The plant growth promoting rhizobacteria (PGPR) and plant growth regulators (PGRs) can be applied to improve the growth and productivity of plants, with potential to be used for genetic improvement of drought tolerance. However, for genetic improvement to be achieved, a solid understanding of the physiological and biochemical changes in plants induced by PGPR and PGR is required. The present study was carried out to investigate the role of PGPR and PGRs on the physiology and biochemical changes in chickpea grown under drought stress conditions and their association with drought tolerance. The PGPR, isolated from the rhizosphere of chickpea, were characterized on the basis of colony morphology and biochemical characters. They were also screened for the production of indole-3-acetic acid (IAA), hydrogen cyanide (HCN), ammonia (NH₃), and exopolysaccharides (EPS) production. The isolated PGPR strains, named P1, P2, and P3, were identified by16S-rRNAgene sequencing asBacillus subtilis,Bacillus thuringiensis, andBacillus megaterium, respectively. The seeds of two chickpea varieties, Punjab Noor-2009 (drought sensitive) and 93127 (drought tolerant) were soaked for 2–3 h prior to sowing in 24 h old cultures of isolates. The salicylic acid (SA) and putrescine (Put) were sprayed (150 mg/L) on 25 day old chickpea seedlings. The results showed that chickpea plants treated with a consortium of PGPR and PGRs significantly enhanced the chlorophyll, protein, and sugar contents compared to irrigated and drought conditions. Leaf proline content, lipid peroxidation, and activities of antioxidant enzymes (CAT, APOX, POD, and SOD) all increased in response to drought stress but decreased due to the PGPR and PGRs treatment. An ultrahigh performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) analysis was carried out for metabolic profiling of chickpea leaves planted under controlled (well-irrigated), drought, and consortium (drought plus PGPR and PGRs) conditions. Proline, L-arginine, L-histidine, L-isoleucine, and tryptophan were accumulated in the leaves of chickpea exposed to drought stress. Consortium of PGPR and PGRs induced significant accumulation of riboflavin, L-asparagine, aspartate, glycerol, nicotinamide, and 3-hydroxy-3-methyglutarate in the leaves of chickpea. The drought sensitive chickpea variety showed significant accumulation of nicotinamide and 4-hydroxy-methylglycine in PGPR and PGR treated plants at both time points (44 and 60 days) as compared to non-inoculated drought plants. Additionally, arginine accumulation was also enhanced in the leaves of the sensitive variety under drought conditions. Metabolic changes as a result of drought and consortium conditions highlighted pools of metabolites that affect the metabolic and physiological adjustments in chickpea that reduce drought impacts.

Genetic improvement for drought tolerance in chickpea requires a solid understanding of biochemical processes involved with different physiological mechanisms. The objective of this study is to demonstrate genetic variations in altered metabolic levels in chickpea varieties (tolerant and sensitive) grown under contrasting water regimes through ultrahigh‐performance liquid chromatography/high‐resolution mass spectrometry‐based untargeted metabolomic profiling. Chickpea plants were exposed to drought stress at the 3‐leaf stage for 25 days, and the leaves were harvested at 14 and 25 days after the imposition of drought stress. Stress produced significant reduction in chlorophyll content,F_v/F_m, relative water content, and shoot and root dry weight. Twenty known metabolites were identified as most important by 2 different methods including significant analysis of metabolites and partial least squares discriminant analysis. The most pronounced increase in accumulation due to drought stress was demonstrated for allantoin,l‐proline,l‐arginine,l‐histidine,l‐isoleucine, and tryptophan. Metabolites that showed a decreased level of accumulation under drought conditions were choline, phenylalanine, gamma‐aminobutyric acid, alanine, phenylalanine, tyrosine, glucosamine, guanine, and aspartic acid. Aminoacyl‐tRNA and plant secondary metabolite biosynthesis and amino acid metabolism or synthesis pathways were involved in producing genetic variation under drought conditions. Metabolic changes in light of drought conditions highlighted pools of metabolites that affect the metabolic and physiological adjustment in chickpea that reduced drought impacts.

High temperature (heat) stress during grain filling is a major problem in most of the wheat growing areas. Developing heat tolerant cultivars has become a principal breeding goal in the Southern and Central Great Plain areas of the USA. Traits associated with high temperature tolerance can be used to develop heat tolerant cultivars in wheat. The present study was conducted to identify chromosomal regions associated with thylakoid membrane damage (TMD), plasmamembrane damage (PMD), and SPAD chlorophyll content (SCC), which are indicative of high temperature tolerance. In this study we have reported one of the first linkage maps in wheat using genotype by sequencing SNP (GBS-SNP) markers to extreme response to post anthesis heat stress conditions. The linkage map was comprised of 972 molecular markers (538 Bin, 258 AFLPs, 175 SSRs, and an EST). The genotypes of the RIL population showed strong variation for TMD, SCC and PMD in both generations (F10 and F9). Composite interval mapping identified five QTL regions significantly associated with response to heat stress. Associations were identified for PMD on chromosomes 7A, 2B and 1D, SCC on 6A, 7A, 1B and 1D and TMD on 6A, 7A and 1D. The variability (R2) explained by these QTL ranged from 11.9 to 30.6% for TMD, 11.4 to 30.8% for SCC, and 10.5 to 33.5% for PMD. Molecular markers Xbarc113 and AFLP AGCTCG-347 on chromosome 6A, Xbarc121 and Xbarc49 on 7A, gwm18 and Bin1130 on 1B, Bin178 and Bin81 on 2B and Bin747 and Bin1546 on 1D were associated with these QTL. The identified QTL can be used for marker assisted selection in breeding wheat for improved heat tolerance in Ventnor or Karl 92 genetic background.

Plant growth regulators (PGRs) and plant growth promoting rhizobacteria (PGPRs) play an important role in mitigating abiotic stresses. However, little is known about the parallel changes in physiological processes coupled with metabolic changes induced by PGRs and PGPRs that help to cope with drought stress in chickpeas. The present investigation was carried out to study the integrative effects of PGRs and PGPRs on the physiological and metabolic changes, and their association with drought tolerance in two chickpea genotypes. Inoculated seeds of two chickpea genotypes, Punjab Noor-2009 (drought sensitive) and 93127 (drought tolerance), were planted in greenhouse condition at the University of Florida. Prior to sowing, seeds of two chickpea varieties were soaked for 3 h in 24 h old cultures of PGPRs (Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium), whereas, some of the seeds were soaked in distilled water for the same period of time and were treated as control. Plant growth regulators, salicylic acid (SA) and putrescine (Put), were applied on 25 days old seedlings just prior to the induction of drought stress. Drought stress was imposed by withholding the supply of water on 25-day-old seedlings (at the three-leaf stage) and continued for the next 25 days until the soil water content reached 14%. Ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) analysis concomitant with physiological parameters were carried out in chickpea leaves at two-time points i.e. 14 and 25 d after imposition of drought stress. The results showed that both genotypes, treated with PGRs and PGPRs (consortium), performed significantly better under drought condition through enhanced leaf relative water content (RWC), greater biomass of shoot and root, higher Fv/FM ratio and higher accumulation of protein, sugar and phenolic compounds. The sensitive genotype was more responsive than tolerant one. The results revealed that the accumulation of succinate, leucine, disaccharide, saccharic acid and glyceric acid was consistently higher in both genotypes at both time points due to PGRs and PGPRs treatment. Significant accumulation of malonate, 5-oxo-L-proline, and trans-cinnamate occurred at both time points only in the tolerant genotype following the consortium treatment. Aminoacyl-tRNA, primary and secondary metabolite biosynthesis, amino acid metabolism or synthesis pathways, and energy cycle were significantly altered due to PGRs and PGPRs treatment. It is inferred that changes in different physiological and metabolic parameters induced by PGRs and PGPRs treatment could confer drought tolerance in chickpeas.

Understanding the contrasting biochemical changes in different plant parts in response to drought can help to formulate smart strategies to develop drought tolerant genotypes. The current study used metabolomics and physiological approaches to understand the differential biochemical changes coupled with physiological adjustments in leaves and roots to cope with drought stress in two wheat genotypes, LA754 (drought tolerant) and AGS2038 (drought sensitive). The gas chromatography-mass spectrometry (GC-MS) analysis and physiological trait estimation were performed in the roots and leaves after drought imposition. Drought induced reduction was observed in all physiological and yield related traits. In LA754, higher numbers of metabolites were altered in leaves (45) compared to roots (20) which indicates that plants allocated more resources to leaves in tolerant genotype. In addition, the metabolic components of the root were less affected by the stress which supports the idea that the roots are more drought tolerant than the leaf or shoot. In AGS2038, thirty and twenty eight metabolites were altered in the leaves and roots, respectively. This indicates that the sensitive genotype compromised resource allocation to leaves, rather allocated more towards roots. Tryptophan, valine, citric acid, fumaric acid, and malic acid showed higher accumulation in leaf in LA754, but decreased in the root, while glyceric acid was highly accumulated in the root, but not in the leaf. The results demonstrated that the roots and shoots have a different metabolic composition, and shoot metabolome is more variable than the root metabolome. Though the present study demonstrated that the metabolic response of shoots to drought contrasts with that of roots, some growth metabolites (protein, sugar, etc) showed a mirror increase in both parts. Protein synthesis and energy cycle was active in both organs, and the organs were metabolically activated to enhance water uptake and maintain growth to mitigate the effect of drought.

Key message The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Abstract Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.