The 11th International Conference on Agricultural and Biological Sciences (ABS 2025)

Abstract: Shoot branching is an important trait in both agriculture and horticulture, as the number of axillary buds directly influences crop yield and seed production. Strigolactones (SLs) are a class of plant hormones that inhibit shoot branching in plants. In SL biosynthesis, carlactone, a biosynthetic precursor of SLs, is synthesized from β-carotene through sequential reactions catalyzed by the β-carotene isomerase DWARF27 (D27) and carotenoid cleavage dioxygenases 7 and 8 (CCD7 and CCD8). Carlactone is then converted to carlatonoic acid (CLA) via oxidation by cytochrome P450 encoded by the CYP711A gene family. CLA is further metabolized into various types of SLs. To date, more than 30 canonical and non-canonical SLs have been identified from various plants. However, the specific bioactive SLs for shoot branching inhibition remain unidentified. In our previous research, we collected SL biosynthesis mutants in the tomato cultivar Micro-Tom to evaluate the roles of SLs in tomato, but SL signaling mutants were not available. Bioactive SLs are perceived by DWARF14 (D14), a member of the α/β-fold hydrolase superfamily. Since bioactive phytohormones tend to accumulate in signaling mutants, we hypothesized that SLs involved in shoot branching inhibition might be enriched in sld14 mutants. Therefore, we generated sld14 mutants in Micro-Tom by genome editing. Our analysis revealed that 16-hydroxymethyl carlactonoate (16-HO-MeCLA) significantly accumulated in the nodes of the mutants compared to the wild type. We also found that CYP722A is associated with the 16-hydroxylation of CLA. 16-HO-MeCLA or the metabolites may serve as bioactive SLs. To further elucidate the physiological roles of 16-HO-MeCLA, we plan to investigate the function of the CYP722A gene.

Biography: Mikihisa UMEHARA is a Professor of Department of Biological Resources and Graduate School of Life Sciences, Toyo University, Japan. His major is plant physiology and plant biotechnology. He graduated from the University of Tsukuba in 1997, finished a doctor’s course at the Graduate School of Biological Sciences, the University of Tsukuba in 2004, and obtained Ph.D. in Science. He worked on onion breeding in Department of Biotechnology, Fukuoka Agricultural Research Center from 2004 to 2007. He joined RIKEN Plant Science Center as a special postdoctoral researcher in 2007 and worked on a class of plant hormones, strigolactones. In 2011, he moved to Toyo University as a associate professor, and became a full professor in 2015.

Abstract: Genome-wide association study (GWAS) is a key gene mining method and has played a significant role in the genetic dissection of complex traits in animals, plants, and humans. However, it still has shortcomings in both theory and application. The genome-wide scanning framework of existing methods does not take advantage of the AI era. Methods based on allele substitution effect (α) have low power to detect dominant, overdominant, rare, and small-α QTNs. There are almost no feasible methods available to detect QTN-by-environment (QEI) and QTN-by-QTN (QQI) interactions. These result in some important loci and trait heritability being missed. To address these issues, we have established a compressed variance component mixed model and a 3VmrMLM method. In this model, all possible effects are considered and all possible polygenic genetic backgrounds are controlled. The model is then used for genome-wide scanning to identify potential QTNs, QEIs, and QQIs. Some of these are then identified as significant QTNs, QEIs, and QQIs using machine learning. This is our two-stage method with a genome-wide scanning plus machine learning framework. This simple model can be used to detect QTNs, QEIs, and QQIs uniformly. Genetic dissection and breeding by design for polygenic traits remain substantial challenges, and its calculation speed still needs to be improved, particularly in the era of big data and AI. Thus, we have integrated advanced statistical and computer technologies with 3VmrMLM to propose Fast3VmrMLM for identifying abundant and key genes for polygenic traits. The advantages of this approach are as follows. In the 18K rice dataset containing ~3 million SNPs, more dominant, small-effect and rare QTNs were identified; 211 known genes and 384 candidate genes for 14 traits were detected; and each trait took 3.30 h to analyze. Furthermore, the SNP marker expanded to include bin/gene haplotypes, lncRNA types, and structural variations, making mQTL detection possible. In the maize NC II breeding population containing over 30 million markers, 26 known genes and 24 candidate genes were found to be associated with seven yield-related traits. These QTNs were then used to predict excellent hybrid combinations. More importantly, a genetic network for rice yield-related traits was constructed using all the known and candidate genes. The key genes in this network were identified and a breeding strategy utilizing these key genes was proposed. The software packages IIIVmrMLM and Fast3VmrMLM can be downloaded for free from https://github.com/YuanmingZhang65. Additionally, the new methods for QEI and QQI detection in big data were introduced briefly.

Biography: Yuan-Ming Zhang is a professor of statistical genomics at Huazhong Agricultural University (HZAU) in China. His major is quantitative genetics. He obtained his Bachelor's degree from Southwest Agricultural University in 1986, followed by his Master’s degree and PhD from Nanjing Agricultural University (NAU) in 1992 and 2001 respectively. He worked on the Rongchang campus of Southwest University from 1986 to 1999 before moving to NAU, and then to HZAU in 2014. He was a postdoctoral fellow at the University of California, Riverside, from 2003 to 2005, and became an associate professor in 1995, progressing to full professor in 2002. In 2022, he was selected as one of the world's top 2% of scientists. He is an editorial board member of Heredity, BMC Genomics, Front Plant Sci (Guest), and Acta Agronomica Sinica. He is also a council member of the Chinese Society of Agri-Biotechnology, and a full member of Sigma Xi, the Scientific Research Honor Society, and the Genetics Society UK. His team has developed several software packages for identifying genes associated with complex traits. These include mrMLM, IIIVmrMLM and Fast3VmrMLM for GWAS, GCIM for QTL mapping, dQTG-seq for bulked segregant analysis, and SEA for mixed inheritance analysis. They can be downloaded for free from https://github.com/YuanmingZhang65 and https://cran.r-project.org/web/packages/. He has published over 120 articles in journals such as Mol Plant, Plant Cell, Brief Bioinform and Heredity.

Abstract: The primary goal of the educational activities of universities is to provide specialized education for the development of human resources in the relevant field. On the other hand, in recent years, universities are also required to contribute to society. Agricultural science is an academic discipline that plays an important role in the agricultural industry, and its scope is extremely diverse. It is also a field related to food, which is directly related to human survival. With the modernization of society, roles have become increasingly divided, and people live by eating food without understanding how it is produced. As the world population exceeds 10 billion in the 21st century, and hunger is difficult to solve, it is necessary to develop food education that will develop all people to understand food production.

Biography: Dr. Hisayoshi Hayashi graduated from University of Tsukuba in 1980. After working as an extension officer in Nagano Prefecture for one year, he moved to Chushin Agricultural Experiment Station, where he worked in the field crop cultivation department for six years. He then moved to University of Tsukuba, where he served as a professor at the Laboratory of Crop Production Systems and the Laboratory of Crop Science, before being appointed professor emeritus at University of Tsukuba in April 2023. He is a former president of the Japanese Society of Farm Work Research and a fellow of Japan Association of International Commission of Agricultural and Biosystems Engineering. Since April 2023, he has been leading training programs for extension workers, researchers, and government officials in developing countries as a training advisor at Japan International Cooperation Agency Tsukuba Center (JICA Tsukuba).

Abstract: Large language models (LLMs), trained on massive datasets, are opening new frontiers in biology, especially when combined with prompt-based learning, retrieval-augmented generation (RAG), and AI agents. This presentation showcases our work leveraging these tools across multiple biological domains, such as plant science. We developed RAG and prompt refinement techniques to improve gene relationship prediction. We built AI agents for protein annotation and Fatplants (https://fatplants.net), our database of plant lipid-related genes and metabolism. In protein modeling, we introduced S-PLM, a contrastive learning-based, 3D structure-aware protein language model that enhances sequence-based predictions. Prompting protein language models further boosted tasks like signal peptide and targeting signal prediction. We also applied prompt-based learning to large single-cell RNA-seq models, improving several single-cell analysis tasks. In addition, we developed scPlantAnnotate, a plant-specific large single-cell RNA-seq model, for plant cell type annotation that significantly outperforms current reference-based methods across four plant species. Our findings demonstrate the transformative potential of LLMs and AI agents in advancing biological research.

Biography: Dong Xu is Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his Ph.D. from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016. Over the past 30 years, he has conducted research in many areas of computational biology and bioinformatics, including single-cell data analysis, protein structure prediction and modeling, protein post-translational modifications, protein localization prediction, computational systems biology, biological information systems, and bioinformatics applications in human, microbes, and plants. His research since 2012 has focused on the interface between bioinformatics and deep learning. He has published more than 500 papers with more than 28,000 citations and an H-index of 89 according to Google Scholar. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015 and American Institute for Medical and Biological Engineering (AIMBE) Fellow in 2020.