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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

Expression confirmation of eight selected genes using qRT-PCR. a-h The normalized expression of target genes relative to a control gene (actin) in qRT-PCR was shown in light grey, and their corresponding RPKM values from RNA-seq were in black. The correlation coefficient (r) and associated p value (n = 10) were shown accordingly. Standard deviations were shown with the error bars
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Fig9: Expression confirmation of eight selected genes using qRT-PCR. a-h The normalized expression of target genes relative to a control gene (actin) in qRT-PCR was shown in light grey, and their corresponding RPKM values from RNA-seq were in black. The correlation coefficient (r) and associated p value (n = 10) were shown accordingly. Standard deviations were shown with the error bars

Mentions: To evaluate if and how the RPKM values reflected the gene expression levels, a set of eight genes were analyzed using qRT-PCR (Fig. 9). The eight genes include Ma1, the EIN3-like regulator M190273, another most significant gene for acidity M651862 encoding a protein serine/threonine kinase, and five others. The data not only confirmed that the relative expressions of the eight genes in qRT-PCR were significantly (p = 6.663E-3 to 9.647E-5) correlated with their RPKM values in RNA-seq, but also confirmed their differential expression between the two genotypes groups Ma_ and mama.Fig. 9


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)

Expression confirmation of eight selected genes using qRT-PCR. a-h The normalized expression of target genes relative to a control gene (actin) in qRT-PCR was shown in light grey, and their corresponding RPKM values from RNA-seq were in black. The correlation coefficient (r) and associated p value (n = 10) were shown accordingly. Standard deviations were shown with the error bars
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4537561&req=5

Fig9: Expression confirmation of eight selected genes using qRT-PCR. a-h The normalized expression of target genes relative to a control gene (actin) in qRT-PCR was shown in light grey, and their corresponding RPKM values from RNA-seq were in black. The correlation coefficient (r) and associated p value (n = 10) were shown accordingly. Standard deviations were shown with the error bars
Mentions: To evaluate if and how the RPKM values reflected the gene expression levels, a set of eight genes were analyzed using qRT-PCR (Fig. 9). The eight genes include Ma1, the EIN3-like regulator M190273, another most significant gene for acidity M651862 encoding a protein serine/threonine kinase, and five others. The data not only confirmed that the relative expressions of the eight genes in qRT-PCR were significantly (p = 6.663E-3 to 9.647E-5) correlated with their RPKM values in RNA-seq, but also confirmed their differential expression between the two genotypes groups Ma_ and mama.Fig. 9

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