Mining the Wheat Grain Proteome

Abstract

Bread wheat is the most widely cultivated crop worldwide, used in the production of food products and a feed source for animals. Selection tools that can be applied early in the breeding cycle are needed to accelerate genetic gain for increased wheat production while maintaining or improving grain quality if demand from human population growth is to be fulfilled. Proteomics screening assays of wheat flour can assist breeders to select the best performing breeding lines and discard the worst lines. In this study, we optimised a robust LC–MS shotgun quantitative proteomics method to screen thousands of wheat genotypes. Using 6 cultivars and 4 replicates, we tested 3 resuspension ratios (50, 25, and 17 µL/mg), 2 extraction buffers (with urea or guanidine-hydrochloride), 3 sets of proteases (chymotrypsin, Glu-C, and trypsin/Lys-C), and multiple LC settings. Protein identifications by LC–MS/MS were used to select the best parameters. A total 8738 wheat proteins were identified. The best method was validated on an independent set of 96 cultivars and peptides quantities were normalised using sample weights, an internal standard, and quality controls. Data mining tools found particularly useful to explore the flour proteome are presented (UniProt Retrieve/ID mapping tool, KEGG, AgriGO, REVIGO, and Pathway Tools).

Finding the LMA needle in the wheat proteome haystack

“…Even though on one hand genomics can catalogue genes present in a sample and possibly the biological context of their expression and on the other hand transcriptomics can validate expression levels, only proteomics can measure the actual protein abundance, record post-translational modification (PTM), as well as identify interacting proteins (2). We have developed a high-throughput proteomics method to rapidly profile T. aestivum grains and data mine their proteome (41). In the present study, we have applied our optimised procedure to a collection of in excess of 4,000 wheat cultivars and germplasm whose LMA content ranged from 0 to 8 u/g of flour.…”

“…Dirty jars and balls were rinsed to remove excess flour and soaked in 1% decon 90 surfactant (Decon, Hove, UK) for 2 hours followed by a thorough wash in a dishwasher with RO water and left to air dry prior to being used again. A wheat quality control (QC) sample was prepared by sampling 50 mg (±0.05 mg) from each of the 96 flour samples described in (41) and mixing them all thoroughly.…”

“…Protein digests were cleaned using 96-wells solid phase extraction (SPE) plates (Strata C18-E 100 mg P/N 8E-S001-EGB, Phenomenex, Lane Cove, NSW, Australia) and fully evaporated as described in (Vincent, Bui et al 2022). Peptide digests were reconstituted by adding 70 µL of 0.1% FA/water to each well.…”

“…The wet experiment bottlenecks were resolved where possible as explained in (41). Most time was spent grinding, transferring, weighing and extracting the samples as there was no option to greatly up-scale those steps (Figure 2).…”

“…The Genedata Refiner workflow described in (41) This step had to be performed on all 4,076 reproducible data files simultaneously and therefore could only be attempted when the LC-MS1 run was finalised. The experiment metadata captured in Excel was associated to the quantitative data and exported to Genedata Analyst for data normalisation purpose.…”