Latest 4 Papers

MATERNAL AUTOPHAGY CONTRIBUTES TO GRAIN YIELD IN MAIZE

Authors: Tang, J., Avin-Wittenberg, T., Vollbrecht, E., Bassham, D.

Date: 2025-12-31 · Version: 1
DOI: 10.64898/2025.12.30.697098

Category: Plant Biology

Model Organism: Zea mays

AI Summary

The study shows that maize plants carrying autophagy-defective atg10 mutations exhibit delayed flowering and significant reductions in kernel size, weight, and number, culminating in lower grain yield. Reciprocal crossing experiments reveal that the maternal genotype, rather than the seed genotype, primarily drives the observed kernel defects, suggesting impaired nutrient remobilization from maternal tissues during seed development.

autophagy atg10 mutant maize yield maternal effect nutrient remobilization

Predicting complex phenotypes using multi-omics data in maize

Authors: Creach, M., Webster, B., Newton, L., Turkus, J., Schnable, J., Thompson, A., VanBuren, R.

Date: 2025-10-01 · Version: 1
DOI: 10.1101/2025.09.30.679283

Category: Plant Biology

Model Organism: Zea mays

AI Summary

The study evaluated whether integrating genomic, transcriptomic, and drone-derived phenomic data improves prediction of 129 maize traits across nine environments, using both linear (rrBLUP) and nonlinear (SVR) models. Multi-omics models consistently outperformed single-omics models, with transcriptomic data especially enhancing cross‑environment predictions and capturing genotype‑by‑environment interactions. The results highlight the added value of combining transcriptomics and phenomics with genotypes for more accurate and generalizable trait prediction in maize.

multi-omics trait prediction transcriptomics phenomics genotype-by-environment interaction

High-resolution transcriptional atlas of growing maize shoot organs throughout plant development under well-watered and drought conditions

Authors: Zhang, J., Verbraeken, L., Sprenger, H., Mertens, S., Wuyts, N., Cannoot, B., De Block, J., Demuynck, K., Natran, A., Maleux, K., Merchie, J., Crafts-Brandner, S., Vogel, J., Bruce, W., Inze, D., Maere, S., Nelissen, H.

Date: 2025-03-13 · Version: 1
DOI: 10.1101/2025.03.12.642568

Category: Plant Biology

Model Organism: Zea mays

AI Summary

The study mapped the macroscopic and cellular development of maize leaves and internodes, revealing a shared growth design with organ‑specific timing. Using high‑resolution spatiotemporal transcriptome profiling of 272 tissue samples under well‑watered and drought conditions, the authors generated a searchable expression atlas and identified conserved and organ‑specific gene regulatory patterns, including genes linked to leaf angle and vascular development. This resource advances understanding of shoot organ development and drought response for targeted trait engineering in maize.

Zea mays leaf and internode development drought stress spatiotemporal transcriptome atlas gene regulatory networks

Temporal analysis of physiological phenotypes identifies novel metabolic and genetic underpinnings of senescence in maize

Authors: Brar, M. S., Kumar, R., Kunduru, B., McMahan, C. S., Tharayil, N., Sekhon, R. S.

Date: 2025-03-12 · Version: 1
DOI: 10.1101/2025.03.07.641920

Category: Plant Biology

Model Organism: Zea mays

AI Summary

The study generated a temporal physiological and metabolomic map of leaf senescence in diverse maize inbred lines differing in stay‑green phenotype, identifying 84 metabolites associated with senescence and distinct metabolic signatures between stay‑green and non‑stay‑green lines. Integration of metabolite data with genomic information uncovered 56 candidate genes, and reverse‑genetic validation in maize and Arabidopsis demonstrated conserved roles for phenylpropanoids such as naringenin chalcone and eriodictyol in regulating senescence.

leaf senescence staygreen metabolomics phenylpropanoids maize