ArticleBMC bioinformatics2015
Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 13 citations in OpenAlex.
- Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses.Scientific reports · 2018Article
- Comparative Histological and Transcriptional Analysis of Maize Kernels Infected withFrontiers in plant science · 2017Article
- Proceedings of the 2015 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.BMC bioinformatics · 2015Article
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5 authors at 3 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMaize, a crop of global significance, is vulnerable to a variety of biotic stresses resulting in economic losses. Fusarium verticillioides (teleomorph Gibberella moniliformis) is one of the key fungal pathogens of maize, causing ear rots and stalk rots. To better understand the genetic mechanisms involved in maize defense as well as F. verticillioides virulence, a systematic investigation of the host-pathogen interaction is needed. The aim of this study was to computationally identify potential maize subnetwork modules associated with its defense response against F. verticillioides.
resultsWe obtained time-course RNA-seq data from B73 maize inoculated with wild type F. verticillioides and a loss-of-virulence mutant, and subsequently established a computational pipeline for network-based comparative analysis. Specifically, we first analyzed the RNA-seq data by a cointegration-correlation-expression approach, where maize genes were jointly analyzed with known F. verticillioides virulence genes to find candidate maize genes likely associated with the defense mechanism. We predicted maize co-expression networks around the selected maize candidate genes based on partial correlation, and subsequently searched for subnetwork modules that were differentially activated when inoculated with two different fungal strains. Based on our analysis pipeline, we identified four potential maize defense subnetwork modules. Two were directly associated with maize defense response and were associated with significant GO terms such as GO:0009817 (defense response to fungus) and GO:0009620 (response to fungus). The other two predicted modules were indirectly involved in the defense response, where the most significant GO terms associated with these modules were GO:0046914 (transition metal ion binding) and GO:0046686 (response to cadmium ion).
conclusionThrough our RNA-seq data analysis, we have shown that a network-based approach can enhance our understanding of the complicated host-pathogen interactions between maize and F. verticillioides by interpreting the transcriptome data in a system-oriented manner. We expect that the proposed analytic pipeline can also be adapted for investigating potential functional modules associated with host defense response in diverse plant-pathogen interactions.
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