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: Kernel dehydration rate (KDR) is a crucial production trait that affects mechanized harvesting and kernel quality in maize; however, the underlying mechanisms remain unclear. Here, we identified a quantitative trait locus (QTL), qKDR1, as a non-coding sequence that regulates the expression of qKDR1 REGULATED PEP- TIDE GENE (RPG). RPG encodes a 31 amino acid micropeptide, microRPG1, which controls KDR by precisely modulating the expression of two genes, ZmETHYLENE-INSENSITIVE3-like 1 and 3, in the ethylene signaling pathway in the kernels after filling. microRPG1 is a Zea genus-specific micropeptide and originated de novo from a non-coding sequence. Knockouts of microRPG1 result in faster KDR in maize. By contrast, overex- pression or exogenous application of the micropeptide shows the opposite effect both in maize and Arabi- dopsis. Our findings reveal the molecular mechanism of microRPG1 in kernel dehydration and provide an important tool for future crop breeding.

Biography: Dr. Yan received his B.S. in 1999 and Ph. D in 2003 from Huazhong Agricultural University (HZAU). 2003-2006, he worked in China Agricultural University as lecture, associate professor. From 2006-2008, he did his post doctorate research in International Maize and Wheat Improvement Center (CIMMYT) and Cornell University. From 2009 to 2011, he worked in CIMMYT as an associate scientist and scientist. 2011-now, he works in HZAU as a professor. His research interests focus on: Maize Genomics and the Genetic Improvement of Quality Traits based on big data and Single Cell Sequencing Technology Development and Application in Plants. Till data, he published more than 100 papers in international journals including of Nature Genetics, Nature Communications, PNAS, PLoS Genet, Plant Cell, New Phytologsit, Molecular Plant and so on.

Home page: www.maizego.org

Honors:
Ø Japan International Award for Yong Agricultural Researchers 2010
Ø Dupont Young Professor Award 2011
Ø Hubei Chutian Scholar Professor, 2011 (湖北楚天特聘教授)
Ø New Century Excellent Talents in University, 2011 （教育部新世纪人才计划）
Ø Natural Science Foundation for Excellent Youth, 2012 （优秀青年基金）
Ø Hubei “Five Four” Youth Medal, 2012（湖北五四青年奖章）
Ø The National Top-notch Youth Support Program, 2013 （“万人计划”青年拔尖人才）
Ø China National Funds for Distinguished Young Scientists, 2015 （国家自然科学基金委“杰青”）
Ø Yangtze River Scholars Distinguished Professor, 2016 （教育部“长江学者”）
Ø Overseas Chinese Contribution Award, 2018 （第七届中国侨界贡献奖二等奖）
Ø The L. Stadler Mid-Career Award, 2022 （刘易斯•斯塔德勒中期职业生涯奖）
Ø The 3rd National Award for Outstanding Innovation, 2023 （第三届全国创新争先奖）

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.