Carlos D. Messina

An indirect organogenesis-based citrus transformation system monitored using visible reporter markers

This paper establishes an efficient citrus transformation system using indirect organogenesis and evaluates two visible reporters for monitoring transgenic tissues during regeneration.

Ectopic expression of ARGOS8 mediates silk elongation rate response to water deficit in maize (Zea mays L.)

This study shows that ectopic ARGOS8 expression in maize improves silk elongation under water deficit, increasing silk emergence and reproductive performance.

SPIN: A Scalable Bioinformatics Pipeline for Screening Pathogenicity Related Host-Pathogen Protein INteractions Using AlphaFold3

SPIN uses AlphaFold3 to predict host-pathogen protein interactions in plant pathogens, improving prediction reliability with a multi-metric scoring framework.

Leveraging Crop Wild Relatives and Ploidy Level for Climate-Resilient Annual Ryegrass

This study evaluates how polyploidy and genetic background affect annual ryegrass responses to climate stress, finding that wild populations can match cultivars in productivity under elevated CO2 and reduced water.

Crop improvement can accelerate agriculture adaptation to societal demands and climate change.

The paper shows that maize breeding in the US corn-belt has increased genetic gain over time, adapting to climate change and improving yield despite environmental changes.

Extending the breeder's equation to take aim at the Target Population of Environments

The paper discusses the importance of the MET-TPE relationship in genomic prediction for improving breeding outcomes in target populations of environments.

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

The paper explores using crop models as gene-to-phenotype link functions to optimize selection in plant breeding programs by integrating quantitative genetic theory and trait modeling.

Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction (WGP)

This paper integrates crop growth models with whole genome prediction to improve phenotypic prediction by accounting for genotype-by-environment-by-management interactions in maize breeding.

Integrating crop growth models with whole genome prediction through approximate Bayesian computation

This paper integrates crop growth models with whole genome prediction using approximate Bayesian computation to improve prediction accuracy for non-additive gene effects and genotype by environment interactions.