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AI-summarized plant biology research papers from bioRxiv

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Effects of atmospheric CO2 levels on the susceptibility of maize to diverse pathogens

Authors: Khwanbua, E., Qi, Y., Ssengo, J., Liu, P., Graham, M. A., Whitham, S.

Date: 2026-01-02 · Version: 1
DOI: 10.64898/2025.12.31.697224

Category: Plant Biology

Model Organism: Zea mays

AI Summary

The study examined how elevated atmospheric CO₂ (550 ppm) affects immunity in the C₄ cereal maize (Zea mays L.) by exposing plants grown under ambient and elevated CO₂ to a range of pathogens. Elevated CO₂ increased susceptibility to sugarcane mosaic virus, decreased susceptibility to several bacterial and fungal pathogens, and left susceptibility to others unchanged, with reduced bacterial disease linked to heightened basal immune responses. These findings provide a baseline for future investigations into CO₂‑responsive defense mechanisms in C₄ crops.

elevated CO₂ maize plant immunity pathogen susceptibility C4 crops

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