Blocked chlorophyll synthesis leads to the production of golden snap bean pods

Segmentation and Phenotype Calculation of Rapeseed Pods Based on YOLO v8 and Mask R-Convolution Neural Networks

Rapeseed is a significant oil crop, and the size and length of its pods affect its productivity. However, manually counting the number of rapeseed pods and measuring the length, width, and area of the pod takes time and effort, especially when there are hundreds of rapeseed resources to be assessed. This work created two state-of-the-art deep learning-based methods to identify rapeseed pods and related pod attributes, which are then implemented in rapeseed pots to improve the accuracy of the rapeseed yield estimate. One of these methods is YOLO v8, and the other is the two-stage model Mask R-CNN based on the framework Detectron2. The YOLO v8n model and the Mask R-CNN model with a Resnet101 backbone in Detectron2 both achieve precision rates exceeding 90%. The recognition results demonstrated that both models perform well when graphic images of rapeseed pods are segmented. In light of this, we developed a coin-based approach for estimating the size of rapeseed pods and tested it on a test dataset made up of nine different species of Brassica napus and one of Brassica campestris L. The correlation coefficients between manual measurement and machine vision measurement of length and width were calculated using statistical methods. The length regression coefficient of both methods was 0.991, and the width regression coefficient was 0.989. In conclusion, for the first time, we utilized deep learning techniques to identify the characteristics of rapeseed pods while concurrently establishing a dataset for rapeseed pods. Our suggested approaches were successful in segmenting and counting rapeseed pods precisely. Our approach offers breeders an effective strategy for digitally analyzing phenotypes and automating the identification and screening process, not only in rapeseed germplasm resources but also in leguminous plants, like soybeans that possess pods.

The molecular mechanism on suppression of climacteric fruit ripening with postharvest wax coating treatment via transcriptome

Wax coating is an important means to maintain fruit quality and extend fruit shelf life, especially for climacteric fruits, such as apples (Malus domestica). Here, we found that wax coating could inhibit ethylene production, chlorophyll degradation, and carotenoid synthesis, but the molecular mechanism remains unclear. The regulatory mechanism of wax coating on apple fruit ripening was determined by subjecting wax-treated apple fruits to transcriptome analysis. RNA-seq revealed that 1,137 and 1,398 genes were upregulated and downregulated, respectively. These differentially expressed genes (DEGs) were shown to be related to plant hormones, such as ethylene, auxin, abscisic acid, and gibberellin, as well as genes involved in chlorophyll degradation and carotenoid biosynthesis. Moreover, we found that some genes related to the wax synthesis process also showed differential expression after the wax coating treatment. Among the DEGs obtained from RNA-seq analysis, 15 were validated by quantitative RT-PCR, confirming the results from RNA-seq analysis. RNA-seq and qRT-PCR of pear (Pyrus ussuriensis) showed similar changes after wax treatment. Our data suggest that wax coating treatment inhibits fruit ripening through ethylene synthesis and signal transduction, chlorophyll metabolism, and carotenoid synthesis pathways and that waxing inhibits endogenous wax production. These results provide new insights into the inhibition of fruit ripening by wax coating.

Identifying key genes involved in yellow leaf variation in 'Menghai Huangye' based on biochemical and transcriptomic analysis

Albino tea plants generally have higher theanine, which causes their tea leaves to taste fresher, and they are an important mutant for the breeding of tea plant varieties. Earlier, we reported an albino germplasm, 'Menghai Huangye' (MHHY), from Yunnan Province and found that it has a lower chlorophyll content during the yellowing stage, but the mechanism underlying low chlorophyll and the yellowing phenotype is still unclear. In this study, the pigment contents of MHHY_May (yellowing, low chlorophyll), MHHY_July (regreening, normal chlorophyll), and YK10_May (green leaves, normal chlorophyll) were determined, and the results showed that the lower chlorophyll content might be an important reason for the formation of the yellowing phenotype of MHHY. Through transcriptome sequencing, we obtained 654 candidates for differentially expressed genes (DEGs), among which 4 genes were related to chlorophyll synthesis, 10 were photosynthesis-related, 34 were HSP family genes, and 19 were transcription factor genes. In addition, we analysed the transcription levels of the key candidate genes in MHHY_May and MHHY_July and found that they are consistent with the expression trends in MHHY_May and YK10_May, which further indicates that the candidate differential genes we identified are likely to be key candidate factors involved in the low chlorophyll content and yellowing of MHHY. In summary, our findings will assist in revealing the low chlorophyll content of MHHY and the formation mechanism of yellowing tea plants and will be applied to the selection and breeding of albino tea cultivars in the future.

Identification and fine genetic mapping of the golden pod gene (pv-ye) from the snap bean (Phaseolus vulgaris L.)

Using bulked segregant analysis combined with next-generation sequencing, we delimited the pv-ye gene responsible for the golden pod trait of snap bean cultivar A18-1. Sequence analysis identified Phvul.002G006200 as the candidate gene. The pod is the main edible part of snap beans (Phaseolus vulgaris L.). The commercial use of the pods is mainly affected by their color. Consumers seem to prefer golden pods. The aim of the present study was to identify the gene responsible for the golden pod trait in the snap bean. 'A18-1' (a golden bean cultivar) and 'Renaya' (a green bean cultivar) were chosen as the experimental materials. Genetic analysis indicated that a single recessive gene, pv-ye, controls the golden pod trait. A candidate region of 4.24 Mb was mapped to chromosome Pv 02 using bulked-segregant analysis coupled with whole-genome sequencing. In this region, linkage analysis in an F₂ population localized the pv-ye gene to an interval of 182.9 kb between the simple sequence repeat markers SSR77 and SSR93. This region comprised 16 genes (12 annotated genes from the P. vulgaris database and 4 functionally unknown genes). Combined with transcriptome sequencing results, we identified Phvul.002G006200 as the potential candidate gene for pv-ye. Sequencing of Phvul.002G006200 identified a single-nucleotide polymorphism (SNP) in pv-ye. A pair of primers covering the SNP were designed, and the fragment was sequenced to screen 1086 F₂ plants with the 'A18-1' phenotype. Our findings showed that among the 1086 mapped individuals, the SNP cosegregated with the 'A18-1' phenotype. The findings presented here could form the basis to reveal the molecular mechanism of the golden pod trait in the snap bean.