Mineral concentrations of chickpea and lentil cultivars and breeding lines grown in the U.S.Pacific Northwest

Diseases and health complications caused by mineral deficiencies afflict billions of people globally. Developing pulse crops with elevated seed mineral concentrations can contribute to reducing the incidence of these deficiencies. The objectives of this study were to estimate variance components conditioning seed mineral concentrations of chickpea and lentil grown in Washington and Idaho, determine correlations between different mineral concentrations and between mineral concentrations and yield, 100-seed weight, and days to flowering, and compare seed mineral concentrations between chickpeas and lentils grown in adjacent plots. Genotype effects, although significant in chickpea and lentil for all minerals except selenium, tended to be minimal compared to location, year, and their interaction effects. In both chickpeas and lentils high positive correlations were observed between seed concentrations of phosphorus and potassium, phosphorus and zinc, and potassium and zinc. Correlations between mineral concentration and yield, and mineral concentration and days to 50% flowering were similar for chickpeas and lentils across the majority of minerals. These results may reflect similarities between the two crops in physiological processes for mineral uptake and partitioning. Lentils had higher concentrations of iron and zinc than chickpea when the two crops were grown in adjacent plots,whereas chickpeas had higher concentrations of calcium and manganese. Plant genotypes that are more efficient at obtaining minerals from growing environments will be useful as parental materials to develop improved chickpea and lentil cultivars that have good yield potential coupled with high seed mineral concentrations.

Evaluation of Expression Stability of Candidate References Genes among Green and Yellow Pea Cultivars (<i>Pisum sativum</i>L.) Subjected to Abiotic and Biotic Stress

Dry pea (Pisum sativum L.) is grown as human and animal feed throughout the world. Large yield losses in pea due to biotic and abiotic stresses compel an improved understanding of mechanisms of stress tolerance and genetic determinants conditioning these tolerances. The availability of stably expressed reference genes is a prerequisite for examining differential gene expression. The objective of this study was to examine the expression profile of several candidate reference genes across a broad range of commercial pea cultivars. Expression profiles of five candidate reference genes;18s rRNA, actin, TIF, β tubulin-2 and β tubulin-3 were examined. Relative quantifications of candidate reference genes were estimated from control plants, plants after 48 h of cold treatment, and plants 24 and 48 h after inoculation with Sclerotinia sclerotiorum, the causal agent of white mold disease of pea. RT-qPCR was performed on cDNA synthesized from three food grade spring peas, Ariel, Aragorn, and Sterling, and two spring yellow peas, Delta and Universal, which are used as animal feed. Analysis of variance (ANOVA) of CT values demonstrated significant variation between varieties and treatments under cold and disease conditions. The most abundant transcripts among tested reference genes were for 18s rRNA. Stability analysis indicated that TIF and β tubulin-3 genes were the most stably expressed candidate genes under both cold and disease stress and could serve as reference genes across a wide range of pea cultivars.

Development of a panel of unigene-derived polymorphic EST–SSR markers in lentil using public database information

Lentil(Lens culinaris Medik.), a diploid(2n = 14) with a genome size greater than 4000 Mbp, is an important cool season food legume grown worldwide. The availability of genomic resources is limited in this crop species. The objective of this study was to develop polymorphic markers in lentil using publicly available curated expressed sequence tag information(ESTs). In this study, 9513 ESTs were downloaded from the National Center for Biotechnology Information(NCBI) database to develop unigene-based simple sequence repeat(SSR) markers. The ESTs were assembled into 4053 unigenes and then analyzed to identify 374 SSRs using the MISA microsatellite identification tool. Among the 374 SSRs, 26 compound SSRs were observed.Primer pairs for these SSRs were designed using Primer3 version 1.14. To classify the functional annotation of ESTs and EST–SSRs, BLASTx searches(using E-value 1 × 10-5) against the public UniP rot(http://www.uniprot.org/) and NCBI(http://www.ncbi.nlh.nih.gov/) databases were performed. Further functional annotation was performed using PLAZA(version3.0) comparative genomics and GO annotation was summarized using the Plant GO slim category. Among the synthesized 312 primers, 219 successfully amplified Lens DNA. A diverse panel of 24 Lens genotypes was used to identify polymorphic markers. A polymorphic set of 57 markers successfully discriminated the test genotypes. This set of polymorphic markers with functional annotation data could be used as molecular tools in lentil breeding.

Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology

The pinto bean is one of widely consumed legume crop that constitutes over 42%of the U.S dry bean production.However,limited studies have been conducted in past to assess its quantitative and qualitative yield potentials.Emerging remote sensing technologies can help in such assessment.Therefore,this study evaluates the role of ground-based multispectral imagery derived vegetation indices(VIs)for irrigated the pinto bean stress and yield assessments.Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52%and 100%of required evapotranspiration.Imagery data was acquired using a five-band multispectral imager at early,mid and late growth stages.Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential.Principal component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation(rs)with abiotic stress at each growth stage.Transformed difference vegetation index,nonlinear vegetation index(NLI),modified NLI and infrared percentage vegetation index(IPVI)were consistent in accounting the stress response and crop yield at all growth stages(rs>0.60,coefficient of determination(R2):0.50–0.56,P<0.05).Ten other VIs significantly accounted for crop stress at early and late stages.Overall,identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments.

Generalized Linear Model with Elastic Net Regularization and Convolutional Neural Network for Evaluating Aphanomyces Root Rot Severity in Lentil

Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping approaches.The utilization of such technologies has enabled the generation of multidimensional plant traits creating big datasets.However,to harness the power of phenomics technologies,more sophisticated data analysis methods are required.In this study,Aphanomyces root rot(ARR)resistance in 547 lentil accessions and lines was evaluated using Red-Green-Blue(RGB)images of roots.We created a dataset of 6,460 root images that were annotated by a plant breeder based on the disease severity.Two approaches,generalized linear model with elastic net regularization(EN)and convolutional neural network(CNN),were developed to classify disease resistance categories into three classes:resistant,partially resistant,and susceptible.The results indicated that the selected image features using EN models were able to classify three disease categories with an accuracy of up to 0:91±0:004(0:96±0:005 resistant,0:82±0:009 partially resistant,and 0:92±0:007 susceptible)compared to CNN with an accuracy of about 0:84±0:009(0:96±0:008 resistant,0:68±0:026 partially resistant,and 0:83±0:015 susceptible).The resistant class was accurately detected using both classification methods.However,partially resistant class was challenging to detect as the features(data)of the partially resistant class often overlapped with those of resistant and susceptible classes.Collectively,the findings provided insights on the use of phenomics techniques and machine learning approaches to provide quantitative measures of ARR resistance in lentil.

Evaluation of biogenic markers-based phenotyping for resistance to Aphanomyces root rot in field pea

Biogenic volatile organic compounds(VOCs)emitted by plants can reveal information about plant adaptation,defense processes,and biological pathways.Thus,such VOC data may be utilized to capture phenotypic plant responses to the environment.In this study,the main objective was to evaluate the potential of biogenic compounds,including VOCs,to phenotype two pea cultivars,Ariel(susceptible)and Hampton(high levels of partial resistance)for resistance to Aphanomyces root rot disease.Plants were monitored non-destructively for VOC emission at three-time points(15,20,and 30 days after inoculation,DAI)using dynamic headspace sampling with gas chromatography-flame ionization detec-tion(GC-FID)system,as well as destructively at the end of the experiments,using solvent extraction and pyrolysis of both shoot and root tissues.A non-inoculated control(mock-inoculated with distilled water)was utilized to compare the plant responses within a cul-tivar.The common chemical peaks between control and inoculated samples of both culti-vars(RT_(cm))were analyzed after normalizing the relative peak intensity of inoculated samples with those of control samples,prior to a comparison between cultivars.In addi-tion,unique chemical peaks(RT_(uq))present in inoculated samples,but not in control sam-ples were also identified and their relative peak intensities were compared.Among the released green leaf volatiles(RT_(cm)),the normalized relative peak intensity of hexanal emis-sion,at 20 DAI,was higher in Ariel than that of Hampton.In addition,several putative chemical peaks(both RT_(cm) and RT_(uq)),previously known as indicators for disease response,exhibited some differences in their emission rates between pea cultivars in at least one of the time points.The destructive sampling revealed that shoot samples produced more putative unique biomarkers(RT_(uq))than the root samples.Based on the differences in puta-tive chemical peaks between cultivars,this initial study supports the concept of utilization of biogenic biomarker-based phenotyping in distinguishing levels of resistance in the eval-uated pea cultivars.More research is needed to further this approach for phenotyping other plant cultivars.Upon validation,the VOC profile integrated with high-throughput VOC sensing techniques can serve as a novel mechanism for phenotyping disease responses in crops.