Genetic and phenotypic investigation of nitrogen use efficiency in Rice

Abstract

Rice is an important staple crop, serving as a primary food source for more than half of the global population. Its production depends upon rice cultivars and applied fertilisers, and nitrogen (N) is an essential nutrient for rice plants. Nitrogen plays a crucial role in the growth and development of rice plants and in the production of high-quality grains. It is a key component of chlorophyll, the pigment responsible for photosynthesis and impacts other phenotype expression of plants. Among different rice populations, aus rice has rich genetic diversity with wide variation in nitrogen use efficiency (NUE). This work evaluated NUE in the Bengal and Assam Aus Panel (BAAP, 266 out of 300 are aus rice), quantitative trait loci (QTLs), candidate genes were identified, and RNA-seq was evaluated. Two Preliminary experiments were conducted in different N levels to identify suitable N conditions and associated phenotype expression for screening the large rice BAAP sub-population. Six N treatments applied were selected as a screening method for subsequent experiments. The N response at the rice seedling stage was evaluated in a subset of BAAP grown in mixed (50%/50%) agricultural topsoil and sand under controlled conditions and different levels of N treatments using 15 rice cultivars in the first experiment (these cultivars have been used in the previous studies and some of them are my favorite) and 12 rice cultivars in the second experiment (the best 12 cultivars of 15 from the first experiment in terms of the result). Then, another experiment tested the effect of below-ground competition between 12 rice cultivars with and without bags under controlled greenhouse conditions at three N levels (0%,50% and 100% RDN). I used two nitrogen scales (one scale starting from 0 to 150 N% this was not too much high than optimum dose of nitrogen and the second sacle was taken lower dose as well as the higher that was much more doses than the optimum level of nitrogen which was 120 kg/ha). The main reason to select these levels of nitrogen only to know NUE threshold of plants. For that, I use the higher scale to find if plants grow similarly as they were growing in the first experiment. But I found that, the plants have stopped their growth at 150kg/ha. In the first experiment, various traits related to plant growth and nitrogen utilisation were investigated across different levels of nitrogen applications. The traits examined included plant height, nitrogen balance index (NBI), biomass production, shoot nitrogen percentage, and nitrogen uptake per plant. The results demonstrated a wide range of responses among the tested cultivars—Lemont, BRRI Dhan 28, IR 64, Nipponbare, Black Gora, and BJ1—indicating diverse reactions to varying nitrogen levels. Specifically, the phenotypic response to nitrogen was comparatively lower in Lemont, BRRI Dhan 28, IR 64, and Nipponbare, while it was xix higher in Black Gora and BJ1. These findings suggest distinct variations in how these cultivars express their phenotypic traits in response to nitrogen availability, providing valuable insights into their phenotypic responses under different nitrogen conditions. In the second experiment, the focus was on identifying cultivars that exhibited distinct responses to low and high nitrogen (N) conditions. Among the cultivars studied, N22 and BRRI Dhan 28 were identified as low nitrogen-responsive cultivars, indicating that these varieties demonstrated notable changes or adaptations in their traits under conditions of limited nitrogen availability. On the other hand, BJ1 and Niyan Wee were identified as high nitrogen-responsive cultivars, suggesting that these varieties displayed significant alterations in their characteristics or performance in response to ample nitrogen supply. These findings highlight the variability among different rice cultivars in their sensitivity and adaptation to nitrogen levels, providing valuable information for understanding and optimsing their growth under varying nutrient conditions. The notable genetic variation observed in shoot biomass among the tested rice cultivars within the same nitrogen treatment underscores the inherent diversity in their responses to environmental conditions. Specifically, the lower shoot biomass values observed in cultivars N22, IR 64, and BRRI 28 indicate that these varieties exhibit a tendency toward reduced above-ground plant material production under the specified nitrogen treatment. This may suggest that these cultivars are less efficient or responsive in utilizing nitrogen for shoot growth. On the contrary, the higher shoot biomass values observed in BJ1, Black Gora, and Niyan Wee signify that these particular cultivars have a genetic predisposition for enhanced above-ground biomass production under similar nitrogen conditions. This variability in shoot biomass responses highlights the intricate interplay between genetic factors and environmental cues, providing valuable insights for crop management strategies and genetic improvement programs aimed at tailoring rice varieties to specific nitrogen regimes for optimal agricultural productivity. Genome-wide association (GWA) mapping was conducted on nitrogen use efficiency associated traits with 2 million SNPs on a large BAAP population (230 rice cultivars) at two N levels (0% and 100% RDN). The total number of QTLs associated with traits related to NUE identified was 26 at N0, 48 at N100, and 30 of the ratio of N0/N100. A total of 109 candidate genes were found, including well-known genes OsNAR2.1, OsAAP, and OsbZIP59. Among identified QTLs, four notable QTLs on several chromosomes of different traits were selected for haplotyping. The haplotype variants of three candidate genes (OsbZIP59, OsNAR2.1, and OsCDKD-1) are discussed. xx N-responsive and unresponsive genotypes selected from large experiment for RNA-seq were used to identify differential expression of candidate genes for NUE in rice. This study categorised six rice genotypes into responsive and unresponsive groups based on their trait performance under the two nitrogen treatments. To investigate the transcriptomic differences, RNA was sequenced from shoots to gain insights into the gene expression patterns related to nitrogen treatments. Transcriptomics revealed the gene expression patterns and levels of RNA transcripts in a given sample, providing insights into which genes are active and to what extent they are being expressed for NUE relevant to GWAS. The N efficient accessions, QTLs and candidate genes identified in this work will be important resources for further research in rice NUE and breeding programs for sustainable rice production.