Rice (Oryza sativa L.) is an important staple grain because more than half of the world's population depends on it for their livelihood. White rice contributes to the daily caloric intake (20–40%) of Asian countries, but its nutritional quality is poor compared to that of pigmented rice. Pigmented rice varieties (black and red) are gaining popularity among consumers due to nutritional health benefits. Despite having nutritional importance, pigmented rice is usually low-yielding due to the narrow genetic base that occurred during domestication and artificial selection. Considering the potentials of wild rice, two distinct RIL populations (each 100 lines) were developed through interspecific hybridization BWF (Badshabhog x O. rufipogon) and CWF (Chenga x O. rufipogon) to break the yield plateaus. In the present study, we have created novel and inventive aromatic black lines from non-black parental lines. Significant genetic diversity prevailed in the RIL lines, which were assessed through genetic variability parameters, path coefficient, Mahalanobis D2 test, and PCA using 15 agromorphological traits. High heritability (>90%), high GA and GAM were found in RIL lines, suggesting additive gene action for the characteristics. The first four principal components together accounted for 73.74% of the variability in BWF, and the first six PCs showed 71.90% cumulative variability in CWF (eigenvalue >1). Total anthocyanin content was varying at 261 mg/100 g, 253 mg/100 g, and 259 mg/100 g in BW23, CW16, and control black, respectively. The DPPH activity was highest in BWF (78.37%) compared to control black (76.29%). Amylose content varies from 7.57 to 24.59%, and protein content was recorded at 8.76 to 8.81 g/100 g in the RIL lines. RIL lines (BW23, CW16) contain high-quality essential amino acids, including anthocyanin petunidin 3-O glucoside, quantified through HR-LCMS-QTOF. The Kala4 gene-specific PCR amplification product supports the acquired mutation with neo-functionalization activity through LINE1 insertional rearrangement near the Kala4 promoter, suggesting that the Kala4 gene is activated in black lines. Moreover, the RIL lines may provide some insight into the evolutionary and domestication history of black rice origin and can be used as important genetic resources for improving black rice for food and nutritional security.
Research Article
Genetic Diversity Analysis of the Innovative Black Rice RILs Developed by Interspecific Hybridization (O. sativa x O. rufipogon) Based on Agromorphology and Grain Quality
https://doi.org/10.21203/rs.3.rs-5092686/v1
This work is licensed under a CC BY 4.0 License
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Wild rice Oryza rufipogon
interspecific hybridization
innovative black rice
Kala4 gene
neo-functionalization
metabolomics
amino acid profiling
HRLCMS-QTOF.
Rice (Oryza sativa L.) is an important staple food grain because more than half of the world's population (>3.5 billion) depends on it for their livelihood and considered as model crop plant under the family Poaceae (Khush 2005; Huang et al. 2012A; Huang et al. 2024). Cultivated Asian rice (O. sativa L.) can be divided into five groups based on genetics and culinary properties, such as japonica temperate, japonica tropical, indica, aus, and aromatic (Garris et al. 2005). It is needed to produce a double amount of rice by 2050 to feed the more than 9 billion people in this world from diminishing acreage, deteriorating soil health, and environmental stresses induced by global climate change (Ray et al. 2013). The impact of climate change on agriculture predicts that global rice production will decline by 12-14% by 2050 compared to the 2000 production baseline. Thus developed varieties must be with high-yielding potential and resistant to biotic and abiotic stresses, including adaptability to climate change (Godfray et al. 2010; Tester and Langridge 2010) and should have low input efficiency. The intensive selection and breeding efforts targeted towards the improvement of yield in the green revolution era inadvertently resulted in the significant reduction in genetic diversity due to limited utilization of genetic resources and consequently narrow genetic bases of improved varieties, which have resulted in yield plateaus (Tanksley and McCouch 1997; Tian et al. 2006). Wild rice species, Oryza rufipogon Griff.is an immediate ancestral progenitor of cultivated rice (Huang et al. 2010), and considered as a reservoir of many untapped gene/QTLs for abiotic and biotic stress tolerance and can be utilized in the pre-breeding program for broadening the genetic base of released varieties to break the yield plateaus (Tanksley and McCouch 1997; Sanchez et al. 2013; Siddiq and Vemireddy 2021; Padmavathi et al. 2024). Yield related QTLs have been transferred to cultivated rice from O. rufipogon for yield enhancement through interspecific hybridization (pre-breeding) (Thomson et al. 2003; McCouch et al. 2007; Siddiq and Vemireddy 2021). White rice makes a major contribution to the daily calorific intake (20–40%) of Asian countries, but its nutritional quality is poor compared to that of pigmented rice (Ito and Lacerda 2019; Mbanjo et al. 2020). Grain colour varies from white, brown, greenish, red, and purple to black (Rahman et al. 2013; Maeda et al. 2014; Devi et al. 2020). Grain color has been a target during domestication, given that the color of most cultivated rice is white and that of wild rice (Oryza rifupogon) is red due to accumulation of flavonoid pigment proanthocyanidins (condensed tannins) in pericarp (Sweeney et al. 2007; Gross and Zhao 2014; Zhu et al. 2024), with important deterrent effects on pathogens and predators (Shirley 1998). Two complementary genes Rc/rc and Rd/rd is responsible for red pericarp (Rc/Rd) development in rice and their dominant/recessive combinations produce brown (Rc-rd) and white grain (rc/rc-Rd/rd) (Sweeney et al. 2006; 2007; Furukawa et al. 2007; Mbanjo et al. 2020; Xia et al. 2021; Zhu et al. 2024). Flavonoid anthocyanins are responsible for black pericarp pigmentation, and there is no black grain colour in wild rice accessions (O. rufipogon) indicating that the black rice pericarp phenotype is a newly acquired trait (neo-functionalization) and incorporated during domestication or after domestication into the cultivated rice (Maeda et al. 2014; Oikawa et al. 2015; Kim et al. 2021). Three genes OsKala1 (Rd) (dihydroflavonol reductase), OsKala3 (R2R3-MYB-transcription factor), and OsKala4 (bHLH-transcription factor), were reported to be associated with black pericarp in a near-isogenic line derived from the black rice cultivar Hong xie nuo and white cultivar Koshihikari (Maeda et al. 2014). It was an acquired neo-functionalization of Kala4 allele (gain-of-function mutation) that happened in black rice development during the domestication of cultivated rice (Oiwaka et al. 2015). Genome analysis evidence has suggested that an 11.02 kb genome segment was inserted into the LINE1 transposon region of the Kala4 promoter, the segment was taken from the upstream (-83 kb) region of the Kaka4 allele and consequently activated the anthocyanin biosynthesis genes in the grain pericarp, which was reported to be the original genetic change that gave birth to black rice (Oikawa et al. 2015; Xia et al. 2021; Kim et al. 2021). The report explained that neo-functionalization of Kala4 allele through LINE1 insertional mutation, had happened in the genetic background of tropical japonica first and then spread to indica and other subspecies of rice (Oikawa et al. 2015). This is without any genetic proof or breeding evidence, and the origin of black pericarp in an indica-type background is still under debate (Roy and Shil 2020A; Xie et al. 2023; Sakulsingharoj et al. 2024). There is still a possibility that some additional genes and allelic variants thereof remain to be exposed for pericarp and vegetative parts pigmentation (Mbanjo et al. 2020; Lap et al. 2024).
Pigmented rice accumulates various types of secondary metabolites such as phytosterols, polyphenols, flavonoids, anthocyanins, proanthocyanidins, vitamins, and micronutrients (Shao et al. 2018; Mendoza‑Sarmiento et al. 2023; Zhu et al. 2024; Idrishi et al. 2024), which are recognized to have high nutritional value and medicinal properties with antioxidant, anti-mutagenic, anti-cancer, anti-viral, anti-diabetic, anti-inflammatory, and anti-aging protection potentialities (Mbanjo et al. 2020; Sakulsingharoj et al. 2024; Gogoi et al. 2024). The nutritive value of pigmented rice is greatly influenced by genetics, genotypic variation, and environmental factors (Rhowell et al. 2023), along with several external influences such as soil fertility status, the degree of milling, and the method of preparation before consumption (Gogoi et al. 2024). In India, different varieties of black rice are locally grown in different states, specifically Chakhao landraces (Chakhao means delicious) in Manipur (North-Esatern state of India), Kavuni in South India, Njavara in Kerala, and Kalabhat in West Bengal (Deepa et al. 2008; Jose et al. 2018; Bhuvaneswari et al. 2020; Mazumdar et al. 2022). Despite having nutritional importance (rich source of phytonutrients), pigmented rice is usually low yielding, prone to lodging, susceptible to diseases, and late-maturing (Devi et al. 2020; Bhuvaneswari et al. 2020; Sedeek et al. 2023). Pigmented rice varieties (black, red, or brown) are gaining popularity among consumers due to nutritional health benefits, and market demands are expected to rise (Kushwaha 2016; Ito and Lacerda 2019; Bhuvaneswari et al. 2023). Looking forward, there is a remarkable opportunity for breeding programs to develop nutritionally enriched (phytonutrients) productive pigmented rice varieties. Good quality black rice lines were developed through crossing between black rice ‘Okunomurasaki’ and white rice Koshihikari including black Hong Xie Nuo with white Koshihikari (Maeda et al. 2014). Transgressive segregant lines were selected from the cross between black rice Chakhao Poireiton and Sahbhagi dhan (white rice) with colored pericarp, high anthocyanin content, and increased yield compared to their parental lines (Lap et al. 2024).
Considering the above literature review, it is evident that there is no report about the development of black rice varieties from any wide crosses (O. sativa x O. rufipogon) using non-black parental lines. In the present study, we are reporting for the first time in the history of rice breeding and genetics about the development of innovative black rice RIL lines (black pericarp) with improved yield potential and enhanced grain quality from two wide croses, BWF (Badshabhog x O. rufipogon) and CWF (Chenga x O. rufipogon). Breeding lines are with the new, unique, and novel trait, i.e., black pericarp; thus, it is an innovative discovery because parents were non-black lines. Anthocyanin pigment petunidin 3-O glucoside was qualitatively identified from the black rice lines using the HR-LCMS-QTOF technique, signifying that anthocyanin biosynthesis genes were activated in the breeding lines. The Kala4 gene-specific PCR amplification also supports the acquired mutation with neo-functionalization activity to accumulate anthocyanin pigment in the pericarp, leading to black rice development.
Plant materials for Interspecific Hybridization
Wild rice Oryza rufipogon Griff. of Raiganj was used as one of the parental line as donor parent. This wild rice is growing naturally in the shallow marshy land/ditches of Raiganj block, Uttar Dinajpur district, West Bengal, India; at latitude 25.62˚N and longitude 88.12˚E, elevation- 40 m (130 ft) (Figure 1). Well adapted farmer’s varieties Oryza sativa L. subspecies indica cultivar Badshabhog, and Chenga were used as parents in this interspecific hybridization.
Interspecific Hybridization between cultivated rice and Asian wild rice O. rufipogon
Two different crosses were made BWF (O. sativa cv. Badshabhog x O. rufipogon) and CWF (O. sativa cv. Chenga x O. rufipogon) for the creation of F1 progenies in 2016 according to standard protocols (Sleper and Poehlmer, 2007; Sha 2013, Roy 2017). All F2 seeds were highly shattering and collected within a nylon net by bagging the panicles to harvest the seeds (Figure 1). Progeny populations from F2 generation (in 2017) were allowed to self-fertilize to develop recombinant inbred lines (RIL) (Figure 2). From F3, F4 generations, shatteredness was reduced, and from F5 generations, shattering was stopped in most of the breeding lines and maintained. From the F6 generation (2021 kharif crop), we had selected 100 distinct types of breeding lines based on 15 agromorphological traits from each of the crosses (BWF, CWF) for further evaluation. RIL lines of each of the progeny populations (BWF, CWF) were plotted in the field in a randomized complete block design (RCBD) with three replications for two seasons (F7 in 2022 and F8 in 2023 kharif crop) (Figure 2). The 30-day-old seedlings were transplanted with a spacing of 20 x 20 cm. Each genotype constituted ten rows of 2.5 m long spaced 20 cm apart with one seedling per hill. The fertilizer application and intercultural agronomic practices were carried out as per the recommended standard.
Phenotypic Evaluation
Agro-Morphological Characterization
The observation of fifteen quantitativeagro-morphological traits, viz., plant height (PH), flag leaf length (FFL), flag leaf width (FLW), panicle length (PnL), panicle weight (PnWt), grain per panicle (GrPn), grain length (GL), grain breadth (GB), kernel length (KL), kernel breadth (KB), 1000 grain weight (GrWt), tillering number (Till), heading date (HD), maturity time in days (MT), and single plant yield (PY), were ramdomly recorded in 5 plants of each RIL lines (BWF and CWF) in each replication using DUS guideline (PPV&FR Act 2001, Govt. of India). Data was recorded from the middle two rows to avoid border effects, and mean values of the 15 traits were used for further analysis. Other agromorphological and grain quality parameters such as awn length (AwnL), aroma (Aroma), ASV, GT, and GC, pericarp pigmentation colour (PC), seed shattering habit (Sh), and seed coat phenol test were recorded and analyzed.
Statistical analysis of agro-morphological data andGenetic diversity studies
The analysis of variance (ANOVA) and descriptive statistics of fifteen agromorphological data was analyzed using the following statistical softwares: IBM SPSSv-22, XLSTAT, PAST4.03, and Origin 2024 (Ahmad et al. 2015). Genetic variability parameters were calculated using the standard methods (Burton and de Vane 1953; Johnson et al. 1955; Allard 1960). Phenotypic coefficients of correlation were calculated based on Burton and de Vane’s formula (1953). Path coefficient analysis was performed based on the formula of Dewey and Lu (1959) using software R4.4.0. The Mahalanobis D2 statistics and clustering by Tocher’s method were done in software R4.4.0 to estimate the level of genetic divergence among the 100 RIL genotypes of both the crosses (BWF and CWF) (Mahalanobis 1936). By using Tocher’s principle, genotypes were categorized into distinct clusters based on the inter-cluster and intra-cluster generalized distances (D2). The multivariate principal component analysis (PCA) was performed based on the original concept of Pearson (Hotelling 1933), and PCA scores were graphically represented on a biplot distribution. The PCA was utilized to estimate the relative contribution of various traits to total variability.
The following formulas were used to calculate the genetic variability parameters:
Heritability (Broad sense) measurement (H%):
Broad sense Heritability of the breeding lines estimated by the formula given by Allard (1960).
Estimation of phytochemical and nutrients
Estimation of anthocyanin, total phenol content, and antioxidant activity in grains
Estimation of total anthocyanin content (TAC) was performed from pigmented and non-pigmented RIL lines (BWF, CWF) with pericarp colour variation either black, red, brown and white according to standard protocol (Sompong et al. 2011; Lim et al. 2017). Briefly, dehusked rice kernels were finely powdered in a mortar by manual grinding and stored at 4°C. About 100 mg of the flour powder was extracted with 1000 µl of acidified methanol (methanol containing 1% HCl) at 40°C for 12 hours with ultrasonication two to three times to ensure complete color extraction. After that 300 μl water and 300 μl of chloroform was added to the extraction mixture and centrifugation was done at 10,000 rpm for 5 minutes under 4°C. Supernatant was collected into a 2 ml centrifuge tube and absorbance was taken at 530 nm. and 657 nm. using UV-Vis spectrophotometer (Parkin Elmer LAMDA 365) to estimate the pigment anthocyanin. The anthocyanin content was estimated using following equation (μg/100 mg) = A530 – 0.33 х A657.
Quantification of total phenol content (TPC)
Total phenol content (TPC) was determined using the Folin-Ciocalteu method with a minor modification (Du et al. 2021). In brief, 0.5 ml methanolic sample extract (70% methanol) was added to 2.5 ml 10% Folin-ciocalceu reagent. After 5 minutes of reaction in dark, 2.5 ml of 7.5% sodium carbonate solution is added and incubate for 45 minutes in 45°C. After that absorbance measured at 765 nm against black in UV-Vis spectrophotometer (Parkin Elmer LAMDA 365). TPC is calculated as gallic acid equivalent (GAE) as gallic acid is used as standard. A standard curve was prepared using different concentrations of gallic acid (100, 200, 300, 400, 500 µg ml−1) from a stock solution of 10 mg ml−1. The total phenolic content was calculated by the formula (CxV)/W, where C is the gallic acid equivalent (GAE) of the sample (mg ml-1) obtained from the standard curve, V is the volume of the extract in ml, and W is the weight of the sample (g). Total phenolic content is expressed as mg GAE per 100g dry weight (DW).
Antioxidant activity
Antioxidant activity of the pigmented (red, black breeding lines) and non-pigmented (white breeding lines) rice extract was tested using the 2,2- diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity (RSA) (Brand-Williams et al. 1995). Fresh DPPH solution (0.066 mM) was prepared by dissolving 0.0026 g in 100 ml of 95% methanol. 100 µl of the sample extract was added to 2.9 ml of freshly prepared DPPH solution and incubated in the dark at room temperature (at 25°C) for 30 min. The absorbance was measured using a UV-Vis spectrophotometer (Parkin Elmer LAMDA 365) at 517 nm against methanol as a blank and 100 µl of 0.1% acidified water in 2.9 ml of DPPH solution as a control. RSA was calculated and expressed in percentage of DPPH activity (%) as follows [(A0-As)/A0] × 100, where A0 is the absorbance of control and As is the absorbance of the sample extract.
Quantification of amylose content (AC)
The modified procedure of Juliano (Juliano 1971) was used for spectrophotometric estimation of amylose content from the RIL lines (BWF and CWF). About 100 mg grain powder of each samples are prepared by grinding it in a mortar-pestle. Then put the grain powder into a 100 ml volumetric flask and mixed with 1ml of 95% ethanol and 1ml of 1(N) sodium hydroxide. The contents are heated on a boiling water-bath at 95°C to gelatinize the starch. After cooling distilled water is added and contents are shaken well. Then 5ml of sample extract are taken in a volumetric flask and mixed with 1ml of 1 N acetic acid, 2 ml of iodine solution (a mixture of 2.0 g potassium iodide and 0.2 g iodine in 100 ml aqueous solution). After that volume is made up with distilled water and let it stand for 20 minutes. Finally, the absorbance is taken at 620 nm. against blank. Amylose content is calculated by taking pure amylose (Sigma, US) as standard using UV-Vis spectrophotometer (Parkin Elmer LAMDA 365).
Phenol reaction of seed coat
Phenol reaction of seed coat was tested according to the protocol (Kumar et al. 2021). Freshly harvested grains are collected. Fifteen healthy grains of each cultivar and breeding lines are soaked in 1.5 percent aqueous phenol solution for 24 hours. After that solutions are drained and air dried. Hull color was then recorded unstained and stained as compared to control treatment in which grained are treated with distilled water.
Estimation of total protein by Micro-Kjeldahl method
Nitrogen was estimated in the samples of dehusked rice of the selected accessions by the slightly modifed Micro-Kjeldahl method (Juliano1985; Bordoloi et al. 2024). The percentage of nitrogen was multiplied by the conversion factor of 5.95 to estimate the total protein content. About 0.5 g of rice flour was digested at 400 °C in the presence of concentrated H2SO4 and a mixture of K2SO4 and CuSO4, followed by distillation using 4% boric acid and 40% NaOH solution. The distilled samples were titrated against the 0.1 N sulphuric acid until the first pink colour appeared at the last point. The titer value was used to calculate the percent nitrogen. Nitrogen % = 14 × (Normality of acid) × Titrant value (burette reading)/ Sample weight (g) ×100. That means Protein % is equal to = Nitrogen % × 5.95.
Metabolomics Analysis through HR-LCMS-QTOF method
Anthocyanin pigments were qualitatively identified from the grain of black rice lines (BWF and CWF) according to the standard methods (Bhuvaneswari et al. 2020). In brief, the dried pigmented black rice grain samples (1g) were ground in with 5 mL of 70% aqueous methanol at room temperature. After centrifugation at 10,000×g for 10 min, the extracts were filtered (0.22 μm) before HR-LCMS-QTOF analysis at the SAIF, IIT Bombay, India. The instrument used: hrlcms-qtof-Agilent Technologies, USA, Data Acquisition Software was used- Agilent MAss Hunter, Data Processing Software was applied- Agilent MAss Hunter Qualitative Analysis B.06, Column details- ZORBAX Eclipse Plus -C18 150 x 2.1 MM, 5 microns (Agilent). Following solvent were used -Solvent A: 0.1% formic acid in Milli-Q water, Solvent B: Acetonitrile. The instrument scanned over the mass (m)/charge (z) range of 100–1100 in the both (positive/negative) ion mode.
Identification of amino acids in black rice lines using HR-LC/MS-QTOF
Total amino acid (TAA) identification was performed using standard protocols (Liyanaarachchi et al. 2021; Tyagi et al. 2022). Briefly 100 mg of black rice flour (BW23 and CW16) were hydrolyzed in 10 ml of 6N HCI at 110 °C for 24h. About 20µl solution taken from hydrolysed samples and evaporated by Speed vac. Then reconstitured by adding 50µl of 0.1N HCl. From this extract, 1µl sample loaded to LCMS system for amino acid profiling along with standard amino acids. Amino acid identification and quantitative analysis was performed with an HR-LCMS-QTOF Mass Spectrometer (Agilent Technologies, USA; SAIF, IIT Bombay, India) with following parameters- dual ion source AJS ESI, HiP sampler, binary pump, with diode array detection (DAD) with gradient elution in Q-TOF Column Comp, and column-Poroshell HPH-C18, 2.7µ, 4.6 x100 mm was used.
Physicochemical properties and sensory based aroma test
Alkali spreading value (ASV) (in a scale of 1 to 7) was measured according to the standard method (Little et al. 1958). A low ASV corresponds to a high gelatinization temperature (GT), conversely, a high ASV indicates a low GT (Little et al. 1958). Sensory based aroma (in a scale of 0 to 3) was evaluated using standard procedure (Sood and Siddiq 1978). The gel consistency (GC) was carried out as per standard protocol (Little et al. 1958).
Scanning electron microsecopy (SEM) for caryopsis ultrastructure
Scanning electron microsecopy (SEM) was carried out to study the histo-anatomical ultrastructural features of the rice caryopsis, mainly BWF, and control black Chakhao using standard protocol (Roy and Shil 2020). Briefly, fractured surfaces (solid round ring) facing upwards were mounted on a specimen stub and coated with a thin film of gold by means of a sputter coater (Jeol Model Smart Coater PF 18001006-2) for about 2 minutes at high vacuum evaporator condition. Then ultrastructure was viewed with a scanning electron microscope (SEM) (Jeol Model JSM-IT100, Japan) at various magnifications with an accelerating voltage of 10 kV to study the histo-anatomical ultrastructure of the different rice caryopses.
Genomic DNA isolation and Kala4 gene specific PCR amplification
Total genomic DNA was extracted and purified from fresh leaf of 10 days old seed ling (parental and breeding lines) following manufacturer protocol (DNeasy Plant Mini Kit, QIAGEN, Germany). Gene specific PCR amplification (Kala4 and BAD2) was carried out using standard protocol (Oikawa et al. 2015; Kim et al. 2021). Kala4 gene specific different primers was used to understand the genetic architecture of Kala4 gene of the progeny (BWF and CWF) and parental lines (Badshabhog, Chenga, Wild rice) and control black Chakhao (Table 1). Total six rice lines were used for the present investigation such as –four parental lines (Wild rice, Badshabhog, Chenga; and local black control Chakhao); two RIL lines (BW23 and CW16). PCR amplification was carried out using standard protocol (Oikawa et al. 2015; Kim et al. 2021). In brief PCR reaction of 20 μl contains nuclease free water 10.75μl, 10X reaction buffer with template DNA (50ng/ μl) 1ul, primer (10 picomoles/μl) 4μl, MgCl2 (1.5mM) 2μl, dNTP mix (2.5mM) 2μl, 0.25μl Taq DNA polymerase (5U). Following PCR profiling was set for the reaction, initial denaturation at 94°C for 5 min, then 30 cycles of denaturation at 94°C for 1 min, annealing 58°C for 30 secs, extension 72°C for 2 min, lastly final extension at 72°C for 10 min. PCR product was fractionated in 1% agarose gel electrophoresis and visualized under UV-Transilluminator after staining with ethidium bromide and photographed.
Table 1. Kala4 gene specific primers used in PCR amplification and BAD2 gene.
Phenotypic variability
Genetic divergence based on agro-morphological variability in the RIL lines
The progeny population of two wide crosses BWF (Badshabhog x O. rufipogon) and CWF (Chenga x O. rufipogon) comprising 100 RIL lines in each cross was evaluated in the present study using DUS guideline (PPV&FR Act 2001, Govt. of India). All the RIL lines of BWF and CWF showed higher significant differences at p<0.001 based on the 15 quantitative traits, as revealed by ANOVA (Tables 2-3). An unexpected range of phenotypic variation was recorded among the RIL lines of BWF (Table 4, Figures 3-7). Shortest plant height of only 60 cm was recorded in BW98 line with small flag leaf length of 15.17 cm and width of only 5.07 mm with small shattered seeds (Table 4, Figures 3-7; Supplementary Table S1). In contrast to that tallest plant height was observed in line BW97 with shattered seeds. Grain pericarp colour in both the RIL lines (BWF, CWF), varied from white, brown, red, greenish to black with distinctive grain quality parameters viz., ASV, GT, GC, aroma (Figure 8; Supplementary Tables S1-S2). Considering the plant height (121.07 cm to 142.14 cm), maturity time (125 to 140 days), grain per panicle (190.40 to 387.14), 1000 grain weight (21.34 g to 27.20 g), and single plant yield (32.48 g to 61.89 g), the following BWF RIL lines BW6, BW17, BW18, BW23, BW24, BW25, BW26, BW77, BW84, BW85, BW88, BW90, BW91, BW94, and BW95 can be considered as promising aromatic black rice lines, including one white grained line BW99 (Table 4). In case of CWF, the following promising potential RIL lines CW1, CW11, CW16, CW20, CW23, CW79, CW78, CW79, CW90, CW94, CW95, CW96, CW97, CW98, and CW99 can be considered for further evaluation before release to the farmers’ field (Table 5, Figures 3-7). The presence of variability is a prerequisite for any breeding program and germplasm characterization. In this context, a previous study reported that grain weight is one of the most important trait for determining phenotypic variability in the rice landraces of the Majuli Islands, Assam, India (Mudhale et al. 2024).
In our breeding lines (BWF, CWF), wide range of phenotypic diversity was observed in regard to 15 quantitative agromorphological traits and grain quality parameters. Some of the RIL lines (BWF, CWF) showed transgressive segregation with respect to 1000 GrWt, grain length, panicle length, panicle weight, and others (Tables 4-5, Figures 3-7; Supplementary Tables S1-S2). Therefore, our present results are consistent with the previous studies and emphasizes that prior knowledge about the agro-morphological characterization of rice germplasm is fundamental to the plant breeders (Mudhale et al. 2024; Bordoloi et al. 2024).
Genetic variability parameters (Heritability, Genetic advance, GAM)
To investigate the genetic diversity of any genetic resources, several indicators like genotypic variance (GV), phenotypic variance (PV), and genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), as well as its broad sense heritability (H%), genetic advance (GA), and GAM (genetic advance as percentage of mean), were frequently exploited and accordingly conducted in our breeding lines (BWF, CWF) (Tables 6-7). The 15 quantitative traits studied indicated the presence of gigantic variability for the yield and its allied traits, which provides more opportunity to utilize these traits for the further rice improvement programs. The magnitude of PCV was higher than GCV for all the traits studied, indicating that the environment may have an impact on their phenotypic expression. Differences between GCV and PCV were less (BWF, CWF), indicating a higher correlation between phenotype and genotype, less environmental effect, and a larger role of genetic factors in these traits expression (Tables 6-7). High PCV and GCV values suggest that there is a great amount of genetic diversity existed in RIL lines (BWF, CWF) and are consistent with the previous results (Pathak et al. 2019; Gogoi et al. 2024). Heritability in the broad sense was classified as high (>60%), moderate (40–60%), and low (40%), whereas the levels of GA were ranked as low, moderate, and high, with corresponding ranges of 10%, 10–20%, and >20%, respectively. Some of the traits were recorded for high heritability in concurrence with high genetic advance, indicating additive gene effects in our present study (Tables 6-7). The broad sense heritability for agro-morphological traits ranged from 73.20% (PnL) to 99.87% (HD) in BWF lines and 76.58% (GB) to 99.72% (KL) in CWF (Tables 6-7). Broad sense heritability was found to be high for heading date (99.87%), maturity time (98.81%), 1000 grain weight (98.41%), panicle weight (91.98), plant height (91.55), yield per plant (90.01%), panicle length (73.20), and grain per panicle (80.85%) in BWF lines (Table 6). Similar results were also demonstrated by previous researchers (Pathak et al. 2019; Gogoi et al. 2024). Broad sense heritability was high (>60%) in all the characters studied (Tables 6-7), which indicates little environmental influence in our breeding lines and is consistent with an earlier study of Mondal et al. (2024). High heritability coupled with high GA (> 20%) of a trait is crucial for the selection of a trait in any breeding program. In our present study, high heritability coupled with high GA was reported for plant height; panicle length; grain per panicle; thousand grain weight; tillering; and single plant yield in BWF RIL lines, suggesting the additive gene action for the traits, so selection based on these traits could contribute largely to the improvement of rice (Table 6). High heritability (>90%) in combination with high score of genetic advance as percent of mean (GAM) was observed for the traits FLW, panicle weight, grain length, and kernel length in the CWF RIL lines, respectively (Table 7). This signifies that the traits are governed by additive gene action in nature and can be introgressed into the cultivars for germplasm enhancement through selection. Present results were consistent with the earlier studies in respect to genetic parameters studied (Ahmad et al. 2015; Roy and Shil 2020B; Faysal et al. 2022; Gogoi et al. 2024; Bordoloi et al. 2024). Our results ratify the proposition that significant genetic gain can be achieved in improving varieties by utilizing novel genes of neglected wild rice to restore genetic diversity and allelic variation lost during domestication (Siddiq and Vemireddy 2021; Eizenga et al. 2024).
Correlation among the agro-morphological traits data
Based on correlation analysis, it was observed that yield per plant (PY) had positive correlation with the traits such as grain per panicle (0.78), panicle weight (0.66), 1000 grain weight (0.57), and grain length (0.57) in BWF (Figure 9). The highest value of positive correlation was observed (0.88) between the traits kernel length (KL) and grain length (GL), followed by grain length and 1000 grain weight (0.79), grain length, and panicle weight (0.61). Yield per plant was negatively correlated with maturity time in days (MT) (-0.26), followed by heading date (-0.23). Heading date (HD) was negatively correlated with three traits: 1000 grain weight (-0.22), grain breadth (-.0.23), and kernel breadth (-0.03). In the CWF RIL lines, single plant yield was positively correlated with the traits panicle length (0.26), panicle weight (0.32), grain per panicle (0.63), and grain bredth (0.23) (Figure 9). The highest positive correlation (0.76) was found between heading date (HD) and maturity time in days (MT) and (0.61) between grain length and kernel length. The correlation analysis therefore indicate that grain per panicle, panicle length, panicle weight, 1000 grain weight, and grain length are the most important traits that need to be considered in the production of high-yield breeding lines.
Character Association and Path Coefficient Analysis
The correlation analysis illustrated only the relationship between two traits while path coefficient analysis permits separation of the direct and indirect effects via other attributes by partitioning the associations. Additionaliy, by separating the genotypic (G) and phenotypic (P) correlation coefficients into direct and indirect effects, the path analysis module gives insight into the true impact of independent factors on yield (dependent factor) (Saleh et al. 2020). In our current investigation, single plant yield (PY) was measured as a resultant (dependent) variable and PH, FFL, FLW, PnL, PnWt, GrPn, GL, GB, KL, KB, GrWt, Till, HD and MT were causal (independent) variables, which is illustrated through a path coefficient diagram (Figure 10; Tables 8-9). Partitioning of genotypic correlation coefficient with PY into direct (bold) and indirect path coefficient (PC) in 100 CWF and 100 BWF RIL lines are also summarized (Supplementary Tables S3-S4). In BWF lines, residual effect was 0.1743, indicating that 82.57% of the variability was explained by the 14 characters studied (Table 8). The positive direct impact on plant yield was majorly influenced by GL (0.5977**), GB (0.4916**) GrWt (0.5787**), PnWt (0.656**), PnL (0.4275**), and GrPn (0.797**) in the present study in case of BWF (Table 8; Figure 10). Thus, selection directly based on these characters would be appropriate for increasing plant yield (PY) in BWF. On the other hand, in case of CWF lines, plant height (PH) had a significant positive correlation (SPC) with FLL (r = 0.6869**, 0.5932**), FLW (r= 0.4341**, 0.3983**), PnL (r=0.5034**, 0.3677**), PnWt (r=0.2258*, 0.1543**), respectively at both genotypic and phenotypic (G and P) level (Table 9; Supplementary Table S4), suggesting that yield might be improved by regulating plant height. The SPC was found between panicle length (PnL) and panicle weight (r=0.5073**, 0.4600 **), grain per panicle (0.3652**, 0.3348 **), grain weight (0.3042**, 0.2378 **), GL (0.2207**, 0.1754 **), GB (0.3658**, 0.2967 **) KL (0.3301**, 0.2441 **) at the G and P levels, respectively, indicating that PnL and plant yield could be increased by improving these traits. However, the negative correlation (NC) between grain per panicle and HD (r = -0.4501 **, -0.4171 **), MT (r = -0.3705 **, -0.3582 **) was observed in our study at both genotypic and phenotypic level. The results indicate the association among the traits, that number of grain per panicle may increased the HD and MT (Table 9). Our results were in agreement with that of Nguyen et al. (2023), which revealed that MT had a negative indirect impact on grain yield per plant via HD. Positive direct impact was detected on plant yield through KL (0.5259**), GrWt (0.5113**), PnWt (0.6469**), PnL (0.4043**), and GrPn (0.7323) in the present study in CWF (Figure 10). Thus, selection directly based on these characters would be achievable for increasing plant yield (PY). The residual effect was 0.1364 indicating that 86.36% of the variability was explained by the 14 characters studied. However, there were other contributors (13.64%) which were responsible for yield but were not taken into consideration in the present investigation (Table 9). Our findings corroborate the earlier results that these traits had strong positive direct effects on yield (Surekha et al. 2016; Jeke et al. 2021; Nguyen et al. 2023). Positive direct effects of various traits on grain yield reported in the present research are in agreement with the findings of Faysal et al (2022). Therefore, the present study suggested that GrPn, PnWt, GrWt, and PnL which are the main components of the yield of these genotypes should be given high priority in selection for future breeding programs.
Dendrogram construction from agro-morphological traits data in BWF and CWF
The genetic relationship among the 100 RIL lines (BWF, CWF) was assessed by constructing a dendrogram to visualize the closeness. The dendrogram for agro-morphological traits was constructed on the basis of a matrix of average taxonomic distance based on euclidean estimates using the UPGMA method for 15 quantitative agro-morphological traits in both RIL populations (BWF and CWF). All the 100 BWF RIL lines, including three parental lines (parentals Badshabhog, wild rice Oryza rufipogon, and one control local black, Chakhao), were broadly grouped into three clusters. Each cluster is closely associated with the parental and control black lines. The cluster I comprises 40 RIL lines and is closely associated with the parental line Badshabhog; cluster II is related to control black rice and consists of 53 lines, whereas cluster III is associated with wild rice parental line O. rufipogon, consisting of 10 RIL lines (Figure 11A). On the other hand, dendrogram showed three clusters based on 15 agro-morphological data of 100 CWF RIL lines (Figure 11B). Cluster I, consisting of twenty-six RIL lines along with the parental line Chenga; cluster II, comprising of 59 RIL lines along with the local control black; whereas cluster I incorporates sixteen RIL lines close to wild rice, although O. rufipogon is placed in a separate clade outside of the cluster III, indicating high genetic dissimilarity with the RIL lines. Our present findings are consistent with the earlier studies of Ahmad et al. (2015) and Bordoloi et al. (2024).
Mahalanobis D2 test for genetic diversity analysis in BWF and CWF
Mahalanobis distances (D2) distinguished the BWF RIL lines (100 lines) into seven Tocher’s clusters (Supplementary Table S5). Each of the fifteen traits has contributed to the overall genetic divergence in the 100 BWF RIL lines (Table 10). The contribution towards the total variation was the maximum for GrPn (19.00), followed by 1000 GrWt (13.90), KB (13.60), GB (10.50), GL (8.80), PH (8.50), PnWt (5.30), KL (5.00), and PY (4.30). The average intra- and inter-cluster distances within the seven clusters indicate the degree of divergence within and between the groups (Table 11). The largest inter-cluster distances were found between clusters IV and VII (25865.50) in BWF lines. On the other hand, based on the Mahalanobis distance (D2) matrix, 100 CWF RIL lines are grouped into elevan Tocher’s clusters, seven multi-genotypic and four mono-genotypic (Supplementary Table S6). Each of the fifteen traits that has contributed to the overall genetic divergence in the CWF was categorized and displayed in Table 12. The contribution towards the total variation was the maximum for 1000 GrWt (24.32), followed by the other traits (Table 12). Cluster XI had the highest PY (34.95 g) with maximum contributions from GrWt, GL, KB, PH, GB, PnL, GrPn, and PnWt. Moreover, PY benefited most from Clusters VIII (32.01 g) and X (33.65 g) (Table 12). The average inter-cluster distances were observed to be greater than the average intra-cluster distances, suggesting that the CWF lines possess a greater degree of genetic diversity (Table 13). The average intra- and inter-cluster distances within the elevan clusters indicate the degree of divergence within and between the groups (Table 13). The largest inter-cluster distances (25817.49) were found between clusters II (CW27, CW71, CW95, CW23, CW16, CW84, CW85, and CW81) and cluster IV (CW44, CW48, CW26, CW31), containing genotypes that were found most divergent with maximum inter-cluster distance. According to the D2 cluster matrix, cluster VII had the largest intra-cluster distance (2942.63) with RIL genotypes CW20 and CW34. Maximum heterosis would result from a cross between genotypes from Clusters II and IV that had the greatest genetic distance (25817.49). Genotypes with the largest genetic distance in yield-attributing parameters would result in the complementation of gene effects in the hybridization program. Our present studies are substantiating the previous explanation that natural wild rice accessions are regarded as poor agronomic performers; despite this, the recovery of widely adaptable cultivars to diverse challenging environments would be higher when wild relatives of rice are used in the crossing program (Sanchez et al. 2013). The present reports are consistent with the earlier analyses that higher inter-cluster distances existed in the breeding lines and cultivars (Faysal et al. 2022; Mondal et al. 2024).
Principal component analysis (PCA)
Principal component analysis (PCA) is a multivariate powerful statistical tool to identify the minimum number of components to explain the maximum variability out of the total variability and rank genotypes based on PC scores. Plant breeders often measure many variables, some of which may not be of sufficient discriminatory power for germplasm evaluation, characterization, and management. In such cases, principal component analysis (PCA) may reveal the patterns and eliminate redundancy in data sets. Principal component analysis (PCA) was performed using phenotypic diversity based on 15 morphological traits from the 100 BWF RIL and 100 CWF RIL lines (Tables 14-15). According to the scree plot (Figure 12), principal components (PCs) assume importance when the eigen value is greater than one and the PC explains at least 3-5% of the variation in the data. Significant variables were indicated by high positive loading values (Tables 14-15, Figure 12). Out of fifteen, only four principal components (PCs) exhibited eigen value greater than one (Eigen value > 1) and explained 73.74% cumulative variability among the traits studied, indicating significant variability in BWF RIL lines (Table 14). The first four PCs explained 35.52, 17.43, 13.09 and 7.68% of the variability among the BWF RIL. The traits panicle weight (0.350), 1000 grain weight (0.346), grain length (0.335), grain breadth (0.324), kernel length (0.332) and single plant yield (0.356) positively contributed to the first PC1. In contrast, heading date (-0.1384) and maturity time in days (0.1242) contributed negatively to PC1. PC2, PC3 and PC4 which accounted for 17.43, 13.09 and 7.68% of the total variability respectively (Table 14; Supplementary Tables S7 & S9). PC1 and PC2 in the biplot diagram explained the dispersion and nature of diversity for both variables and breeding lines (BWF and CWF) in our present study (Figure 9). The vectors in the first quadrant, viz., plant height and flag leaf length, strongly correlated among themselves and loaded on the PC2 (Table 14). The vectors in the second quadrant- productive tillers, grain per panicle, and single plant yield, were highly correlated variables loaded on PC4. Similarly, the vectors in the fourth quadrant, heading date (HD) and maturity time in days (MT) were highly correlated variables and loaded on PC2. The traits heading date and maturity time were negatively correlated with single plant yield (PY). The RIL lines (BW9, BW19, BW81, BW44, BW86, BW88) projected on the vectors of grain per panicle, grain weight, GL, KL and single plant yield (PY) were close to them, demonstrating a positive interaction (Figure 9). Comparing the 100 RIL lines of BWF based on PCA biplot analysis, the RIL lines BW18, BW23, BW24, BW25, BW44, BW52, BW77, BW83, BW88, BW90, and BW99 were superior for panicle weight, 1000 grain weight, single plant yield, grain length, grain breadth, and kernel length. Hence, these results of PCA will be of greater benefit to the breeder to identify parents and the selection of characters for future hybridization programs for varietal improvement.
The PC analysis of yield and yield-contributing traits of 100 CWF RIL lines generated six PCs, and the first six components together explained more than 71.90 % of the total variation in CWF RIL lines (Table 15; Figure 9). PC1, PC2, PC3, PC4, PC5 and PC6 accounted for 20.026, 14.267, 11.569, 9.680, 8.394, and 7.961%, respectively, of the total variability in CWF RIL lines. In PC1, the traits GrPn (0.384) and PH (0.390) contributed positively and accounted for 20.026 % of the variation as a whole (Figure 9). The vectors in the first quadrant, viz., FLL, GrWt, GL, GB, KB, strongly correlated among themselves and loaded on the PC2 (Table 15). The vectors in the second quadrant, PY, GrPn, PnWt, and PnL were highly correlated variables loaded on PC5. Similarly, the vectors in the fourth quadrant, PH, Till, and KL, were highly correlated variables and loaded on PC5. Comparing the 100 CWF RIL lines based on PCA biplot analysis, the RIL lines CW1, CW11, CW16, CW26, CW29, CW32, CW36, CW40, CW41, CW44, CW57, CW79, CW81, CW84, CW85, CW93, CW96, and CW98 were superior for PnWt, PnL, GrWt, PY, GL, GB, and KL (Table 15; Figure 9; Supplementary Table S8 & S10). The genetic diversity of breeding lines (BWF and CWF) was clarified, and component traits contributing to variability were broken down through the combination of principal component analysis; this could provide the framework for a well-run hybridization program (Supplementary Tables S7-S10). Previous studies also found similar results that the first five principal components (PCs) exhibited eigen values of more than 1.00 and explained 85.87% variability (Ahmad et al. 2015; Bordoloi et al. 2024).
Selection of newly developed innovative aromatic black rice lines with purple leaf
The most innovative and novel genetic change that we have observed in the progeny populations (BWF and CWF) was the appearance of black pericarp containing rice lines. Many phenotypic variations were detected in the grain colour which ranged from white, light brown, redish brown, brown, deep brown, redish, red, blackish red, greenish, blackish brown, black, to deep black (Figures 3-7; Supplementary Tables S1-S2), broadly showing 9:6:1 and 9:3:4 ratios in some of the generations. This supports the view that grain colour is a polygenic inheritance in nature and controlled by many genes, or quantitative trait loci (QTL) or/ involving as yet unidentified genes (Oikawa et al. 2015; Ham et al. 2015; Devi et al. 2020; Pham et al. 2024). In the present study, we have observed many breeding lines with purple leaf colouration in the CWF cross with black pericarp and black husk colour (Figure 2). Purple leaf trait is inherited from F3 generation, suggesting that the trait has been newly acquired by the breeding lines although parental lines were devoid of such trait. The evolutionary history of anthocyanin biosynthesis genes reveals that the purple-leaf trait was negatively selected during the domestication of rice. The reason for this negative selection (mutant allele of OsC1 and Rb gene and normal Rd) may be that anthocyanin in rice leaves reduces the efficiency of photosynthesis, in turn reducing yield (Xia et al. 2021). However, anthocyanins in various plant tissues is crucial for diverse biological functions, including UV damage protection, defense responses to biotic and abiotic stresses, hormonal regulation and defense against pathogens, insects, herbivores and environmental stresses (Chalker Scott, 1999; Steyn et al. 2002; Ithal et al. 2004; Landi et al. 2015; Zaidi et al. 2019).
Physicochemical properties, Biochemical tests and Nutritional Facts of Breeding Lines
All the physicochemical properties and sensory-based aroma test results of the black rice breeding lines were summarized in Figure 8, supplementary Tables S1-S2. The amount of amylose content ranged from 11.31% in CW97 (black non-scented) to 19.13% in BW57 (red non-scented), 14.46% in BW23 (black scented) and 13.72 % in CW16 (black scented) (Figure 8; Table 16). Higher amylose content generally results in a slower digestion rate, leading to a lower glycemic index compared to rice varieties with lower amylose content. The amount of TPC ranged from 154.859 mg GAE/100g dry weight basis in BW99 (white, non-scented) to 520.016 mg GAE/100g in CW40 (red, scented), which was quite high compared to some of the parental lines. The present results are consistent with the previous report of Bhuvaneswari et al. (2020) and Idrishi et al. (2024). The promising black rice breeding lines also contained a comparatively high amount of anthocyanin pigment, 261 mg/100 g in BW23 (black-scented) in comparison to control black (259 mg/100g) and other parental lines. Our present investigation is consistent with the findings of other researchers (Gogoi et al. 2024; Bhubaneswari et al. 2024; Lap et al. 2024). The DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging activity in breeding lines ranged from 70.88 to 78.43% in pigmented rice and 11.89% to 19.06% in non-pigmented rice line genotypes (Table 16). Our findings in respect to DPPH antioxidant activity support the earlier views that black rice breeding lines (BWF, CWF) have higher levels of total phenols, flavonoids, and anthocyanins than white rice, indicating greater antioxidant activity with high grain quality (Table 16; Figure 8) (Roy and Reddy 2017; Bhuvaneswari et al. 2020; Zhu et al. 2024; Gogoi et al. 2024). Nutritional facts of black rice breeding lines, including parental lines, were also summarized in Table 17, indicating that our breeding lines were nutritionally enriched, comprising of high amount of quality protein, fiber content, various minerals, magnesium, manganese, phosphorus, calcium, sodium, zinc, iron, PUFAs (polyunsaturated fatty acids), and MUFAs (monounsaturated fatty acids) (Table 17). Both PUFAs and MUFAs play crucial roles in human health and have been associated with a decreased risk of cardiovascular diseases (CVDs), and our present result was consistent with the earlier report (Bordoloi et al. 2024).
Moreover, the HR-LCMS-QTOF analysis revealed the detection of anthocyanin compounds in our black rice breeding lines (BW23 and CW16), mainly petunidin 3-O-glucoside, including 41 metabolites (Table 18; Figure 13C-D). The most common metabolites identified were as follows: catechin, oryzanol, gallic acid, caffeic acid, quinic acid, quercetin, 3,5-dihydroxybenzoic acid, rutin, luteolin 4'-O-glucoside, heptadecatrienoic acid, PAB/4-Aminobenzoic acid, kaempferol, 7-O-glucoside, peganine, maritimetin, mitoxantrone, methyl 2-(10-heptadecenyl)-6-hydroxybenzoate, zinnimidine, azafrin, tubulosine, and other metabolite compounds having medicinal properties (Table 18). Similar patterns of metabolites were reported by previous studies in pigmented rice varieties (Bhuvaneswari et al. 2020; Zhu et al. 2024). Total amino acid content was quantitatively estimated in the rice grain of our breeding lines through the HR-LCMS-QTOF method and ranged from 8.76 mg/100 g (BW23) to 8.81 mg/100 g (CW16) on a dry weight basis with the following amino acid compositions: aspartic acid, alanine, arginine, cysteine, glutamate, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline (hydroxyproline), serine, threonine, tyrosine, glutamine, and valine (Table 18 and Figure 13A-B). In the present study, glutamic acid was found to be the highest amount (1650 mg/100 g) detected in BW23, and methionine was the lowest amount (70 mg/100 g) detected in CW16. The amino acid profile of rice grains showed that it is high in glutamic and aspartic acids, while methionine is the limiting amino acid, similar to other analyses (Carcea 2021). Protein content was 8.76 g/100 g in BW23 and 8.81 g/100 g in CW16, which is quite high in respect to the parental lines (Table 17). Comparatively higher amounts of total protein were observed in aromatic rice compared to non-aromatic rice (Liyanaarachchi et al., 2021; Tyagi et al., 2022). Proteins containing the following amino acids, such as lysine, leucine, isoleucine, and threonine, are considered high-quality proteins (Huang et al., 2019; Liyanaarachchi et al., 2021; Tyagi et al., 2022; Jayaprakash et al., 2022). Our results showed that both the black rice breeding lines (BW23 and CW16) are nutritionally enriched due to the presence of high-quality proteins in the endosperm containing the high quality amino acids (Table 19) and other important neutraceuticals, i.e., oryzanol, anthocyanin, catechin, iron, and zinc (Tables 17-19) (Ahmad et al. 2015; Huang et al. 2019; Liyanaarachchi et al. 2021; Tyagi et al. 2022; Jayaprakash et al. 2022) (Table 19). Therefore, our breeding lines can be considered as Super Food or Panacea. Glutelin protein is considered a high-quality protein due to the lysine, leucine, isoleucine, and threonine richness present in PB-II (protein body) located in the rice endosperm and visualized in caryopsis ultrastructural anatomy using SEM (Figure 14). Grain protein prolamin is present in the PB-I and located in the endoplasmic reticulum (RE) (Jayaprakash et al. 2022).
Genetic characterization of Kala4 gene through PCR amplification in BWF and CWF
The Kala4 gene (bHLH TF) is mostly responsible for black pericarp development in cultivated rice by rearranging its promoter region through a LINE1 insertional mutation, including 11.02 kb of genome segment insertion within the LINE1 transposon (Figure 15). The 11.02 kb genomic segment insertion at the Kala4 promoter has been considered as a key regulatory genetic rearrangement completely responsible for black pericarp development in the Asian cultivated rice (acquired neo-functionalization trait) (Oikawa et al. 2015; Kim et al. 2021). The report explained that neo-functionalization of Kala4 allele through LINE1 insertional mutation, had happened in the genetic background of tropical japonica first and then spread to indica and other subspecies of rice (Oikawa et al. 2015). The Kala4 gene is approximately 25.6 kb in size and composed of 8 exons and 7 introns. In the present study, PCR-amplified products were detected in all the parental and breeding lines when LINE1 insertion-specific primer set 1 was used in the reaction mixture (Table 1), which signified that LINE1 had been inserted into the intron 2 position of the Kala4 gene otherwise, a PCR product was not possible with this primer set 1 (Figure 15A). Genomic segment of 11.02 kb insertion was checked by the PCR product in presence of primer set 2, which was formed as a junction1 of either type I or type II insertional pattern (Figure 15B). PCR amplified product was observed in all the six lines in presence of primer set 3 confirming that the intron 2 construct of Kala4 gene is inserted in the correct position in BWF and CWF lines (Figure 15C). In our present investigation, we judged the availability of Kala4 ORF in the breeding lines based on the information of chromosomal location (Os4g0557500), with the help of primer set 4 (Table 1). PCR product was detected about 700 bp long in all the breeding lines along with other coamplification bands about 200-300 bp, indicating that Kala4 ORF of chromosomal location at position Os4g0557500 is in a functional structural position (Figure 15D). PCR profiling based on fragrant gene BAD2 (Table 1) also indicated that our black rice breeding lines are scented, BWF (Figure 15E) is heterozygous in nature and CWF (Figure 15E) is double recessive homozygous in nature and scented. It can be summarized from the PCR results that primers used (Table 1) for the detection of LINE1 transposon and an 11.02 kb genomic segment insertion near the Kala4 promoter has been indorsed, supporting the view (partially) of earlier study regarding Kala4 gene construct rearrangement in black rice varieties (Oikawa et al. 2015) except the black conversion process from tropical japonica to black indica. Present study therefore, provides a direct evidence of the earlier proposed concept of Kala4 genetic rearrangement of black rice origin, which was totally based on genome analysis and not from any breeding experiment (Oikawa et al. 2015). Our report explained that acquired neo-functionalization of the Kala4 allele (gain-of-function mutation) had occurred in the black rice RIL lines (BWF, CWF). If no such insertional mutation happened (LINE1 and 11.02 Kb insertion), then it would not have been possible to develop black pericarp in our breeding lines (BWF and CWF). The LINE1 rearrangement consequently induce the ectopic expression of genes involved in anthocyanin production, which in turn gives rise to black rice.
The subspecies indica/aus is originated from a subgroup of O. rufipogon type OrI and japonica from a subgroup type OrIII (Huang et al. 2012B; Civan and Brown 2018; Wang et al. 2018; Zhang et al. 2021). Reproductive barriers between japonica and indica rice emphasizes that they have resulted from independent domestication process or a single domestication with multiple origins (Choi et al. 2017). This lead to a pertinent question that how black rice of the indica type be originated from black tropical japonica (whose progenitor is type OrIII)? The subspecies japonica originates from distinct populations of wild rice (OrIII) and indica originates from other subgroup OrI. At this point of dispute, we are not supporting the earlier view that neo-functionalization of Kala4 allele through LINE1 insertional mutation, had happened in the genetic background of tropical japonica first and then spread to indica and other subspecies of rice (Oikawa et al. 2015). Therefore, black indica subspecies must be originated from the wild rice OrI type population. Based on our classical breeding and genetic evidence, we support the view of multigeographical independent origin of cultivated rice, that means indica and japonica subspecies originated from distinct genetic resources of O. rufipogon (OrI and OrIII). In this argument, we have put up a novel theory that black rice, primarily of the indica subspecies, originated independently on the Indian subcontinent during the domestication process from the wild rice of India through natural outcrossing, gene flow, and artificial selection. This is a new innovative information and unravels the potential of breeding knowledge that can contribute to overcoming the many unsolved problems about the evolutionary origin of cultivated rice, the history of rice domestication (single or multiple independent origins), and more specifically, the origin of black pericarp pigmentation in the cultivated rice. Also the high yield potential with good nutritional quality of our RIL lines will help to boost up the food and nutritional security of the world by 2050.
This is the first study in the history of rice breeding and genetics describing the development of novel black rice lines via interspecific hybridization between Indian wild rice (O. rufipogon) and locally grown rice (O. sativa) cultivars Badshabhog and Chenga. Because the breeding lines' unique and novel trait—a black pericarp—comes from non-black parental lines, this is an inventive and original finding. Based on our classical breeding and genetic evidence, we support the view of multigeographical independent origin of cultivated rice, that means indica and japonica subspecies originated from distinct genetic resources of O. rufipogon (OrI and OrIII). Simultaneously, we have proposed a new concept that black rice (mainly indica type) of the Indian subcontinent originated independently through natural outcrossing, gene flow, and artificial selection in the course of domestication from the wild rice of India. Underutilized wild rice contributed immensely to enhancing the genetic base of the RIL lines, with unusual genetic diversity associated with agromorphological traits and grain pigmentation. RIL lines may provide some insight into the evolutionary and domestication history of black rice origin and can be used as an important genetic resource for improving black rice for food and nutritional security. Potential breeding lines of BWF (BW6, BW18, BW23, BW24, BW25, BW26, BW33, BW44, BW50, BW77, BW83, BW88, BW90, BW91 and BW99) and CWF (CW1, CW11, CW16, CW20, CW23, CW79, CW78, CW79, CW90, CW94, CW95, CW96, CW97, CW98 and CW99) have been considered as promising aromatic black rice lines with quality grains. Single plant yield (PY) was measured as a yield evaluation parameter in the breeding lines and showed yield increase on an average of 10 to 19% compared to control black cultivar Chakhao with early maturity time (135-140 days). Our black rice lines may be considered as functional pigmented Super Food and Panacea due to their high nutritional values (antioxidants, high-quality proteins with essential amino acids, anthocyanin pigment petunidin-3-O glucoside, and many others). As a whole, the genetic base of the recipient cultivars (Badshabhog, Chenga) has been widened (broadened) through this pre-breeding system via alien introgression of untapped hidden genes from underutilized wild rice (O. rufipogon). Therefore, wild rice germplasms are an essential component of nature-positive sustainable agriculture, agrobiodiversity enhancement, and overall food and nutritional security; thus, efforts are being taken for their in situ conservation in our university campus.
Awn- awn length
BWF – Badshabhog x Wild rice progeny
CWF- Chenga x Wild rice progeny
DUS Guideline-Distinctiveness Uniformity and Stability
FLL- flag leaf length
FLW- flag leaf width
GrPn-grain per panicle
GL- grain length
GB-grain breadth
GrWt-1000 grain weight
HD-Heading date
PC-Pericarp colour
PH- plant height
PnL-panicle length
Till- active tillering number
HYV- High yielding variety
Kala- key activator locifor anthocyanin
LINE1-Long Interspersed Nuclear Element-1
MT-maturity time in days
QTL- Quantitative Trait Locus
PCA- Principal component analysis
PCV- Phenotypic coefficient of variation
GCV- Genotypic coefficient of variation
HR-LCMS-QTOF- High resolution-liquid chromatography–mass spectrometry-QTOF
Ethics approval and consent to participate:
Not Applicable in this research work.
Consent to publish:
All the authors have given the consent to publish the works in Rice journal.
Availability of data and materials: Not applicable
Competing interests: The authors declare that they have no competing interests.
Funding: No funding.
Authors' Contributions
Subhas Chandra Roy- contributed to the conception and design of the project, innovative idea and written the whole manuscript based on this Interspecific Hybridization (Wide hybridization) and selection of breeding lines and first observed black grain pericarp among the progeny populations of different wide crosses, agromorphological data acquisition from the trial field, overall supervision.
Pankaj Shil- Rice seeds germination, data measurement, data sheet preparation with analysis and reference correction.
Acknowledgements
SCR is thankful to the North Bengal University authority for providing necessary Laboratory facilities and establishment of ‘Rice Germplasm Conservation & Breeding Centre’ Department of Botany, University of North Bengal with an ‘Experimental Rice Field’ to conduct the trial of large number of breeding population and selection. And also thankful to DST, Govt. of India for providing DST-FIST Fund, [Sanc No. SR/FST/LS-I/2021/900 dt. 25.03.2022, duration 2022 to 2027] to the Department of Botany, University of North Bengal for upgradation of the Laboratory with Sophisticated Instruments. Thanks to the USIC, NBU for SEM facility.
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Tables 1 to 19 are available in the Supplementary Files section
No competing interests reported.
- Table4..xls
- Table5..xls
- Table8..xls
- Table9..xls
- Table12..xls
- Table13..xls
- Table18..xls
- Tables.doc
- SupplementaryTableS1.ASVGTGCAroma.xls
Supplementary Table S1. Grain quality parameters such as ASV, GT, GC, PC, aroma and others morphological traits in 100 BWF lines.
- SupplementaryTableS2.ASVGTGCAromaCWF.xls
Supplementary Table S2. Grain quality parameters such as ASV, GT, GC, PC, aroma and others morphological traits in 100 CWF lines.
- SupplementaryTablesS3S10..xls
Supplementary Table S3. The genotypic correlation coefficient among 15 characters in 100 BWF RIL lines. Supplementary Table S4. The genotypic correlation coefficient among 15 characters in 100 CWF RIL lines. Supplementary Table S5: Composition of the Tocher’s clusters based on Mahalanobis D2 test analysis of 100 BWF RIL lines. Supplementary Table S6: Composition of the Tocher’s clusters based on Mahalanobis D2 test analysis of 100 CWF RIL lines. Supplementary Table S7. Interpretation of rotated component matrix for the traits having values >0.3 in each PCs of RIL BWF. Supplementary Table S8. Interpretation of rotated component matrix for the traits having values >0.3 in each PCs of RIL CWF. Supplementary table S9. Parental and progeny lines of BWF RIL lines are selected on the basis of PC score in each component having positive values more than >1.0 in each PCs. Supplementary Table S10. Parental and progeny lines of 100 CWF RIL are selected on the basis of PC score in each component having positive values more than >1.0 in each PCs.