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Uncovering co-expression gene network modules regulating fruit acidity in diverse apples.

Bai Y, Dougherty L, Cheng L, Zhong GY, Xu K - BMC Genomics (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Horticulture Section, School of Integrative Plant Science, Cornell University, New York State Agricultural Experiment Station, Geneva, NY, 14456, USA. yb63@cornell.edu.

ABSTRACT

Background: Acidity 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.

Results: Guided 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.

Conclusions: The 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.

No MeSH data available.


Related in: MedlinePlus

Regulator genes and their assigned tight clusters. a Regulator M190273 (upper panel) and Cluster 1 (mid panel) of 48 genes. The expression of genes is color coded from low (dark blue) through high (bright yellow). Each row stands for a gene (listed on the right) and each column for a sample named at the bottom (see the legend in Fig. 1 for keys). The regulator is assigned based on the hierarchical tree on top, which indicates how samples were clustered together with the red vertical lines. The short red vertical line in the sample list shows where the two primary clades diverge while the short green vertical line marks where the secondary clades depart within a primary clade. The genotype names in red indicate they are not in agreement with one of the two primary clusters Ma_ or mama. b Regulator M525602 and Cluster 22 of 13 genes. c Regulator M319170 and Cluster 0 of 20 genes. Elements in (b) and (c) and their contents, formats and messages are the same as those in (a)
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Fig8: Regulator genes and their assigned tight clusters. a Regulator M190273 (upper panel) and Cluster 1 (mid panel) of 48 genes. The expression of genes is color coded from low (dark blue) through high (bright yellow). Each row stands for a gene (listed on the right) and each column for a sample named at the bottom (see the legend in Fig. 1 for keys). The regulator is assigned based on the hierarchical tree on top, which indicates how samples were clustered together with the red vertical lines. The short red vertical line in the sample list shows where the two primary clades diverge while the short green vertical line marks where the secondary clades depart within a primary clade. The genotype names in red indicate they are not in agreement with one of the two primary clusters Ma_ or mama. b Regulator M525602 and Cluster 22 of 13 genes. c Regulator M319170 and Cluster 0 of 20 genes. Elements in (b) and (c) and their contents, formats and messages are the same as those in (a)

Mentions: Regulator genes are not readily identifiable from the WGCNA co-expression network modules as they are non-directed. To identify regulator genes, we used Lemon-Tree software [36] first to generated 50 tight clusters (Clusters 0–49) covering 839 of the 1301 FAAGs through ten Gibbs sampler runs [38], and then to examine the 96 candidate regulators (transcription factors or signal transducers annotated in the 1301 FAAGs) with the clusters. To statistically validate the results obtained from the 96 candidate regulators, another 96 randomly selected genes were used as control (CK) and examined in parallel with the 96 candidate regulators. As expected, the probabilistic (P.) scores for genes assigned as a regulator for the 50 tight clusters showed significant difference (p = 0 in z test) between the 96 candidate regulators (0.1–81.0) and CK (0.1–6.3). At the top 1 % of P. scores (35.5–81.0), 12 of the 96 candidate regulators were identified as regulators and assigned to 21 of 50 tight clusters (Additional file 8: Table S7). Of the 12 regulators, five (M190273, M525602, M319170, M239684 and M134341) were from module Turquoise (Fig. 8, Additional file 9: Figure S2, Additional file 10: Figure S3), two (M753318 and M175481) from Brown (Additional file 10: Figure S3), and five from modules Green, Red and Pink which were irrelevant for acidity (Additional file 8: Table S7). Below are brief descriptions of three regulator genes, which were among the 18 MSAGs or among the 12 most connected intramodular hub genes in module Turquoise.Fig. 8


Uncovering co-expression gene network modules regulating fruit acidity in diverse apples.

Bai Y, Dougherty L, Cheng L, Zhong GY, Xu K - BMC Genomics (2015)

Regulator genes and their assigned tight clusters. a Regulator M190273 (upper panel) and Cluster 1 (mid panel) of 48 genes. The expression of genes is color coded from low (dark blue) through high (bright yellow). Each row stands for a gene (listed on the right) and each column for a sample named at the bottom (see the legend in Fig. 1 for keys). The regulator is assigned based on the hierarchical tree on top, which indicates how samples were clustered together with the red vertical lines. The short red vertical line in the sample list shows where the two primary clades diverge while the short green vertical line marks where the secondary clades depart within a primary clade. The genotype names in red indicate they are not in agreement with one of the two primary clusters Ma_ or mama. b Regulator M525602 and Cluster 22 of 13 genes. c Regulator M319170 and Cluster 0 of 20 genes. Elements in (b) and (c) and their contents, formats and messages are the same as those in (a)
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Fig8: Regulator genes and their assigned tight clusters. a Regulator M190273 (upper panel) and Cluster 1 (mid panel) of 48 genes. The expression of genes is color coded from low (dark blue) through high (bright yellow). Each row stands for a gene (listed on the right) and each column for a sample named at the bottom (see the legend in Fig. 1 for keys). The regulator is assigned based on the hierarchical tree on top, which indicates how samples were clustered together with the red vertical lines. The short red vertical line in the sample list shows where the two primary clades diverge while the short green vertical line marks where the secondary clades depart within a primary clade. The genotype names in red indicate they are not in agreement with one of the two primary clusters Ma_ or mama. b Regulator M525602 and Cluster 22 of 13 genes. c Regulator M319170 and Cluster 0 of 20 genes. Elements in (b) and (c) and their contents, formats and messages are the same as those in (a)
Mentions: Regulator genes are not readily identifiable from the WGCNA co-expression network modules as they are non-directed. To identify regulator genes, we used Lemon-Tree software [36] first to generated 50 tight clusters (Clusters 0–49) covering 839 of the 1301 FAAGs through ten Gibbs sampler runs [38], and then to examine the 96 candidate regulators (transcription factors or signal transducers annotated in the 1301 FAAGs) with the clusters. To statistically validate the results obtained from the 96 candidate regulators, another 96 randomly selected genes were used as control (CK) and examined in parallel with the 96 candidate regulators. As expected, the probabilistic (P.) scores for genes assigned as a regulator for the 50 tight clusters showed significant difference (p = 0 in z test) between the 96 candidate regulators (0.1–81.0) and CK (0.1–6.3). At the top 1 % of P. scores (35.5–81.0), 12 of the 96 candidate regulators were identified as regulators and assigned to 21 of 50 tight clusters (Additional file 8: Table S7). Of the 12 regulators, five (M190273, M525602, M319170, M239684 and M134341) were from module Turquoise (Fig. 8, Additional file 9: Figure S2, Additional file 10: Figure S3), two (M753318 and M175481) from Brown (Additional file 10: Figure S3), and five from modules Green, Red and Pink which were irrelevant for acidity (Additional file 8: Table S7). Below are brief descriptions of three regulator genes, which were among the 18 MSAGs or among the 12 most connected intramodular hub genes in module Turquoise.Fig. 8

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Horticulture Section, School of Integrative Plant Science, Cornell University, New York State Agricultural Experiment Station, Geneva, NY, 14456, USA. yb63@cornell.edu.

ABSTRACT

Background: Acidity 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.

Results: Guided 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.

Conclusions: The 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.

No MeSH data available.


Related in: MedlinePlus