Dissection of quantitative trait nucleotides and candidate genes associated with agronomic and yield-related traits under drought stress in rapeseed varieties: integration of genome-wide association study and transcriptomic analysis.

Introduction: An important strategy to combat yield loss challenge is the development of varieties with increased tolerance to drought to maintain production. Improvement of crop yield under drought stress is critical to global food security. Methods: In this study, we performed multiomics analysis in a collection of 119 diverse rapeseed (Brassica napus L.) varieties to dissect the genetic control of agronomic traits in two watering regimes [well-watered (WW) and drought stress (DS)] for 3 years. In the DS treatment, irrigation continued till the 50% pod development stage, whereas in the WW condition, it was performed throughout the whole growing season. Results: The results of the genome-wide association study (GWAS) using 52,157 single-nucleotide polymorphisms (SNPs) revealed 1,281 SNPs associated with traits. Six stable SNPs showed sequence variation for flowering time between the two irrigation conditions across years. Three novel SNPs on chromosome C04 for plant weight were located within drought tolerance-related gene ABCG16, and their pleiotropically effects on seed weight per plant and seed yield were characterized. We identified the C02 peak as a novel signal for flowering time, harboring 52.77% of the associated SNPs. The 288-kbps LD decay distance analysis revealed 2,232 candidate genes (CGs) associated with traits. The CGs BIG1-D, CAND1, DRG3, PUP10, and PUP21 were involved in phytohormone signaling and pollen development with significant effects on seed number, seed weight, and grain yield in drought conditions. By integrating GWAS and RNA-seq, 215 promising CGs were associated with developmental process, reproductive processes, cell wall organization, and response to stress. GWAS and differentially expressed genes (DEGs) of leaf and seed in the yield contrasting accessions identified BIG1-D, CAND1, and DRG3 genes for yield variation. Discussion: The results of our study provide insights into the genetic control of drought tolerance and the improvement of marker-assisted selection (MAS) for breeding high-yield and drought-tolerant varieties.

Transcriptome analysis in Aegilops tauschii unravels further insights into genetic control of stripe rust resistance.

MAIN CONCLUSION: The Aegilops tauschii resistant accession prevented the pathogen colonization by controlling the sugar flow and triggering the hypersensitive reaction. This study suggested that NBS-LRRs probably induce resistance through bHLH by controlling JA- and SA-dependent pathways. Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst) is one of wheat's most destructive fungal diseases that causes a severe yield reduction worldwide. The most effective and economically-friendly strategy to manage this disease is genetic resistance which can be achieved through deploying new and effective resistance genes. Aegilops tauschii, due to its small genome and co-evolution with Pst, can provide detailed information about underlying resistance mechanisms. Hence, we used RNA-sequencing approach to identify the transcriptome variations of two contrasting resistant and susceptible Ae. tauschii accessions in interaction with Pst and differentially expressed genes (DEGs) for resistance to stripe rust. Gene ontology, pathway analysis, and search for functional domains, transcription regulators, resistance genes, and protein-protein interactions were used to interpret the results. The genes encoding NBS-LRR, CC-NBS-kinase, and phenylalanine ammonia-lyase, basic helix-loop-helix (bHLH)-, basic-leucine zipper (bZIP)-, APETALA2 (AP2)-, auxin response factor (ARF)-, GATA-, and LSD-like transcription factors were up-regulated exclusively in the resistant accession. The key genes involved in response to salicylic acid, amino sugar and nucleotide sugar metabolism, and hypersensitive response contributed to plant defense against stripe rust. The activation of jasmonic acid biosynthesis and starch and sucrose metabolism pathways under Pst infection in the susceptible accession explained the colonization of the host. Overall, this study can fill the gaps in the literature on host-pathogen interaction and enrich the Ae. tauschii transcriptome sequence information. It also suggests candidate genes that could guide future breeding programs attempting to develop rust-resistant cultivars.

Genetic variation and response to selection of photosynthetic and forage characteristics in Kentucky bluegrass (Poa pratensis L.) ecotypes under drought conditions.

Introduction: Evaluation of the effects of water-limited conditions on the photosynthetic characteristics and forage yield is important for enhancing the forage productivity and drought tolerance in Kentucky bluegrass (Poa pratensis L.). Methods: In the present study, 100 P. pratensis ecotypes collected from different geographical areas in Iran were assessed under well-watered and drought stress conditions. Genetic variation and response to selection for the photosynthetic characteristics [i.e., net photosynthesis rate (A), stomatal conductance (gs), transpiration rate (Tr), chlorophyll content (Chl), and photochemical efficiency (Fv/Fm)] and forage yield [fresh forage yield (FY) and dry forage yield (Dy)] traits were analyzed during the 2018 and 2019 growing seasons. Results and discussion: Drought stress had negative effects on evaluated photosynthesis parameters and significantly reduced dry and fresh forage yields. On average, FY with a 45% decrease and gs with a 326% decrease under drought stress conditions showed the highest reduction rate among forage yield and photosynthesis traits, respectively. Genotypic coefficients of variation (GCV) for FY were lower under drought stress. The estimates of heritability, genetic advance, and genetic advance as percentage of mean showed the predominance of additive gene action for the traits. Overall, the results showed that "Ciakhor", "Damavand", "Karvandan", "Basmenj", "Abr2", "Abrumand", "Borhan", "Hezarkanian", "LasemCheshmeh", "Torshab", and "DoSar" have higher forage yield production with little change between two irrigation regimes, which makes them promising candidates for developing high-yielding drought-tolerant varieties through breeding programs.

Comparative profiling of polyphenols and antioxidants and analysis of antiglycation activities in rapeseed (Brassica napus L.) under different moisture regimes.

Genotype, growth stages, and moisture regimes affect polyphenols as beneficial compounds in rapeseed with edible and medicinal properties. The aims of this study were to assess the effects of tissue, genotype background and moisture on growth, pigment composition, phenolic acids, flavonoids, antioxidant, and antiglycation activities in rapeseed. Treatments included two moisture regimes (10% field capacity as drought-treated and 30% field capacity as control), tissue (leaf, flower and seed), and 12 rapeseed genotypes. The range of loss in growth traits under drought compared with control was between 23% and 47%. Drought reduced number of leaves, leaf area, fresh and dry weights by 23%, 31%, 37%, and 36%, respectively whilst increased chlorophylls, carotenoids, total pigment, phenolic compounds, flavonoids, and antioxidant activities. Analysis for antiglycation properties in two genotypes (G01 and G08) which accumulated higher phenolic compounds showed that higher antiglycation property was associated with higher epicatechin, and caffeic, and syringic acids. Flower extracts showed higher phenolics than leaf and seed suggesting flowering stage is a preferred timing to harvest a higher polyphenols from rapeseed. Overall, our results demonstrated role of specific polyphenols in antiglycation activities and the importance of growth stage and genotype in attaining higher polyphenols and antioxidants that affect edible and medicinal values of rapeseed under water limited conditions.

Integration of genome-wide association studies, metabolomics, and transcriptomics reveals phenolic acid- and flavonoid-associated genes and their regulatory elements under drought stress in rapeseed flowers.

Introduction: Biochemical and metabolic processes help plants tolerate the adverse effects of drought. In plants accumulating bioactive compounds, understanding the genetic control of the biosynthesis of biochemical pathways helps the discovery of candidate gene (CG)-metabolite relationships. Methods: The metabolic profile of flowers in 119 rapeseed (Brassica napus) accessions was assessed over two irrigation treatments, one a well-watered (WW) condition and the other a drought stress (DS) regime. We integrated information gained from 52,157 single-nucleotide polymorphism (SNP) markers, metabolites, and transcriptomes to identify linked SNPs and CGs responsible for the genetic control of flower phenolic compounds and regulatory elements. Results: In a genome-wide association study (GWAS), of the SNPs tested, 29,310 SNPs were qualified to assess the population structure and linkage disequilibrium (LD), of which several SNPs for radical scavenging activity (RSA) and total flavanol content (TFLC) were common between the two irrigation conditions and pleiotropic SNPs were found for chlorogenic and coumaric acids content. The principal component analysis (PCA) and stepwise regression showed that chlorogenic acid and epicatechin in WW and myricetin in DS conditions were the most important components for RSA. The hierarchical cluster analysis (HCA) showed that vanillic acid, myricetin, gallic acid, and catechin were closely associated in both irrigation conditions. Analysis of GWAS showed that 60 CGs were identified, of which 18 were involved in stress-induced pathways, phenylpropanoid pathway, and flavonoid modifications. Of the CGs, PAL1, CHI, UGT89B1, FLS3, CCR1, and CYP75B137 contributed to flavonoid biosynthetic pathways. The results of RNA sequencing (RNA-seq) revealed that the transcript levels of PAL, CHI, and CYP75B137 known as early flavonoid biosynthesis-related genes and FLS3, CCR1, and UGT89B1 related to the later stages were increased during drought conditions. The transcription factors (TFs) NAC035 and ERF119 related to flavonoids and phenolic acids were upregulated under drought conditions. Discussion: These findings expand our knowledge on the response mechanisms to DS, particularly regarding the regulation of key phenolic biosynthetic genes in rapeseed. Our data also provided specific linked SNPs for marker-assisted selection (MAS) programs and CGs as resources toward realizing metabolomics-associated breeding of rapeseed.

The interacting effects of irrigation, sowing date and nitrogen on water status, protein and yield in pea (Pisum sativum L.).

Management for agronomic practices might improves growth and grain yield in pea. The main objective of this experiment was to assess the interacting effects of different irrigation regimes, sowing date and nitrogen fertilizer treatments on pea traits. We evaluated three irrigation regimes (50, 75, and 100% of the plant irrigation requirement), two sowing dates (February and March), and nitrogen [application of nitroregn (N1) and without nitrogen as control (N0)] in 2019 and 2020 under field conditions. Chlorphyll content, leaf area index, leaf water potential, grain yield and water productivity were higher in the late sowing (March) than in early sowing (February) treatment. Percentage of vegetation cover in late sowing (60%) was significantly higher than in early sowing (52.7%) treatment. Grain yield in 75% water requirement treatment was not significantly different from yield in full irrigation treatment. Application of nitrogen fertilizer significantly reduced grain yield, grain protein and seeds per pod whilst increased chlorophyll content only. The 100% irrigation requirement treatment showed higher evaporation form the soil in N0 than in 50% and 75% irrigation treatments in late sown pea. Leaf evapotranspiration (ET) was lower in 50% water requirement irrigation regime than in the other irrigation treatments. Water use efficiency (WUE) which was higher in the late than early sowing treatment did not differ between 50% and full irrigation treatments in N0. In conclusion, the results of the current study suggested that application of nitrogen fertilizer did not benefit pea growth and that management of irrigation regime in late sowing might improve grain yield in pea and save irrigation water in regions with limited water availability.

Assessing the potential of native ecotypes of Poa pratensis L. for forage yield and phytochemical compositions under water deficit conditions.

Evaluation of forage yield and antioxidant activity in Poa pratensis with high quality and good spring green-up forage might help variety improvement for use under water deficit condition. Germplasm and phenotypic diversity evaluations lay a foundation for genotype selection and improvement of varieties for drought tolerance in P. pratensis. The present study was conducted to assess the genetic potential of a collection of P. pratensis accessions for drought stress and to identify the association between polyphenol compounds and forage yield traits. Vegetative clone samples of 100 accessions collected from a diverse geographical area of Iran were clonally propagated in a greenhouse and evaluated in the field under two moisture regimes (non-stress and drought stress) in 2018 and 2019. Drought stress had negative effects on fresh and dry forage yields and reduced phenotypic variances. On average, drought stress reduced fresh and dry forage yields by 45% and 28%, respectively. The results of Mantel test showed no significant correlation between forage yield traits and geographical distances. Genetic coefficients of variation for forage yield and most of the phytochemicals were lower under drought stress, suggesting that deficit irrigation may reduce genetic variation for the tested traits. The estimates of heritability were higher under non-stress conditions than under drought stress treatment for forage yield traits and few polyphenols. However, the majority of polyphenol compounds had higher heritability than forage yield traits under drought stress, which suggests the potential for indirect selection. The 'Ciakhor', 'Damavand', 'Karvandan', 'Abrumand', and 'Abr2' accessions had high quantities for polyphenols and yield traits under both moisture regimes. These accessions are promising candidates for use in variety crossing programs and for developing high-yielding varieties under water-deficit conditions.

Comparative transcriptome analysis of two contrasting resistant and susceptible Aegilops tauschii accessions to wheat leaf rust (Puccinia triticina) using RNA-sequencing.

Leaf rust, caused by Puccinia triticina Eriks., is the most common rust disease of wheat (Triticum aestivum L.) worldwide. Owing to the rapid evolution of virulent pathotypes, new and effective leaf rust resistance sources must be found. Aegilops tauschii, an excellent source of resistance genes to a wide range of diseases and pests, may provide novel routes for resistance to this disease. In this study, we aimed to elucidate the transcriptome of leaf rust resistance in two contrasting resistant and susceptible Ae. tauschii accessions using RNA-sequencing. Gene ontology, analysis of pathway enrichment and transcription factors provided an apprehensible review of differentially expressed genes and highlighted biological mechanisms behind the Aegilops-P. triticina interaction. The results showed the resistant accession could uniquely recognize pathogen invasion and respond precisely via reducing galactosyltransferase and overexpressing chromatin remodeling, signaling pathways, cellular homeostasis regulation, alkaloid biosynthesis pathway and alpha-linolenic acid metabolism. However, the suppression of photosynthetic pathway and external stimulus responses were observed upon rust infection in the susceptible genotype. In particular, this first report of comparative transcriptome analysis offers an insight into the strength and weakness of Aegilops against leaf rust and exhibits a pipeline for future wheat breeding programs.

Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat (Triticum aestivum L.).

Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.

Genomic analysis of ionome-related QTLs in Arabidopsis thaliana.

Ionome contributes to maintain cell integrity and acts as cofactors for catalyzing regulatory pathways. Identifying ionome contributing genomic regions provides a practical framework to dissect the genetic architecture of ionomic traits for use in biofortification. Meta-QTL (MQTL) analysis is a robust method to discover stable genomic regions for traits regardless of the genetic background. This study used information of 483 QTLs for ionomic traits identified from 12 populations for MQTL analysis in Arabidopsis thaliana. The selected QTLs were projected onto the newly constructed genetic consensus map and 33 MQTLs distributed on A. thaliana chromosomes were identified. The average confidence interval (CI) of the drafted MQTLs was 1.30 cM, reduced eight folds from a mean CI of 10.88 cM for the original QTLs. Four MQTLs were considered as stable MQTLs over different genetic backgrounds and environments. In parallel to the gene density over the A. thaliana genome, the genomic distribution of MQTLs over the genetic and physical maps indicated the highest density at non- and sub-telomeric chromosomal regions, respectively. Several candidate genes identified in the MQTLs intervals were associated with ion transportation, tolerance, and homeostasis. The genomic context of the identified MQTLs suggested nine chromosomal regions for Zn, Mn, and Fe control. The QTLs for potassium (K) and phosphorus (P) were the most frequently co-located with Zn (78.3%), Mn (76.2%), and Fe (88.2% and 70.6%) QTLs. The current MQTL analysis demonstrates that meta-QTL analysis is cheaper than, and as informative as genome-wide association study (GWAS) in refining the known QTLs.

Impact of Aceria alhagi (Acari: Eriophyidae) as a potential biological control agent on the invasive weed Alhagi maurorum (Fabaceae) in its native range.

Camelthorn, Alhagi maurorum Medik. (Fabaceae, Leguminosae), a native component of the Asian flora, is invasive in Australia, South Africa and the USA where it is considered a noxious weed in several states. To date there is no biological control program against this weed; however, initial investigations into potential biocontrol agents revealed an eriophyid mite, Aceria alhagi Vidovic & Kamali, causing considerable damage in the native range. The mite attacks the growing tips as well as the flowers of the plants, not only reducing height and plant vigor but also reducing seed set. To assess the host range and impact of this potential biological control agent, on the target weed, no-choice tests as well as an open-field impact experiment were conducted at the research farm of the School of Agriculture, Shiraz University, Shiraz, Iran, over 2 years (2018 and 2019). Results from the no-choice tests suggest that A. alhagi poses negligible risk to the non-target plants tested in this study. In the first year of the open field impact test, plants did not flower; however, plant height was significantly reduced by mite attack. In the second year, significant reductions in plant biomass (26%), seed production (95%) and photosynthesis (53%) were observed in response to mite attack that would potentially limit the competitiveness of camelthorn as well as long-distance dispersal through seed in the invaded range. These results suggest that A. alhagi is a promising candidate for the biological control of camelthorn and should be prioritized for any future studies, expanding on the host range testing and safety.

Development of an SNP Assay for Marker-Assisted Selection of Soil-Borne Rhizoctonia solani AG-2-2-IIIB Resistance in Sugar Beet.

Rhizoctonia solani, causing Rhizoctonia crown and root rot, is a major risk to sugar beet (Beta vulgaris L.) cultivation. The development of resistant varieties accelerated by marker-assisted selection is a priority of breeding programs. We report the identification of a single-nucleotide polymorphism (SNP) marker linked to Rhizoctonia resistance using restriction site-associated DNA (RAD) sequencing of two geographically discrete sets of plant materials with different degrees of resistance/susceptibility to enable a wider selection of superior genotypes. The variant calling pipeline utilized SAMtools for variant calling and the resulting raw SNPs from RAD sequencing (15,988 and 22,439 SNPs) were able to explain 13.40% and 25.45% of the phenotypic variation in the two sets of material from different sources of origin, respectively. An association analysis was carried out independently on both the datasets and mutually occurring significant SNPs were filtered depending on their contribution to the phenotype using principal component analysis (PCA) biplots. To provide a ready-to-use marker for the breeding community, a systematic molecular validation of significant SNPs distributed across the genome was undertaken to combine high-resolution melting, Sanger sequencing, and rhAmp SNP genotyping. We report that RsBv1 located on Chromosome 6 (9,000,093 bp) is significantly associated with Rhizoctonia resistance (p < 0.01) and able to explain 10% of the phenotypic disease variance. The related SNP assay is thus ready for marker-assisted selection in sugar beet breeding for Rhizoctonia resistance.

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models.

Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020).

OBJECTIVES: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. METHODS: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. RESULTS: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the World Health Organisation (WHO) data, Iran's fatality rate (deaths/0.1M pop) is 10.53. Other countries' fatality rates were, for comparison, Belgium - 83.32, UK - 61.39, Spain - 58.04, Italy - 56.73, Sweden - 48.28, France - 45.04, USA - 35.52, Canada - 21.49, Brazil - 20.10, Peru - 19.70, Chile - 16.20, Mexico- 12.80, and Germany - 10.58. The fatality rate for China is 0.32 (deaths/0.1M pop). Over time, the heatmap of the infected areas identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks were separate from the others. The heatmap of countries of the world shows that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidence of turning. A polynomial relationship was identified between the coronavirus infection rate and the province population density. Also, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the world's, but Iran's shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11 to March 18 showed an increasing trend of COVID-19 in Iran's provinces. It is worth noting that using the LASSO MLT to evaluate variables' importance, indicated that the most important variables were the distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. CONCLUSIONS: We believe that this study's risk maps are the primary, fundamental step to take for managing and controlling COVID-19 in Iran and its provinces.

Expression Profiling of Candidate Genes in Sugar Beet Leaves Treated with Leonardite-Based Biostimulant.

Leonardite-based biostimulants are a large class of compounds, including humic acid substances. Foliar application of biostimulants at field level improves plant growth, yield and quality through metabolic changes and stimulation of plant proton pumps. The present study aimed at identifying optimum dosage of BLACKJAK, a humic acid-based substance, which is able to modify genes involved in sugar beet growth. Thirty-three genes belonging to various biochemical pathway categories were tested in leaves of treated sugar beet (Beta vulgaris L.) samples to assess gene expression profiling in response to BLACKJAK. Seedlings of a diploid and multigerm variety were grown in plastic pots and sprayed with two dilutions of BLACKJAK (dilution 1:500-1.0 mg C L-1 and dilution 1:1000-0.5 mg C L-1). Leaf samples were collected after 24, 48, and 72 h treatment with BLACKJAK for each dilution. RNA was extracted and the quantification of gene expression was performed while using an OpenArray platform. Results of analysis of variance demonstrated that, 15 genes out of a total of 33 genes tested with OpenArray qPCR were significantly affected by treatment and exposure time. Analysis for annotation of gene products and pathways revealed that genes belonging to the mitochondrial respiratory pathways, nitrogen and hormone metabolisms, and nutrient uptake were up-regulated in the BLACKJAK treated samples. Among the up-regulated genes, Bv_PHT2;1 and Bv_GLN1 expression exerted a 2-fold change in 1:1000 and 1:500 BLACKJAK concentrations. Overall, the gene expression data in the BLACKJAK treated leaves demonstrated the induction of plant growth-related genes that were contributed almost to amino acid and nitrogen metabolism, plant defense system, and plant growth.

Dissection of genotype × environment interactions for mucilage and seed yield in Plantago species: Application of AMMI and GGE biplot analyses.

Genotype × environment interaction (GEI) is an important aspect of both plant breeding and the successful introduction of new cultivars. In the present study, additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects and genotype (G) × environment (E) interaction (GGE) biplot analyses were used to identify stable genotypes and to dissect GEI in Plantago. In total, 10 managed field trials were considered as environments to analyze GEI in thirty genotypes belonging to eight Plantago species. Genotypes were evaluated in a drought stress treatment and in normal irrigation conditions at two locations in Shiraz (Bajgah) for three years (2013-2014- 2015) and Kooshkak (Marvdasht, Fars, Iran) for two years (2014-2015). Three traits, seed yield and mucilage yield and content, were measured at each experimental site and in natural Plantago habitats. AMMI2 biplot analyses identified genotypes from several species with higher stability for seed yield and other genotypes with stable mucilage content and yield. P. lanceolata (G26), P. officinalis (G10), P. ovata (G14), P. ampleexcaulis (G11) and P. major (G4) had higher stability for seed yield. For mucilage yield, G21, G18 and G20 (P. psyllium), G1, G2 and G4 (P. major), G9 and G10 (P. officinalis) and P. lanceolata were identified as stable. G13 (P. ovata), G5 and G6 (P. major) and G30 (P. lagopus) had higher stability for mucilage content. No one genotype was found to have high levels of stability for more than one trait but some species had more than one genotype exhibiting stable trait performance. Based on trait variation, GGE biplot analysis identified two representative environments, one for seed yield and one for mucilage yield and content, with good discriminating ability. The identification of stable genotypes and representative environments should assist the breeding of new Plantago cultivars.

Targeted Next-Generation Sequencing Identification of Mutations in Disease Resistance Gene Analogs (RGAs) in Wild and Cultivated Beets.

Resistance gene analogs (RGAs) were searched bioinformatically in the sugar beet (Beta vulgaris L.) genome as potential candidates for improving resistance against different diseases. In the present study, Ion Torrent sequencing technology was used to identify mutations in 21 RGAs. The DNA samples of ninety-six individuals from six sea beets (Beta vulgaris L. subsp. maritima) and six sugar beet pollinators (eight individuals each) were used for the discovery of single-nucleotide polymorphisms (SNPs). Target amplicons of about 200 bp in length were designed with the Ion AmpliSeq Designer system in order to cover the DNA sequences of the RGAs. The number of SNPs ranged from 0 in four individuals to 278 in the pollinator R740 (which is resistant to rhizomania infection). Among different groups of beets, cytoplasmic male sterile lines had the highest number of SNPs (132) whereas the lowest number of SNPs belonged to O-types (95). The principal coordinates analysis (PCoA) showed that the polymorphisms inside the gene Bv8_184910_pkon (including the CCCTCC sequence) can effectively differentiate wild from cultivated beets, pointing at a possible mutation associated to rhizomania resistance that originated directly from cultivated beets. This is unlike other resistance sources that are introgressed from wild beets. This gene belongs to the receptor-like kinase (RLK) class of RGAs, and is associated to a hypothetical protein. In conclusion, this first report of using Ion Torrent sequencing technology in beet germplasm suggests that the identified sequence CCCTCC can be used in marker-assisted programs to differentiate wild from domestic beets and to identify other unknown disease resistance genes in beet.

Microsatellite markers for the Triticum timopheevi-derived leaf rust resistance gene Lr18 on wheat 5BL chromosome.

Leaf rust, caused by Puccinia triticina, is a common wheat disease worldwide. Developing resistant cultivars through deploying new or pyramiding resistance genes in a suitable line, is the most effective approach to control this disease. However, to stack genes in a genotype, efficient and reliable markers are required. In the present study, F2 plants and their corresponding F3 families from a cross between the resistant line; Thatcher (Tc) Lr18, and the susceptible cultivar 'Boolani' were used to map rust resistance gene, Lr18 using SSR markers on chromosome 5BL of hexaploid wheat. The P. triticina pathotype no 15 was used to inoculate plants. Out of 20 primers tested, eight showed polymorphism between the two parents and were subsequently genotyped in the entire F2 population. The markers Xgpw7425 and Xwmc75 flanked the locus at a distance of 0.3 and 1.2 cM, respectively. Analysis of 81 genotypes from different backgrounds with these two markers confirmed their usefulness in screening absence or presence of Lr18. Therefore, these markers can be used for gene postulation and marker-assisted selection (MAS) of this gene in wheat breeding programs in future.

Mapping QTLs associated with agronomic and physiological traits under terminal drought and heat stress conditions in wheat (Triticum aestivum L.).

Wheat crops frequently experience a combination of abiotic stresses in the field, but most quantitative trait loci (QTL) studies have focused on the identification of QTLs for traits under single stress field conditions. A recombinant inbred line (RIL) population derived from SeriM82 × Babax was used to map QTLs under well-irrigated, heat, drought, and a combination of heat and drought stress conditions in two years. A total of 477 DNA markers were used to construct linkage groups that covered 1619.6 cM of the genome, with an average distance of 3.39 cM between adjacent markers. Moderate to relatively high heritability estimates (0.60-0.70) were observed for plant height (PHE), grain yield (YLD), and grain per square meter (GM2). The most important QTLs for days to heading (DHE), thousand grain weight (TGW), and YLD were detected on chromosomes 1B, 1D-a, and 7D-b. The prominent QTLs related to canopy temperature were on 3B. Results showed that common QTLs for DHE, YLD, and TGW on 7D-b were validated in heat and drought trials. Three QTLs for chlorophyll content in SPAD unit (on 1A/6B), leaf rolling (ROL) (on 3B/4A), and GM2 (on 1B/7D-b) showed significant epistasis × environment interaction. Six heat- or drought-specific QTLs (linked to 7D-acc/cat-10, 1B-agc/cta-9, 1A-aag/cta-8, 4A-acg/cta-3, 1B-aca/caa-3, and 1B-agc/cta-9 for day to maturity (DMA), SPAD, spikelet compactness (SCOM), TGW, GM2, and GM2, respectively) were stable and validated over two years. The major DHE QTL linked to 7D-acc/cat-10, with no QTL × environment (QE) interaction increased TGW and YLD. This QTL (5.68 ≤ LOD ≤ 10.5) explained up to 19.6% variation in YLD in drought, heat, and combined stress trials. This marker as a candidate could be used for verification in other populations and identifying superior allelic variations in wheat cultivars or its wild progenitors to increase the efficiency of selection of high yielding lines adapted to end-season heat and drought stress conditions.

Mapping QTL for grain yield, yield components, and spike features in a doubled haploid population of bread wheat.

A doubled haploid (DH) population derived from a cross between the Japanese cultivar 'Fukuho-kumogi' and the Israeli wheat line 'Oligoculm' was used to map genome regions involved in the expression of grain yield, yield components, and spike features in wheat (Triticum aestivum L). A total of 371 markers (RAPD, SSR, RFLP, AFLP, and two morphological traits) were used to construct the linkage map that covered 4190 cM of wheat genome including 28 linkage groups. The results of composite interval mapping for all studied traits showed that some of the quantitative trait loci (QTL) were stable over experiments conducted in 2004 and 2005. The major QTL located in the Hair-Xpsp2999 interval on chromosome 1A controlled the expression of grains/spike (R(2) = 12.9% in 2004 and 22.4% in 2005), grain weight/spike (R(2) = 21.4% in 2004 and 15.8% in 2005), and spike number (R(2) = 15.6% in 2004 and 5.4% in 2005). The QTL for grain yield located on chromosomes 6A, 6B, and 6D totally accounted for 27.2% and 31.7% of total variation in this trait in 2004 and 2005, respectively. Alleles inherited from 'Oligoculm' increased the length of spikes and had decreasing effects on spike number. According to the data obtained in 2005, locus Xgwm261 was associated with a highly significant spike length QTL (R(2) = 42.33%) and also the major QTL for spikelet compactness (R(2) = 26.1%).