Integrated Analysis of the Metabolome and Transcriptome on Anthocyanin Biosynthesis in Four Developmental Stages of Cerasus humilis Peel Coloration

Cerasus humilis is a unique dwarf shrub and fruit color is an important trait in the species. In this study, we evaluated the transcriptomic and metabolomic profiles of the plant at different developmental stages to elucidate the mechanism underlying color formation. In a metabolomics analysis, 16 anthocyanin components were identified at four developmental stages, and high levels of cyanidin O-syringic acid and pelargonidin 3-O-beta-d-glucoside (callitephin chloride) were correlated with the reddening of the fruit peel. A co-expression analysis revealed that ANS and UFGT play key roles in pigmentation (PCC > 0.82). Additionally, transcriptome data showed that most anthocyanin biosynthetic genes and two MYB transcription factors were significantly up-regulated. QRT-PCR results for these differentially expressed genes were generally consistent with the high-throughput sequencing. Moreover, the overexpression of ChMYB1 (TRINITY_DN21536_c0_g1) in apple calli could contribute to the accumulation of anthocyanin. It was also found that UFGT (TRINITY_DN19893_c1_g5) and ChMYB1 (TRINITY_DN21536_c0_g1) have similar expression patterns. These findings provide insight into the mechanisms underlying anthocyanin accumulation and coloration during fruit peel development, providing a basis for the breeding of anthocyanin-rich C. humilis cultivars.

Identification of LecRLK gene family in Cerasus humilis through genomic-transcriptomic data mining and expression analyses

Lectin receptor-like protein kinases (LecRLKs) have been shown to be involved in plants’ responses to various biotic and abiotic stresse factors. Cerasus humilis is an important fruit species widely planted for soil and water conservation in northern China due to its strong tolerance to drought and salinity stresses. In this study, a total of 170 LecRLK family genes (125 G-types, 43 L-types and 2 C-types) were identified in the newly released whole-genome sequences of C. humilis. Furthermore, nine representative LecRLK genes in young plants of C. humilis under varying drought and salinity stresses were selected for qRT-PCR analysis. Our systematic comparative analyses revealed the active participation of these nine LecRLK genes in the salt and drought stress responses of C. humilis. The results from our study have provided a solid foundation for future functional verification of these LecRLK family genes and will likely help facilitate the more rapid and effective development of new stress resistant Cerasus humilis cultivars.

ON-LINE DETECTION OF CERASUS HUMILIS FRUIT BASED ON VIS/NIR SPECTROSCOPY COMBINED WITH VARIABLE SELECTION METHODS AND GA-BP MODEL

In order to realize the rapid and non-destructive detection of fresh Cerasus Humilis’ (CH) classification, and promote the deep-processing of post-harvest fresh fruit and improve market competitiveness, this study proposed a nonlinear identification method based on genetic algorithm (GA) optimized back propagation (BP) neural network of different varieties of fresh CH fruit. “Nongda-4”, “Nongda-5”, and “Nongda-7” fresh CH fruit were selected as research objects to collect their visible/near-infrared spectral data dynamically. The original spectra were preprocessed by moving smoothing (MS) and standard normal variate (SNV) methods, for the characteristic wavelengths were extracted with four dimension-reducing methods, namely principal components analysis (PCA), competitive adaptive reweighed sampling (CARS), CARS-mean impact value (CARS-MIV), and random frog (RF) algorithm. Finally, the BP prediction models were established based on full-spectrum and characteristic wavelengths. At the same time, the GA optimization was used to optimize the initial weight and threshold of the BP neural network and compared with the partial least squares’ discrimination analysis (PLS-DA) linear model. Through comparing the MS (7)+SNV was proved to be the best preprocessing method, the CARS-MIV-GA-BP model had the best discriminant accuracy, the prediction set accuracy was 98.76%, of which the variety “Nongda-4” and “Nongda-5” recognition rate were 100%, the variety “Nongda-7” recognition rate was 96.29%. The results show that the GA can effectively optimize the initial weights and threshold randomization of the BP neural network, improve the discrimination accuracy of CH varieties, and the CARS-MIV algorithm can effectively reduce the number of input nodes of the BP neural network model, simplify the structure of BP neural network. This study provides a new theoretical basis for the detection of fresh CH fruit classification.

Differential Regulation of Anthocyanins in Cerasus humilis Fruit Color Revealed by Combined Transcriptome and Metabolome Analysis

Coloring is an important appearance quality of fruit. In order to evaluate the relationship between metabolites and fruit color, we analyzed the metabolites and transcriptional profiles of two different Cerasus humilis cultivars: “RF” (cv. Zhangwu, red fruit) and “YF” (cv. Nongda No.5, yellow fruit). The results of identification and quantification of metabolites showed that there were significant differences in the contents of 11 metabolites between RF and YF. Transcriptomics was used to analyze the expression patterns of genes related to the anthocyanin biosynthesis pathway, and subsequently, the regulation network of anthocyanin biosynthesis was established to explore their relationship with color formation. QRT-PCR, performed for 12 key genes, showed that the expression profiles of the differentially expressed genes were consistent with the results of the transcriptome data. A co-expression analysis revealed that the late genes were significantly positively correlated with most of the different metabolites. The results of the study provide a new reference for improving the fruit color of Cerasus humilis in the future.