Dataset Information

Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses.

ABSTRACT: Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional subnetwork modules likely involved in virulence. Each subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus fsr1 mutant. We also identified putative hub genes from predicted subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR assays. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.

SUBMITTER: Kim MS

PROVIDER: S-EPMC5974142 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses.

Kim Man S MS Zhang Huan H Yan Huijuan H Yoon Byung-Jun BJ Shim Won Bo WB

Scientific reports 20180529 1

Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expres ...[more]

PMID: 29844502

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Project description:BACKGROUND:Maize, 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. RESULTS:We 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). CONCLUSION:Through 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.

Project description:Fusarium stem rot disease caused by Fusarium verticillioides has become one of the most serious issues confronting Indonesian farmers in recent years. An alternative option for suppressing this disease is to use indigenous microbes as an eco-friendly method to reduce synthetic fungicides. The objective of the research was to identify the molecular characteristics and effectiveness of an indigenous microbial consortium in controlling Fusarium stem rot disease. Identification of indigenous microbes is carried out based on molecular characters using universal primers, namely ITS-1/ITS-4 for fungi and 27F/1492R for bacteria. Nucleotide sequences were analyzed using Bioedit 7.2 version and MEGAX software. In vitro testing was carried out using the dual culture method for indigenous fungi and the disc diffusion method for indigenous bacteria. Meanwhile, in planta testing was conducted by evaluating a consortium of fungi and bacteria to control F. verticillioides in the field using a randomized block design with three replications, followed by a 5% DMRT test. The use of universal primer pairs ITS-1/ITS-4 and 27F/1492R succeeded in amplifying DNA bands of indigenous microbial isolates measuring ±600 bp and ±1465 bp, respectively. S6 and S9 bacterial isolates were identified as Bacillus cereus. JRP 7 and SEDF 6A isolates were identified as Trichoderma asperellum and JRP 10 isolate was identified as Penicillium raperi. All identification resulted in homology of >99%. The in vitro inhibitory reactions of isolates JRP 7, JRP 10, SEDF 6A, S6, and S9 against F. verticillioides were >60%. Disease severity of B6B9C10, B6B9C6, B6B9C7, B9C6C7, B9C7C10, and C6C7C10 treatments significantly proved their ability to control F. verticillioides in the field with a lower percentage of disease severity than positive controls, which are 23.33%, 18.89%, 23.33%., 21.85%, 14.07%, and 15.93%, respectively. The B9C7C10 consortium (S9 + JRP 7 + JRP 10 isolates) containing three species of indigenous microbes, i.e. B. cereus, T. asperellum, and P. raperi is the most effective at controlling F. verticillioides and may be developed for use as biopesticide products.

Dataset Information

Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses.

Publications

Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses.

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