Poster

  • P-II-0599

Proteomics allows differentiation of growing location of faba bean varieties

Beitrag in

One Health Approaches (Plant, Food, Nutrition, Animal, Environment)

Posterthemen

Mitwirkende

Leigh Donnellan (Adelaide / AU), Clifford Young (Adelaide / AU), Peter Hoffmann (Adelaide / AU), Arineh Thamasian (Perth / AU)

Abstract

It is estimated that global food systems will need to double production by 2050 to meet the requirements of a growing population. To meet this demand and mitigate the environmental impacts of meat production, alternative protein sources such as pulse crops are being investigated for their sustainability. Faba bean (Vicia faba) is an important pulse crop to Australian farmers, with approximately 678,000 tonnes produced in 2020-2021. In addition to being a cash crop, it plays important roles in nitrogen fixation, disease break and weed control within crop rotations. Nutritionally, faba bean is desirable because of its high-quality plant protein and dietary fibre. It is therefore one of the few crops that could satisfy the criteria for sustainable alternative protein sources, while advancing the current market-trade for Australia.

Most research on the faba bean has focussed on the removal of vicine and convicine, which are antinutritional compounds that cause favism (haemolytic anaemia) in glucose-6-phosphate dehydrogenase deficient individuals. Conversely, only a few studies have investigated attributes such as crop quality, yield, resistance to environmental stressors or protein content. Furthermore, proteomic investigation to potentially address these questions have been extensively hampered by the low number of annotated proteins in the corresponding UniProt database (125 proteins).

Herein, we describe the first large scale investigation of the faba bean proteome from 20 varieties grown at two different locations in Australia. Utilising theoretical protein sequences generated from a recently sequenced faba bean genomes, we produced the first extensively annotated faba bean protein database. From this, we identified ~2800 proteins and ~27,000 precursors using a 35-minute gradient at 5 µl/min on a ZenoTOF 7600 acquired with 64 variable window SWATH. Proteomics data allowed discrimination of the growing location, suggesting proteome adaptions to site-specific environmental influences. These results may allow for the identification of protein markers for suitability of different varieties grown at specific sites. This study is the first step in generating a comprehensive proteomic resource to aid both industry and researchers in identifying proteomic markers which can help guide the suitability of different varieties across growing locations

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