Miscanthus × giganteus is wildly cultivated as a potential biofuel feedstock around the world; however, the narrow genetic basis and sterile characteristics have become a limitation for its utilization. As a progenitor of M. × giganteus, M. sinensis is widely distributed around East Asia providing well abiotic stress tolerance. To enrich the M. sinensis genomic databases and resources, we sequenced and annotated the transcriptome of M. sinensis by using an Illumina HiSeq 2000 platform. Approximately 316 million high-quality trimmed reads were generated from 349 million raw reads, and a total of 114,747 unigenes were obtained after de novo assembly. Furthermore, 95,897 (83.57%) unigenes were annotated to at least one database including NR, Swiss-Prot, KEGG, COG, GO, and NT, supporting that the sequences obtained were annotated properly. Differentially expressed gene analysis indicates that drought stress 15 days could be a critical period for M. sinensis response to drought stress. The high-throughput transcriptome sequencing of M. sinensis under drought stress has greatly enriched the current genomic available resources. The comparison of DEGs under different periods of drought stress identified a wealth of candidate genes involved in drought tolerance regulatory networks, which will facilitate further genetic improvement and molecular studies of the M. sinensis.
Genomic prediction of nitrogen use efficiency (NUE) has not been studied in perennial grass species exposed to low N stress. The experiment was designed to conduct genomic prediction of physiological traits and NUE in 184 global accessions of perennial ryegrass (Lolium perenne L.) in response to the normal (7.5 mM) and low (0.75 mM) N supply. Significant variations in plant height (ΔHT), leaf fresh weight (LFW), leaf dry weight (LDW), chlorophyll index (Chl), chlorophyll fluorescence, leaf N and carbon (C) content, C/N ratio and NUE were observed in accessions after 21 days of both N treatments in a greenhouse, but to a greater extent under low N stress. Six genomic prediction models (BayesC, BL, BRR, RRBLUP, RKHS, RF) produced similar prediction accuracy of traits within the normal N or low N treatment, but prediction accuracy differed between the two N treatments. ΔHT, LFW, LDW, and C were slightly better predicted under the normal N with an average r = 0.26 in comparison with r = 0.22 under low N, while prediction accuracy for Chl, N, C/N, and NUE was significantly improved under low N stress with an average r = 0.45, compared to the r = 0.26 under normal N. The population panel contained three population structures, which generally had no effect on prediction accuracy. The moderate prediction accuracy for N, C and NUE under low N stress is promising, which indicates a feasible way that may be used to assess valuable germplasm for further conducting genomic prediction of NUE for enhancing breeding programs of perennial grass species grown under N-deficient conditions.
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