Aflatoxins are toxic and potent carcinogenic metabolites produced by Aspergillus flavus and A. parasiticus. Aflatoxins can contaminate cottonseed under conducive environmental conditions. Much success has been achieved by the application of atoxigenic strains of A. flavus for controlling aflatoxin contamination in cotton, peanut and maize. Development of aflatoxin-resistant cultivars overexpressing resistance-associated genes and/or knocking down aflatoxin biosynthesis of A. flavus could be an effective strategy for controlling aflatoxin contamination in cotton. In this study, differentially expressed genes (DEGs) were identified in response to infection with both toxigenic and atoxigenic strains of A. flavus pericarp and seed of cotton through genome-wide transcriptome profiling. The genes involved in antifungal response, oxidative burst, transcription factors, defense signaling pathways and stress response were highly differentially expressed in pericarp and seed tissues in response to A. flavus infection. The cell-wall modifying genes and genes involved in the production of antimicrobial substances were more active in pericarp than seed. Genes involved in defense response in cotton were highly induced in pericarp. The DEGs will serve as the source for identifying biomarkers for breeding, potential candidate genes for transgenic manipulation, and will help in understanding complex plant-fungal interaction for future downstream research.
The increasing volume of sequence data generated by the rapidly decreasing cost of RNA sequencing (RNA-Seq) necessitates the development of software pipeline(s) that can analyze the massive amounts of RNA-Seq data in an efficient manner. Through the present study, a comprehensive and flexible Standalone RNA-Seq Analysis Pipeline (SRAP) implemented with the parallel programming approach was developed, which can analyze transcriptome for any genome. SRAP consists of high-level modules, including sequence reads filtering, mapping to reference genome (or transcriptome), sequence assembly, gene expression analysis and variant discovery along with low-level modules for other common NGS utilities. The high-level modules, unlike low-level modules, require intense computation in terms of memory and processor. SRAP is developed with in-house developed scripts (Python), parallel computing and open source bioinformatics tools. It can be executed as a batch and/or individual mode for single or multiple sample files. SRAP generates RNA-Seq data analysis output files with statistical summary and graphic visualization.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-07112016-114436 |
| Date | 27 July 2016 |
| Creators | Bedre, Renesh |
| Contributors | Chen, Zhiyuan, Brandt, Steven, Damann, Kenneth, Myers, Gerald, Harrison, Stephen, Baisakh, Niranjan |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-07112016-114436/ |
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