PPIM: A Protein-Protein Interaction Database for Maize

Zhu, Guijin; Wu, Aibo; Xu, Xiaoyan; Xiao, Peipei; Lü, Le; Liu, Jingdong; Cao, Yongwei; Chen, Luonan; Wu, Jun; Zhao, Xing‐Ming

doi:10.1104/pp.15.01821

Cited by 80 publications

(33 citation statements)

References 41 publications

(33 reference statements)

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“…We also assessed GO term enrichment for the candidate genes for each phenotype’s components and their validated and predicted interaction partners in a maize PPI network 37 using BiNGO 38 (Supplementary Tables 7, 8, and 9) and a 1% FDR threshold. This analysis provided greater insight into the different biological mechanisms by which plastic responses might influence the expression of different phenotypes.…”

Section: Resultsmentioning

confidence: 99%

“…In this scenario we would expect to see evidence of interactions between candidate genes for the two phenotypes or their co-localization in similar pathways. We performed a non-exhaustive survey of a maize protein-protein interaction network 37 and found predicted interactions between some pairs of candidate genes for the mean and plasticity of the same phenotype (unpublished results), a result that is at least consistent with “mediated pleiotropy”.…”

Section: Discussionmentioning

confidence: 95%

“…High-confidence experimentally validated and predicted protein-protein interactions (PPI) were downloaded from a maize PPI network 37 . Subnetworks were defined for each measure of each phenotype by selecting candidate genes and any proteins with which they interacted from the full network.…”

Section: Methodsmentioning

confidence: 99%

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Distinct genetic architectures for phenotype means and plasticities in Zea mays

et al. 2017

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Phenotypic plasticity describes the phenotypic variation of a trait when a genotype is exposed to different environments. Understanding the genetic control of phenotypic plasticity in crops such as maize is of paramount importance for maintaining and increasing yields in a world experiencing climate change. Here, we report the results of genome-wide association analyses of multiple phenotypes and two measures of phenotypic plasticity in the maize nested association mapping (US-NAM) population grown in multiple environments and genotyped with ~2.5 million single nucleotide polymorphisms (SNPs). We show that across all traits the candidate genes for mean phenotype values and plasticity measures form structurally and functionally distinct groups. Such independent genetic control suggests that breeders will be able to select semi-independently for mean phenotype values and plasticity, thereby generating varieties with both high mean phenotype values and levels of plasticity that are appropriate for the target performance environments.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 95%

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Distinct genetic architectures for phenotype means and plasticities in Zea mays

et al. 2017

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“…This result can be attributed to either biased GOBP annotations toward evolutionarily conserved processes rather than plant-specific ones in maize, or biased availability of high-quality functional genomics data toward animals and yeast. Subsequently, we compared MaizeNet with other maize gene networks based on the protein-protein interaction database for maize (PPIM) (Zhu et al, 2016), gene coexpression (GCN) (Huang et al, 2017) and integration of multiple types of interactions (STRING10.5) (Szklarczyk et al, 2017) (Table 2). This comparison revealed that the range of AUROC (FPR < 0.01) for MaizeNet was significantly higher than that of the other maize gene networks (P < 0.001, Wilcoxon signed rank test) ( Figure 2b), thereby indicating that MaizeNet is among the most predictive gene networks for biological processes in maize.…”

Section: Assessment Of the Predictive Power Of Maizenetmentioning

confidence: 99%

MaizeNet: a co‐functional network for network‐assisted systems genetics in Zea mays

Lee

Yang

et al. 2019

The Plant Journal

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Summary Maize (Zea mays) has multiple uses in human food, animal fodder, starch and sweetener production and as a biofuel, and is accordingly the most extensively cultivated cereal worldwide. To enhance maize production, genetic factors underlying important agricultural traits, including stress tolerance and flowering, have been explored through forward and reverse genetics approaches. Co‐functional gene networks are systems biology resources useful in identifying trait‐associated genes in plants by prioritizing candidate genes. Here, we present MaizeNet (http://www.inetbio.org/maizenet/), a genome‐scale co‐functional network of Z. mays genes, and a companion web server for network‐assisted systems genetics. We describe the validation of MaizeNet network quality and its ability to functionally predict molecular pathways and complex traits in maize. Furthermore, we demonstrate that MaizeNet‐based prioritization of candidate genes can facilitate the identification of cell wall biosynthesis genes and detect network communities associated with flowering‐time candidate genes derived from genome‐wide association studies. The demonstrated gene prioritization and subnetwork analysis can be conducted by simply submitting maize gene models based on the commonly used B73 RefGen_v3 and the latest B73 RefGen_v4 reference genomes on the MaizeNet web server. MaizeNet‐based network‐assisted systems genetics will substantially accelerate the discovery of trait‐associated genes for crop improvement.

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“…Recognizing the need for a more comprehensive plant interactome, several computational methods have been used to infer genome‐wide PPI networks using a variety of strategies (Cui et al ., ; Lin et al ., ; Wang et al ., , ; Szklarczyk et al ., ; Zhu et al ., ). For instance, Gu et al .…”

Section: Introductionmentioning

confidence: 97%

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)

et al. 2017

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Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice.

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PPIM: A Protein-Protein Interaction Database for Maize

Cited by 80 publications

References 41 publications

Distinct genetic architectures for phenotype means and plasticities in Zea mays

Distinct genetic architectures for phenotype means and plasticities in Zea mays

MaizeNet: a co‐functional network for network‐assisted systems genetics in Zea mays

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)

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