Back

Isogenic reciprocal grafts with transgenic HaHB11 plants dissect shoot and root contributions to yield in field-grown soybean: a multi-omic study

Raineri, J.; Rositto, G.; Arce, A. L.; Otegui, M. E.; Chan, R. L.

2026-08-20 plant biology
10.64898/2026.08.15.744928 bioRxiv
Show abstract

Soybean must coordinate root and shoot signals to optimize yield. Grafting is a powerful tool to study this communication. However, most studies compare contrasting genotypes and cannot separate genotype from graft combination effects. Here we used isogenic soybean lines to dissect root and shoot contributions in the field. These lines differ from controls in a single gene, either HaHB11 or HaHB4, two sunflower HD-Zip I transcription factors associated with increased grain number. Unexpectedly, heterografted plants outperformed homografts in several yield-related traits, an effect not previously documented in soybean. This advantage was reproduced with both HaHB11 and HaHB4 scions, suggesting the effect is not gene-specific. Under non-stress conditions the scion governed yield-related traits, particularly pod number, as well as the leaf transcriptome, whereas both organs left subtle metabolic signatures. The root contribution was minor and confined to the R6-R7 transition, where it was specific to HaHB11. The highest-yielding combination was a control rootstock with an HaHB11 scion (CH11), which increased grain number by [~]30% over the best homograft. CH11 showed higher stomatal conductance and lower leaf temperature; yet CH11 and HaHB11 homografts were remarkably similar, sharing higher stomatal density, differing in only four leaf-expressed genes, and lacking a metabolomic signature. Thus, under non-stress conditions, soybean grain number is governed by the scion and the graft combination, and accompanied by early physiological differences rather than by leaf molecular reprogramming.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.