Acidity levels greatly affect the taste and flavor of fruit, and consequently its market value. In mature apple fruit, malic acid is the predominant organic acid. Several studies have confirmed that the major quantitative trait locus Ma largely controls the variation of fruit acidity levels. The Ma locus has recently been defined in a region of 150 kb that contains 44 predicted genes on chromosome 16 in the Golden Delicious genome. In this study, we identified two aluminum-activated malate transporter-like genes, designated Ma1 and Ma2, as strong candidates of Ma by narrowing down the Ma locus to 65-82 kb containing 12-19 predicted genes depending on the haplotypes. The Ma haplotypes were determined by sequencing two bacterial artificial chromosome clones from G.41 (an apple rootstock of genotype Mama) that cover the two distinct haplotypes at the Ma locus. Gene expression profiling in 18 apple germplasm accessions suggested that Ma1 is the major determinant at the Ma locus controlling fruit acidity as Ma1 is expressed at a much higher level than Ma2 and the Ma1 expression is significantly correlated with fruit titratable acidity (R (2) = 0.4543, P = 0.0021). In the coding sequences of low acidity alleles of Ma1 and Ma2, sequence variations at the amino acid level between Golden Delicious and G.41 were not detected. But the alleles for high acidity vary considerably between the two genotypes. The low acidity allele of Ma1, Ma1-1455A, is mainly characterized by a mutation at base 1455 in the open reading frame. The mutation leads to a premature stop codon that truncates the carboxyl terminus of Ma1-1455A by 84 amino acids compared with Ma1-1455G. A survey of 29 apple germplasm accessions using marker CAPS(1455) that targets the SNP(1455) in Ma1 showed that the CAPS(1455A) allele was associated completely with high pH and highly with low titratable acidity, suggesting that the natural mutation-led truncation is most likely responsible for the abolished function of Ma for low pH or high acidity in apple.
The reference genome of apple (Malus × domestica) has been available since 2010. Despite being a milestone in apple genomics, the reference genome is difficult to be used as a reference in RNA-seq (RNA sequencing) analysis, a widespread technology in transcriptomic studies. One of the major limitations appears to be the low coverage of the reference transcriptome in RNA-seq mapping of reads. To improve the reference transcriptome, we obtained 14 sets of strand-specific RNA-seq data of 168.5 million reads in total from fruit of Golden Delicious (GD, the source of the reference genome) in varying growth and developmental stages. Using a combination of genome-guided assembly and de novo assembly, the apple reference transcriptome was improved to a collection of 71,178 genes or transcripts, which includes 53,654 genes predicted originally (with MDP prefixed in their IDs) and 17,524 novel transcripts. Of these novel transcripts, 8,144 were identified from reads directly mapped to the reference genome while the remaining 9,380 were extracted from de novo assemblies of reads that could not be initially mapped to the reference genome. Evaluating the improved apple reference transcriptome with reads from Golden Delicious and other genotypes used in this and other studies showed that it allowed 62.5 ± 9.3-82.3 ± 2.7 % of reads to be mapped, a marked increase from the low rates of 37.4 ± 7.7-46.6 ± 7.1 % offered by the original reference transcriptome. The improved reference transcriptome therefore represents a step forward towards a complete reference transcriptome in apple.
BackgroundAcidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates.ResultsGuided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P < 0.001) correlation with malate. Of these, the Ma1 containing module (Turquoise) of 336 genes showed the highest correlation (0.79). We also identified 12 intramodular hub genes from each of the five modules and 18 enriched gene ontology (GO) terms and MapMan sub-bines, including two GO terms (GO:0015979 and GO:0009765) and two MapMap sub-bins (1.3.4 and 1.1.1.1) related to photosynthesis in module Turquoise. Using Lemon-Tree algorithms, we identified 12 regulator genes of probabilistic scores 35.5–81.0, including MDP0000525602 (a LLR receptor kinase), MDP0000319170 (an IQD2-like CaM binding protein) and MDP0000190273 (an EIN3-like transcription factor) of greater interest for being one of the 18 MSAGs or one of the 12 intramodular hub genes in Turquoise, and/or a regulator to the cluster containing Ma1.ConclusionsThe most relevant finding of this study is the identification of the MSAGs, intramodular hub genes, enriched photosynthesis related processes, and regulator genes in a WGCNA module Turquoise that not only encompasses Ma1 but also shows the highest modular correlation with acidity. Overall, this study provides important insight into the Ma1-mediated gene network controlling acidity in mature apple fruit of diverse genetic background.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1816-6) contains supplementary material, which is available to authorized users.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.