Stress response functional annotation using RNA expression in maize

Authors: HAYFORD, RITA - Orise Fellow; Woodhouse, Margaret; Portwood, John; SEN, SHATABDI - Iowa State University; GARDINER, JACK - University Of Missouri; Cannon, Ethalinda; Andorf, Carson

Submitted to: Maize Annual Meetings
Publication Type: Abstract Only
Publication Acceptance Date: 2/10/2023
Publication Date: 3/16/2023
Citation: Hayford, R., Woodhouse, M.H., Portwood II, J.L., Sen, S., Gardiner, J., Cannon, E.K., Andorf, C.M. 2023. Stress response functional annotation using RNA expression in maize. Maize Annual Meetings. 68.

Interpretive Summary: N/A

Technical Abstract: Maize (Zea mays ssp. mays) is a major crop widely grown throughout the world. In addition to food, maize is used as fuel and as feed for animals. In spite of the diverse use of maize, this important crop is exposed to several environmental cues reducing yield and quality. Transcriptome profiling studies have been used to provide insights into the molecular mechanisms underlying stress response. Although MaizeGDB browsers and tools have been used to assist with the functional annotation of genes, there still remain unknown functions of many genes. To enhance the functional annotation of maize-specific genes, MaizeGDB has outlined a data-driven approach with emphasis on identifying genes and traits related to biotic and abiotic stress. Many RNA-Seq reads have been mapped to older versions of the reference genomes which could lead to omission of gene model annotations. Hence, our goal is to map high quality RNA-Seq expression reads to the recent version of the reference genome B73 (B73v5) and to deduce stress-related functional annotation of gene models. Publicly available RNA-Seq datasets related to biotic and abiotic stress generated from seeds of B73 cultivar were used in this analysis. We use heat stress as a case study to illustrate the use of the pipeline and its implications. In the future, we will conduct meta-analysis of all mapped datasets to identify common differentially expressed genes and pathways from both biotic and abiotic data. Our analysis will facilitate identifying multiple stress response gene models and annotation in maize.