Amber L. Hauvermale

Background and Objectives: Expression of alpha‐amylase proteins during germination or other physiological events may lower grain soundness and have a negative impact on end‐use quality. The objective of the current study was to validate two new monoclonal peptide antibodies for wheat alpha‐amylases and describe alpha‐amylase expression in response to gibberellin and during germination. Findings: The antibodies detected heterologously expressed alpha‐amylase protein targets and native alpha‐amylases from wheat aleurones and from germinating seeds. Conclusions: The validated antibodies detected wheat alpha‐amylases expressed in the presence of gibberellin and during germination. Targeted proteomics demonstrated that alpha‐amylase expression profiles were different in the presence of gibberellin or during germination at the subgenome level. Significance and Novelty: The antibodies recognized both native and heterologously expressed alpha‐amylase proteins. Anti‐TaAMY1‐gen, developed as a generalist antibody, detected all four alpha‐amylase protein isoforms (TaAMY1‐4). Anti‐TaAMY2 was specific for TaAMY2. Furthermore, targeted proteomics allowed for identification of homeoform identity and revealed the proportion of alpha‐amylases belonging to each wheat subgenome in the presence of gibberellin and during germination. In addition, this is the first report of a simple water extraction method of alpha‐amylase proteins from crude wheatmeal that is compatible with immunoblotting.

Introduction: The falling number (FN) method is the industry standard for evaluating wheat soundness by estimating the impacts to end-use quality from starch damage, typically caused by the enzyme α-amylase. Wheat that fails to meet standard industry thresholds for FN may receive price discounts, costing the wheat industry millions of dollars annually. Although FN has been the industry standard test for decades, it has several limitations such as slow runtime, expensive equipment, and low throughput, which limit consistent implementation at certain levels of the grain chain including initial grain receival stations, variety testing programs, and individual farms. Methods: The current study validated a novel rapid immunoassay, called the TotalTarget for Sprout Damage, that estimates FN (immuno-FN) in milled wheat samples based on α-amylase activity. In total, 1,288 soft white wheat samples representing a wide range of FN, protein content, and levels of the “grain chain” (plot, elevator, and composite) were analyzed. Each sample was evaluated for FN, immuno-FN, and protein content. Agreement between FN and immuno-FN was assessed according to parameters established by the immunoassay manufacturer. Results: Overall agreement between immuno-FN and FN exceeded 90% in samples with market-class acceptable protein, with approximately 80% agreement across samples regardless of protein content. The likelihood of agreement between FN and immuno-FN increased as the protein content approached targeted levels, and was highest within the acceptable protein range for soft white wheat. Agreement between immuno-FN and FN increased at successive levels of the grain chain. Discussion: Cases of disagreement between immuno-FN and FN highlight limitations in relying on FN alone, especially in samples with low protein and no α-amylase or high protein and elevated α-amylase. The high throughput of immuno-FN presents multiple possible use-cases for incorporating immuno-FN testing within variety development and in the grain chain. Increased screening efficiency may allow for earlier identification of elevated α-amylase within the grain chain and may facilitate more targeted blending.

Background and Objectives: Non‐alpha‐amylase induced low falling number was identified in 2024 soft white wheat. The objectives of this work were to characterize these low falling number samples and determine the impact on end‐product quality. The results were analyzed to determine differences in flour characteristics that might result in a low falling number. Findings: Analysis suggests that physiochemical changes in starch properties and low protein content may be contributing to the 2024 low falling numbers. Low falling number Japanese sponge cakes were comparable to the control cakes and produced an acceptable product. Conclusions: The falling number method did not correctly determine grain end‐product quality for the 2024 samples. Starch pasting properties identified by the Rapid Visco Analyzer provided significant diagnostic information and will be important for future work. Significance and Novelty: To our knowledge, this is the first characterization of grain with low falling number in the absence of elevated alpha‐amylase activity and the impact on end‐product quality.

The introduction of quinoa into new growing regions and environments is of interest to farmers, consumers, and stakeholders around the world. Many plant breeding programs have already started to adapt quinoa to the environmental and agronomic conditions of their local fields. Formal quinoa breeding efforts in Washington State started in 2010, led by Professor Kevin Murphy out of Washington State University. Preharvest sprouting appeared as the primary obstacle to increased production in the coastal regions of the Pacific Northwest. Preharvest sprouting (PHS) is the undesirable sprouting of seeds that occurs before harvest, is triggered by rain or humid conditions, and is responsible for yield losses and lower nutrition in cereal grains. PHS has been extensively studied in wheat, barley, and rice, but there are limited reports for quinoa, partly because it has only recently emerged as a problem. This study aimed to better understand PHS in quinoa by adapting a PHS screening method commonly used in cereals. This involved carrying out panicle-wetting tests and developing a scoring scale specific for panicles to quantify sprouting. Assessment of the trait was performed in a diversity panel (N = 336), and the resulting phenotypes were used to create PHS tolerance rankings and undertake a GWAS analysis (n = 279). Our findings indicate that PHS occurred at varying degrees across a subset of the quinoa germplasm tested and that it is possible to access PHS tolerance from natural sources. Ultimately, these genotypes can be used as parental lines in future breeding programs aiming to incorporate tolerance to PHS.

Accurate, rapid testing platforms are essential for early detection and mitigation of late maturity α-amylase (LMA) and preharvest sprouting (PHS) in wheat. These conditions are characterized by elevated α-amylase levels and negatively impact flour quality, resulting in substantial economic losses. The Hagberg–Perten Falling Number (FN) method is the industry standard for measuring α-amylase activity in wheatmeal. However, FN does not directly detect α-amylase and has major limitations. Developing α-amylase immunoassays would potentially enable early, accurate detection regardless of testing environment. With this goal, we assessed an expression of α-amylase isoforms during seed development. Transcripts of three of the four isoforms were detected in developing and mature grain. These were cloned and used to develop E. coli expression lines expressing single isoforms. After assessing amino acid conservation between isoforms, we identified peptide sequences specific to a single isoform (TaAMY1) or that were conserved in all isoforms, to develop monoclonal antibodies with targeted specificities. Three monoclonal antibodies were developed, anti-TaAMY1-A, anti-TaAMY1-B, and anti-TaAMY1-C. All three detected endogenous α-amylase(s). Anti-TaAMY1-A was specific for TaAMY1, whereas anti-TaAMY1-C detected TaAMY1, 2, and 4. Thus, confirming that they possessed the intended specificities. All three antibodies were shown to be compatible for use with immuno-pulldown and immuno-assay applications.

Introduction: Quinoa is a high-value, nutritious crop that performs well in variable environments, marginal soils, and in diverse crop rotations. Quinoa’s many attributes make it an ideal crop for supporting human health in global communities and economies. To date, quinoa research has largely focused on traits in adult plants important for enhancing plant phenotypic plasticity, abiotic stress, disease resistance, and yield. Fewer studies have evaluated quinoa seed dormancy and suggest that most modern quinoa varieties have weak or no seed dormancy, and a narrow window of seed viability post-harvest. In other crops, diminished seed dormancy is a major risk factor for preharvest sprouting (PHS; germination on the panicle due to rain prior to harvest) and may also pose a similar risk for quinoa.Methods: This study (1) developed a dormancy screening assay to characterize seed dormancy strength in a large collection of quinoa varieties, (2) investigated if morphological variables including seed coat color, seed coat thickness, seed shape including eccentricity which evaluates the roundness or flatness of a seed, and other agronomic traits like crude protein content and seed moisture, contribute to quinoa seed dormancy, and (3) evaluated the use of a phenetic modeling approach to explore relationships between seed morphology and seed dormancy.Results: Dormancy screening indicated seed dormancy ranges in quinoa varieties from none to strong dormancy. Further, phenetic modeling approaches indicate that seed coat thickness and eccentricity are important morphological variables that impact quinoa seed dormancy strength.Conclusions: While dormancy screening and phenetic modeling approaches do not provide a direct solution to preventing PHS in quinoa, they do provide new tools for identifying dormant varieties as well as morphological variables contributing to seed dormancy.

Late maturity alpha‐amylase (LMA) and preharvest sprouting (PHS) lead to elevated alpha‐amylase in wheat (Triticum aestivum L.) grain. Risk of poor end‐product quality due to elevated alpha‐amylase is detected in the wheat industry using the Hagberg–Perten falling number (FN) method. In breeding programs, selection for PHS and LMA tolerance requires higher throughput methods requiring a smaller sample size than the 7 g required for the FN method. Specifically, LMA can only be screened only using detection of alpha‐amylase activity or protein after cold treatment of individual wheat spikes at a specific stage of grain development resulting in very small samples (≤1 g). This study developed and evaluated a high throughput 96‐well method for the Phadebas alpha‐amylase enzyme assay for small wheat grain samples and compared this method to FN and the Megazyme Alpha‐Amylase SD (Sprout Damage) Assay Kit performed on the automated Awareness Technology ChemWell‐T Analyzer. In parallel, the efficacy of low‐cost small‐scale milling methods was evaluated relative to traditional larger scale mills. The Phadebas enzyme activity was highly reproducible and showed a strong correlation to the SD enzyme assay and FN method regardless of which mill was used to process the grain. The SD assay offers simpler standardization and calculation of enzyme activity, whereas the Phadebas assay offers higher sensitivity and lower expense. Both the 96‐well Phadebas and automated Megazyme SD assays are suitable for alpha‐amylase detection from small samples, and the use of low‐cost coffee grinders to process small samples did not significantly impact assay performance.

The plant Ubiquitin Regulatory X (UBX) domain-containing protein 1 (PUX1) functions as a negative regulator of gibberellin (GA) signaling. GAs are plant hormones that stimulate seed germination, the transition to flowering, and cell elongation and division. Loss of Arabidopsis (Arabidopsis thaliana) PUX1 resulted in a “GA-overdose” phenotype including early flowering, increased stem and root elongation, and partial resistance to the GA-biosynthesis inhibitor paclobutrazol during seed germination and root elongation. Furthermore, GA application failed to stimulate further stem elongation or flowering onset suggesting that elongation and flowering response to GA had reached its maximum. GA hormone partially repressed PUX1 protein accumulation, and PUX1 showed a GA-independent interaction with the GA receptor GA-INSENSITIVE DWARF-1 (GID1). This suggests that PUX1 is GA regulated and/or regulates elements of the GA signaling pathway. Consistent with PUX1 function as a negative regulator of GA signaling, the pux1 mutant caused increased GID1 expression and decreased accumulation of the DELLA REPRESSOR OF GA1-3, RGA. PUX1 is a negative regulator of the hexameric AAA+ ATPase CDC48, a protein that functions in diverse cellular processes including unfolding proteins in preparation for proteasomal degradation, cell division, and expansion. PUX1 binding to GID1 required the UBX domain, a binding motif necessary for CDC48 interaction. Moreover, PUX1 overexpression in cell culture not only stimulated the disassembly of CDC48 hexamer but also resulted in co-fractionation of GID1, PUX1, and CDC48 subunits in velocity sedimentation assays. Based on our results, we propose that PUX1 and CDC48 are additional factors that need to be incorporated into our understanding of GA signaling.

This review examines the application, limitations, and potential alternatives to the Hagberg–Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end‐product quality mainly due to starch degradation by the enzyme α‐amylase. By viscometry, the FN test indirectly detects the presence of α‐amylase, the primary enzyme that digests starch. Elevated α‐amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α‐amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α‐amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract‐specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α‐amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high‐ and low‐FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near‐infrared spectroscopy, and hyperspectral imaging.

Quinoa (Chenopodium quinoa Willd.) is a culturally significant staple food source that has been grown for thousands of years in South America. Due to its natural drought and salinity tolerance, quinoa has emerged as an agronomically important crop for production in marginal soils, in highly variable climates, and as part of diverse crop rotations. Primary areas of quinoa research have focused on improving resistance to abiotic stresses and disease, improving yields, and increasing nutrition. However, an evolving issue impacting quinoa seed end-use quality is preharvest sprouting (PHS), which is when seeds with little to no dormancy experience a rain event prior to harvest and sprout on the panicle. Far less is understood about the mechanisms that regulate quinoa seed dormancy and seed viability. This review will cover topics including seed dormancy, orthodox and unorthodox dormancy programs, desiccation sensitivity, environmental and hormonal mechanisms that regulate seed dormancy, and breeding and non-breeding strategies for enhancing resistance to PHS in quinoa.