Generation of Wheat Near-Isogenic Lines Overexpressing 1Bx7 Glutenin with Increased Protein Contents and SDS-Sedimentation Values

Overexpression of Glu-1Bx7 via allele 1Bx7^OE significantly contributes to high dough strength in some wheat varieties and is useful for improving wheat quality. However, the proportion of wheat varieties containing Bx7^OE is quite low. In this study, four cultivars containing 1Bx7^OE were selected, and among the selected varieties, Chisholm (1Ax2*, 1Bx7^OE + 1By8*, and 1Dx5 + 1Dx10) was crossed with Keumkang, a wheat variety that contains 1Bx7 (1Ax2*, 1Bx7 + 1By8, and 1Dx5 + 1Dx10). SDS-PAGE and UPLC analyses showed that the expression of the high-molecular-weight glutenin subunit (HMW-GS) 1Bx7 was significantly higher in NILs (1Ax2*, 1Bx7^OE + 1By8*, and 1Dx5 + 1Dx10) compared with that in Keumkang. Wheat quality was analyzed with near infrared reflectance spectroscopy by measuring the protein content and SDS-sedimentation of NILs. The protein content of NILs (12.94%) was 21.65% higher than that of Chisholm (10.63%) and 4.54% higher than that of Keumkang (12.37%). In addition, the SDS-sedimentation value of NILs (44.29 mL) was 14.97% and 16.44% higher than that of Keumkang (38.52 mL) and Chisholm (38.03 mL), respectively. This study predicts that the quality of domestic wheat can be improved by crossbreeding with 1Bx7^OE-containing cultivars.

Genome-Wide Identification of QTLs for Grain Protein Content Based on Genotyping-by-Resequencing and Verification of qGPC1-1 in Rice

To clarify the genetic mechanism underlying grain protein content (GPC) and to improve rice grain qualities, the mapping and cloning of quantitative trait loci (QTLs) controlling the natural variation of GPC are very important. Based on genotyping-by-resequencing, a total of 14 QTLs were detected with the Huanghuazhan/Jizi1560 (HHZ/JZ1560) recombinant inbred line (RIL) population in 2016 and 2017. Seven of the fourteen QTLs were repeatedly identified across two years. Using three residual heterozygote-derived populations, a stably inherited QTL named as qGPC1-1 was validated and delimited to a ~862 kb marker interval JD1006–JD1075 on the short arm of chromosome 1. Comparing the GPC values of the RIL population determined by near infrared reflectance spectroscopy (NIRS) and Kjeldahl nitrogen determination (KND) methods, high correlation coefficients (0.966 and 0.983) were observed in 2016 and 2017. Furthermore, 12 of the 14 QTLs were identically identified with the GPC measured by the two methods. These results indicated that instead of the traditional KND method, the rapid and easy-to-operate NIRS was suitable for analyzing a massive number of samples in mapping and cloning QTLs for GPC. Using the gel-based low-density map consisted of 208 simple sequence repeat (SSR) and insert/deletion (InDel) markers, the same number of QTLs (fourteen) were identified in the same HHZ/JZ1560 RIL population, and three QTLs were repeatedly detected across two years. More stably expressed QTLs were identified based on the genome resequencing, which might be attributed to the high-density map, increasing the detection power of minor QTLs. Our results are helpful in dissecting the genetic basis of GPC and improving rice grain qualities through molecular assisted selection.

Rapid Determination of the Oil and Moisture Contents in Camellia gauchowensis Chang and Camellia semiserrata Chi Seeds Kernels by Near-infrared Reflectance Spectroscopy

A fast and effective determination method of different species of vegetable seeds oil is vital in the plant oil industry. The near-infrared reflectance spectroscopy (NIRS) method was developed in this study to analyze the oil and moisture contents of Camelliagauchowensis Chang and C.semiserrata Chi seeds kernels. Calibration and validation models were established using principal component analysis (PCA) and partial least squares (PLS) regression methods. In the prediction models of NIRS, the levels of accuracy obtained were sufficient for C.gauchowensis Chang and C.semiserrata Chi, the correlation coefficients of which for oil were 0.98 and 0.95, respectively, and those for moisture were 0.92 and 0.89, respectively. The near infrared spectrum of crush seeds kernels was more precise compared to intact kernels. Based on the calibration models of the two Camellia species, the NIRS predictive oil contents of C.gauchowensis Chang and C.semiserrata Chi seeds kernels were 48.71 ± 8.94% and 58.37 ± 7.39%, and the NIRS predictive moisture contents were 4.39 ± 1.08% and 3.49 ± 0.71%, respectively. The NIRS technique could determine successfully the oil and moisture contents of C.gauchowensis Chang and C.semiserrata Chi seeds kernels.

Estimates of diet selection in cattle grazing cornstalk residues by measurement of chemical composition and near infrared reflectance spectroscopy of diet samples collected by ruminal evacuation

Six ruminally cannulated cows (570 ± 73 kg) fed corn residues were placed in a 6 × 6 Latin square to evaluate predictions of diet composition from ruminally collected diet samples. After complete ruminal evacuation, cows were fed 1-kg meals (dry matter [DM]-basis) containing different combinations of cornstalk and leaf and husk (LH) residues in ratios of 0:100, 20:80, 40:60, 60:40, 80:20, and 100:0. Diet samples from each meal were collected by removal of ruminal contents after 1-h and were either unrinsed, hand-rinsed or machine-rinsed to evaluate effects of endogenous compounds on predictions of diet composition. Diet samples were analyzed for neutral (NDF) and acid (ADF) detergent fiber, acid detergent insoluble ash (ADIA), acid detergent lignin (ADL), crude protein (CP), and near infrared reflectance spectroscopy (NIRS) to calculate diet composition. Rinsing type increased NDF and ADF content and decreased ADIA and CP content of diet samples (P < 0.01). Rinsing tended to increase (P < 0.06) ADL content of diet samples. Differences in concentration between cornstalk and LH residues within each chemical component were standardized by calculating a coefficient of variation (CV). Accuracy and precision of estimates of diet composition were analyzed by regressing predicted diet composition and known diet composition. Predictions of diet composition were improved by increasing differences in concentration of chemical components between cornstalk and LH residues up to a CV of 22.6 ± 5.4%. Predictions of diet composition from unrinsed ADIA and machine-rinsed NIRS had the greatest accuracy (slope = 0.98 and 0.95, respectively) and large coefficients of determination (r2 = 0.86 and 0.74, respectively). Subsequently, a field study (Exp. 2) was performed to evaluate predictions of diet composition in cattle (646 ± 89 kg) grazing corn residue. Five cows were placed in 1 of 10 paddocks and allowed to graze continuously or to strip-graze corn residues. Predictions of diet composition from ADIA, ADL, and NIRS did not differ (P = 0.99), and estimates of cornstalk intake tended to be greater (P = 0.09) in strip-grazed compared to continuously grazed cows. These data indicate that diet composition can be predicted by chemical components or NIRS by ruminal collection of diet samples among cattle grazing corn residues.