TILLING-by-Sequencing+ to Decipher Oil Biosynthesis Pathway in Soybeans: A New and Effective Platform for High-Throughput Gene Functional Analysis

Comparative genomics points to tandem duplications of SAD gene clusters as drivers of increased α-linolenic (ω-3) content in S. hispanica seeds

Salvia hispanica L. (chia) is a source of abundant ω-3 polyunsaturated fatty acids (ω-3-PUFAs) that are highly beneficial to human health. The genomic basis for this accrued ω-3-PUFA content in this emerging crop was investigated through the assembly and comparative analysis of a chromosome-level reference genome for S. hispanica. The highly contiguous 321.5-Mbp genome assembly covering all six chromosomes enabled the identification of 32,922 protein-coding genes. Two whole-genome duplications (WGD) events were identified in the S. hispanica lineage. However, these WGD events could not be linked to the high α-linolenic acid (ALA, ω-3) accumulation in S. hispanica seeds based on phylogenomics. Instead, our analysis supports the hypothesis that evolutionary expansion through tandem duplications of specific lipid gene families, particularly the stearoyl-acyl carrier protein desaturase (ShSAD) gene family, is the main driver of the abundance of ω-3-PUFAs in S. hispanica seeds. The insights gained from the genomic analysis of S. hispanica will help establish a molecular breeding target that can be leveraged through genome editing techniques to increase ω-3 content in oil crops.

Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage

Soybean (Glycine max (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide use, leading to reduced yields. Identifying the level of leaf damage aids yield projections, pesticide, and fertilizer decisions. Deep learning models (DLMs) and neural networks mastering tasks from abundant data have been used for binary healthy/unhealthy leaf classification. However, no DLM predicts and categorizes soybean leaf damage severity (five levels) for tailored pesticide use and yield forecasts. This paper introduces a novel DLM for accurate damage prediction and classification, trained on 2930 near-field soybean leaf images. The model quantifies damage severity, distinguishing healthy/unhealthy leaves and offering a comprehensive solution. Performance metrics include accuracy, precision, recall, and F1-score. This research presents a robust DLM for soybean damage assessment, supporting informed agricultural decisions based on specific damage levels and enhancing crop management and productivity.

Is it the end of TILLING era in plant science?

Since its introduction in 2000, the TILLING strategy has been widely used in plant research to create novel genetic diversity. TILLING is based on chemical or physical mutagenesis followed by the rapid identification of mutations within genes of interest. TILLING mutants may be used for functional analysis of genes and being nontransgenic, they may be directly used in pre-breeding programs. Nevertheless, classical mutagenesis is a random process, giving rise to mutations all over the genome. Therefore TILLING mutants carry background mutations, some of which may affect the phenotype and should be eliminated, which is often time-consuming. Recently, new strategies of targeted genome editing, including CRISPR/Cas9-based methods, have been developed and optimized for many plant species. These methods precisely target only genes of interest and produce very few off-targets. Thus, the question arises: is it the end of TILLING era in plant studies? In this review, we recap the basics of the TILLING strategy, summarize the current status of plant TILLING research and present recent TILLING achievements. Based on these reports, we conclude that TILLING still plays an important role in plant research as a valuable tool for generating genetic variation for genomics and breeding projects.

TILLING-by-Sequencing+ Reveals the Role of Novel Fatty Acid Desaturases (GmFAD2-2s) in Increasing Soybean Seed Oleic Acid Content

Soybean is the second largest source of oil worldwide. Developing soybean varieties with high levels of oleic acid is a primary goal of the soybean breeders and industry. Edible oils containing high level of oleic acid and low level of linoleic acid are considered with higher oxidative stability and can be used as a natural antioxidant in food stability. All developed high oleic acid soybeans carry two alleles; GmFAD2-1A and GmFAD2-1B. However, when planted in cold soil, a possible reduction in seed germination was reported when high seed oleic acid derived from GmFAD2-1 alleles were used. Besides the soybean fatty acid desaturase (GmFAD2-1) subfamily, the GmFAD2-2 subfamily is composed of five members, including GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E. Segmental duplication of GmFAD2-1A/GmFAD2-1B, GmFAD2-2A/GmFAD2-2C, GmFAD2-2A/GmFAD2-2D, and GmFAD2-2D/GmFAD2-2C have occurred about 10.65, 27.04, 100.81, and 106.55 Mya, respectively. Using TILLING-by-Sequencing+ technology, we successfully identified 12, 8, 10, 9, and 19 EMS mutants at the GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E genes, respectively. Functional analyses of newly identified mutants revealed unprecedented role of the five GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E members in controlling the seed oleic acid content. Most importantly, unlike GmFAD2-1 members, subcellular localization revealed that members of the GmFAD2-2 subfamily showed a cytoplasmic localization, which may suggest the presence of an alternative fatty acid desaturase pathway in soybean for converting oleic acid content without substantially altering the traditional plastidial/ER fatty acid production.