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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Research Project #435969

Research Project: Predictive Modeling of Mature Oat Seed Composition Using Metabolomics and Transcriptomics

Location: Plant, Soil and Nutrition Research

Project Number: 8062-21000-045-004-A
Project Type: Cooperative Agreement

Start Date: Sep 20, 2019
End Date: Sep 19, 2022

Objective:
Obtain and analyze seed composition using metabolomics and gene expression using RNA sequencing on a core collection of oat lines that have been characterized for nutritional quality of mature grain. Gene expression and seed composition at three time points of development will be used to model mature seed nutritional quality. These data will also enable prediction of nutritional quality for newly developed oat lines.

Approach:
Funds available are sufficient to implement RNA sequencing and metabolite profiling on 96 oat lines at three points in the development of the oat seed. These lines will be grown in the field, planted in spring 2020. Careful monitoring of flowering will take place to sample oat florets at 13, 20, and 27 days after anthesis for RNA-Seq, and at maturity for metabolomics. Florets will be sampled and frozen in liquid nitrogen. RNA will be extracted from developing oat grains excised from the florets, and sequenced at the Cornell Genomics facility in libraries with 48 samples per lane of sequence to obtain on average 5M reads per sample. Gas and liquid chromatography mass spectroscopy will be performed at the Colorado State University Metabolomics and Proteomics facility. Previous research has shown that expression levels follow a limited number of temporal patterns. These patterns will be captured and leveraged through the three RNA-Seq timepoints to increase the power and accuracy of modeling the mature seed metabolome. In an effort to validate findings from this research a mutagenized oat population has been created using the “Targeted induced local lesions in genomes” (TILLING) approach. The objective of this work will be to obtain and analyze gene expression data using RNA sequencing on this TILLING population. Oat lines in this population that have altered gene expression at loci identified to be important will then be subjected to metabolomics assays that are more expensive. These data will validate the importance of specific loci and their expression. Validated loci, in turn will be used as predictors in oat breeding populations, and will be sequenced to identify diversity in those populations for relevant allelic diversity. We will run RNA-Seq at 20 days after anthesis on a large panel of mutagenized oat. Information from the prior modeling will identify transcripts most relevant to components of oat seed. Expression of mutated oat for those transcripts will show from which lines to obtain detailed seed composition and sequence.