Advances and opportunities in unraveling cold-tolerance mechanisms in the world's primary staple food crops.

Temperatures below or above optimal growth conditions are among the major stressors affecting productivity, end-use quality, and distribution of key staple crops including rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays L.). Among temperature stresses, cold stress induces cellular changes that cause oxidative stress and slowdown metabolism, limit growth, and ultimately reduce crop productivity. Perception of cold stress by plant cells leads to the activation of cold-responsive transcription factors and downstream genes, which ultimately impart cold tolerance. The response triggered in crops to cold stress includes gene expression/suppression, the accumulation of sugars upon chilling, and signaling molecules, among others. Much of the information on the effects of cold stress on perception, signal transduction, gene expression, and plant metabolism are available in the model plant Arabidopsis but somewhat lacking in major crops. Hence, a complete understanding of the molecular mechanisms by which staple crops respond to cold stress remain largely unknown. Here, we make an effort to elaborate on the molecular mechanisms employed in response to low-temperature stress. We summarize the effects of cold stress on the growth and development of these crops, the mechanism of cold perception, and the role of various sensors and transducers in cold signaling. We discuss the progress in cold tolerance research at the genome, transcriptome, proteome, and metabolome levels and highlight how these findings provide opportunities for designing cold-tolerant crops for the future.

Development of High-Yielding Upland Cotton Genotypes with Reduced Regrowth after Defoliation Using a Combination of Molecular and Conventional Approaches.

Cotton is an economically important crop. However, the yield gain in cotton has stagnated over the years, probably due to its narrow genetic base. The introgression of beneficial variations through conventional and molecular approaches has helped broaden its genetic base to some extent. The growth habit of cotton is one of the crucial factors that determine crop maturation time, yield, and management. This study used 44 diverse upland cotton genotypes to develop high-yielding cotton germplasm with reduced regrowth after defoliation and early maturity by altering its growth habit from perennial to somewhat annual. We selected eight top-scoring genotypes based on the gene expression analysis of five floral induction and meristem identity genes (FT, SOC1, LFY, FUL, and AP1) and used them to make a total of 587 genetic crosses in 30 different combinations of these genotypes. High-performance progeny lines were selected based on the phenotypic data on plant height, flower and boll numbers per plant, boll opening date, floral clustering, and regrowth after defoliation as surrogates of annual growth habit, collected over four years (2019 to 2022). Of the selected lines, 8×5-B3, 8×5-B4, 9×5-C1, 8×9-E2, 8×9-E3, and 39×5-H1 showed early maturity, and 20×37-K1, 20×37-K2, and 20×37-D1 showed clustered flowering, reduced regrowth, high quality of fiber, and high lint yield. In 2022, 15 advanced lines (F8/F7) from seven cross combinations were selected and sent for an increase to a Costa Rica winter nursery to be used in advanced testing and for release as germplasm lines. In addition to these breeding lines, we developed molecular resources to breed for reduced regrowth after defoliation and improved yield by converting eight expression-trait-associated SNP markers we identified earlier into a user-friendly allele-specific PCR-based assay and tested them on eight parental genotypes and an F2 population.

Alterations in Growth Habit to Channel End-of-Season Perennial Reserves towards Increased Yield and Reduced Regrowth after Defoliation in Upland Cotton (Gossypium hirsutum L.).

Cotton (Gossypium spp.) is the primary source of natural textile fiber in the U.S. and a major crop in the Southeastern U.S. Despite constant efforts to increase the cotton fiber yield, the yield gain has stagnated. Therefore, we undertook a novel approach to improve the cotton fiber yield by altering its growth habit from perennial to annual. In this effort, we identified genotypes with high-expression alleles of five floral induction and meristem identity genes (FT, SOC1, FUL, LFY, and AP1) from an Upland cotton mini-core collection and crossed them in various combinations to develop cotton lines with annual growth habit, optimal flowering time, and enhanced productivity. To facilitate the characterization of genotypes with the desired combinations of stacked alleles, we identified molecular markers associated with the gene expression traits via genome-wide association analysis using a 63 K SNP Array. Over 14,500 SNPs showed polymorphism and were used for association analysis. A total of 396 markers showed associations with expression traits. Of these 396 markers, 159 were mapped to genes, 50 to untranslated regions, and 187 to random genomic regions. Biased genomic distribution of associated markers was observed where more trait-associated markers mapped to the cotton D sub-genome. Many quantitative trait loci coincided at specific genomic regions. This observation has implications as these traits could be bred together. The analysis also allowed the identification of candidate regulators of the expression patterns of these floral induction and meristem identity genes whose functions will be validated.

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat.

BACKGROUND: Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. RESULTS: Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker-trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. CONCLUSION: The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.).

In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and ß-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013-2014 from Powerkheda (Madhya Pradesh) and for 2014-2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.

Lipid modulation contributes to heat stress adaptation in peanut.

At the cellular level, membrane damage is a fundamental cause of yield loss at high temperatures (HT). We report our investigations on a subset of a peanut (Arachis hypogaea) recombinant inbred line population, demonstrating that the membrane lipid remodeling occurring at HT is consistent with homeoviscous adaptation to maintain membrane fluidity. A major alteration in the leaf lipidome at HT was the reduction in the unsaturation levels, primarily through reductions of 18:3 fatty acid chains, of the plastidic and extra-plastidic diacyl membrane lipids. In contrast, levels of 18:3-containing triacylglycerols (TGs) increased at HT, consistent with a role for TGs in sequestering fatty acids when membrane lipids undergo remodeling during plant stress. Polyunsaturated acyl chains from membrane diacyl lipids were also sequestered as sterol esters (SEs). The removal of 18:3 chains from the membrane lipids decreased the availability of susceptible molecules for oxidation, thereby minimizing oxidative damage in membranes. Our results suggest that transferring 18:3 chains from membrane diacyl lipids to TGs and SEs is a key feature of lipid remodeling for HT adaptation in peanut. Finally, QTL-seq allowed the identification of a genomic region associated with heat-adaptive lipid remodeling, which would be useful for identifying molecular markers for heat tolerance.

Heterologous expression of the TaPI-PLC1-2B gene enhanced drought and salt tolerance in transgenic rice seedlings.

Drought and salt stress are important factors that affect plant growth and development and cause crop yield reductions worldwide. Phospholipase C is a class of enzymes that can hydrolyze phospholipids, and it has been shown to play an important role in plant growth regulation and stress response. We used rice as a model to investigate the function of the wheat TaPI-PLC1-2B gene in salt and drought tolerance. For this purpose, we heterologously expressed the TaPI-PLC1-2B gene in rice and studied the transcriptional differences in transgenic and wide-type rice plants in the presence and absence of drought and salt stress. Our results showed that 2130 and 1759 genes expressed differentially in the TaPI-PLC1-2B overexpression rice line under salt and drought stress, respectively. Gene ontology enrichment results showed that differentially expressed genes (DEGs) were significantly enriched in cellular process, metabolic process, stimulus-response, cell, organelle, catalytic activity, and other functional processes under salt and drought stress. In addition, the Kyoto Encyclopedia of Genes and Genomes pathway analysis showed DEG enrichment in plant-pathogen interaction, phosphoinositol, plant hormones, and other signaling pathways under the two stress treatments. Furthermore, the chromosomal localization of salt and drought stress-responsive DEGs showed a clear distribution pattern on specific rice chromosomes. For instance, the greatest number of drought stress-responsive genes mapped to rice chromosomes 1 and 6. The current analysis has built the basis for future explorations to decipher the TaPI-PLC1-2B-mediated plant stress response mechanism in the relatively challenging wheat system.

Plant biomacromolecule delivery methods in the 21st century.

The 21st century witnessed a boom in plant genomics and gene characterization studies through RNA interference and site-directed mutagenesis. Specifically, the last 15 years marked a rapid increase in discovering and implementing different genome editing techniques. Methods to deliver gene editing reagents have also attempted to keep pace with the discovery and implementation of gene editing tools in plants. As a result, various transient/stable, quick/lengthy, expensive (requiring specialized equipment)/inexpensive, and versatile/specific (species, developmental stage, or tissue) methods were developed. A brief account of these methods with emphasis on recent developments is provided in this review article. Additionally, the strengths and limitations of each method are listed to allow the reader to select the most appropriate method for their specific studies. Finally, a perspective for future developments and needs in this research area is presented.

ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants.

One of the thrust areas of research in plant breeding is to develop crop cultivars with enhanced tolerance to abiotic stresses. Thus, identifying abiotic stress-responsive genes (SRGs) and proteins is important for plant breeding research. However, identifying such genes via established genetic approaches is laborious and resource intensive. Although transcriptome profiling has remained a reliable method of SRG identification, it is species specific. Additionally, identifying multistress responsive genes using gene expression studies is cumbersome. Thus, endorsing the need to develop a computational method for identifying the genes associated with different abiotic stresses. In this work, we aimed to develop a computational model for identifying genes responsive to six abiotic stresses: cold, drought, heat, light, oxidative, and salt. The predictions were performed using support vector machine (SVM), random forest, adaptive boosting (ADB), and extreme gradient boosting (XGB), where the autocross covariance (ACC) and K-mer compositional features were used as input. With ACC, K-mer, and ACC + K-mer compositional features, the overall accuracy of ∼60-77, ∼75-86, and ∼61-78% were respectively obtained using the SVM algorithm with fivefold cross-validation. The SVM also achieved higher accuracy than the other three algorithms. The proposed model was also assessed with an independent dataset and obtained an accuracy consistent with cross-validation. The proposed model is the first of its kind and is expected to serve the requirement of experimental biologists; however, the prediction accuracy was modest. Given its importance for the research community, the online prediction application, ASRpro, is made freely available (https://iasri-sg.icar.gov.in/asrpro/) for predicting abiotic SRGs and proteins.

Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs.

Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program's relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.

Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.

We evaluated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy was measured using Pearson's correlation coefficient between the genomic estimated breeding value (GEBV) and the observed phenotypic data using a fivefold cross-validation approach with 100 replications. The Bayesian alphabets performed better for the traits governed by a few genes/QTLs with relatively larger effects. On the contrary, the BLUP alphabets (GBLUP and CBLUP) exhibited higher genomic prediction accuracy for the traits controlled by several small-effect QTLs. Additionally, Bayesian methods performed better for the highly heritable traits and, for other traits, performed at par with the BLUP methods. Further, genomic BLUP (GBLUP) was identified as the least biased method for the GEBV estimation. Among the Bayesian methods, the Bayesian ridge regression and Bayesian LASSO were less biased than other Bayesian alphabets. Nonetheless, genomic prediction accuracy increased with an increase in trait heritability, irrespective of the sample size, marker density, and the QTL type (major/minor effect). In sum, this study provides valuable information regarding the choice of the selection method for genomic prediction in different breeding programs.

Targeting Induced Local Lesions in the Wheat DEMETER and DRE2 Genes, Responsible for Transcriptional Derepression of Wheat Gluten Proteins in the Developing Endosperm.

Wheat is a major source of energy and nutrition worldwide, but it is also a primary cause of frequent diet-induced health issues, specifically celiac disease, for which the only effective therapy so far is strict dietary abstinence from gluten-containing grains. Wheat gluten proteins are grouped into two major categories: high-molecular-weight glutenin subunits (HMWgs), vital for mixing and baking properties, and gliadins plus low-molecular-weight glutenin subunits (LMWgs) that contain the overwhelming majority of celiac-causing epitopes. We put forth a hypothesis that eliminating gliadins and LMWgs while retaining HMWgs might allow the development of reduced-immunogenicity wheat genotypes relevant to most gluten-sensitive individuals. This hypothesis stems from the knowledge that the molecular structures and regulatory mechanisms of the genes encoding the two groups of gluten proteins are quite different, and blocking one group's transcription, without affecting the other's, is possible. The genes for gliadins and LMWgs have to be de-methylated by 5-methylcytosine DNA glycosylase/lyase (DEMETER) and an iron-sulfur (Fe-S) cluster biogenesis enzyme (DRE2) early during endosperm development to permit their transcription. In this study, a TILLING (Targeting Induced Local Lesions IN Genomes) approach was undertaken to identify mutations in the homoeologous DEMETER (DME) and DRE2 genes in common and durum wheat. Lines with mutations in these genes were obtained that displayed reduced content of immunogenic gluten proteins while retaining essential baking properties. Although our data at first glance suggest new possibilities for treating celiac disease and are therefore of medical and agronomical interest, it also shows that inducing mutations in the DME and DRE2 genes analyzed here affected pollen viability and germination. Hence there is a need to develop other approaches in the future to overcome this undesired effect.

Various potentially toxic element tolerances in different rice genotypes correlate with distinct physiological responses and alterations in DNA methylation.

Potentially toxic elements (PTEs) are harmful to plant growth and reduce crop productivity. In this work, we studied three rice genotypes (T-35, RZ-1, and RZ-2) to quantify the diverse PTE effects and tolerances by examining morphology, physiology, and DNA methylation patterns. Morphological results showed that T-35 exhibits the highest tolerance to all studied PTE stressors (Cu, Cd, Cr). Physiological responses under PTE stresses confirmed earlier findings, where T-35 showed a higher potassium (K+) content and more peroxidase (POD) accumulation in the roots than the other two rice genotypes. The differences in PTE tolerance levels observed among the three rice genotypes were also associated with variations in the heavy metal transportation (HMT) gene expression level. Moreover, methylation-sensitive blotting analysis of the selected genes showed that the DNA methylation changes occurring due to PTE treatments are mainly CHG hypomethylation in T-35 but hypermethylation in RZ-1 and RZ-2. Our results demonstrate a tight relationship among physiological response, expression levels of the HMT genes, and DNA methylation pattern under PTEs stresses. It is also indicated that plants use generic mechanisms to tolerate stresses; however, different genotypes employ different combinations of such tactics to confer tolerance, which results in diverse PTE stress tolerances. These findings shed light on the PTE stresses tolerance mechanism and help direct future breeding activities in rice.

PRAT Proteins Operate in Organellar Protein Import and Export in Arabidopsis thaliana.

Chloroplasts need to import preproteins and amino acids from the cytosol during their light-induced differentiation. Similarly, chloroplasts have to export organic matter including proteins and amino acids during leaf senescence. Members of the PRAT (preprotein and amino acid transporter) family are candidate transporters for both processes. Here, we defined the role of two small PRAT gene families, At4g26670 and At5g55510 (HP20 subfamily) versus At3g49560 and At5g24650 (HP30 subfamily) during greening of etiolated plants and during leaf senescence. Using a combination of reverse genetics, protein biochemistry and physiological tools, evidence was obtained for a role of chloroplast HP20, HP30 and HP30-2 in protein, but not amino acid, import into chloroplasts. HP20, HP30 and HP30-2 form larger complexes involved in the uptake of transit sequence-less cytosolic precursors. In addition, we identified a fraction of HP30-2 in mitochondria where it served a similar function as found for chloroplasts and operated in the uptake of transit sequence-less cytosolic precursor proteins. By contrast, HP22 was found to act in the export of proteins from chloroplasts during leaf senescence, and thus its role is entirely different from that of its orthologue, HP20. HP22 is part of a unique protein complex in the envelope of senescing chloroplasts that comprises at least 11 proteins and contains with HP65b (At5g55220) a protein that is related to the bacterial trigger factor chaperone. An ortholog of HP65b exists in the cyanobacterium Synechocystis and has previously been implicated in protein secretion. Whereas plants depleted of either HP22 or HP65b or even both were increasingly delayed in leaf senescence and retained much longer stromal chloroplast constituents than wild-type plants, HP22 overexpressors showed premature leaf senescence that was associated with accelerated losses of stromal chloroplast proteins. Together, our results identify the PRAT protein family as a unique system for importing and exporting proteins from chloroplasts.

tRNA-Dependent Import of a Transit Sequence-Less Aminoacyl-tRNA Synthetase (LeuRS2) into the Mitochondria of Arabidopsis.

Aminoacyl-tRNA synthetases (AaRS) charge tRNAs with amino acids for protein translation. In plants, cytoplasmic, mitochondrial, and chloroplast AaRS exist that are all coded for by nuclear genes and must be imported from the cytosol. In addition, only a few of the mitochondrial tRNAs needed for translation are encoded in mitochondrial DNA. Despite considerable progress made over the last few years, still little is known how the bulk of cytosolic AaRS and respective tRNAs are transported into mitochondria. Here, we report the identification of a protein complex that ties AaRS and tRNA import into the mitochondria of Arabidopsis thaliana. Using leucyl-tRNA synthetase 2 (LeuRS2) as a model for a mitochondrial signal peptide (MSP)-less precursor, a ≈30 kDa protein was identified that interacts with LeuRS2 during import. The protein identified is identical with a previously characterized mitochondrial protein designated HP30-2 (encoded by At3g49560) that contains a sterile alpha motif (SAM) similar to that found in RNA binding proteins. HP30-2 is part of a larger protein complex that contains with TIM22, TIM8, TIM9 and TIM10 four previously identified components of the translocase for MSP-less precursors. Lack of HP30-2 perturbed mitochondrial biogenesis and function and caused seedling lethality during greening, suggesting an essential role of HP30-2 in planta.

Population structure and genetic diversity of the Pee Dee cotton breeding program.

Accelerated marker-assisted selection and genomic selection breeding systems require genotyping data to select the best parents for combining beneficial traits. Since 1935, the Pee Dee (PD) cotton germplasm enhancement program has developed an important genetic resource for upland cotton (Gossypium hirsutum L.), contributing alleles for improved fiber quality, agronomic performance, and genetic diversity. To date, a detailed genetic survey of the program's eight historical breeding cycles has yet to be undertaken. The objectives of this study were to evaluate genetic diversity across and within-breeding groups, examine population structure, and contextualize these findings relative to the global upland cotton gene pool. The CottonSNP63K array was used to identify 17,441 polymorphic markers in a panel of 114 diverse PD genotypes. A subset of 4597 markers was selected to decrease marker density bias. Identity-by-state pairwise distance varied substantially, ranging from 0.55 to 0.97. Pedigree-based estimates of relatedness were not very predictive of observed genetic similarities. Few rare alleles were present, with 99.1% of SNP alleles appearing within the first four breeding cycles. Population structure analysis with principal component analysis, discriminant analysis of principal components, fastSTRUCTURE, and a phylogenetic approach revealed an admixed population with moderate substructure. A small core collection (n < 20) captured 99% of the program's allelic diversity. Allele frequency analysis indicated potential selection signatures associated with stress resistance and fiber cell growth. The results of this study will steer future utilization of the program's germplasm resources and aid in combining program-specific beneficial alleles and maintaining genetic diversity.

Development and use of miRNA-derived SSR markers for the study of genetic diversity, population structure, and characterization of genotypes for breeding heat tolerant wheat varieties.

Heat stress is an important abiotic factor that limits wheat production globally, including south-east Asia. The importance of micro (mi) RNAs in gene expression under various biotic and abiotic stresses is well documented. Molecular markers, specifically simple sequence repeats (SSRs), play an important role in the wheat improvement breeding programs. Given the role of miRNAs in heat stress-induced transcriptional regulation and acclimatization, the development of miRNA-derived SSRs would prove useful in studying the allelic diversity at the heat-responsive miRNA-genes in wheat. In the present study, efforts have been made to identify SSRs from 96 wheat heat-responsive miRNA-genes and their characterization using a panel of wheat genotypes with contrasting reactions (tolerance/susceptible) to heat stress. A set of 13 miRNA-derived SSR markers were successfully developed as an outcome. These miRNA-SSRs are located on 11 different common wheat chromosomes (2A, 3A, 3B, 3D, 4D, 5A, 5B, 5D, 6A, 6D, and 7A). Among 13 miRNA-SSRs, seven were polymorphic on a set of 37 selected wheat genotypes. Within these polymorphic SSRs, three makers, namely HT-169j, HT-160a, and HT-160b, were found promising as they could discriminate heat-tolerant and heat-susceptible genotypes. This is the first report of miRNA-SSR development in wheat and their deployment in genetic diversity and population structure studies and characterization of trait-specific germplasm. The study suggests that this new class of molecular makers has great potential in the marker-assisted breeding (MAB) programs targeted at improving heat tolerance and other adaptability or developmental traits in wheat and other crops.

Proteomics Reveals the Changes that Contribute to Fusarium Head Blight Resistance in Wheat.

Fusarium head blight (FHB) is a devastating disease of wheat, causing yield losses and quality reduction as a result of mycotoxin production. In this study, iTRAQ (isobaric tags for relative and absolute quantification)-labeling-based mass spectrometry was employed to characterize the proteome in wheat cultivars Xinong 538 and Zhoumai 18 with contrasting levels of FHB resistance as a means to elucidate the molecular mechanisms contributing to FHB resistance. A total of 13,669 proteins were identified in the two cultivars 48 h after Fusarium graminearum inoculation. Among these, 2,505 unique proteins exclusively accumulated in Xinong 538 (resistant) and 887 proteins in Zhoumai 18 (susceptible). Gene Ontology enrichment analysis showed that most differentially accumulated proteins (DAPs) from both cultivars were assigned to the following categories: metabolic process, single-organism process, cellular process, and response to stimulus. Kyoto Encyclopedia of Genes and Genomes analysis showed that a greater number of proteins belonging to different metabolic pathways were identified in Xinong 538 compared with Zhoumai 18. Specifically, DAPs from the FHB-resistant cultivar Xinong 538 populated categories of metabolic pathways related to plant-pathogen interaction. These DAPs might play a critical role in defense responses exhibited by Xinong 538. DAPs from both genotypes were assigned to all wheat chromosomes except chromosome 6B, with approximately 30% mapping to wheat chromosomes 2B, 3B, 5B, and 5D. Twenty single nucleotide polymorphism markers, flanking DAPs on chromosomes 1B, 3B, 5B, and 6A, overlapped with the location of earlier mapped FHB-resistance quantitative trait loci. The data provide evidence for the involvement of several DAPs in the early stages of the FHB-resistance response in wheat; however, further functional characterization of candidate proteins is warranted.