Spike architecture influences grain yield in wheat. We report the map-based cloning of a gene determining the number of spikelet nodes per spike in common wheat. The cloned gene is named TaCOL-B5 and encodes a CONSTANS-like protein that is orthologous to COL5 in plant species. Constitutive overexpression of the dominant TaCol-B5 allele but without the region encoding B-boxes in a common wheat cultivar increases the number of spikelet nodes per spike and produces more tillers and spikes, thereby enhancing grain yield in transgenic plants under field conditions. Allelic variation in TaCOL-B5 results in amino acid substitutions leading to differential protein phosphorylation by the protein kinase Ta K4. The TaCol-B5 allele is present in emmer wheat but is rare in a global collection of modern wheat cultivars.

Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI₁ and WFCI₂). Considering the average classification accuracy (ACA) in both years of experiments, WFCI₁ manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI₁ at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI₁ and WFCI₂ with independent data produced R² and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI₁ and WFCI₂) manifested the maximum accuracy for disease estimation with RMSE of 11.61 and R² = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.

Lineage-specific genes (LSGs) are a set of genes in a given taxon without significant sequence similarity to genes and intergenic sequences of other taxa and are functional. The tribe Triticeae mainly includes species of different ploidy levels, such as staple food crops wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). This study is aimed at mining and characterizing the Triticeae-specific genes (TSGs) using expressed sequence data of wheat. A total of 3812 TSGs was identified and they were generally characterized by smaller size, fewer exons, shorter open reading frames and lower expression levels. Most TSGs were expressed with tissue preference and many of them were predominantly expressed in reproduction related tissues, especially in young stamen. Nearly one third of the TSGs were stress-responsive and inducible under abiotic and/or biotic stresses. A co-expression-based annotation supported the relevance of some TSGs with reproduction and stress responses, indicating their potential economic importance.

Programmed cell death (PCD) and apoptosis have key functions in development and disease resistance in diverse organisms; however, the induction of necrosis remains poorly understood. Here, we identified a semi-dominant mutant allele that causes the necrotic death of the entire seedling (DES) of wheat (Triticum aestivum L.) in the absence of any pathogen or external stimulus. Positional cloning of the lethal allele mDES1 revealed that this premature death via necrosis was caused by a point mutation from Asp to Asn at amino acid 441 in a nucleotide-binding leucine-rich repeat protein containing nucleotide-binding domain and leucine-rich repeats. The overexpression of mDES1 triggered necrosis and PCD in transgenic plants. However, transgenic wheat harboring truncated wild-type DES1 proteins produced through gene editing that exhibited no significant developmental defects. The point mutation in mDES1 did not cause changes in this protein in the oligomeric state, but mDES1 failed to interact with replication protein A leading to abnormal mitotic cell division. DES1 is an ortholog of Sr35, which recognizes a Puccinia graminis f. sp. tritici stem rust disease effector in wheat, but mDES1 gained function as a direct inducer of plant death. These findings shed light on the intersection of necrosis, apoptosis, and autoimmunity in plants.