PSI-22 Effects of miR156 overexpression and silencing of SPL6 and SPL13 genes on the carbohydrate molecular structures in alfalfa. (7th December 2018) - Lei, Y; Hannoufa, A; Wang, Y; Yu, P
Abstract: The objectives of this study were to investigate the effect of overexpression of miR156 (miR156 OE) on carbohydrate molecular structures of alfalfa, in comparison with silencing SPL6 and SPL13 genes (SPL RNAi). Five miR156 OE, three SPL6 -silenced (SPL6 RNAi) and three SPL13 -silenced (SPL13 RNAi) alfalfa plants were grown with wild type (WT) under greenhouse conditions. Three batches of each genotype were harvested at mid-vegetative stage, freeze-dried and ground through 0.02mm sieve. Spectra were collected with attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR). Univariate analysis was performed with OMNIC software and spectral parameters were analyzed with PROC MIXED from SAS 9.4. Hierarchical cluster analysis (HCA) and principle component analysis (PCA) were performed with R software using both original spectra and second derivatives. Results showed that miR156 OE and SPL6/13 RNAi had lower heights and areas of total carbohydrate (TC), cellulosic compounds (CEC) and structural carbohydrate (STC) compared to WT, except that both WT and miR156 OE had higher TC4 and STC4 heights than SPL RNAi. Moreover, miR156 OE had greater STC2 and STC3 heights, and STC area (STCA) compared to SPL6/13 RNAi, and lower CEC height than SPL13 RNAi. As for PCA results, WT could be separated from SPL RNAi in CEC and STC regions with original spectra, and in STC and total carbohydrate (CHO) regions by using second derivatives. In terms of HCA, original spectra clusters distinguished WT from SPL RNAi in TC and STC regions. In contrast, second derivatives clusters could separate WT from both SPL RNAi and miR156 OE in STC and CHO regions. In conclusion, overexpression of miR156 altered the carbohydrate structures of alfalfa, which was similar as silencing of SPL6 and SPL13 genes. The STC region had the most spectral variations in carbohydrate region and could be used for clustering analysis.