MicroRNA166 (miR166) is highly conserved and has diverse functions across plant species. The highbush blueberry (Vaccinium corymbosum) genome is thought to harbor 10 miRNA166 loci (Vco-miR166), but the extent of their evolutionary conservation or functional diversification remains unknown. In this study, we identified six additional Vco-miR166 loci based on conserved features of the miR166 family. Phylogenetic analyses showed that mature Vco-miR166s and their precursor cluster in several clades are evolutionary conserved with diverse species. The cis-regulatory elements in the Vco-miR166 promoters indicated functions related to different phytohormones and defense responses. We also identified putative targets of vco-miR166s, which targeted the same gene families, suggesting the functional conservation and diversification of Vco-miR166 family members. Furthermore, we examined the accumulation patterns of six mature Vco-miR166s in response to abiotic stresses by stem-loop reverse RT-qPCR, which revealed their upregulation under freezing, cold, and heat stress, while they were downregulated by drought compared to control growth conditions. However, Vco-miR166 members showed different expression patterns when exposed to salt stress. These results showed that conserved Vco-miR166 family members display functional diversification but also coordinately influence plant responses to abiotic stress.

BACKGROUND: Blueberry fruits contain large amounts of phenolic compounds derived from the phenylpropanoid pathway. Their biosynthesis is complex, involving many enzymes. OBJECTIVE: We sought to investigate the content of phenylpropanoid-derived compounds and identify key genes involved in the phenylpropanoid metabolite pathway during half-highbush blueberry fruit development. METHODS: Phenylpropanoid metabolite contents were determined by high-performance liquid chromatography (HPLC) and spectrophotometry. Gene expression was examined through reverse-transcription PCR. RESULTS: Phloretin, chlorogenic acid, total flavonol, quercetin, catechin, and proanthocyanidin contents were highest in small-sized green fruits; myricetin and epicatechin contents were highest in pink fruits; and lignin and anthocyanin were highest in blue fruits. Genes from the 4-coumarate CoA ligase (4CL) family regulate the biosynthesis of phenylpropanoid metabolites. Phenylalanine ammonia-lyase (PAL) and cinnamoyl-CoA reductase (CCR) are key genes in the lignin biosynthetic pathway. Flavonol synthase (FLS) is a key gene affecting total flavonols and the quercetin biosynthetic pathway. PAL and chalcone isomerase (CHI) are key genes in the epicatechin and anthocyanin biosynthetic pathways, respectively. CONCLUSION: Phenylpropanoid metabolites are regulated by multiple genes from the same or different families. Enzymes in different metabolic pathways compete for precursors to form a complex regulatory network for phenylalanine metabolism.

This article examines data from the Shihuiyao, Nierji, Tongmeng, Jiangqiao, and Dalai hydrological stations in the Nen River basin to understand the hydrological processes occurring in the catchments. Daily precipitation and runoff data from 1955 to 1973 were combined using the smoothed minima trial method to determine the surface runoff concentration time. Then, a genetic algorithm was used to optimize the parameters and obtain an optimal empirical formula. An improved empirical formula was implemented with the genetic algorithm and optimized parameters then incorporated variable average rainfall intensity, correlation between basin area, surface runoff average concentration time, and average rainfall intensity. Finally, an optimized empirical formula (using genetic algorithm to optimize the parameters) and improved empirical formula (incorporating variable average rainfall intensity) were tested by using the daily precipitation and runoff data from the Baishan and Hongshi hydrological stations of the Second Songhua River. The results show that an optimized and improved formula can be used to more accurately estimate hydrologic conditions in the Nen River. Therefore, the improved formula is an efficient method for calculating surface runoff concentration time. Surface runoff concentration time is an important basis for differentiating source waters, which include surface runoff and underground runoff.