Clear effects on root system architecture of winter wheat cultivars (Triticum aestivum L.) from cultivation environment and practices.

Roots play a pivotal role in the adaption of a plant to its environment, with different root traits adapting the plant to different stresses. The environment affects the Root System Architecture (RSA), but the genetic factors determine to what extent, and whether stress brought about by extreme environmental conditions is detrimental to a specific crop. This study aimed to identify differences in winter wheat RSA caused by cultivation region and practice, in the form of preceding crop (precrop), and to identify if modern cultivars used in Sweden differ in their reaction to these environments. This was undertaken using high-throughput phenotyping to assess the RSA. Clear differences in the RSA were observed between the Swedish cultivation regions, precrop treatments, and interaction of these conditions with each other and the genetics. Julius showed a large difference between cultivars, with 9.3-17.1% fewer and 12-20% narrower seminal roots. Standardized yield decreased when grown after wheat, 23% less compared to oilseed rape (OSR), and when grown in the Southern region, 14% less than the Central region. Additionally, correlations were shown between the root number, angle, and grain yield, with different root types being correlated depending on the precrop. Cultivars on the Swedish market show differences that can be adapted to the region-precrop combinations. The differences in precrop effect on RSA between regions show global implications and a need for further assessment. Correlations between RSA and yield, based on root-type × precrop, indicate different needs of the RSA depending on the management practices and show the potential for improving crop yield through targeting genotypic and environmental conditions in a holistic manner. Understanding this RSA variance, and the mechanisms of conditional response, will allow targeted cultivar breeding for specific environments, increasing plant health and food security.

Cookability of 24 pea accessions-determining factors and potential predictors of cooking quality.

BACKGROUND: Cooking time and cooking evenness are two critical quantities when determining the cooking quality (termed cookability) of pulses. Deciphering which factors contribute to pulse cookability is important for breeding new cultivars, and the identification of potential cookability predictors can facilitate breeding efforts. Seeds from 24 morphologically diverse pea accessions were tested to identify contributing factors and potential predictors of the observed cookability using a Mattson cooker. Size- and weight-based measures were recorded, and seed-coat hardness was obtained with a penetrometer. Content of protein, starch (amylose and amylopectin), and phytate was also determined. RESULTS: Distinct differences were found between wrinkled and non-wrinkled seeds in terms of water-absorption capacity, seed-coat hardness, and plunger-perforation speed. Potential predictive indicators of cooking time and cooking evenness were seed-coat hardness (r = 0.49 and r = 0.38), relative area gained (r = -0.59 and r = -0.8), and percentage of swelled seeds after soaking (r = -0.49 and r = -0.58), but only for non-wrinkled seeds. Surprisingly, the coefficients of variation for the profile area of both dry and swelled seeds appeared to be potential cookability predictors of all pea types (correlation coefficients around r = 0.5 and supported by principal component analysis). However, no strong correlation was observed between cookability and protein, starch, or phytate levels. CONCLUSION: Using three types of instruments together with chemical components enabled the identification of novel cookability predictors for both cooking time and cooking evenness in pea. This study unveils the diverse quantitative aspects influencing cookability in pea. Considering both cooking time and cooking evenness, as well as seed-coat hardness, underscores the multifaceted nature of pulse cookability and offers important insights for future breeding strategies to enhance pea cultivars. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Exploring GWAS and genomic prediction to improve Septoria tritici blotch resistance in wheat.

Septoria tritici blotch (STB) is a destructive foliar diseases threatening wheat grain yield. Wheat breeding for STB disease resistance has been identified as the most sustainable and environment-friendly approach. In this work, a panel of 316 winter wheat breeding lines from a commercial breeding program were evaluated for STB resistance at the seedling stage under controlled conditions followed by genome-wide association study (GWAS) and genomic prediction (GP). The study revealed a significant genotypic variation for STB seedling resistance, while disease severity scores exhibited a normal frequency distribution. Moreover, we calculated a broad-sense heritability of 0.62 for the trait. Nine single- and multi-locus GWAS models identified 24 marker-trait associations grouped into 20 quantitative trait loci (QTLs) for STB seedling-stage resistance. The seven QTLs located on chromosomes 1B, 2A, 2B, 5B (two), 7A, and 7D are reported for the first time and could potentially be novel. The GP cross-validation analysis in the RR-BLUP model estimated the genomic-estimated breeding values (GEBVs) of STB resistance with a prediction accuracy of 0.49. Meanwhile, the GWAS assisted wRR-BLUP model improved the accuracy to 0.58. The identified QTLs can be used for marker-assisted backcrossing against STB in winter wheat. Moreover, the higher prediction accuracy recorded from the GWAS-assisted GP analysis implies its power to successfully select superior candidate lines based on their GEBVs for STB resistance.

Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging.

Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost-benefit seed image analysis methods, the free software "SmartGrain" and the fully automated commercially available instrument "Cgrain Value™" by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R 2 = 0.52 compared with SmartGrain for which R 2 = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R 2 = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains.

Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning.

BACKGROUND: High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial. RESULTS: The results show that 40-42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of rs = 0.50-0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI). CONCLUSIONS: The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust.

Characterizing Winter Wheat Germplasm for Fusarium Head Blight Resistance Under Accelerated Growth Conditions.

Fusarium head blight (FHB) is one of the economically important diseases of wheat as it causes severe yield loss and reduces grain quality. In winter wheat, due to its vernalization requirement, it takes an exceptionally long time for plants to reach the heading stage, thereby prolonging the time it takes for characterizing germplasm for FHB resistance. Therefore, in this work, we developed a protocol to evaluate winter wheat germplasm for FHB resistance under accelerated growth conditions. The protocol reduces the time required for plants to begin heading while avoiding any visible symptoms of stress on plants. The protocol was tested on 432 genotypes obtained from a breeding program and a genebank. The mean area under disease progress curve for FHB was 225.13 in the breeding set and 195.53 in the genebank set, indicating that the germplasm from the genebank set had higher resistance to FHB. In total, 10 quantitative trait loci (QTL) for FHB severity were identified by association mapping. Of these, nine QTL were identified in the combined set comprising both genebank and breeding sets, while two QTL each were identified in the breeding set and genebank set, respectively, when analyzed separately. Some QTLs overlapped between the three datasets. The results reveal that the protocol for FHB evaluation integrating accelerated growth conditions is an efficient approach for FHB resistance breeding in winter wheat and can be even applied to spring wheat after minor modifications.

Genome-Wide Association Analysis and Genomic Prediction for Adult-Plant Resistance to Septoria Tritici Blotch and Powdery Mildew in Winter Wheat.

Septoria tritici blotch (STB) caused by the fungal pathogen Zymoseptoria tritici and powdery mildew (PM) caused by Blumeria graminis f.sp tritici (Bgt) are among the forefront foliar diseases of wheat that lead to a significant loss of grain yield and quality. Resistance breeding aimed at developing varieties with inherent resistance to STB and PM diseases has been the most sustainable and environment-friendly approach. In this study, 175 winter wheat landraces and historical cultivars originated from the Nordic region were evaluated for adult-plant resistance (APR) to STB and PM in Denmark, Estonia, Lithuania, and Sweden. Genome-wide association study (GWAS) and genomic prediction (GP) were performed based on the adult-plant response to STB and PM in field conditions using 7,401 single-nucleotide polymorphism (SNP) markers generated by 20K SNP chip. Genotype-by-environment interaction was significant for both disease scores. GWAS detected stable and environment-specific quantitative trait locis (QTLs) on chromosomes 1A, 1B, 1D, 2B, 3B, 4A, 5A, 6A, and 6B for STB and 2A, 2D, 3A, 4B, 5A, 6B, 7A, and 7B for PM adult-plant disease resistance. GP accuracy was improved when assisted with QTL from GWAS as a fixed effect. The GWAS-assisted GP accuracy ranged within 0.53-0.75 and 0.36-0.83 for STB and PM, respectively, across the tested environments. This study highlights that landraces and historical cultivars are a valuable source of APR to STB and PM. Such germplasm could be used to identify and introgress novel resistance genes to modern breeding lines.

Rust expression browser: an open source database for simultaneous analysis of host and pathogen gene expression profiles with expVIP.

BACKGROUND: Transcriptomics is being increasingly applied to generate new insight into the interactions between plants and their pathogens. For the wheat yellow (stripe) rust pathogen (Puccinia striiformis f. sp. tritici, Pst) RNA-based sequencing (RNA-Seq) has proved particularly valuable, overcoming the barriers associated with its obligate biotrophic nature. This includes the application of RNA-Seq approaches to study Pst and wheat gene expression dynamics over time and the Pst population composition through the use of a novel RNA-Seq based surveillance approach called "field pathogenomics". As a dual RNA-Seq approach, the field pathogenomics technique also provides gene expression data from the host, giving new insight into host responses. However, this has created a wealth of data for interrogation. RESULTS: Here, we used the field pathogenomics approach to generate 538 new RNA-Seq datasets from Pst-infected field wheat samples, doubling the amount of transcriptomics data available for this important pathosystem. We then analysed these datasets alongside 66 RNA-Seq datasets from four Pst infection time-courses and 420 Pst-infected plant field and laboratory samples that were publicly available. A database of gene expression values for Pst and wheat was generated for each of these 1024 RNA-Seq datasets and incorporated into the development of the rust expression browser ( http://www.rust-expression.com ). This enables for the first time simultaneous 'point-and-click' access to gene expression profiles for Pst and its wheat host and represents the largest database of processed RNA-Seq datasets available for any of the three Puccinia wheat rust pathogens. We also demonstrated the utility of the browser through investigation of expression of putative Pst virulence genes over time and examined the host plants response to Pst infection. CONCLUSIONS: The rust expression browser offers immense value to the wider community, facilitating data sharing and transparency and the underlying database can be continually expanded as more datasets become publicly available.

Diverse Wheat-Alien Introgression Lines as a Basis for Durable Resistance and Quality Characteristics in Bread Wheat.

Wheat productivity has been significantly improved worldwide through the incorporation of novel genes from various gene pools, not least from wild relatives of wheat, into the commonly cultivated bread and durum wheat. Here, we present and summarize results obtained from a diverse set of wheat-alien introgression lines with mainly introgressions of rye, but also of Leymus spp. and Thinopyrum junceiforme into bread-wheat (Triticum aestivum L.). From this material, lines carrying 2RL were found with good agronomic performance and multiple resistance not least towards several races of powdery mildew. A novel resistance gene, one of few showing resistance towards all today identified stem rust races, designated Sr59, was also found originating from 2RL. Lines with multiple introgressions from 4R, 5R, and 6R were found resistant towards the majority of the stripe rust races known today. Due to lack of agricultural adaptation in these lines, transfer of useful genes into more adapted wheat material is a necessity, work which is also in progress through crosses with the CSph1b mutant, to be able to only transfer small chromosome segments that carry the target gene. Furthermore, resistance towards Russian wheat aphid was found in lines having a substitution of 1R (1D) and translocations of 3DL.3RS and 5AL.5RS. The rye chromosomes 1R, 2R, and 6R were found responsible for resistance towards the Syrian Hessian fly. High levels of especially zinc was found in several lines obtained from crosses with Leymus racemosus and Leymus mollis, while also some lines with 1R, 2R, or 5R showed increased levels of minerals and in particular of iron and zinc. Moreover, lines with 1R, 2R, 3R, and Leymus spp. introgressions were also found to have a combination of high iron and zinc and low cadmium concentrations. High variation was found both in grain protein concentration and gluten strength, measured as %UPP, within the lines, indicating large variation in bread-making quality. Thus, our study emphasizes the impact that wheat-alien introgression lines can contribute to current wheat lines and shows large opportunities both to improve production, resistance, and quality. To obtain such improvements, novel plant breeding tools, as discussed in this paper, opens unique opportunities, to transfer suitable genes into the modern and adapted wheat cultivars.

Fusarium Head Blight Modifies Fungal Endophytic Communities During Infection of Wheat Spikes.

Fusarium head blight (FHB) is a devastating disease of wheat heads. It is caused by several species from the genus Fusarium. Several endophytic fungi also colonize wheat spikes asymptomatically. Pathogenic and commensal fungi share and compete for the same niche and thereby influence plant performance. Understanding the natural dynamics of the fungal community and how the pre-established species react to pathogen attack can provide useful information on the disease biology and the potential use of some of these endophytic organisms in disease control strategies. Fungal community composition was assessed during anthesis as well as during FHB attack in wheat spikes during 2016 and 2017 in two locations. Community metabarcoding revealed that endophyte communities are dominated by basidiomycete yeasts before anthesis and shift towards a more opportunistic ascomycete-rich community during kernel development. These dynamics are interrupted when Fusarium spp. colonize wheat spikes. The Fusarium pathogens appear to exclude other fungi from floral tissues as they are associated with a reduction in community diversity, especially in the kernel which they colonize rapidly. Similarly, the presence of several endophytes was negatively correlated with Fusarium spp. and linked with spikes that stayed healthy despite exposure to the pathogen. These endophytes belonged to the genera Cladosporium, Itersonillia and Holtermanniella. These findings support the hypothesis that some naturally occurring endophytes could outcompete or prevent FHB and represent a source of potential biological control agents in wheat.

MARPLE, a point-of-care, strain-level disease diagnostics and surveillance tool for complex fungal pathogens.

BACKGROUND: Effective disease management depends on timely and accurate diagnosis to guide control measures. The capacity to distinguish between individuals in a pathogen population with specific properties such as fungicide resistance, toxin production and virulence profiles is often essential to inform disease management approaches. The genomics revolution has led to technologies that can rapidly produce high-resolution genotypic information to define individual variants of a pathogen species. However, their application to complex fungal pathogens has remained limited due to the frequent inability to culture these pathogens in the absence of their host and their large genome sizes. RESULTS: Here, we describe the development of Mobile And Real-time PLant disEase (MARPLE) diagnostics, a portable, genomics-based, point-of-care approach specifically tailored to identify individual strains of complex fungal plant pathogens. We used targeted sequencing to overcome limitations associated with the size of fungal genomes and their often obligately biotrophic nature. Focusing on the wheat yellow rust pathogen, Puccinia striiformis f.sp. tritici (Pst), we demonstrate that our approach can be used to rapidly define individual strains, assign strains to distinct genetic lineages that have been shown to correlate tightly with their virulence profiles and monitor genes of importance. CONCLUSIONS: MARPLE diagnostics enables rapid identification of individual pathogen strains and has the potential to monitor those with specific properties such as fungicide resistance directly from field-collected infected plant tissue in situ. Generating results within 48 h of field sampling, this new strategy has far-reaching implications for tracking plant health threats.

Specalyzer-an interactive online tool to analyze spectral reflectance measurements.

Low-cost phenotyping using proximal sensors is increasingly becoming popular in plant breeding. As these techniques generate a large amount of data, analysis pipelines that do not require expertise in computer programming can benefit a broader user base. In this work, a new online tool Specalyzer is presented that allows interactive analysis of the spectral reflectance data generated by proximal spectroradiometers. Specalyzer can be operated from any web browser allowing data uploading, analysis, interactive plots and exporting by point and click using a simple graphical user interface. Specalyzer is evaluated with case study data from a winter wheat fertilizer trial with two fertilizer treatments. Specalyzer can be accessed online at http://www.specalyzer.org.

Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat.

Phenotyping with proximal sensors allow high-precision measurements of plant traits both in the controlled conditions and in the field. In this work, using machine learning, an integrated analysis was done from the data obtained from spectroradiometer, infrared thermometer, and chlorophyll fluorescence measurements to identify most predictive proxy measurements for studying Septoria tritici blotch (STB) disease of wheat. The random forest (RF) models for chlorosis and necrosis identified photosystem II quantum yield (QY) and vegetative indices (VIs) associated with the biochemical composition of leaves as the top predictive variables for identifying disease symptoms. The RF model for chlorosis was validated with a validation set (R2: 0.80) and in an independent test set (R2: 0.55). Based on the results, it can be concluded that the proxy measurements for photosystem II, chlorophyll content, carotenoid, and anthocyanin levels and leaf surface temperature can be successfully used to detect STB. Further validation of these results in the field will enable application of these predictive variables for detection of STB in the field.

A transnational and holistic breeding approach is needed for sustainable wheat production in the Baltic Sea region.

The Baltic Sea is one of the largest brackish water bodies in the world. Eutrophication is a major concern in the Baltic Sea due to the leakage of nutrients to the sea with agriculture being the primary source. Wheat (Triticum aestivum L.) is the most widely grown crop in the countries surrounding the Baltic Sea and thus promoting sustainable agriculture practices for wheat cultivation will have a major impact on reducing pollution in the Baltic Sea. This approach requires identifying and addressing key challenges for sustainable wheat production in the region. Implementing new technologies for climate-friendly breeding and digital farming across all surrounding countries should promote sustainable intensification of agriculture in the region. In this review, we highlight major challenges for wheat cultivation in the Baltic Sea region and discuss various solutions integrating transnational collaboration for pre-breeding and technology sharing to accelerate development of low input wheat cultivars with improved host plant resistance to pathogen and enhanced adaptability to the changing climate.

The wheat Phs-A1 pre-harvest sprouting resistance locus delays the rate of seed dormancy loss and maps 0.3 cM distal to the PM19 genes in UK germplasm.

The precocious germination of cereal grains before harvest, also known as pre-harvest sprouting, is an important source of yield and quality loss in cereal production. Pre-harvest sprouting is a complex grain defect and is becoming an increasing challenge due to changing climate patterns. Resistance to sprouting is multi-genic, although a significant proportion of the sprouting variation in modern wheat cultivars is controlled by a few major quantitative trait loci, including Phs-A1 in chromosome arm 4AL. Despite its importance, little is known about the physiological basis and the gene(s) underlying this important locus. In this study, we characterized Phs-A1 and show that it confers resistance to sprouting damage by affecting the rate of dormancy loss during dry seed after-ripening. We show Phs-A1 to be effective even when seeds develop at low temperature (13 °C). Comparative analysis of syntenic Phs-A1 intervals in wheat and Brachypodium uncovered ten orthologous genes, including the Plasma Membrane 19 genes (PM19-A1 and PM19-A2) previously proposed as the main candidates for this locus. However, high-resolution fine-mapping in two bi-parental UK mapping populations delimited Phs-A1 to an interval 0.3 cM distal to the PM19 genes. This study suggests the possibility that more than one causal gene underlies this major pre-harvest sprouting locus. The information and resources reported in this study will help test this hypothesis across a wider set of germplasm and will be of importance for breeding more sprouting resilient wheat varieties.