Agronomic performance of high oleic,low linolenic soybean in Tennessee

Soybean oil hydrogenation alters the linolenic acid molecule to prevent the oil from becoming rancid, however, health reports have indicated trans-fat caused by hydrogenation, is not generally regarded as safe. Typical soybeans contain approximately 80 g kg⁻¹ to 120 g kg⁻¹ linolenic acid and 240 g kg⁻¹ of oleic acid. In an effort to accommodate the need for high-quality oil, the United Soybean Board introduced an industry standard for a high oleic acid greater than 750 g kg⁻¹ and linolenic acid less than 30 g kg⁻¹ oil. By combing mutations in the soybean plant at four loci, FAD2-1A and FAD2-1B, oleate desaturase genes and FAD3A and FAD3C, linoleate desaturase genes, and seed oil will not require hydrogenation to prevent oxidation and produce high-quality oil. In 2017 and 2018, a study comparing four near-isogenic lines across multiple Tennessee locations was performed to identify agronomic traits associated with mutations in FAD3A and FAD3C loci, while holding FAD2-1A and FAD2-1B constant in the mutant (high oleic) state. Soybean lines were assessed for yield and oil quality based on mutations at FAD2-1 and FAD3 loci. Variations of wild-type and mutant genotypes were compared at FAD3A and FAD3C loci. Analysis using a generalized linear mixed model in SAS 9.4, indicated no yield drag or other negative agronomic traits associated with the high oleic and low linolenic acid genotype. All four mutations of fad2-1A, fad2-1B, fad3A, and fad3C were determined as necessary to produce a soybean with the new industry standard (>750 g kg⁻¹ oleic and <30 g kg⁻¹ linolenic acid) in a maturity group-IV-Late cultivar for Tennessee growers.     

Field Performance of High Oleic Soybeans with Mutant FAD2-1A and FAD2-1B Genes in Tennessee

Soybean [Glycine max (L.) Merr.] oil with high oleic acid (>75%) has increased oxidative stability and health benefits that are valuable for food, fuel, and industrial products. It has been determined that two naturally occurring mutations in genes FAD2-1A and FAD2-1B can combine to produce high oleic soybeans. The objective of this study was to test the effect of these mutant alleles on seed yield and oil and protein concentration. Molecular markers assisted in the creation of a population of 48 BC₃F_2:4 lines (93.75% expected genome commonality). Each line was classified into one of four genotypic groups where both FAD2-1A and FAD2-1B genes were either homozygous wild type or mutant, respectively. Twelve lines for each genotypic group were evaluated in three replications at six locations across Tennessee. There was no seed yield difference between the high oleic genotypic group and the other groups (P < 0.05). On the other hand, there were differences in fatty acid profiles and oil and protein concentrations. In combination, the mutant FAD2-1A and FAD2-1B alleles produced a mean of 803.1 g kg⁻¹ oleic acid. This is, on average, approximately 500 g kg⁻¹ more oleic acid compared to soybean lines with only one mutant FAD2-1 allele. The high oleic double mutant group had more total oil (228.0 g kg⁻¹) and protein (401.0 g kg⁻¹) compared to all other genotypic groups (P < 0.05). Overall, this specific combination of mutant FAD2-1A and FAD2-1B alleles appears to generate conventional high oleic soybeans without a yield drag.     

Non-destructive Determination of High Oleic Acid Content in Single Soybean Seeds by Near Infrared Reflectance Spectroscopy

Soybean [Glycine max (L.) Merr] with increased oleic acid is desirable to improve oxidative stability and functionality of soybean seed oil. Recently, soybean genotypes with high oleic acid (≥70 %) were developed by breeding programs. Efficient and effective identification of high oleic acid soybean genotypes using non-destructive near infrared reflectance (NIR) on whole seeds would greatly enhance progress in breeding programs. The objective of this study was to develop a calibration equation for NIR determination of high oleic acid from single soybean seeds. A total of 600 intact, single F₂ seeds were scanned by NIR. Spectral data were collected between 400 and 2,500 nm at 2 nm intervals. The relationship between NIR spectral patterns of each soybean seed and its oleic acid content was examined. The best predicted equations for oleic acid were selected on the basis of minimizing the standard error of cross-validation and increasing the coefficient of determination. Validation demonstrated that the equations for determining total oleic acid and over 50 % oleic acid content had high predictive ability (r ² = 0.91 and r ² = 0.99, respectively). To validate the newly developed equation, F₂ seeds from a different genetic background were tested. Again, high oleic acid from single soybean seeds was accurately predicted from various genetic backgrounds. Therefore, applying the calibration equations to NIR will be useful to rapidly and efficiently select high oleic acid soybean genotypes in breeding programs.     

Counting Fluorescent Dye Molecules on DNA Origami by Means of Photon Statistics

Obtaining quantitative information about molecular assemblies with high spatial and temporal resolution is a challenging task in fluorescence microscopy. Single‐molecule techniques build on the ability to count molecules one by one. Here, a method is presented that extends recent approaches to analyze the statistics of coincidently emitted photons to enable reliable counting of molecules in the range of 1–20. This method does not require photochemistry such as blinking or bleaching. DNA origami structures are labeled with up to 36 dye molecules as a new evaluation tool to characterize this counting by a photon statistics approach. Labeled DNA origami has a well‐defined labeling stoichiometry and ensures equal brightness for all dyes incorporated. Bias and precision of the estimating algorithm are determined, along with the minimal acquisition time required for robust estimation. Complexes containing up to 18 molecules can be investigated non‐invasively within 150 ms. The method might become a quantifying add‐on for confocal microscopes and could be especially powerful in combination with STED/RESOLFT‐type microscopy.     

A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient''s disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [¹⁸F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.