A QTL on Maize Chromosome 5 is Associated with Root-Knot Nematode Resistance.

This study provides the first report of a quantitative trait locus (QTL) in maize (Zea mays) for resistance to the southern root-knot nematode (SRKN) (Meloidogyne incognita). The SRKN can feed on the roots of maize in the U.S. Southern Coastal Plain region and can cause yield losses of 30% or greater in heavily infested fields. Increases in SRKN density in the soil may reduce the yield for subsequently planted susceptible crops. The use of maize hybrids with resistance to SRKN could prevent an increase in SRKN density, yet no genetic regions have been identified that confer host resistance. In this study, a B73 (susceptible) x Ky21 (resistant) S5 recombinant inbred line (RIL) population was phenotyped for total number of eggs (TE) and root weight. This population has been previously genotyped using single nucleotide polymorphisms (SNPs). By utilizing the SNP data with the phenotype data, a single QTL was identified on chromosome 5 that explained 15% of the phenotypic variation (PV) for the number of eggs and 11% of the PV for the number of eggs per g of root (EGR). Plants that were homozygous for the Ky21 allele for the most associated marker PZA03172.3 had fewer eggs and fewer EGR than the plants that were homozygous or heterozygous for the B73 allele. Thus, the first QTL for SRKN resistance in maize has been identified and could be incorporated into maize hybrids.

Genetic diversity, population structure and anthracnose resistance response in a novel sweet sorghum diversity panel.

Sweet sorghum is an attractive feedstock for the production of renewable chemicals and fuels due to the readily available fermentable sugars that can be extracted from the juice, and the additional stream of fermentable sugars that can be obtained from the cell wall polysaccharides in the bagasse. An important selection criterion for new sweet sorghum germplasm is resistance to anthracnose, a disease caused by the fungal pathogen Colletotrichum sublineolum. The identification of novel anthracnose-resistance sources present in sweet sorghum germplasm offers a fast track towards the development of new resistant sweet sorghum germplasm. We established a sweet sorghum diversity panel (SWDP) of 272 accessions from the USDA-ARS National Plant Germplasm (NPGS) collection that includes landraces from 22 countries and advanced breeding material, and that represents ~15% of the NPGS sweet sorghum collection. Genomic characterization of the SWDP identified 171,954 single nucleotide polymorphisms (SNPs) with an average of one SNP per 4,071 kb. Population structure analysis revealed that the SWDP could be stratified into four populations and one admixed group, and that this population structure could be aligned to sorghum's racial classification. Results from a two-year replicated trial of the SWDP for anthracnose resistance response in Texas, Georgia, Florida, and Puerto Rico showed 27 accessions to be resistant across locations, while 145 accessions showed variable resistance response against local pathotypes. A genome-wide association study identified 16 novel genomic regions associated with anthracnose resistance. Four resistance loci on chromosomes 3, 6, 8 and 9 were identified against pathotypes from Puerto Rico, and two resistance loci on chromosomes 3 and 8 against pathotypes from Texas. In Georgia and Florida, three resistance loci were detected on chromosomes 4, 5, 6 and four on chromosomes 4, 5 (two loci) and 7, respectively. One resistance locus on chromosome 2 was effective against pathotypes from Texas and Puerto Rico and a genomic region of 41.6 kb at the tip of chromosome 8 was associated with resistance response observed in Georgia, Texas, and Puerto Rico. This publicly available SWDP and the extensive evaluation of anthracnose resistance represent a valuable genomic resource for the improvement of sorghum.

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

OBJECTIVES: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project.

OBJECTIVES: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

Yield prediction through integration of genetic, environment, and management data through deep learning.

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield-those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

Insect Feeding on Sorghum bicolor Pollen and Hymenoptera Attraction to Aphid-Produced Honeydew.

Pollinators are declining globally, potentially reducing both human food supply and plant diversity. To support pollinator populations, planting of nectar-rich plants with different flowering seasons is encouraged while promoting wind-pollinated plants, including grasses, is rarely recommended. However, many bees and other pollinators collect pollen from grasses which is used as a protein source. In addition to pollen, Hymenoptera may also collect honeydew from plants infested with aphids. In this study, insects consuming or collecting pollen from sweet sorghum, Sorghum bicolor, were recorded while pan traps and yellow sticky card surveys were placed in grain sorghum fields and in areas with Johnsongrass, Sorghum halepense to assess the Hymenoptera response to honeydew excreted by the sorghum aphid (SA), Melanaphis sorghi. Five genera of insects, including bees, hoverflies, and earwigs, were observed feeding on pollen in sweet sorghum, with differences observed by date, but not plant height or panicle length. Nearly 2000 Hymenoptera belonging to 29 families were collected from grain sorghum with 84% associated with aphid infestations. About 4 times as many Hymenoptera were collected in SA infested sorghum with significantly more ants, halictid bees, scelionid, sphecid, encyrtid, mymarid, diapriid and braconid wasps were found in infested sorghum plots. In Johnsongrass plots, 20 times more Hymenoptera were collected from infested plots. Together, the data suggest that sorghum is serving as a pollen food source for hoverflies, earwigs, and bees and sorghum susceptible to SA could provide energy from honeydew. Future research should examine whether planting strips of susceptible sorghum at crop field edges would benefit Hymenoptera and pollinators.

Enhancer Regulation of Dopaminergic Neurochemical Transmission in the Striatum.

The trace amine-associated receptor 1 (TAAR1) is a Gs protein-coupled, intracellularly located metabotropic receptor. Trace and classic amines, amphetamines, act as agonists on TAAR1; they activate downstream signal transduction influencing neurotransmitter release via intracellular phosphorylation. Our aim was to check the effect of the catecholaminergic activity enhancer compound ((-)BPAP, (R)-(-)-1-(benzofuran-2-yl)-2-propylaminopentane) on neurotransmitter release via the TAAR1 signaling. Rat striatal slices were prepared and the resting and electrical stimulation-evoked [3H]dopamine release was measured. The releaser (±)methamphetamine evoked non-vesicular [3H]dopamine release in a TAAR1-dependent manner, whereas (-)BPAP potentiated [3H]dopamine release with vesicular origin via TAAR1 mediation. (-)BPAP did not induce non-vesicular [3H]dopamine release. N-Ethylmaleimide, which inhibits SNARE core complex disassembly, potentiated the stimulatory effect of (-)BPAP on vesicular [3H]dopamine release. Subsequent analyses indicated that the dopamine-release stimulatory effect of (-)BPAP was due to an increase in PKC-mediated phosphorylation. We have hypothesized that there are two binding sites present on TAAR1, one for the releaser and one for the enhancer compounds, and they activate different PKC-mediated phosphorylation leading to the evoking of non-vesicular and vesicular dopamine release. (-)BPAP also increased VMAT2 operation enforcing vesicular [3H]dopamine accumulation and release. Vesicular dopamine release promoted by TAAR1 evokes activation of D2 dopamine autoreceptor-mediated presynaptic feedback inhibition. In conclusion, TAAR1 possesses a triggering role in both non-vesicular and vesicular dopamine release, and the mechanism of action of (-)BPAP is linked to the activation of TAAR1 and the signal transduction attached.

Genome-wide association mapping of resistance to the sorghum aphid in Sorghum bicolor.

Since 2013, the sorghum aphid (SA), Melanaphis sorghi (Theobald), has been a serious pest that hampers all types of sorghum production in the U.S. Known sorghum aphid resistance in sorghum is limited to a few genetic regions on SBI-06. In this study, a subset of the Sorghum Association Panel (SAP) was used along with some additional lines to identify genomic regions that confer sorghum aphid resistance. SAP lines were grown in the field and visually evaluated for SA resistance during the growing seasons of 2019 and 2020 in Tifton, GA. In 2020, the SAP accessions were also evaluated for SA resistance in the field using drone-based high throughput phenotyping (HTP). Flowering time was recorded in the field to confirm that our methods were sufficient for identifying known quantitative trait loci (QTL). This study combined phenotypic data from field-based visual ratings and reflectance data to identify genome-wide associated (GWAS) marker-trait associations (MTA) using genotyping-by-sequencing (GBS) data. Several MTAs were identified for SA-related traits across the genome, with a few common markers that were consistently identified on SBI-08 and SBI-10 for aphid count and plant damage, as well as loci for reflectance-based traits on SBI-02, SBI-03, and SBI-05. Candidate genes encoding leucine-rich repeats (LRR), Avr proteins, lipoxygenases (LOXs), calmodulins (CAM) dependent protein kinase, WRKY transcription factors, flavonoid biosynthesis genes, and 12-oxo-phytodienoic acid reductase were identified near SNPs that had significant associations with different SA traits. In this study, flowering time-related genes were also identified as a positive control for the methods. The total phenotypic variation explained by significant SNPs across SA-scored traits, reflectance data, and flowering time ranged from 6 to 61%, while the heritability value ranged from 4 to 69%. This study identified three new sources of resistant lines to sorghum aphid. These results supported the existing literature, and also revealed several new loci. Markers identified in this study will support marker-assisted breeding for sorghum aphid resistance.

Transfer of Meloidogyne incognita Resistance Using Marker-assisted Selection in Sorghum.

Meloidogyne incognita is a wide-spread and damaging pathogen of many important crops in the southern United States, and most sorghum genotypes allow significant levels of reproduction by the nematode. A series of greenhouse evaluations were conducted to determine whether a quantitative trait locus (QTL) that imparts a high level of resistance to Meloidogyne incognita in sorghum can effectively be transferred into diverse sorghum genotypes using marker assisted selection. Using marker-assisted selection, the resistance QTL, QTL-Sb.RKN.3.1, from 'Honey Drip' sorghum was crossed into five different sorghum backgrounds that included forage, sweet, and grain sorghum until the BC1F6 generation. Repeated greenhouse experiments documented that the recurrent parent genotypes were all susceptible to M. incognita and statistically similar to each other. In contrast, the BC1F6 genotypes were all highly resistant and similar to each other and similar to the resistant standard, 'Honey Drip'. These results suggest that this resistance QTL could be introgressed using marker assisted selection into many sorghum genotypes and confer a high level of resistance to M. incognita. Thus, this QTL and its associated markers will be useful for sorghum breeding programs to incorporate M. incognita resistance into their sorghum lines.

The inheritance of anthracnose (Colletotrichum sublineola) resistance in sorghum differential lines QL3 and IS18760.

Anthracnose caused by the fungal pathogen C. sublineola is an economically important constraint on worldwide sorghum production. The most effective strategy to safeguard yield is through the introgression of resistance alleles. This requires elucidation of the genetic basis of the different resistance sources that have been identified. In this study, 223 recombinant inbred lines (RILs) derived from crossing anthracnose-differentials QL3 (96 RILs) and IS18760 (127 RILs) with the common susceptible parent PI609251 were evaluated at four field locations in the United States (Florida, Georgia, Texas, and Puerto Rico) for their anthracnose resistance response. Both RIL populations were highly susceptible to anthracnose in Florida and Georgia, while in Puerto Rico and Texas they were segregating for anthracnose resistance response. A genome scan using a composite linkage map of 982 single nucleotide polymorphisms (SNPs) detected two genomic regions of 4.31 and 0.85 Mb on chromosomes 4 and 8, respectively, that explained 10-27% of the phenotypic variation in Texas and Puerto Rico. In parallel, a subset of 43 RILs that contained 67% of the recombination events were evaluated against anthracnose pathotypes from Arkansas (2), Puerto Rico (2) and Texas (4) in the greenhouse. A genome scan showed that the 7.57 Mb region at the distal end of the short arm of chromosome 5 is associated with the resistance response against the pathotype AMP-048 from Arkansas. Comparative analysis identified the genomic region on chromosome 4 overlaps with an anthracnose resistance locus identified in another anthracnose-differential line, SC414-12E, indicating this genomic region is of interest for introgression in susceptible sorghum germplasm. Candidate gene analysis for the resistance locus on chromosome 5 identified an R-gene cluster that has high similarity to another R-gene cluster associated with anthracnose resistance on chromosome 9.

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Electroactivity of polyphenols in sweet sorghum (Sorghum bicolor (L.) Moench) cultivars.

Polyphenols and other potential health-promoting components of sorghum (Sorghum bicolor (L.) Moench) drove its recent growth in the U.S. consumer food industry. Linear sweep (cyclic voltammetry, CV) and differential (cyclic differential pulse) voltammetry methods were developed to detect target polyphenols and amino acids in sweet sorghum juice without interference from the dominant secondary (trans-aconitic acid) and primary (sucrose) metabolites. Of 24 cultivars investigated, No.5 Gambela showed the highest electron-donating capacity, as indicated by the highest peak area, height, and peak anodic potential. Pearson's correlation analysis indicated the contribution of polyphenols (rather than amino acids) on CV voltammograms of juice samples. The Eh-pH values of 173 sweet sorghum juice samples collected in 2017 aligned with quercetin model polyphenol. Accumulation of quercetin-like polyphenols in No.5 Gambela could offer antioxidant-rich juice for conversion to edible syrup as well as an increased tolerance against a recently emerged pest, sugarcane aphid [(Melanaphis sacchari (Zehntner)].

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

OBJECTIVES: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Genomic Dissection of Anthracnose (Colletotrichum sublineolum) Resistance Response in Sorghum Differential Line SC112-14.

Sorghum production is expanding to warmer and more humid regions where its production is being limited by multiple fungal pathogens. Anthracnose, caused by Colletotrichum sublineolum, is one of the major diseases in these regions, where it can cause yield losses of both grain and biomass. In this study, 114 recombinant inbred lines (RILs) derived from resistant sorghum line SC112-14 were evaluated at four distinct geographic locations in the United States for response to anthracnose. A genome scan using a high-density linkage map of 3,838 single nucleotide polymorphisms (SNPs) detected two loci at 5.25 and 1.18 Mb on chromosomes 5 and 6, respectively, that explain up to 59% and 44% of the observed phenotypic variation. A bin-mapping approach using a subset of 31 highly informative RILs was employed to determine the disease response to inoculation with ten anthracnose pathotypes in the greenhouse. A genome scan showed that the 5.25 Mb region on chromosome 5 is associated with a resistance response to nine pathotypes. Five SNP markers were developed and used to fine map the locus on chromosome 5 by evaluating 1,500 segregating F2:3 progenies. Based on the genotypic and phenotypic analyses of 11 recombinants, the locus was narrowed down to a 470-kb genomic region. Following a genome-wide association study based on 574 accessions previously phenotyped and genotyped, the resistance locus was delimited to a 34-kb genomic interval with five candidate genes. All five candidate genes encode proteins associated with plant immune systems, suggesting they may act in synergy in the resistance response.

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Synthetic enhancer compounds, besides acting on biogenic amine system, influence the glutamate transmission and stress response.

Pharmaceutically available enhancer selegiline/(-)-deprenyl (DEP) in the clinically used dose shows antidepressant effect, but nothing is known about this effect in enhancer dose, and its effect on co-morbid anxiety. Moreover, data about the antidepressant/antianxiety effects of the serotonin-influencing enhancer, (2R)-1-(1-benzofuran-2-yl)-N-propylpentane-2-amine (BPAP) are also missing. The aim of the present paper is to establish the role of enhancer regulation in anxiety and follow the changes in the phosphorylation of glutamate subunits in prefrontal cortex as well as stress-related organ and hormonal changes as possible background mechanism. The effect of 3-week-treatment of rats with specific (0.001 mg/kg for DEP, 0.0001 mg/kg for BPAP) and non-specific (0.1 mg/kg for DEP, 0.05 mg/kg for BPAP) enhancer doses were evaluated on anxiety-like behavior in the elevated plus maze (EPM) and open-field (OF) tests. Phosphorylated glutamatergic GluR1 and GluN2B subunits were analyzed by Western blot. Changes in the stress-regulatory system were evaluated by measuring the organ weights and blood corticosterone concentrations. Non-specific enhancer doses had a tendency for anxiolysis on EPM, while only 0.1 mg/kg DEP elevated motility in OF. Specific enhancer doses significantly increased the expression of both glutamatergic receptor subunits; non-specific doses elevated only pGluR1. Treatments had no effects on stress-like organ weights; however, the specific enhancer doses significantly reduced the dark phase resting corticosterone levels. The study proved the enhancer-sensitivity of the glutamatergic transmitter system and suggested enhancer-induced stabilization of stress-hormone levels without major impact on non-stimulated anxiety-like behavior.

Accumulation of Carboxylate and Aromatic Fluorophores by a Pest-Resistant Sweet Sorghum [Sorghum bicolor (L.) Moench] Genotype.

The sugary juice from sweet sorghum [Sorghum bicolor (L.) Moench] stalks can be used to produce edible syrup, biofuels, or bio-based chemical feedstock. The current cultivars are highly susceptible to damage from sugarcane aphids [Melanaphis sacchari (Zehntner)], but development of new cultivars is hindered by a lack of rapid analytical methods to screen for juice quality traits. The mechanism of aphid resistance/tolerance is also largely unknown, though the importance of defense phytochemicals has been suggested. The purpose of this study was to develop low-cost methods sensitive to fluorescent fingerprints in sweet sorghum juice, which is a complex mixture of saccharides, carboxylates, polyphenols, and metal ions. Of primary juice components, tryptophan and trans-aconitic acid were the highest intensity contributors to the overall fluorescence and UV/visible absorbance, respectively, while tyrosine and polyphenols contributed to a less extent. In a test of 24 sweet sorghum cultivars, tryptophan and tyrosine contents were the highest in the aphid-susceptible hybrid N109A x Chinese, while sucrose, trans-aconitic acid, and polyphenols were the highest in the resistant line No. 5 Gambela. This suggests that the accumulation of carboxylate (trans-aconitic acid) and polyphenolic secondary products in No. 5 Gambela may contribute to its aphid resistance, thus allowing it to maintain sucrose production. Rapid detection of these chemical signatures could be used to prescreen the breeding material for potential resistance and juice quality traits, without analytical separation required for metabolomics.

A Novel QTL for Root-Knot Nematode Resistance is Identified from a South African Sweet Sorghum Line.

Southern root-knot nematodes, Meloidogyne incognita, feed on the underground portions of hundreds of plant species and affect nutrient partitioning and water uptake of the host plants. Sorghum (Sorghum bicolor) is often not significantly damaged by southern root-knot nematodes (RKN) but some sorghum genotypes support greater population densities of RKN than other genotypes. These higher nematode populations increase the risk of damage to subsequently planted susceptible crops. A previous study identified a major quantitative trait locus (QTL) for RKN resistance on sorghum chromosome (chr.) 3. To maintain long-term resistance, multiple resistance genes should be pyramided in a cultivar. In this study, we identified a new source of RKN resistance, created a mapping population, and identified single-nucleotide polymorphism markers using genotyping-by-sequencing of the segregating population. Use of single-marker analysis and composite interval mapping identified a single QTL on chr. 5 that was associated with egg number and egg number per gram of root from the resistant sweet sorghum line PI 144134. This region on chr. 5 and the prior QTL on chr. 3 can be potentially moved from PI 144134 and Honey Drip, respectively, into elite sorghum germplasm via marker-assisted selection for more durable resistance.

Rapid Data Analytics to Relate Sugarcane Aphid [(Melanaphis sacchari (Zehntner)] Population and Damage on Sorghum (Sorghum bicolor (L.) Moench).

Sugarcane aphid [(Melanaphis sacchari (Zehntner)] emerged in the United States in 2013 as a new pest infesting sorghum (Sorghum bicolor (L.) Moench). Aphid population and plant damage are assessed by field scouting with mean comparison tests or repeated regression analysis. Because of inherently large replication errors from the field and interactions between treatments, new data analytics are needed to rapidly visualize the pest emergence trend and its impact on plant damage. This study utilized variable importance in the projection (VIP) and regression vector statistics of partial least squares (PLS) modeling to deduce directional relationships between aphid population and leaf damage from biweekly field monitoring (independent variable) and chemical composition (dependent variable) of 24 sweet sorghum cultivars. Regardless of environment, aphid population increase preceded the maximum damage rating. Greater damage rating at earlier growth stage in 2015 than 2016 led to an overall higher damage rating in 2015 than 2016. This trend in damage coincided with higher concentrations of trans-aconitic acid and polyphenolic secondary products in stem juice in 2016 than 2015, at the expense of primary sugar production. Developed rapid data analytics could be extended to link phenotypes to perturbation parameters (e.g., cultivar and growth stage), enabling integrated pest management.