Probing Differential Metabolome Responses among Wheat Genotypes to Heat Stress Using Fourier Transform Infrared-Based Chemical Fingerprinting

Heat stress is one of the major environmental constraints for wheat production; thus, a comprehensive understanding of the metabolomic responses of wheat is required for breeding heat-tolerant varieties. In this study, the metabolome responses of heat-tolerant genotypes Imam and Norin 61, and susceptible genotype Chinese Spring were comparatively analyzed using Fourier transform infrared (FTIR) spectroscopy in combination with chemometric data mining techniques. Principal component analysis of the FTIR data suggested a spectral feature partially overlapping between the three genotypes. FTIR spectral biomarker assay showed similar heat responses between the genotypes for markers Fm482 and Fm1502, whereas genotype-dependent variations were observed for other markers. The markers Fm1251 and Fm1729 showed contrasting behaviors between heat-tolerant and susceptible genotypes, suggesting that these markers may potentially serve as a tool for distinguishing heat-tolerant genotypes. Linear discriminant analysis (LDA) of the spectra demonstrated a clear separation between the three genotypes in terms of the heat stress effect. Analysis of LDA coefficients identified several spectral regions that were potentially responsible for the discrimination of FTIR spectra between different genotypes and environments. These results suggest that a combination of FTIR and chemometrics can be a useful technique for characterizing the metabolic behavior of diverse wheat genotypes under heat stress

Chemical Fingerprinting of Heat Stress Responses in the Leaves of Common Wheat by Fourier Transform Infrared Spectroscopy

Wheat (Triticum aestivum L.) is known to be negatively affected by heat stress, and its production is threatened by global warming, particularly in arid regions. Thus, efforts to better understand the molecular responses of wheat to heat stress are required. In the present study, Fourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress. Wheat plants at the three-leaf stage were subjected to heat stress at a 42 °C daily maximum temperature for 3 days, and this led to delayed growth in comparison to that of the control. Measurement of FTIR spectra and their principal component analysis showed partially overlapping features between heat-stressed and control leaves. In contrast, supervised machine learning through linear discriminant analysis (LDA) of the spectra demonstrated clear discrimination of heat-stressed leaves from the controls. Analysis of LDA loading suggested that several wavenumbers in the fingerprinting region (400–1800 cm−1) contributed significantly to their discrimination. Novel spectrum-based biomarkers were developed using these discriminative wavenumbers that enabled the successful diagnosis of heat-stressed leaves. Overall, these observations demonstrate the versatility of FTIR-based chemical fingerprints for use in heat-stress profiling in wheat

Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan

Developing climate-resilient crop varieties with better performance under variable environments is essential to ensure food security in a changing climate. This process is significantly influenced, among other factors, by genotype × environment (G × E) interactions. With the objective of identifying high-yielding and stable genotypes, 20 elite durum wheat lines were evaluated in 24 environments (location–season combination) during 5 crop seasons (2010/11–2014/15). The REML (residual maximum likelihood)-predicted means of grain yield of 16 genotypes that were common across all environments ranged from 3522 kg/ha in G201 to 4132 kg/ha in G217. Results of additive main effect and multiplicative interaction (AMMI) analysis showed that genotypes (G), environments (E), and genotype × environment interaction (GEI) significantly affected grain yield. From the total sum of squares due to treatments (G + E + GEI), E attributed the highest proportion of the variation (90.0%), followed by GEI (8.7%) and G (1.3%). Based on the first four AMMI selections for grain yield in the 24 environments, genotypes G217, G219, G211, and G213 were selected in 23, 12, 11, and 9 environments, respectively. The genotype and genotype × environment biplot (GGE) biplot polygon view showed that the environments were separated into three mega-environments. The winning genotypes in these mega-environments were G217, G214, and G204. Genotypes G212, G220, G217, G215, and G213 showed low AMMI stability values (ASV), whereas genotypes G217, G220, G212, G211, and G219 showed low genotype selection index (GSI), indicating their better stability and adaptability to the test environments. The results indicated that genotypes G217, G219, G211, G213, and G220 combined both high grain yield and stability/adaptability under dry but irrigated and heat-prone environments. An in-depth analysis of the superior genotypes could help better understand the stress-adaptive traits that could be targeted to further increase durum wheat yield and stability under the changing climate