Metabolomic Profiling of Dongxiang Wild Rice Under Salinity Demonstrates the Significant Role of Amino Acids in Rice Salt Stress

Dongxiang common wild rice is a precious rice germplasm resource for the study and improvement of salt tolerance in rice.The metabolism profile of Dongxiang wild rice (DXWR) under salinity was determined by high performance liquid chromatography-mass spectrometry (HPLC-MS) to find differential metabolites and screen potential biomarkers for salt-tolerant rice varieties. A global untargeted metabolism analysis showed 4,878 metabolites accumulated in seedlings of Dongxiang wild rice. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) results provided a clear metabolism discrimination between DXWR under control and DXWR under salinity. A total of 90 metabolites were significantly changed (49 upregulated and 41 downregulated) under salinity, of which the largest increase was in DL-2-Aminoadipic acid (27.08-fold) and the largest decrease was in L-Carnitine (0.014-fold). Amino acids and nuclear glycosides were mainly upregulated, while carbohydrates and organic acids were mainly downregulated in the salt-treated group. Among the top 10 upregulated metabolites, five kinds of differential metabolites were amino acids. According to the survival rates of the seedlings under salinity, we selected three backcross inbred lines of DXWR with survival rates above 80% as salt-tolerant progenies (pro-DS) and three backcross inbred lines with survival rates below 10% as non-salt-tolerant progenies (pro-NDS) for an amino acid change analysis. This analysis found that the change in L-Asparagine (2.59-fold) was the biggest between pro-DS and pro-NDS under salinity, revealing that the contents of L-Asparagine may be one of the indices we can use to evaluate the salt tolerance of rice varieties.

Identification and Validation of QTLs for Macronutrient Contents in Brown and Milled Rice Using Two Backcross Populations between Oryza sativa and O. rufipogon

Mineral malnutrition as a prevalent public health issue can be alleviated by increasing the intake of dietary minerals from major staple crops, such as rice. Identification of the gene responsible for mineral contents in rice would help breed cultivars enriched with minerals through marker-assisted selection. Two segregating populations of backcross inbred lines (BIL) were employed to map quantitative trait loci (QTLs) for macronutrient contents in brown and milled rice, BC1F5, and BC2F4:5 derived from an interspecific cross of Xieqingzao B (Oryza sativa) and Dongxiang wild rice (O. rufipogon). Phenotyping the populations was conducted in multiple locations and years, and up to 169 DNA markers were used for the genotyping. A total of 17 QTLs for P, K, Na, Ca, and Mg contents in brown and milled rice distributed on eight regions were identified in the BC1F5 population, which is explained to range from 5.98% to 56.80% of phenotypic variances. Two regions controlling qCa1.1 and qCa4.1 were validated, and seven new QTLs for Ca and Mg contents were identified in the BC2F4:5 population. 18 of 24 QTLs were clustered across seven chromosomal regions, indicating that different mineral accumulation might be involved in common regulatory pathways. Of 24 QTLs identified in two populations, 16 having favorable alleles were derived from O. rufipogon and 10 were novel. These results will not only help understand the molecular mechanism of macronutrient accumulation in rice but also provide candidate QTLs for further gene cloning and grain nutrient improvement through QTL pyramiding.

Genetic analysis of dormancy and shattering traits in the backcross inbred lines derived from Oryza sativa cv. Swarna / O. nivara Ac. CR100008

Seed dormancy and shattering are important traits influencing the economics of rice farming. The genetic basis of dormancy and shattering traits were investigated in 174 Backcross Inbred Lines (BILs) derived from Oryza sativa cv. Swarna and O. nivara ac. CR100008. Significant variation was observed among the BILs for dormancy and shattering traits. Dormancy of 4-40 days was observed among BILs harvested at 35 days after heading and all the BILs attained > 80% germination by 6th week. Among all the BILs, least dormancy period (4 days) was found in SN-1, 13, 23, 25 and SN-28. Highest dormancy period (40 days) was found in 4 BILs i.e., SN-108, SN-116, SN-117 and SN-122 (40 days). None of the BILs were found to have non-shattering trait, while 2 BILs (SN-38 and SN-163) showed low shattering and 18 BILs were found with very high grain shattering percent. Of the 312 SSRs screened, 94 were polymorphic between the parents. A strategy of combining the DNA pooling from phenotypic extremes and genotyping was employed to detect the putative markers associated with dormancy and shattering traits. Single marker analysis revealed co-segregation of two putative markers RM488 on chromosome 1 and RM247 on chromosome 12 were with dormancy and shattering traits respectively. The putative marker RM488 identified is suitable for the marker-assisted transfer of the dormancy shown by O. nivara accession CR100008 for addressing pre harvest sprouting in modern cultivars. Interestingly, O. nivara type allele at RM247 was observed in BILs with low shattering phenotype.

Mapping genomic regions of moisture deficit stress tolerance using backcross inbred lines in wheat (Triticum aestivum L.)

Identification of markers associated with major physiological and yield component traits under moisture deficit stress conditions in preferred donor lines paves the way for marker-assisted selection (MAS). In the present study, a set of 183 backcross inbred lines (BILs) derived from the cross HD2733/2*C306 were genotyped using 35K Axiom genotyping array and SSR markers. The multi-trait, multi-location field phenotyping of BILs was done at three locations covering two major wheat growing zones of India, north-western plains zone (NWPZ) and central zone (CZ) under varying moisture regimes. A linkage map was constructed using 705 SNPs and 86 SSR polymorphic markers. A total of 43 genomic regions and QTL × QTL epistatic interactions were identified for 14 physiological and yield component traits, including NDVI, chlorophyll content, CT, CL, PH, GWPS, TGW and GY. Chromosomes 2A, 5D, 5A and 4B harbors greater number of QTLs for these traits. Seven Stable QTLs were identified across environment for DH (QDh.iari_6D), GWPS (QGWPS.iari_5B), PH (QPh.iari_4B-2, QPh.iari_4B-3) and NDVI (QNdvi1.iari_5D, QNdvi3.iari_5A). Nine genomic regions identified carrying major QTLs for CL, NDVI, RWC, FLA, PH, TGW and biomass explaining 10.32–28.35% of the phenotypic variance. The co-segregation of QTLs of physiological traits with yield component traits indicate the pleiotropic effects and their usefulness in the breeding programme. Our findings will be useful in dissecting genetic nature and marker-assisted selection for moisture deficit stress tolerance in wheat.

QTL mapping for heat tolerance related traits using backcross inbred lines in wheat (Triticum aestivum L.)

Heat stress is one of the most limiting factors for the production of wheat. Global warming and consequent changes in climate adversely affect wheat plant growth and yield. To elucidate genetic basis and map heat tolerance traits, a set of 134 backcross inbred lines (BILs) derived from the cross between WH730/*2 HD2733 was used. The population was evaluated under late sown (LS) and very late sown (VLS) conditions, by exposing to heat stress during rabi season. Positive association of normalized difference vegetation index (NDVI), thousand grain weight (TGW), grain weight per spike (GWS), biomass and grain yield (GY) under both production conditions was observed. However, canopy temperature (CT) and days to heading (DH) showed negative correlation with GY under heat stress. A total of 9 Quantitative trait loci (QTL) were discovered on 7 chromosomes, which includes 4 QTLs in LS and 5 QTLs under VLS condition. Combining the results of these QTLs revealed a major stable QTL for DH (qDH_iari_5A) on chromosome 5A with 23% and 26% explaining phenotypic variance under both sowing conditions. QTL for NDVI was detected on chromosome 1B while QTL for SL and GY on chromosome 2A. The identified QTLs in the genomic regions could be targeted for genetic improvement and marker assisted selection for heat tolerance in wheat.