Genetic Dissection of Grain Nutritional Traits and Leaf Blight Resistance in Rice

Cruz PC, Borromeo TH, Lalusin AG, Mauleon R, McNally KL, Swamy BPM. Molecular dissection of connected rice populations revealed important genomic regions for agronomic and biofortification traits

Breeding staple crops with increased micronutrient concentration is a sustainable approach to address micronutrient malnutrition. We carried out Multi-Cross QTL analysis and Inclusive Composite Interval Mapping for 11 agronomic, yield and biofortification traits using four connected RILs populations of rice. Overall, MC-156 QTLs were detected for agronomic (115) and biofortification (41) traits, which were higher in number but smaller in effects compared to single population analysis. The MC-QTL analysis was able to detect important QTLs viz: qZn_5.2, qFe_7.1, qGY_10.1, qDF_7.1, qPH_1.1, qNT_4.1, qPT_4.1, qPL_1.2, qTGW_5.1, qGL_3.1 , and qGW_6.1 , which can be used in rice genomics assisted breeding. A major QTL (qZn_5.2 ) for grain Zn concentration has been detected on chromosome 5 that accounted for 13% of R². In all, 26 QTL clusters were identified on different chromosomes. qPH_6.1 epistatically interacted with qZn_5.1 and qGY_6.2 . Most of QTLs were co-located with functionally related candidate genes indicating the accuracy of QTL mapping. The genomic region of qZn_5.2 was co-located with putative genes such as OsZIP5, OsZIP9, and LOC_OS05G40490 that are involved in Zn uptake. These genes included polymorphic functional SNPs, and their promoter regions were enriched with cis-regulatory elements involved in plant growth and development, and biotic and abiotic stress tolerance. Major effect QTL identified for biofortification and agronomic traits can be utilized in breeding for Zn biofortified rice varieties.

Detection of QTLs Regulating Six Agronomic Traits of Rice Based on Chromosome Segment Substitution Lines of Common Wild Rice (Oryza rufipogon Griff.) and Mapping of qPH1.1 and qLMC6.1

Wild rice is a primary source of genes that can be utilized to generate rice cultivars with advantageous traits. Chromosome segment substitution lines (CSSLs) are consisting of a set of consecutive and overlapping donor chromosome segments in a recipient's genetic background. CSSLs are an ideal genetic population for mapping quantitative traits loci (QTLs). In this study, 59 CSSLs from the common wild rice (Oryza rufipogon Griff.) accession DP15 under the indica rice cultivar (O. sativa L. ssp. indica) variety 93-11 background were constructed through multiple backcrosses and marker-assisted selection (MAS). Through high-throughput whole genome re-sequencing (WGRS) of parental lines, 12,565 mapped InDels were identified and designed for polymorphic molecular markers. The 59 CSSLs library covered 91.72% of the genome of common wild rice accession DP15. The DP15-CSSLs displayed variation in six economic traits including grain length (GL), grain width (GW), thousand-grain weight (TGW), grain length-width ratio (GLWR), plant height (PH), and leaf margin color (LMC), which were finally attributed to 22 QTLs. A homozygous CSSL line and a purple leave margin CSSL line were selected to construct two secondary genetic populations for the QTLs mapping. Thus, the PH-controlling QTL qPH1.1 was mapped to a region of 4.31-Mb on chromosome 1, and the LMC-controlling QTL qLMC6.1 was mapped to a region of 370-kb on chromosome 6. Taken together, these identified novel QTLs/genes from common wild rice can potentially promote theoretical knowledge and genetic applications to rice breeders worldwide.

Metabolomic analysis of sheath blight disease of rice (Oryza sativa L.) induced by Rhizoctonia solani phytotoxin

AIM

To understand the mechanism of necrosis incited by a host-selective phytotoxin designated as Rhizoctonia solani toxin (RST) identified to be a potential pathogenic factor of Rhizoctonia solani AG1 IA, causing sheath blight (ShB) of rice.

METHODS AND RESULTS

The metabolomic changes induced by the phytotoxic metabolite in a ShB susceptible rice cultivar were elucidated by Gas Chromatography-Mass Spectrometry (GC-MS) analysis and compared with that of the pathogen to identify rice metabolites targeted by the phytotoxin. The profiles of about 29 metabolites with various physiological roles in rice plants have been identified worldwide. Unsupervised and supervised multivariate chemometrics (Principal Component Analysis, PCA and Partial Least Squares-Discriminant Analysis, PLS-DA) and cluster (Heat maps) analyses were used to compare the metabolites obtained from chemical profiles of the treatments with sterile distilled water (SDW) control. The results indicated that the rice plant expressed more metabolites in response to the pathogen than the phytotoxin and was lowest in SDW control. The key metabolites expressed in rice in response to the treatments were investigated by the Variable Importance in Projection (VIP) analysis using P< 0.05 VIP >15. The analysis identified 7 and 11 upregulating metabolites in the phytotoxin and the pathogen treatments, respectively, compared to the untreated control. Among the phytotoxin-treated and the pathogen inoculated samples, the phytotoxin treated sample recorded upregulation of 6 metabolites, whereas 9 metabolites were upregulated in the pathogen inoculated samples. These upregulating metabolites are speculated for the necrotic symptoms characteristic to both the phytotoxin and pathogen. In this analysis, hexadecanoic acid and dotriacontane were highly expressed metabolites specific to the phytotoxin and pathogen-treated samples, respectively. Besides upregulation, the metabolites also have a VIP score of >1.5 and hence fulfilled the criteria of classifying them as reliable potential biomarkers. In the pathway analysis, hexadecanoic acid and dotriacontane were identified to be involved in several important biosynthetic pathways of rice, such as the biosynthesis of saturated fatty acid and unsaturated fatty acids cutin, suberin, and wax.

CONCLUSIONS

The study concludes that though certain metabolites induced by the phytotoxin in the susceptible variety during necrosis shares with that of the pathogen, the identification of metabolites specific to the phytotoxin in comparison to the pathogenic and SDW controls indicated that the phytotoxin modulates the host metabolism differently and hence can be a potential pathogenicity factor of the ShB fungus.

SIGNIFICANCE AND IMPACT OF THE STUDY

Due to lack of knowledge on the pathway genes of RST and in the absence of an ShB resistant variety, understanding differentially expressed metabolic changes induced in the susceptible variety by the phytotoxin in comparison to that of the pathogenic and uninoculated controls enables us to identify the key metabolite changes during the ShB infection. Such metabolomic changes can further be used to infer gene functions for exploitation in ShB control.

Omics Technologies to Enhance Plant Based Functional Foods: An Overview

Functional foods are natural products of plants that have health benefits beyond necessary nutrition. Functional foods are abundant in fruits, vegetables, spices, beverages and some are found in cereals, millets, pulses and oilseeds. Efforts to identify functional foods in our diet and their beneficial aspects are limited to few crops. Advances in sequencing and availability of different omics technologies have given opportunity to utilize these tools to enhance the functional components of the foods, thus ensuring the nutritional security. Integrated omics approaches including genomics, transcriptomics, proteomics, metabolomics coupled with artificial intelligence and machine learning approaches can be used to improve the crops. This review provides insights into omics studies that are carried out to find the active components and crop improvement by enhancing the functional compounds in different plants including cereals, millets, pulses, oilseeds, fruits, vegetables, spices, beverages and medicinal plants. There is a need to characterize functional foods that are being used in traditional medicines, as well as utilization of this knowledge to improve the staple foods in order to tackle malnutrition and hunger more effectively.

Potato biofortification: an effective way to fight global hidden hunger

Hidden hunger is leading to extensive health problems in the developing world. Several strategies could be used to reduce the micronutrient deficiencies by increasing the dietary uptake of essential micronutrients. These include diet diversification, pharmaceutical supplementation, food fortification and crop biofortification. Among all, crop biofortification is the most sustainable and acceptable strategy to overcome the global issue of hidden hunger. Since most of the people suffering from micronutrient deficiencies, have monetary issues and are dependent on staple crops to fulfil their recommended daily requirements of various essential micronutrients. Therefore, increasing the micronutrient concentrations in cost effective staple crops seems to be an effective solution. Potato being the world's most consumed non-grain staple crop with enormous industrial demand appears to be an ideal candidate for biofortification. It can be grown in different climatic conditions, provide high yield, nutrition and dry matter in lesser time. In addition, huge potato germplasm have natural variations related to micronutrient concentrations, which can be utilized for its biofortification. This review discuss the current scenario of micronutrient malnutrition and various strategies that could be used to overcome it. The review also shed a light on the genetic variations present in potato germplasm and suggest effective ways to incorporate them into modern high yielding potato varieties.

Iron Biofortification in Rice: An Update on Quantitative Trait Loci and Candidate Genes

Rice is the most versatile model for cereals and also an economically relevant food crop; as a result, it is the most suitable species for molecular characterization of Fe homeostasis and biofortification. Recently there have been significant efforts to dissect genes and quantitative trait loci (QTL) associated with Fe translocation into rice grains; such information is highly useful for Fe biofortification of cereals but very limited in other species, such as maize (Zea mays) and wheat (Triticum aestivum). Given rice's centrality as a model for Poaceae species, we review the current knowledge on genes playing important roles in Fe transport, accumulation, and distribution in rice grains and QTLs that might explain the variability in Fe concentrations observed in different genotypes. More than 90 Fe QTLs have been identified over the 12 rice chromosomes. From these, 17 were recorded as stable, and 25 harbored Fe-related genes nearby or within the QTL. Among the candidate genes associated with Fe uptake, translocation, and loading into rice grains, we highlight the function of transporters from the YSL and ZIP families; transporters from metal-binding molecules, such as nicotianamine and deoxymugineic acid; vacuolar iron transporters; citrate efflux transporters; and others that were shown to play a role in steps leading to Fe delivery to seeds. Finally, we discuss the application of these QTLs and genes in genomics assisted breeding for fast-tracking Fe biofortification in rice and other cereals in the near future.

Identification of Promising RILs for High Grain Zinc Through Genotype × Environment Analysis and Stable Grain Zinc QTL Using SSRs and SNPs in Rice (Oryza sativa L.)

Polished rice is one of the commonly consumed staple foods across the world. However, it contains limited nutrients especially iron (Fe) and zinc (Zn). To identify promising recombinant inbred lines (RILs) for grain Zn and single plant yield, 190 RILs developed from PR116 and Ranbir Basmati were evaluated in two environments (E1 and E2). A subset of 44 contrasting RILs for grain Zn was screened in another two environments (E3 and E4). Phenotypic data was collected for 10 traits, viz., days to 50% flowering, plant height, panicle length, number of tillers, single plant yield (SPY), test weight, Fe and Zn in brown (IBR, ZBR), and polished rice (IPR, ZPR). Stepwise regression analysis of trait data in 190 RILs and a subset of 44 RILs revealed the interdependence of ZPR, ZBR, IPR, and IBR and the negative association of grain Zn with single plant yield. Based on the additive main effect and multiplicative interaction (AMMI) and genotype and genotype × environment interaction (GGE) analyses of the subset of 44 RILs across four environments (E1-E4), six promising RILs were identified for ZPR with >28 ppm. Mapping of 190 RILs with 102 simple sequence repeats (SSRs) resulted in 13 QTLs for best linear unbiased estimates (BLUEs) of traits including advantage over check (AOC). Using genotype-based sequencing (GBS), the subset of 44 RILs was mapped with 1035 single-nucleotide polymorphisms (SNPs) and 21 QTLs were identified. More than 100 epistatic interactions were observed. A major QTL qZPR.1.1 (PV 37.84%) and another QTL qZPR.11.1 (PV 15.47%) were identified for grain Zn in polished rice. A common major QTL (qZBR.2.1 and qZPR.2.1) was also identified on chromosome 2 for grain Zn content across SSR and SNP maps. Two potential candidate genes related to transporters were identified based on network analyses in the genomic regions of QTL < 3 Mb. The RILs identified for grain Zn and SPY were nominated for national evaluation as under rice biofortification, and two QTLs identified based on BLUEs could be used in the rice biofortification breeding programs.

Profiling of Nutraceuticals and Proximates in Peanut Genotypes Differing for Seed Coat Color and Seed Size

A total of 60 genotypes of peanut comprising 46 genotypes selected from ICRISAT mini core collection and 14 elite cultivars with differing kernel color and size were used to profile the nutritional parameters such as proximates (moisture, fat, ash, crude protein, crude fiber, carbohydrate content) and nutraceuticals (total polyphenol content and total antioxidant activity). The genotypes showed varied kernel color ranging from white to purple. Kernel skin color was quantified using colorimetry, and the color parameters were expressed as CIELAB color parameters. In total, nine morphological traits, six yield related traits, eight nutritional traits and eleven color parameters were observed across 60 genotypes. The sixty genotypes were grouped into ten clusters based on the color strength. Among them, Cluster-III with dark red seeds had the maximum fat content and total polyphenol content (TPC). Cluster-VI with light pink colored seeds had high antioxidant activity (AOA) and Cluster-X with white colored seeds had highest moisture and crude protein content. Color strength (K/S) was found to be positively correlated with TPC. Another color parameter, redness/greenness (a*) was found to be positively correlated with AOA. However, seed size was positively correlated with the crude protein content, but not with any other nutritional traits under study. The population studies based on the genotypic data indicated two distinct groups pertaining to botanical types of peanut. The marker-trait association (MTA) using single marker analysis indicated 75 major MTAs for most of the nutritional traits except for moisture content. The markers associated with nutritional parameters and other important yield related traits can further be utilized for genomics-assisted breeding for nutrient-rich peanuts.

Genetic Analysis of Agronomic Traits and Grain Iron and Zinc Concentrations in a Doubled Haploid Population of Rice (Oryza sativa L.)

The development of micronutrient dense rice varieties with good agronomic traits is one of the sustainable and cost-effective approaches for reducing malnutrition. Identification of QTLs for high grain Fe and Zn, yield and yield components helps in precise and faster development of high Fe and Zn rice. We carried out a three-season evaluation using IR05F102 x IR69428 derived doubled-haploid population at IRRI. Inclusive composite interval mapping was carried out using SNP markers and Best Linear Unbiased Estimates of the phenotypic traits. A total of 23 QTLs were identified for eight agronomic traits and grain Fe and Zn concentration that explained 7.2 to 22.0% PV. A QTL by environment interaction analysis confirmed the stability of nine QTLs, including two QTLs for Zn on chromosomes 5 and 12. One epistatic interaction for plant height was significant with 28.4% PVE. Moreover, five QTLs were identified for Fe and Zn that harbor several candidate genes, e.g. OsZIP6 on QTL qZn_5.1. A number of QTLs were associated with a combination of greater yield and increased grain Zn levels. These results are useful for development of new rice varieties with good agronomic traits and high grain Zn using MAS, and identification of genetic resources with the novel QTLs for grain Zn.