Transcriptomic analysis of the maize inbred line Chang7-2 and a large-grain mutant tc19

Backgrounds Grain size is a key factor in crop yield that gradually develops after pollination. However, few studies have reported gene expression patterns in maize grain development using large-grain mutants. To investigate the developmental mechanisms of grain size, we analyzed a large-grain mutant, named tc19, at the morphological and transcriptome level at five stages corresponding to days after pollination (DAP). Results After maturation, the grain length, width, and thickness in tc19 were greater than that in Chang7-2 (control) and increased by 3.57, 8.80, and 3.88%, respectively. Further analysis showed that grain width and 100-kernel weight in tc19 was lower than in Chang7-2 at 14 and 21 DAP, but greater than that in Chang7-2 at 28 DAP, indicating that 21 to 28 DAP was the critical stage for kernel width and weight development. For all five stages, the concentrations of auxin and brassinosteroids were significantly higher in tc19 than in Chang7-2. Gibberellin was higher at 7, 14, and 21 DAP, and cytokinin was higher at 21 and 35 DAP, in tc19 than in Chang7-2. Through transcriptome analysis at 14, 21, and 28 DAP, we identified 2987, 2647 and 3209 differentially expressed genes (DEGs) between tc19 and Chang7-2. By using KEGG analysis, 556, 500 and 633 DEGs at 14, 21 and 28 DAP were pathway annotated, respectively, 77 of them are related to plant hormone signal transduction pathway. ARF3, AO2, DWF4 and XTH are higher expressed in tc19 than that in Chang7-2. Conclusions We found some DEGs in maize grain development by using Chang7-2 and a large-grain mutant tc19. These DEGs have potential application value in improving maize performance.

Wheat Grain Width: A Clue for Re-Exploring Visual Indicators of Grain Weight

Background Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated thanks to the automated image processing systems, MGW estimations has been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore potentially more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which was harvested from a two-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images taken from an overall number of more than 72000 grains). Results It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area×Circularity, Perimeter×Circularity, and Area/Perimeter indices. In general, two main common factors were detected in the structure of the major indices, i.e. either grain width or the Area/Perimeter ratio. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. Conclusions It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using the new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.

Characterization of Functional Genes GS3 and GW2 and their Effect on the Grain Size of Various Landraces of Rice

Grain size is an essential factor in grain quality and yield. In the existing agricultural lands in Pakistan and even all over the world, genetics in rice works better for yield potential and quality improvement. GS3 and GW2 with functional mutation responsible for grain size in rice. In the current study, 17 different Pakistani landraces of various genetic and geographic backgrounds were evaluated for grain phenotypic traits (thousand-grain weight, length, width, and thickness) and characterized genotypes for GS3 gene (grain length) and GW2 (grain width). The two accessions JP5 and Bas370, were used as control. Phenotypic data revealed the range for grain weight from 16.86g (Lateefy) to 26.91g (PS2), grain length ranged from 7.27 mm (JP-5) to 12.18 mm (PS2), grain width ranged from 2.01 mm (Lateefy) to 3.51 mm (JP5), and grain thickness ranged from 1.79 mm to 2.19. Pearson correlation revealed a negative and significant correlation between grain width and length. There was no significant correlation between grain length and 1000-grain weight and grain width. LSD test displayed that the means of three variables grain length, grain width, and 1000-grain weight were statistically different from one another except grain width and grain breadth. GS3 is a negative regulator of grain length. Fifteen accessions GA-5015, PS-2, Swat-1, Swat-2, DR-2, Dilrosh, Malhar-346, Kashmir Basmati, Rachna Basmati, KS-282, Basmati-370, KSK-133, KSK-434, MG-Basmati, and Lateefy, carried the domesticated allele of GS3 while JP5 and Fakhr-e-Malakand carried the dominant allele. Similarly, the GW2 is a negative regulator of grain width. Fifteen accessions, i.e., Bas-370, GA-5015, PS-2, Swat-1, Swat-2, DR-2, Dilrosh, Malhar-346, Kashmir Basmati, Rachna Basmati, KS-282, KSK-133, KSK-434, MG-Basmati, and Lateefy carried the dominant allele while JP-5 and Fakhr-e-Malakand carried the mutant allele. The current phenotypic evaluation of the Germplasm revealed a diverse range of grain size of Pakistani landraces and also suggests that the selection of grain length in Pakistani landraces was independent of 1000-grain weight. The accessions with genotypic characterization will aid in marker-assisted breeding programs to break the stagnant yield prevail for the last few decades in Pakistan.

CRISPR-Cas9 mediated mutation in GRAIN WIDTH and WEIGHT2 (GW2) locus improves aleurone layer and grain nutritional quality in rice

Enhancing crop productivity and their nutritional quality are the key components and primary focus of crop improvement strategy for fulfilling future food demand and improving human health. Grain filling and endosperm development are the key determinants of grain yield and nutritional quality. GRAIN WIDTH and WEIGHT2 (GW2) gene encodes a RING-type E3 ubiquitin ligase and determines the grain weight in cereal crops. Here we report GW2 knockout (KO) mutants in Indica (var. MTU1010) through CRISPR/Cas9 genome editing. The endosperm of GW2-KO mutant seed displays a thick aleurone layer with enhanced grain protein content. Further the loss of function of OsGW2 results in improved accumulation of essential dietary minerals (Fe, Zn, K, P, Ca) in the endosperm of rice grain. Additionally, the mutants displayed an early growth vigour phenotype with an improved root and shoot architecture. The hull morphology of GW2-KO lines also showed improved, grain filling thereby promoting larger grain architecture. Together, our findings indicate that GW2 may serve as a key regulator of improved grain architecture, grain nutritional quality and an important modulator of plant morphology. The study offers a strategy for the development of improved rice cultivars with enriched nutritional quality and its possible implementation in other cereals as well.

Wheat grain width: A clue for re-exploring visual indicators of grain weight

Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated thanks to the automated image processing systems, MGW estimations has been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore potentially more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which was harvested from a two-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images taken from an overall number of more than 72000 grains). It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area*Circularity, Perimeter*Circularity, and Area/Perimeter indices. In general, two main common factors were detected in the structure of the major indices, i.e. either grain width or the Area/Perimeter ratio. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. It is expected that the findings of the present study improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.

Identification and Pyramid of QTLs Based on Rice Short-wide Grain CSSL-Z414, SSSL, DSSL and Candidate Gene Analysis of qGL11 and qGW5

BackgroundMost of rice agronomic traits as grain length etc. are complex traits controlled by multiple genes. Chromosome segment substitution lines (CSSLs) are ideal materials for dissecting and studying of these complex traits. ResultsA rice short-wide grain CSSL Z414 was identified among progeny of the recipient parent Xihui 18 (an indica restorer line) and the donor parent Huhan 3 (a japonica cultivar). Z414 carried 4 substitution segments (average length was 3.04 Mb), and displayed shorter panicle length and less number of primary branches, shorter, wider and larger grain, higher brown rice rate and chalkiness degree when compared with Xihui 18. Then, 9 quantitative trait loci (QTLs) for associated traits were identified using the secondary F2 population from Xihui 18 / Z414. Among them, 6 QTLs (qPL3, qGW5, qGL11, qRLW5, qRLW11, qGWT5) could be verified by corresponding single segment substitution lines (SSSLs, S1-S6). In addition, 4 QTLs (qGL3, qGL5, qCD3 and qCD5) were detected by S1 and S5, which was not detected by the F2 population. Thus, the grain length of Z414 was controlled by qGL11, qGL3 and qGL5, and the grain width of Z414 was answered by qGW5. Then by substitution mapping, qGL11 and qGW5 were delimited within the estimated substitution length of 1.42 and 1.14 Mb on chromosomes 11 and 5, and 4 and 2 candidate genes were found respectively for qGL11 and qGW5 by sequencing. However, only two had expression differences by qRT-PCR analysis. Finally, Analysis of QTL epistatic effects revealed that pyramid of qGL3 (a= 0.22) and qGL11 (a=-0.19) caused grain length of double segment substitution line (DSSL, D2) shorter than that of S5 (qGL11).ConclusionsWe developed a rice short –wide grain CSSL with 4 substitution segments from Huhan 3 based on the genetic backgrounds of Xihui 18. The grain width of Z414 was controlled by qGW5, and GS5 should be the candidate gene for qGW5 by sequencing and qRT-PCR analysis. The grain length of Z414 was controlled by qGL11, qGL3, and qGL5, and CycT1;3 should be the best candidate gene of qGL11, whose specific function of regulating grain length was still unknown, and qGL11 is epistatic to qGL3.

QTL Mapping for 1000-grain Weight and Its Main Components in Wheat

In order to identify QTLs for 1000-grain weight and its main component traits in wheat, a high-density genetic link map was constructed using a F8:9 recombined inbred line (RIL) population as material and exploiting the single nucleotide polymorphism (SNP) as well as PCR-based molecular markers, and to go a step further, QTL mapping for 1000-grain weight, grain length, and grain width in wheat was conducted. The linkage map was composed of 1257 loci formed by the 3916 markers, including 143 SSR markers and 3773 SNP markers, which were distributed on 22 chromosomes (2A chromosome formed a break point) with the total length of 2291.6 cM and the average genetic distance between loci of 1.82 cM. A total of 41 qualitative trait loci (QTLs) on 19 chromosomes were detected, with contributions to phenotypic variance ranged from 3.59–58.49% for each QTL. Among these QTLs, two were detected in four environments, six in three environments, and 14 in two environments. Fifteen important loci with multi-effect were mapped on 9 chromosomes 1B, 2B, 3A, 4B, 5A, 5B, 6A, 7A, and 7B, involving 37 QTLs that accounted for 90.2% of total numbers of QTLs detected. Of these 15 loci, including one on chromosome 7A flanked by the markers Tdurum_contig77759_5 and BS00062425_51, which consisted of QTLs controlling TGW, GL, and GW and explained 4.2 to 58.49% of the phenotypic variation in these traits. Thus, the chromosome intervals of the 15 loci were important areas controlling the expression of 1000-grain weight and its main component traits in wheat.

Multi-locus genome-wide association studies for five yield-related traits in rice

Background Improving the overall production of rice with high quality is a major target of breeders. Mining potential yield-related loci have been geared towards developing efficient rice breeding strategies. In this study, one single-locus genome-wide association studies (SL-GWAS) method (MLM) in conjunction with five multi-locus genome-wide association studies (ML-GWAS) approaches (mrMLM, FASTmrMLM, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were conducted in a panel consisting of 529 rice core varieties with 607,201 SNPs. Results A total of 152, 106, 12, 111, and 64 SNPs were detected by the MLM model associated with the five yield-related traits, namely grain length (GL), grain width (GW), grain thickness (GT), thousand-grain weight (TGW), and yield per plant (YPP), respectively. Furthermore, 74 significant quantitative trait nucleotides (QTNs) were presented across at least two ML-GWAS methods to be associated with the above five traits successively. Finally, 20 common QTNs were simultaneously discovered by both SL-GWAS and ML-GWAS methods. Based on genome annotation, gene expression analysis, and previous studies, two candidate key genes (LOC_Os09g02830 and LOC_Os07g31450) were characterized to affect GW and TGW, separately. Conclusions These outcomes will provide an indication for breeding high-yielding rice varieties in the immediate future.

Dissection of Closely Linked QTLs Controlling Grain Size in Rice

Grain size is a key constituent of grain weight and appearance in rice. However, insufficient attention has been paid to the small-effect QTLs on grain size. In the present study, residual heterozygous populations were developed for mapping two genetically linked small-effect QTLs for grain size. After genotyping and phenotyping of five successive generations, qGS7.1 was dissected into three QTLs and two were selected for further analysis. qTGW7.2a was finally mapped into a 21.10-kb interval containing four annotated candidate genes. Transcript levels assay showed that the expression of candidates LOC_Os07g39490 and LOC_Os07g39500 were significantly reduced in the NIL- qTGW7.2a BG1 . Cytological observation indicated that qTGW7.2a regulated grain width through controlling cell expansion. Use the same strategy, qTGW7.2b was fine mapped into a 52.71-kb interval, showing a significant effect on grain length and width with opposite allelic directions but little on grain weight. Our study provides new genetic resources for yield improvement and fine-tunes of grain size in rice.