A new bacterial consortia for management of Fusarium head blight in wheat

Fusarium head blight (FHB) is a significantly important disease in cereals primarily caused by Fusarium species. FHB control is largely executed through chemical strategies, which are costlier to sustainable wheat production, resulting in leaning towards sustainable sources such as resistance breeding and biological control methods for FHB. The present investigation was aimed at evaluating newly identified bacterial consortium (BCM) as biocontrol agents for FHB and understanding the morpho-physiological traits associated with the disease resistance of spring wheat. Preliminary evaluation through antagonistic plate assay and in vivo assessment indicated that BCM effectively inhibited Fusarium growth in spring wheat, reducing area under disease progress curve (AUDPC) and deoxynivalenol (DON), potentially causing type II and V resistance, and improving single spike yield (SSPY). Endurance to FHB infection with the application of BCM is associated with better sustenance of spike photosynthetic performance by improving the light energy harvesting and its utilization. Correlation and path-coefficient analysis indicated that maximum quantum yield (QY_max) is directly influencing the improvement of SSPY and reduction of grain DON accumulation, which is corroborated by principal component analysis. The chlorophyll fluorescence traits identified in the present investigation might be applied as a phenotyping tool for the large-scale identification of wheat sensitivity to FHB.

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.

Transcriptomic analysis identifies candidate genes for Aphanomyces root rot disease resistance in pea

BACKGROUND

Aphanomyces euteiches is a soil-borne oomycete that causes root rot in pea and other legume species. Symptoms of Aphanomyces root rot (ARR) include root discoloration and wilting, leading to significant yield losses in pea production. Resistance to ARR is known to be polygenic but the roles of single genes in the pea immune response are still poorly understood. This study uses transcriptomics to elucidate the immune response of two pea genotypes varying in their levels of resistance to A. euteiches.

RESULTS

In this study, we inoculated roots of the pea (P. sativum L.) genotypes 'Linnea' (susceptible) and 'PI180693' (resistant) with two different A. euteiches strains varying in levels of virulence. The roots were harvested at 6 h post-inoculation (hpi), 20 hpi and 48 hpi, followed by differential gene expression analysis. Our results showed a time- and genotype-dependent immune response towards A. euteiches infection, involving several WRKY and MYB-like transcription factors, along with genes associated with jasmonic acid (JA) and abscisic acid (ABA) signaling. By cross-referencing with genes segregating with partial resistance to ARR, we identified 39 candidate disease resistance genes at the later stage of infection. Among the genes solely upregulated in the resistant genotype 'PI180693', Psat7g091800.1 was polymorphic between the pea genotypes and encoded a Leucine-rich repeat receptor-like kinase reminiscent of the Arabidopsis thaliana FLAGELLIN-SENSITIVE 2 receptor.

CONCLUSIONS

This study provides new insights into the gene expression dynamics controlling the immune response of resistant and susceptible pea genotypes to A. euteiches infection. We present a set of 39 candidate disease resistance genes for ARR in pea, including the putative immune receptor Psat7g091800.1, for future functional validation.

Chasing high and stable wheat grain mineral content: Mining diverse spring genotypes under induced drought stress

Climate change-induced drought has an effect on the nutritional quality of wheat. Here, the impact of drought at different plant stages on mineral content in mature wheat was evaluated in 30 spring-wheat lines of diverse backgrounds (modern, old and wheat-rye-introgressions). Genotypes with rye chromosome 3R introgression showed a high accumulation of several important minerals, including Zn and Fe, and these also showed stability across drought conditions. High Se content was found in genotypes with chromosome 1R. Old cultivars (K, Mg, Na, P and S) and 2R introgression lines (Fe, Ca, Mn, Mg and Na) demonstrated high mineral yield at early and late drought, respectively. Based on the low nutritional value often reported for modern wheat and negative climate effects on the stability of mineral content and yield, genes conferring high Zn/Fe, Se, and stable mineral yield under drought at various plant stages should be explicitly explored among 3R, 1R, old and 2R genotypes, respectively.

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.

Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat

Linkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models. Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r² < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.

Phenocave: An Automated, Standalone, and Affordable Phenotyping System for Controlled Growth Conditions

Controlled plant growth facilities provide the possibility to alter climate conditions affecting plant growth, such as humidity, temperature, and light, allowing a better understanding of plant responses to abiotic and biotic stresses. A bottleneck, however, is measuring various aspects of plant growth regularly and non-destructively. Although several high-throughput phenotyping facilities have been built worldwide, further development is required for smaller custom-made affordable systems for specific needs. Hence, the main objective of this study was to develop an affordable, standalone and automated phenotyping system called "Phenocave" for controlled growth facilities. The system can be equipped with consumer-grade digital cameras and multispectral cameras for imaging from the top view. The cameras are mounted on a gantry with two linear actuators enabling XY motion, thereby enabling imaging of the entire area of Phenocave. A blueprint for constructing such a system is presented and is evaluated with two case studies using wheat and sugar beet as model plants. The wheat plants were treated with different irrigation regimes or high nitrogen application at different developmental stages affecting their biomass accumulation and growth rate. A significant correlation was observed between conventional measurements and digital biomass at different time points. Post-harvest analysis of grain protein content and composition corresponded well with those of previous studies. The results from the sugar beet study revealed that seed treatment(s) before germination influences germination rates. Phenocave enables automated phenotyping of plants under controlled conditions, and the protocols and results from this study will allow others to build similar systems with dimensions suitable for their custom needs.