Pepino (Solanum muricatum) Metabolic Profiles and Soil Nutrient Association Analysis in Three Growing Sites on the Loess Plateau of Northwestern China

De novo assembly of the complete mitochondrial genome of pepino (Solanum muricatum) using PacBio HiFi sequencing: insights into structure, phylogenetic implications, and RNA editing

BACKGROUND

Solanum muricatum is an emerging horticultural fruit crop with rich nutritional and antioxidant properties. Although the chromosome-scale genome of this species has been sequenced, its mitochondrial genome sequence has not been reported to date.

RESULTS

PacBio HiFi sequencing was used to assemble the circular mitogenome of S. muricatum, which was 433,466 bp in length. In total, 38 protein-coding, 19 tRNA, and 3 rRNA genes were annotated. The reticulate mitochondrial conformations with multiple junctions were verified by polymerase chain reaction, and codon usage, sequence repeats, and gene migration from chloroplast to mitochondrial genome were determined. A collinearity analysis of eight Solanum mitogenomes revealed high structural variability. Overall, 585 RNA editing sites in protein coding genes were identified based on RNA-seq data. Among them, mttB was the most frequently edited (52 times), followed by ccmB (46 times). A phylogenetic analysis based on the S. muricatum mitogenome and those of 39 other taxa (including 25 Solanaceae species) revealed the evolutionary and taxonomic status of S. muricatum.

CONCLUSIONS

We provide the first report of the assembled and annotated S. muricatum mitogenome. This information will help to lay the groundwork for future research on the evolutionary biology of Solanaceae species. Furthermore, the results will assist the development of molecular breeding strategies for S. muricatum based on the most beneficial agronomic traits of this species.

Metabolomics analysis of quality components metabolism during the growth process of pepino (Solanum muricatum) fruit

Pepino (Solanum muricatum), a horticultural crop that has experienced significant growth in the highlands of China over the past two decades, is widely embraced by consumers due to its distinctive taste and nutritional advantages. This study focused on the cultivar 'Qingcanxiang' of pepino grown on the Qinghai-Tibetan Plateau was analyzed using UPLC-QTOF-MS and RNA-seq transcriptome sequencing. Fruit samples were collected at three distinct stages of development, and the results of the metabolomics and transcriptomics were compared and correlated. The study's findings indicate that the 'Qingcanxiang' fruit contained a total of 187 metabolites, comprising 12 distinct categories of compounds, including amino acids and their derivatives, organic acids, sugars and alcohols, phenols and phenolic acids. Of these metabolites, 94 were identified as differential. Significant variations in nutrient composition were observed across the three growth stages of the fruit. Specifically, the stage spanning from the growth to the maturation was identified as the critical stages for nutrient accumulation and flavor development. Transcriptome sequencing analysis revealed a set of highly associated genes between aspartate and quinic acid, namely SIR2, IRAK4, RP-L29, and CCNH. These genes are potentially involved in the regulation of both amino acid and phenolic acid synthesis. Through the application of metabolomics and transcriptomics, this investigation elucidates the alterations in metabolites and the underlying molecular regulatory mechanisms of pepino fruits during three growth stages. The findings furnish a theoretical foundation for the evaluation of nutritional quality and the enhancement of breeding strategies for pepino.

Assessment of suitable cultivation region for Pepino (Solanum muricatum) under different climatic conditions using the MaxEnt model and adaptability in the Qinghai-Tibet plateau

Pepino (Solanum muricatum), a member of the Solanaceae family originating from South America, is cultivated globally. However, the cultivation range and suitable habitat of Pepino have not been extensively studied, which hampers the further development of its cultivation industry. Therefore, we aimed at enrich and expand the planting scope of Pepino. Currently, the main cultivation areas of Pepino in China are the Yunnan-Guizhou Plateau and the Loess Plateau, where the altitude is above 1000 m. In this study, ArcGIS combined with the MaxEnt model was used for prediction, whose area under curve value was 0.949. The main climatic factors affecting the distribution of Pepino are temperature seasonality, annual means temperature, mean temperature of the coldest quarter, elevation, isothermality, and the climate factors, and their cumulative contribution rate of 87.6%. Pepino's main potential distribution areas are located in Yunnan-Guizhou Plateau, Yunnan Province, Hexi Corridor of Loess Plateau, and low altitude areas of Qinghai-Tibet Plateau. The main distribution ranges from 1000 to 2000 m above sea level, and the total suitable area accounts for 20.09% of China's total land area. The prediction results reveal an expanded potential area for Pepino, with no significant migration in the central region of the main potential distribution area by 2050 and 2070. No studies have been conducted on the open-area cultivation of Pepino in northern China. Our findings revealed that the yield and quality in the four experimental sites and final actual cultivation conditions were consistent with the predicted results of MaxEnt. The yiel d per plant in Xunhua and Minhe was significantly different from that in Xining, which was low, and that in Minhe was the highest. Overall, the fruit quality in the Xining region was the lowest among the three regions, which was related to the climatic differences in each region. These results align with the predicted outcomes, indicating that Xining is the least suitable area. Further, these data verify the accuracy of the prediction results. The climate data of the four regions were analyzed simultaneously to elucidate the influence of different climate conditions on the growth of Pepino. Our findings are of considerable significance for introducing characteristic horticultural crops in the Qinghai-Tibet Plateau and using the MaxEnt model to predict the cultivation range of crops.

Identification of the key metabolites and related genes network modules highly associated with the nutrients and taste components among different Pepino (Solanum muricatum) cultivars

There is considerable knowledge about plant compounds that produce flavor, scent, and aroma. Aside from the similarities, however, groups of plant-produced nutrients and taste components have little in common with each other. Network analysis holds promise for metabolic gene discovery, which is especially important in plant systems where metabolic networks are not yet fully resolved. To bridge this gap, we propose a joint model of gene regulation and metabolic reactions in two different pepino varieties. Differential metabolomics analysis is carried out for detection of eventual interaction of compound. We adopted a multi-omics approach to profile the transcriptome and metabolome analyze differences in phenolic acids, flavonoids, organic acids, lipids, alkaloids, and sugars between LOF and SRF. The two most predominant classes of metabolites are phenolic acids and lipids in pepino. Overall results show enrichment in most DEGs was carbohydrate and biosynthesis of secondary metabolites pathway. Results of DEMs predominantly comprised N-p-coumaroyl agmatine and tryptamine, and significant differences were observed in their expression between LOF and SRF. Integrated DEMs and DEGs specific networks were constructed by combining two types of networks: transcriptional regulatory networks composed of interactions between DEMs and the regulated genes, and pepino metabolite-metabolite interaction networks. Newly discovered features, such as DEGs (USPA, UBE2 and DELLA) involved in the production of secondary metabolites are found in coregulated gene clusters. Moreover, lipid metabolites were most involved in DEMs correlations by OPLS-DA while identifying a significant number of DEGs co-regulated by SENP1, HMGCS et al. These results further that the metabolite discrepancies result from characterized the nutrients and taste components between two pepino genotype. Among the possible causes of the differences between species in pepino metabolite concentrations is co-regulated by these DEGs, continue to suggest that novel features of metabolite biosynthetic pathway remain to be uncovered. Finally, the integrated metabolome and transcriptome analyses have revealed that many important metabolic pathways are regulated at the transcriptional level. The metabolites content differences observed among varieties of the same species mainly originates from different regulated genes and enzymes expression. Overall, this study provides new insights into the underlying causes of differences in the plant metabolites and suggests that genetic data can be used to improve its nutrients and taste components.

An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation

Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as N-acetylhistamine, arginine, and caffeic acid can enhance pepino‘s flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor.