bioRxivPlant BiologyPreprint
Engineering the NAT-ULT module increases seed number per pod in soybean
Cuong Xuan NguyeniD, Jae Hyo SongiD, Vikranth KumariD, Manh V. Nguyen, Sterling Evans, Shunhong Dai, Minviluz G. StaceyiD
Abstract
Seed number per pod is a major soybean yield-component trait, yet experimentally validated causal modules remain poorly defined. Here, we identify and functionally validate a soybean NAT-ULT regulatory module controlling ovule and seed number per pod. Through a visual screen of a fast-neutron-irradiated Glycine max cv. Williams 82 population, we isolated MO27, which increased four-seeded pod frequency from approximately 2-4% in wild type to 27-30%, without major changes in plant architecture, total pod number, or seed morphology. Comparative genome hybridization mapped the phenotype to a 46.7-kb chromosome 2 deletion containing GmNAT1 and chromosome 6 deletions encompassing GmULT-B1 and GmULT-B3. Ovule-clearing analysis showed that increased seed number originated before fertilization through elevated formation of four-ovule ovaries. CRISPR/Cas9-mediated mutagenesis of GmNAT1, GmULT-B1, and GmULT-B3 increased four-seeded pod frequency without detectable penalties in 100-seed weight, total pod number, or plant height. Tomato SlULT triple mutants also increased floral organ and locule number, supporting conserved ULT-like function in reproductive organ patterning. RNA-seq interaction analysis revealed a chromosome 6-dominant transcriptional state, accompanied by selective non-additive reprogramming of hormone/ovule-number and cell-wall-remodeling modules. Integrated genetic and transcriptomic analyses support a hierarchical model in which NAT-ULT perturbation may enhance meristematic competence and remodel reproductive developmental programs, while additional fast-neutron-background-associated transcriptional changes may further modulate the phenotype. These findings define NAT-ULT as a molecular target for engineering soybean seed number per pod.
- organism: plant (other)
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 0
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