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Vacuolar malic acid accumulation largely determines fruit acidity, a key trait for the taste and flavor of apple and other fleshy fruits. Aluminum‐activated malate transporter 9 (ALMT9/Ma1) underlies a major genetic locus, Ma, for fruit acidity in apple, but how the protein transports malate across the tonoplast is unclear. Here, it is shown that overexpression of the coding sequence of Ma1 (Ma1α) drastically decreases fruit acidity in "Royal Gala" apple, leading to uncovering alternative splicing underpins Ma1's function. Alternative splicing generates two isoforms: Ma1β is 68 amino acids shorter with much lower expression than the full‐length protein Ma1α. Ma1β does not transport malate itself but interacts with the functional Ma1α to form heterodimers, creating synergy with Ma1α for malate transport in a threshold manner (When Ma1β/Ma1α ≥ 1/8). Overexpression of Ma1α triggers feedback inhibition on the native Ma1 expression via transcription factor MYB73, decreasing the Ma1β level well below the threshold that leads to significant reductions in Ma1 function and malic acid accumulation in fruit. Overexpression of Ma1α and Ma1β or genomic Ma1 increases both isoforms proportionally and enhances fruit malic acid accumulation. These findings reveal an essential role of alternative splicing in ALMT9‐mediated malate transport underlying apple fruit acidity. [ABSTRACT FROM AUTHOR]

The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential, forming the foundation for precision apple management. Traditionally, 2D imaging technologies were employed to delineate the architectural traits of apple trees, but their accuracy was hampered by occlusion and perspective ambiguities. This study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization, utilizing point clouds collected by a terrestrial laser scanner (TLS). The pipeline consisted of four modules: (a) data preprocessing module, (b) tree instance segmentation module, (c) tree structure segmentation module, and (d) architectural trait extraction module. The developed pipeline was used to analyze 84 trees of two representative apple cultivars, characterizing architectural traits such as tree height, trunk diameter, branch count, branch diameter, and branch angle. Experimental results indicated that the established pipeline attained an R 2 of 0.92 and 0.83, and a mean absolute error (MAE) of 6.1 cm and 4.71 mm for tree height and trunk diameter at the tree level, respectively. Additionally, at the branch level, it achieved an R 2 of 0.77 and 0.69, and a MAE of 6.86 mm and 7.48° for branch diameter and angle, respectively. The accurate measurement of these architectural traits can enable precision management in high-density apple orchards and bolster phenotyping endeavors in breeding programs. Moreover, bottlenecks of 3D tree characterization in general were comprehensively analyzed to reveal future development., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Tian Qiu et al.)