Stripe rust resistance and dough quality of new wheat - Dasypyrum villosum translocation lines T1DL•1V#3S and T1DS•1V#3L and the location of HMW-GS genes.

The transfer of agronomically useful genes from wild wheat species into cultivated wheat is one of the most effective approaches to improvement of wheat varieties. To evaluate the transfer of genes from Dasypyrum villosum into Triticum aestivum, wheat quality and disease resistance was evaluated in two new translocation lines, T1DL•1V#3S and T1DS•1V#3L. We examined the levels of stripe rust resistance and dough quality in the two lines, and identified and located the stripe rust resistant genes and high molecular weight glutenin subunit (HMW-GS) genes Glu-V1 of D. villosum. Compared to the Chinese Spring (CS) variety, T1DL•1V#3S plants showed moderate resistance to moderate susceptibility to the stripe rust races CYR33 and Su11-4. However, T1DS•1V#3L plants showed high resistance or immunity to these stripe rusts. The genes for resistance to stripe rust were located on 1VL of D. villosum. In comparison to CS, the dough from T1DS•1V#3L had a significantly shorter developing time (1.45 min) and stable time (1.0 min), a higher weakness in gluten strength (208.5 FU), and a lower farinograph quality index (18). T1DL•1V#3S had a significantly longer developing time (4.2 min) and stable time (5.25 min), a lower weakness in gluten strength (53 FU) and a higher farinograph quality index (78.5). We also found that T1DS•1V#3L had reduced gluten strength and dough quality compared to CS, but T1DL•1V#3S had increased gluten strength and dough quality. The results of SDS-PAGE analysis indicated that Glu-V1 of D. villosum was located on short arm 1VS and long arm 1VL. These results prove that the new translocation lines, T1DS•1V#3L and T1DS•1V#3L, have valuable stripe rust resistance and dough quality traits that will be important for improving wheat quality and resistance in future wheat breeding programs.

Prediction of disease-related microRNAs by incorporating functional similarity and common association information.

The identification of human disease-related microRNAs (miRNAs) is important for understanding the pathogenesis of diseases, but to do this experimentally is a costly and time-consuming process. Computational prediction of disease-related miRNA candidates is a valuable complement to experimental studies. It is essential to develop an effective prediction method to provide reliable candidates for subsequent biological experiments. In this study, we constructed a miRNA functional similarity network based on calculation of the functional similarity between each pair of miRNAs. Here, we present a new method (DismiPred) for predicting disease-related miRNA candidates based on the network. This method incorporates functional similarity and common association information to achieve an efficient prediction performance. DismiPred has been successfully shown to recover experimentally validated disease-related miRNAs for 12 common human diseases, with an F-measure ranging from 69.49 to 91.69%. Furthermore, a case study examining breast neoplasms showed that DismiPred could uncover novel disease-related miRNAs. DismiPred is useful for further experimental studies on the involvement of miRNAs in the pathogenesis of diseases.