Location: Cereal Crops Research
2020 Annual Report
Objectives
Objective 1: Identify and characterize germplasm for barley malt production in suboptimal environmental conditions.
Sub-objective 1.1: Barley will be assessed for resilience to combined heat
and drought stress.
Sub-objective 1.2: Assess the impact of abiotic stress on malting quality.
Sub-objective 1.3: SNP genotyping of barley lines using Illumina chips and
Genome Wide Association Study (GWAS).
Objective 2: Identify molecular networks associated with malting, and functionally characterize known and putative genes with the potential to improve malt quality.
Sub-objective 2.1a: Determine the transcriptome and the miRNAs involved in
regulating the transcriptome in malting barley.
Sub-objective 2.2: Analyze proteome changes during various stages of barley
malting.
Sub-objective 2.3: Integrate transcriptional, post-transcriptional, and
proteomic changes during various stages of malting.
Sub-objective 2.4: Functionally characterize the putative malting quality
genes Bmy2 and DPE1.
Sub-Objective 2.5: Characterize the molecular mechanisms of barley lys3a and
determine how its function regulates malting quality genes.
Objective 3: Determine biochemical or physiological roles of metabolites in barley and oat.
Sub-objective 3.1: Identify abiotic stress-induced seed solutes in malting
barley.
Sub-objective 3.2: Determine if stress-induced seed solutes function as
osmoprotectant molecules to hydrolytic enzymes during mashing.
Approach
Objective 1.
Accessions from the barley mini-core collection, the Vavilov collection, and selected pre-prohibition and modern elite malting barley cultivars will be grown under optimal and abiotic stress conditions. Evaluation of selected tolerant lines will be for a variety of physical traits including biomass and seed yield, physiological traits such as photosynthesis, transpiration, respiration, stomatal conductance and a variety of malting quality traits including standard metrics of quality plus mashing performance. SNP genotyping of the mini-core collection will aid in GWAS for identification of malting quality and abiotic stress associated QTLs.
Objective 2.
Changes in the transcriptome, miRNAome and the proteome during malting of selected lines will be evaluated. Omics data from these multiple high throughput platforms will be integrated to develop a systems model of the genetic and biochemical pathways involved in the barley malting process. Genetic confirmation of key genes and proteins associated with malting quality and/or abiotic stress tolerance will be conducted via transformation, CRISPER/Cas or via TILLING populations. Barley lys3.a mutants will be evaluated during grain development to determine the mechanism of action on malting quality genes and to identify the causal gene. Select malting quality genes will be evaluated in modern elite malting cultivars during malting.
Objective 3.
Stress induced metabolites present in malts and rendered soluble during mashing will be chromatographically separated, then detected and identified by mass spectrometry. Resource spectral databases used for identification will include NIST, Flavor and Fragrances and our in-house authentic compound database. Metabolites identified that are commercially available will be used in relevant concentrations to determine if they affect the activity and thermostability of key enzymes involved in the production of fermentable sugars during high temperature mashing.
Progress Report
Studies to address Objective 1 included subjecting the barley mini-core collection to combined heat and drought stress during the heading stage. Seed yield, root and shoot weight from stressed plants and corresponding controls were collected for all the 165 lines from replicate 1. A second replication of this population has been subjected to the stress treatment and will be harvested later summer 2020. This research will enable the identification of germplasm for barley malt production in suboptimal environmental conditions. The 165 lines in the mini-core population has been genotyped to detect single nucleotide polymorphisms (SNPs), which are small genetic differences in genes, at the ARS location in Fargo, North Dakota. The seeds of mini-core panel and a wild barley population were phenotyped for eight different chemical types of tocols, commonly called as vitamin E. Genetic analysis led to the identification of several significant SNPs tightly associated with the genes crucial for tocol biosynthesis. A recombinant inbred line population (approximately 200 lines) derived from a cross between stress tolerant Otis and sensitive Golden Promise has been analyzed for their drought responses (root and shoot biomass, and seed yield) under greenhouse conditions. Studies to address Objective 2 (2.2) included the analysis of the synthesized proteins (proteome) during five different malting stages that identified 37 proteins annotated as ‘RNA binding proteins’. This prompted an in-silico genome-wide survey of ‘RNA binding’ proteins in barley. The barley rootlets, byproducts of the malting industry, were subjected to proteomic analysis. The identified proteins suggest that rootlets may be a good resource for amino acids as well as phenylpropanoids and other antioxidants.
Addressing Sub-Objective 2.1a, small RNA genetic libraries from barley undergoing micromalting were sequenced to identify small RNAs involved in regulating malting. Small RNAs are non-coding genetic elements associated with gene regulation. Sequencing data are being analyzed with a public university collaborator to identify small RNAs present during micromalting. Addressing Sub-Objective 2.1b, coding RNA genetic libraries from barley undergoing micromalting were sequenced to identify differentially expressed genes, identify enriched functional categories, conduct network analysis, and identify new genes with the potential to influence malting. Sequencing analysis revealed that malting is a primarily upregulated metabolic process with the vast majority of differential expressed genes found to be upregulated with three quarters of the upregulated genes considered to be turned on. Network and functional analysis of the differentially expressed genes identified four stages that occur during micromalting. These stages have the potential to be exploited to identify the essential suite of gene expression needed to achieve the desired malting quality phenotype. A detailed analysis of the barley genome identified 40 starch degrading genes from the four primary starch degrading gene families with four newly identified starch degrading genes. Sixty percent of the genes were found to be differentially expressed during micromalting. This research expands upon the first demonstration of a novel beta-amylase expressed during micromalting and identifies three more beta-amylases that are upregulated during malting. Additionally, genes for the other three main starch degrading enzymes were tracked and a global view of starch degradation based on gene expression was determined. The understanding of the complex gene expression networks involved in malting is paramount to the success of breeding new malting cultivars in a rapidly changing climate, as well as creating new cultivars that best suit their desired end-use whether that is adjunct brewing, all-malt brewing or distilling. Addressing Sub-Objective 2.4, four malting cultivars had significantly higher Bmy2 gene expression than the other eight malting cultivars studied. Variation in Bmy2 expression during malting could influence malting quality parameters such as diastatic power if the Bmy2 enzyme is present in sufficient quantities. Additionally, Bmy2 expression could result in greater enzyme activity during later mashing times when temperatures are elevated. Bmy2 upregulation during micromalting gives breeders a new target to improve malting quality parameters or to improve upon the ratio of Bmy1 and Bmy2 so that more thermostable Bmy2 is present during mashing, which is a hot water incubation step that commonly inactivates starch degrading enzymes.
To address Objective 3, malts from U.S. germplasm collections submitted to ARS researchers in Madison, Wisconsin, for malt quality analyses were used to generate worts during the course of routine quality analyses. Samples of approximately 500 individual worts were collected, analyzed for solute concentration and for the quantities of a limited suite of metabolites were determined. This metabolites included one heat stress induced metabolite in addition to the precursor and breakdown product of this metabolite. Significant variation was identified both within and between four breeding populations. Data revealed that although significant variation was present within and between these populations, each population was meeting its targeted malt extract values for some lines with a different combination of metabolites. Populations studied were from barley growing regions in the western, mid-western and eastern United States, resulting in a wide range of environments being evaluated. Dry land plots (drought stressed) had a narrower range of stress induced metabolite than did mid-western grown plots. Weather data collected contemporaneously are being correlated with metabolite data.
Accomplishments
Review Publications
Mahalingam, R., Walling, J.G. 2019. Genomic survey of RNA recognition motif (RRM) containing RNA binding proteins from barley (Hordeum vulgare ssp. vulgare). Genomics. 112(2):1829-1839. https://doi.org/10.1016/j.ygeno.2019.10.016.
Mahalingam, R. 2019. Analysis of the barley malt rootlet proteome. International Journal of Molecular Sciences. 21(1):179. http://dx.doi.org/10.3390/ijms21010179.
Henson, C.A., Vinje, M.A., Duke, S.H. 2020. Maltose effects on barley malt beta-amylase activity and thermostability at low isothermal mashing temperatures. Journal of the American Society of Brewing Chemists. https://doi.org/10.1080/03610470.2020.1738811.
Vinje, M.A., Duke, S.H., Henson, C.A. 2020. De novo expression of beta-amylase2 (Bmy2) in barley grains during micromalting. Journal of the American Society of Brewing Chemists. 78(2):126-135. https://doi.org/10.1080/03610470.2019.1705104.
