Proteomic assessment of low-abundance leaf proteins is hindered by the large quantity of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) present within plant leaf tissues. In the present study, total proteins were extracted from wheat (Triticum aestivum L.) leaves by a conventional trichloroacetic acid (TCA)/acetone method and a protocol first developed in this work. Phytate/Ca2+ fractionation and TCA/acetone precipitation were combined to design an improved TCA/acetone method. The extracted proteins were analysed by two-dimensional gel electrophoresis (2-DE). The resulting 2-DE images were compared to reveal major differences. The results showed that large quantities of Rubisco were deleted from wheat leaf proteins prepared by the improved method. As many as (758±4) protein spots were detected from 2-DE images of protein extracts obtained by the improved method, 130 more than those detected by the TCA/acetone method. Further analysis indicated that more protein spots could be detected at regions of pI 4.00-4.99 and 6.50-7.00 in the improved method-based 2-DE images. Our findings indicated that the improved method is an efficient protein preparation protocol for separating low-abundance proteins in wheat leaf tissues by 2-DE analysis. The proposed protocol is simple, fast, inexpensive and also applicable to protein preparations of other plants.
Muhammad A R F SultanLIU HuiCHENG Yu-FengZHANG Pei-peiZHAO Hui-xian
Two microRNA(miRNA) quantification methods, namely, poly(A) reverse transcription(RT)-quantitative real-time polymerase chain reaction(qPCR) and stem-loop RT-qPCR, have been developed for quantifying miRNA expression. In the present study, five miRNAs, including miR166, miR167, miR168, miR159, and miR396, with different sequence frequencies, were selected as targets to compare their expression profiles in five wheat tissues by applying the two methods and deep sequencing. The study aimed to determine a simple, reliable and high-throughput method for detecting miRNA expressions in wheat tissues. Results showed that the miRNA expression profiles determined by poly(A) RT-qPCR were more consistent with those obtained by deep sequencing. Further analysis indicated that the correlation coefficients of the data obtained by poly(A) RT-qPCR and deep sequencing(0.739, P 0.01) were higher than those obtained by stem-loop RT-qPCR and deep sequencing(0.535, P 0.01). The protocol used for poly(A) RT-qPCR is simpler than that for stem-loop RT-qPCR. Thus, poly(A) RT-qPCR was a more suitable high-throughput assay for detecting miRNA expression profiles. To the best of our knowledge, this study was the first to compare these two miRNA quantification methods. We also provided useful information for quantifying miRNA in wheat or other plant species.
