many of the metabolites were mapped onto therapeutically important pathways such as flavonoid, phenylpropanoid, valine, leucine and isoleucine, steroid, carotenoid and terpenoid biosynthesis, as described in KEGG database (Figure 3). Abundance of these metabolites varied greatly between diverse sorghum accessions.

3.3. Grain Metabolome of Temperate and Tropical Sorghums

To understand the grain metabolome differences between the tropical and temperate sorghums, metabolite content of 13 temperate sorghum lines was compared against 48 tropical sorghum lines. PLS-DA analysis revealed that the first two components separated the temperate and tropical sorghum with limited overlaps (Figure 4). Distinct grouping of sorghum lines based on their grain metabolome suggests that tropical and temperate sorghum may have entirely different metabolic machinery for their adaptation to their environments.
To differentiate the tropical and temperate sorghums based on their grain metabolome, PLS-DA analysis and heat map analysis was performed, which revealed 51 variable importance in projection (VIP) metabolites significantly varying between the tropical and temperate sorghums (Figure 5), including eight phenylpropanoids, five flavonoids and two sterols. This analysis identified 42 metabolites higher in temperate and 9 metabolites higher in tropical sorghums. It was observed that phenylpropanoids, sterols, amino acids and flavonoids can be used to differentiate between the tropical and temperate sorghum. Temperate sorghum grains were found to have increased levels of phenylpropanoids, flavonoids, steroids and amino acids (Figure 5). In contrast, tropical sorghums had higher levels of L-Proline, L-Glutamic acids, L-Arabinose, D-Erythrose 4 P and a few other carbohydrates and fatty acids.

3.4. Hierarchical Clustering

Hierarchical clustering was performed to understand the metabolic diversity among the 61 diverse sorghum accessions and grouped into two major clusters with 18 accessions possessing brown and red pericarps (164A, 165A, 166A and 166B) and the second cluster of 43 accessions predominantly possessing light red (164B, 165B) and white pericarps (Figure 6).

3.5. Multivariate and Univariate Analyses

Multivariate analysis such as PCA and PLS-DA was performed to measure the genetic variation for accumulation of secondary metabolites. PCA in the 61 diverse sorghum accessions provides a preliminary estimate of the overall metabolic differences between different grain color groups and the degree of variability between samples within the group. PCA revealed that the first two components, PC1 (24.3%) and PC2 (7.3%), explain a cumulative variance of 31.6% (Figure 7). To group the diverse sorghum genotypes differing in grain color using the grain metabolome data, PLS-DA analysis was performed according to the first two components. PLS-DA model explained a cumulative variance of 26.4% (Figure 8), which identified 94 metabolites having VIP score of more than 1, indicating that these metabolites may be responsible for the metabolic variation between different colored sorghum grains (Table 1). Among the 94 VIP scored metabolites, 29 metabolites were flavonoids, followed by carboxylic acids (15 metabolites) and phenylpropanoids (13 metabolites). Four different uncommon “condensed tannins” such as catechins, epicatechins, epigallocatechins and epiafzelechin were detected in a few brown and black sorghum accessions. Heat map showing the abundance or expression levels of the top 50 metabolites having VIP score >1 is shown as described in Figure 9. Among the 94 metabolites with high VIP scores, flavonoids, phenylpropanoids and steroids contributed significantly to PC1, and they were abundant in red, brown and black grain sorghums.
Univariate analysis of variance (one-way ANOVA) was performed for identifying significant metabolites differing between different colored grain sorghum accessions. Among the 221 metabolites detected, 89 metabolites were significantly different between the accessions (Table 1). Results of one-way ANOVA were similar to the results of PLS-DA analysis.

3.6. Fold Change Analysis

Abundance of the metabolites in the colored grains (red, brown, yellow and black) was compared against their respective abundance in the white grains, which identified 161 up-regulated, 2 down-regulated and 58 unchanged metabolites in colored sorghum grains when compared to white grains (Supplementary Table S3; Figure 10). Pattern of accumulation and changes in the abundance of metabolites indicated that secondary metabolites were relatively more abundant in colored sorghum grains than in the white grain sorghum. Metabolites involved in the mevanolate and non-mevanolate pathways did not show any significant difference between white and colored sorghum types. White and colored sorghum grains did not differ significantly in their glucosinolates, cyanogenic glycosides and phenolics (Figure 10). Metabolites belonging to terpenoids, phenylpropanoids, flavonoids, lignins, alkaloids and carotenoids showed significant difference in their accumulation between the white and colored sorghum grains. Overall, colored sorghum grains were found to contain significantly elevated levels of these health-benefiting secondary metabolites (Figure 10).

3.7. Mapping of Significant Metabolites onto Metabolic Pathways

Pathway mapping using KEGG database identified 13 significant metabolic pathways showing FDR value ≤ 0.05 (Table 2; Figure 11). Flavonoid pathway showed the highest −log (p) value of 18.9 followed by valine, leucine and isoleucine biosynthesis (13.1); phenylalanine, tyrosine and tryptophan biosynthesis (7.9); and phenylpropanoid biosynthesis (7.3).

4. Discussion

Recent advancements in food technology have caused sorghum grain to become one of the major ingredients in the food industry due to its gluten-free nature. Hence, they serve as an alternate food for patients with diabetes, cardiovascular problems, obesity, immunological disorders and celiac disease [28,29,48]. Speciality grain sorghum is reported to contain bioactive compounds such as phenolic compounds, including phenolic acids (benzoic and cinnamic acids), flavonoids (3-deoxyanthocyanidins), condensed tannins (proanthocyanidins, flavin-3-ols), lignin and stilbenes produced through phenylpropanoid pathway [9,16] possessing anti-microbial activity, and anti-inflammatory and anticancer activities [49,50]. Several in-vitro studies have reported on the strong anti-oxidative and anti-inflammatory capacity of sorghum compounds [51,52]. According to the phenolic profile and color, sorghum is broadly classified into five types: white, black, brown, red and yellow [40]. Black sorghum is reported to have the highest total phenolic contents when compared to other colored (brown, red, yellow and white) sorghums [11]. However, a large number of conserved and widely diversified sorghum germplasms are underutilized. This is mainly due to lack of information on the genetic diversity and genetic basis of metabolic traits of economic importance.
Several studies have reported nutritional benefits (e.g., Fe, Zn and carotenoids) of sorghum [53,54,55], but limited attempts have been made to measure the genetic diversity of sorghum for its secondary metabolite accumulation and therapeutic properties. Plant metabolomics may help us to dissect the relationship between biological processes and phenotypes as well as their nutritive potential [56]. Non-targeted metabolomics is applied in various crops to measure genetic variation for nutritional/therapeutic traits and thus allow researchers to achieve genetic improvement of specific metabolites/biomarkers [32,33,36,57]. The present study was performed to measure genetic variation for accumulation of secondary metabolites in a diverse set of sorghum germplasm lines through non-targeted metabolomics using GC-MS/MS, paving the way for breeding fortified sorghum varieties.
Inclusion of sorghum accessions differing in grain color and growth habitat helped to assess the inter relationship with metabolite accumulation. Understanding the pattern of secondary metabolites accumulation helped to identify molecular factors underlying plant adaptation to diverse environments and nutritional/health benefits. GC-MS/MS analysis of grain metabolome in 61 diverse sorghum accessions identified a total of 221 known metabolites (Supplementary Table S2). These 221 metabolites were from different classes of primary and secondary metabolites (Figure 2) with significant role in 27 sub-pathways listed in KEGG database (Figure 3). The accessions were with clear distribution for carboxylic acids (50), flavonoids (35), amino acids (25), phenylpropanoids (21) and sesquiterpenoids (10) and were predominant in sorghum metabolome.
Metabolite profiling enabled one to understand the metabolic basis of accessions adaptation to widely varying environments. First two components of PLS-DA distinguished tropical and temperate sorghum accessions with minimum overlaps (Figure 4). Clustering based on grain metabolome revealed that these accessions possess metabolic machineries for adaptation to their environments. It was noticed that relative abundance of 51 different metabolites was significantly different between tropical and temperate sorghum lines based on PLS-DA (Figure 5). Different metabolites of amino acids, phenylpropanoids, flavonoids and a few sugars can be used to differentiate between the tropical and temperate sorghum (Figure 5). In general, temperate accessions possessed higher levels of metabolites such as phenylpropanoids, amino acids, flavonoids and steroids, whereas tropical accessions were rich in carbohydrate metabolites and stress-related amino acids. Tropical sorghums from Cameroon, Chad, Ethiopia, Kenya, Nigeria, Sudan, Uganda and Israel accumulated significantly higher levels of nine metabolites, including L-proline, L-arabinose, L-glutamic acid and glyceraldehyde 3 phosphate (Figure 5). Similar observations were reported in rice [32], where stress-tolerance-related metabolites were identified in the grains of tropical indica when compared to temperate japonica rice grains. PCA and PLS-DA analyses on grain metabolome showed that the metabolite compositions of brown and red pericarp grains are distinctly different from the white grain sorghum (Figure 7 and Figure 8).
The detected 221 different metabolites from the 61 accessions belonged to 27 classes of metabolites mapped onto 27 KEGG pathways. One-way ANOVA and PLS-DA identified 89 significant and 94 VIP metabolites, respectively. Most of the significant metabolites belonged to flavonoid (flavanones, flavones, flavan-3-ols, flavonols and dihydroflavonols), phenylpropanoids, condensed tannins and sterols, which have tremendous health benefits, including antioxidant anti-cancer properties [58,59]. Out of 221 metabolites detected, 35 were under the class of flavonoids. Heat map and fold change analysis of this study (Figure 9 and Figure 10) indicated that most of these flavonoids and other therapeutic metabolites are more abundant in dark red pericarp than in light red and white pericarp accessions. These metabolites could be used as biomarkers for discriminating diverse colored sorghum genotypes. Overall, the metabolome data generated in the grains of diverse sorghum genotypes suggests that the metabolome of colored grain sorghum are quite different from that of white sorghum.
The rare flavanones metabolites (naringenin and eriodictyol) possessing anti-cancer properties [60] were found in the brown- and red-colored sorghum accessions IS 9442, IS 10877, IS 10634 and IS 8569 (Table 3). No white-colored sorghums were found to contain significantly elevated levels of these secondary metabolites. Similar results were reported earlier in sorghum by Taylor and Awika (2017) [61]. Flavones including luteolin and apigenin were found to be abundant in the grains of brown, red and black sorghum accessions viz. IS 10634, IS 9442, IS 9378, IS 12267, IS 14535, IS 15098 and IS 11818 (Table 3). Apigenin was demonstrated to induce apoptosis of colorectal cancer cells and to activate estrogenic activities [50]. Condensed tannins, including catechin, epicatechin, epigallocatechin and epiafzelechin, detected in the grains of colored sorghum lines (IS 12166, IS 15098, IS 7748, IS 9378 and IS 14861) were reported to exhibit antioxidant, anti-inflammatory and anticancer activities [7,17,62,63]. Very interestingly, sorghum grains studied were found to accumulate nutraceutically important phytosterols viz. stigmasterol (IS 7748, IS 8826, IS 12330 and IS 12267) and sitosterol (IS 7748, IS 8826 and IS 12267) [64]. Phytosterols are functional foods known for their cholesterol-lowering ability [65]. Common dietary sources of phytosterols are vegetable oils, cereal products, vegetables and fruit. On the contrary, this study has identified sorghum lines rich in phytosterols, namely, stigmasterol and sitosterol (cholesterol-lowering ability and anti-cancer). Stigmasterols are used as a precursor for industrial production of semisynthetic progesterone, an important regulatory and tissue rebuilding hormone, as well as an intermediary in the biosynthesis of androgens, estrogens and corticoids [66]. Stigmasterol is also used as one of the precursors in the synthesis of vitamin D3 [67]. The sorghum accessions listed in Table 3 are rich in key nutraceuticals and therapeutic compounds and can be further used for trait introgression to improve adapted breeding lines.
Pathway analysis was performed to identify key metabolic pathways contributing to variation in grain color. Results revealed that grain color diversity in sorghum was attributed to flavonoid pathway (−log (p) = 18.923), followed by valine, leucine and isoleucine biosynthesis (−log (p) = 13.114), phenylalanine, tyrosine and tryptophan biosynthesis (−log(p) = 7.8637) and phenylpropanoid biosynthesis (−log (p) = 7.2676). Similar studies with three cultivars on non-targeted metabolomic analysis were conducted earlier by Xia and Wishart (2010) [46] and Zhou et al., (2020) [36]. In this study, firstly in sorghum, large-scale detailed grain antioxidants and metabolome profiling analyses using advanced bioinformatics tools in sorghum accessions diversified origins, growth habitats and grain colors were studied, with significant results.

5. Conclusions

The present study was aimed at unravelling metabolic signatures in the grains of a subset of 61 diverse sorghum accessions differing in origin, growth habitat and grain color. GC-MS/MS analysis identified a total of 221 different metabolites mapping with a significant role in 27 sub-pathways listed in KEGG database. The results of PCA and PLS-DA analysis revealed distinguished the clustering of accessions differing in their grain color and metabolite variations. Further, metabolic signatures explained the adaptability of sorghum accessions to varied growth conditions (tropical vs. temperate). Antioxidants such as flavonoids, condensed tannins, phenolic acids, phenylpropanoids, phytosterols and amino acids were more predominant in the dark-colored (brown, black and red) sorghum grains than in the white-colored grains. Overall, this study paves the way for further genetic and molecular studies through association mapping and omics approaches to identify genetic factors determining the accumulation of nutraceuticals and therapeutically important metabolites in sorghum. Further, identified genetic stocks of sorghum rich in nutraceutical compounds may be utilized in food fortification and bio-fortification programs.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/antiox10101511/s1. Figure S1: GC-MS/MS acquired peak data processing and annotation using MS-DIAL; Figure S2: GC-MS/MS derived chromatograms of sorghum grains differing in grain color; Table S1: Details of sorghum accessions used (grain color descriptor, origin and type); Table S2. List of metabolites identified in the grains of 61 diverse sorghum accessions; Table S3. Abundance ratio of 163 metabolites showing more than 2 fold change between colored and white grain sorghums.

Author Contributions

Conceptualization, R.P. and R.M.; methodology, A.P.R., W.M. and R.M.; formal analysis, A.P.R., A.P. and W.M.; resources, R.M., R.P. and P.V.V.P.; writing—original draft preparation, A.P.R., A.P. and R.M.; writing, review and editing, R.P., P.V.V.P. and R.M.; supervision, R.M., R.P. and P.V.V.P.; project administration, R.M.; funding acquisition, R.M., R.P. and P.V.V.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within this article and supplementary information.

Acknowledgments

Authors acknowledge the help rendered by U. Sivakumar, (Microbiology), TNAU, Coimbatore in performing GC-MS/MS analysis. P.V.V.P. appreciates the Feed the Future Sustainable Intensification Innovation Lab funded by United States Agency for International Development for supporting his research (Cooperative Agreement Number AID-OAA-L-14-00006) and provided support for open access. This paper is Contribution number 22-068-J from the Kansas Agricultural Experiment Station.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Assigning grain color to the diverse sorghum accessions using Royal Horticulture Society (RHS) color chart.
Figure 1. Assigning grain color to the diverse sorghum accessions using Royal Horticulture Society (RHS) color chart.
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Figure 2. Classification of 221 sorghum grain metabolites into metabolite classes. Number in parentheses indicates number of metabolites mapped against each class.
Figure 2. Classification of 221 sorghum grain metabolites into metabolite classes. Number in parentheses indicates number of metabolites mapped against each class.
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Figure 3. Mapping of sorghum grain metabolites onto KEGG (Kyoto Encyclopaedia of Genes and Genomes) metabolic pathways. Values indicates the number of metabolites mapped against various specific pathways.
Figure 3. Mapping of sorghum grain metabolites onto KEGG (Kyoto Encyclopaedia of Genes and Genomes) metabolic pathways. Values indicates the number of metabolites mapped against various specific pathways.
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Figure 4. Partial least squares discriminant analyses (PLS-DA) analysis of sorghum grain metabolome. The first two principal components (PCs) explain 25.5% of variance separating tropical and temperate sorghum.
Figure 4. Partial least squares discriminant analyses (PLS-DA) analysis of sorghum grain metabolome. The first two principal components (PCs) explain 25.5% of variance separating tropical and temperate sorghum.
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Figure 5. Heatmap of PLS-DA VIP metabolites differentiating temperate and tropical sorghum.
Figure 5. Heatmap of PLS-DA VIP metabolites differentiating temperate and tropical sorghum.
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Figure 6. Hierarchical clustering of sorghum accessions based on its metabolite profiles.
Figure 6. Hierarchical clustering of sorghum accessions based on its metabolite profiles.
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Figure 7. Genetic variation for accumulation of metabolites among diverse sorghum accessions obtained through principal component analysis.
Figure 7. Genetic variation for accumulation of metabolites among diverse sorghum accessions obtained through principal component analysis.
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Figure 8. Score plots of partial least square-discriminant analysis (PLS-DA) using the data generated through non-targeted metabolomics analysis of diverse sorghum genotypes differing in grain color.
Figure 8. Score plots of partial least square-discriminant analysis (PLS-DA) using the data generated through non-targeted metabolomics analysis of diverse sorghum genotypes differing in grain color.
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Figure 9. Sixty-one sorghum genotypes were clustered based on the accumulation pattern of 50 metabolites identified based on VIP scores from PLS-DA model. Color of the cell indicated the relative abundance of metabolites as indicated in the side color scale. Columns represent genotypes and rows represent a metabolite. Color of cells in the header row indicates the grain color category.
Figure 9. Sixty-one sorghum genotypes were clustered based on the accumulation pattern of 50 metabolites identified based on VIP scores from PLS-DA model. Color of the cell indicated the relative abundance of metabolites as indicated in the side color scale. Columns represent genotypes and rows represent a metabolite. Color of cells in the header row indicates the grain color category.
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Figure 10. Pathway mapping of secondary metabolites up-regulated in the colored sorghum grains over the white grains.
Figure 10. Pathway mapping of secondary metabolites up-regulated in the colored sorghum grains over the white grains.
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Figure 11. Major pathways contributing towards diversification of grain color in sorghum.
Figure 11. Major pathways contributing towards diversification of grain color in sorghum.
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Table
Table 1. List of significant metabolites differentiating diverse sorghum accessions.
Table 1. List of significant metabolites differentiating diverse sorghum accessions.
S. No.CompoundClassPLS-DA
VIP Score		ANOVA
p Value
1NaringeninFlavonoids2.13469.53 × 10−9
2(+)-CatechinFlavonoids1.93789.88 × 10−7
3CinnamaldehydePhenylpropanoids1.86461.39 × 10−6
4StigmasterolSteroids1.85923.71 × 10−6
5(−)EpicatechinFlavonoids1.79971.25 × 10−5
6alpha-IsopropylmalateCarboxylic acids1.79050.00013398
7trans-CinnamatePhenylpropanoids1.77030.00015462
8L-LeucineAmino acids1.7550.00017873
9LuteolinFlavonoids1.71471.82 × 10−5
10SitosterolSteroids1.6070.00022826
11AntheraxanthinCarotenoids1.60260.00039374
12L-ValineAmino acids1.56480.00047442
13ApigeninFlavonoids1.54961.54 × 10−7
14Caffeyl alcoholPhenylpropanoids1.54120.00017893
155′-PrenyleriodictyolFlavonoids1.50367.06 × 10−6
16Kaempferol-3-O-arabinosideFlavonoids1.48551.57 × 10−5
17GenisteinFlavonoids1.48520.00021481
18Coniferyl aldehydePhenylpropanoids1.47380.00034236
19EriodictyolFlavonoids1.46967.06 × 10−6
20HesperetinFlavonoids1.45485.14 × 10−5
21(−)-EpigallocatechinFlavonoids1.45339.29 × 10−5
22AbscisateApocarotenoids1.45040.00072293
232-CoumarinatePhenylpropanoids1.42170.00079068
24LuteinCarotenoids1.41090.0012749
25Abscisic aldehydeSesquiterpenoids1.39930.0021929
26L-IsoleucineAmino acids1.39740.00065042
27PhosphoenolpyruvateCarboxylic acids1.35830.00078092
28Shikimate 3-phosphateCarboxylic acids1.3550.00098977
29Propanoyl-CoAFatty acids1.35360.0012642
30L-TryptophanAmino acids1.35120.0019882
314-CoumaratePhenylpropanoids1.34820.0013115
32LupeolTriterpenoids1.33910.0013742
33Geranylgeranyl diphosphateDiterpenoids1.32760.00068171
34Farnesyl diphosphateSesquiterpenoids1.32020.0024475
35PhenylpyruvateCarboxylic acids1.31560.0026778
36Dodecanoic acidFatty acids1.31560.0031825
37SqualeneTriterpenoids1.3140.0023789
38D-CitramalateCarboxylic acids1.30840.0026724
39(9Z)-Hexadecenoic acidFatty acids1.30590.0030008
40(+)-NeomentholMonoterpenoids1.3013Nil
41(S)-2-AcetolactateCarboxylic acids1.29630.0021652
422-trans,6-trans-FarnesalSesquiterpenoids1.29270.0022391
438-OxogeranialMonoterpenoids1.28550.0024556
44ZeaxanthinCarotenoids1.28220.0034374
45CitraconateCarboxylic acids1.268Nil
46Glyceraldehyde 3-phosphateOrganophosphate1.26470.019693
47CarlactoneLactones1.26320.0048748
48SpermidineAmino acids1.26170.0029642
49(−)-EpiafzelechinFlavonoids1.2540.00313
503-DehydroshikimateCarboxylic acids1.25210.0066344
51NaringinFlavonoids1.24990.0072895
52EriocitrinFlavonoids1.24550.0078144
53BrassicasterolSteroids1.24250.0095876
544-Coumaryl alcoholPhenylpropanoids1.24120.010089
559-cis-10′-Apo-beta-carotenalApocarotenoids1.24070.011766
56CampesterolSteroids1.23780.012192
57DihydrokaempferolFlavonoids1.21580.013448
58PentahydroxyflavanoneFlavonoids1.20830.0040772
59(S)-2-Aceto-2-hydroxybutanoateCarboxylic acids1.20220.0052647
60KaempferolFlavonoids1.20110.0067844
61KaempferideFlavonoids1.20.0042311
62Caffeic aldehydePhenylpropanoids1.19810.0075665
63p-CoumaraldehydePhenylpropanoids1.19490.0099842
64(R)-2,3-Dihydroxy-3-methylpentanoateCarboxylic acids1.19190.011751
658′-HydroxyabscisateCarboxylic acids1.19140.0042353
662-OxoisocaproateCarboxylic acids1.16230.0075086
67beta-D-Glucopyranosyl abscisateCarboxylic acids1.16060.0099713
689′-cis-NeoxanthinCarotenoids1.15320.00491
69Presqualene diphosphateTriterpenoids1.15160.0094175
70FerulatePhenylpropanoids1.14630.010938
71L-PhenylalanineAmino acids1.14460.012012
721-Deoxy-D-xylulose 5-phosphateSugar Phosphates1.14380.003749
73beta-TocopherolPrenol lipids1.1416Nil
74Luteolin 7-glucosideFlavonoids1.13860.012662
752′,5-DimethoxyflavoneFlavonoids1.13080.012898
76Eriodictyol-7-O-glucosideFlavonoids1.12930.013856
77HomoeriodictyolFlavonoids1.12170.01464
78Naringenin chalconeFlavonoids1.11730.0147
79Indoleglycerol phosphateSugar Phosphates1.1098Nil
80TaxifolinFlavonoids1.10270.017069
81(S)-3-Methyl-2-oxopentanoateCarboxylic acids1.09960.0040564
82FustinFlavonoids1.09240.015207
83GalanginFlavonoids1.08670.015721
84PhloretinFlavonoids1.08320.016724
85GarbanzolFlavonoids1.07860.016737
86Apigenin-7-O-glucosideFlavonoids1.07370.017055
87(S)-2,3-EpoxysqualeneTriterpenoids1.0709Nil
88ChorismateCarboxylic acids1.06670.020117
892-C-Methyl-D-erythritol 4-phosphateFatty Alcohols1.05430.019126
905-HydroxyconiferaldehydePhenylpropanoids1.05010.017298
91Caffeoyl-CoAPhenylpropanoids1.04920.017933
92p-Coumaroyl-CoAPhenylpropanoids1.03280.01822
93L-TyrosineAmino acids1.01160.01873
94DemethylphylloquinolPrenol lipids1.01040.01881
PLS-DA, partial least square-discriminant analysis; VIP, variable importance in projections; ANOVA, analysis of variance; p, probability level.
Table
Table 2. List of metabolic pathways significantly contributing to grain color diversity.
Table 2. List of metabolic pathways significantly contributing to grain color diversity.
S. No.Pathway NameRaw p−log(p)FDR
1Flavonoid biosynthesis1.19 × 10−1918.9231.15 × 10−17
2Valine, leucine and isoleucine biosynthesis7.69 × 10−1413.1143.69 × 10−12
3Phenylalanine, tyrosine and tryptophan biosynthesis1.37 × 10−87.86374.38 × 10−7
4Phenylpropanoid biosynthesis5.40 × 10−87.26761.30 × 10−6
5Tyrosine metabolism8.19 × 10−87.0871.57 × 10−6
6Ubiquinone and other terpenoid-quinone biosynthesis1.16 × 10−65.93591.85 × 10−5
7C5-Branched dibasic acid metabolism9.51 × 10−65.02180.000114
8Isoquinoline alkaloid biosynthesis9.51 × 10−65.02180.000114
9Sesquiterpenoid and triterpenoid biosynthesis4.03 × 10−54.39470.00043
10Arginine and proline metabolism0.0001333.87670.001275
11Biosynthesis of secondary metabolites - unclassified0.0020162.69550.017596
12Valine, leucine and isoleucine degradation0.0046382.33360.037106
13Carotenoid biosynthesis0.0063742.19560.047071
FDR, false discovery rate is a measure of error; −log(p) represents significance at probability (p) ≤ 0.05.
Table
Table 3. List of accessions enriched with nutraceutical and therapeutic metabolites and their potential applications.
Table 3. List of accessions enriched with nutraceutical and therapeutic metabolites and their potential applications.
SchemeClassMetabolitesEnriched Sorghum
Accessions		UsesReferences
1SqualeneTriterpenoidsIS 7748, IS 9108, IS 9262, IS 10968Anti-cancer, anti-bacterial and cholesterol-lowering ability[68,69,70]
2StigmasterolSteroidsIS 7748, IS 8826, IS 12330, IS 12267Anti-cancer and cholesterol-lowering ability, reduces risk of cardiovascular diseases[71,72,73]
3SitosterolSteroidsIS 7748, IS 8826, IS 12267Prevention of cervical cancer, lowers cholesterol level[74]
4LuteinCarotenoidsIS 9378, IS 12330, IS 14542Delays/inhibits age-related macular degeneration, improves cardiovascular health, and anti-cancer[75,76]
5ZeaxanthinCarotenoidsIS 14861, IS 12290, IS 7748Protective factor in age-related macular degeneration (AMD), reduces diabetic retinopathy and inhibits cataract growth[77,78,79]
6(+)-CatechinFlavonoidsIS 12166, IS 15098, IS 7748, IS 9378, IS 14861Prevents/reduces skin damage; antioxidant; anti-inflammatory; anti-viral; and anti-cancer[80]
7(−)-EpicatechinFlavonoidsIS 15191, IS 7748, IS 8962, IS 14861, IS 12330Anti-diabetic, cytotoxic to cancer cells, antioxidant and anti-angiogenic[81]
8NaringeninFlavonoidsIS 9442, IS 10877, IS 10634, IS 8569, IS 10877Anti-cancer, cardiovascular protection, anti-viral (against hepatitis C virus) and weight control[82]
9ApigeninFlavonoidsIS 12267, IS 9442, IS 10634, IS 14535, IS 15098, IS 11818Anti-cancer, activation of estrogen[50]
10GenisteinFlavonoidsIS 8826, IS 12267Anti-tumor[83]
11(−)-EpigallocatechinFlavonoidsIS 14316, IS 15061, IS 15098, IS 10634, IS 12330Anti-inflammatory, anti-cancer and antioxidant[80]
12HesperetinFlavonoidsIS 8826, IS 12267Antioxidant, lowers blood cholesterol[84]
13Kaempferol-3-O-arabinosideFlavonoidsIS 9442, IS 12267, IS 12166, 1S 8826, IS 15098, IS 10634, IS 10877Chemotherapeutic drug, antioxidant and anti-inflammatory[85]
14LuteolinFlavonoidsIS 10634, IS 9442, IS 9378, IS 12267Anti-cancer, anti-hypertensive and anti-inflammatory[86]
15EriodictyolFlavonoidsIS 12330, IS 8826, IS 9378, IS 14316Anti-cancer, anti-inflammatory and anti- oxidant[87]
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Ramalingam, A.P.; Mohanavel, W.; Premnath, A.; Muthurajan, R.; Prasad, P.V.V.; Perumal, R. Large-Scale Non-Targeted Metabolomics Reveals Antioxidant, Nutraceutical and Therapeutic Potentials of Sorghum. Antioxidants 2021, 10, 1511. https://doi.org/10.3390/antiox10101511

AMA Style

Ramalingam AP, Mohanavel W, Premnath A, Muthurajan R, Prasad PVV, Perumal R. Large-Scale Non-Targeted Metabolomics Reveals Antioxidant, Nutraceutical and Therapeutic Potentials of Sorghum. Antioxidants. 2021; 10(10):1511. https://doi.org/10.3390/antiox10101511

Chicago/Turabian Style

Ramalingam, Ajay Prasanth, Williams Mohanavel, Ameena Premnath, Raveendran Muthurajan, P. V. Vara Prasad, and Ramasamy Perumal. 2021. "Large-Scale Non-Targeted Metabolomics Reveals Antioxidant, Nutraceutical and Therapeutic Potentials of Sorghum" Antioxidants 10, no. 10: 1511. https://doi.org/10.3390/antiox10101511

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