Three-dimensional infection network analysis in maize reveals variation in fungal colonization associated with lesion phenotypes
Abstract
Quantitative disease resistance in plants emerges from complex interactions between host tissues and pathogen growth dynamics, producing a spectrum of phenotypic responses. In plant-fungal interactions, disease is most visibly expressed through lesions that vary in number, size, shape, and color, collectively defining a lesion profile. For Cochliobolus heterostrophus, a fungus causing Southern Corn Leaf Blight of maize (Zea mays ssp. mays), we show that infection on different maize genotypes produces strikingly different lesion profiles. However, it remains unclear whether such macroscopic variation in lesion profiles corresponds to consistent differences in the three-dimensional organization of pathogen colonization within host tissue. We therefore examined variation in the three-dimensional structure of C. heterostrophus-infection networks across host genotypes representing four lesionprofile classes. Using light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue. In this dataset, network depth was similar across genotypes, whereas network morphology (shape and density), spatial association with vascular bundles, hyphal segment length, and branching frequency varied. Notably, genotypes with similar quantitative resistance levels sometimes exhibited distinct patterns of fungal colonization, suggesting that comparable resistance can arise from different underlying infection dynamics. These findings indicate that lesion profiles may not uniquely predict infection network structure and highlight the utility of three-dimensional network metrics for describing variation that likely reflects multiple underlying host and pathogen processes. This multi-scale framework provides tools for linking macroscopic disease phenotypes with microscopic infection processes in quantitative disease resistance.
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BibTeX
@article{nedo2026threedimensional,
title = {{Three-dimensional infection network analysis in maize reveals variation in fungal colonization associated with lesion phenotypes}},
author = {Alexander Nedo and Yael Lupu-Haber and Lilach Pnueli and Neta Holland and Nitsan Dahan and B. Gillian Turgeon and Benjamin A Horwitz and Randall J Wisser and Jeffrey L Caplan},
journal = {Scientific Reports},
year = {2026},
month = jun,
doi = {10.1038/s41598-026-52604-6},
url = {https://hal.inrae.fr/hal-05750047v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Three-dimensional infection network analysis in maize reveals variation in fungal colonization associated with lesion phenotypes
AU - Alexander Nedo
AU - Yael Lupu-Haber
AU - Lilach Pnueli
AU - Neta Holland
AU - Nitsan Dahan
AU - B. Gillian Turgeon
AU - Benjamin A Horwitz
AU - Randall J Wisser
AU - Jeffrey L Caplan
PY - 2026
DA - 2026/06/04
JO - Scientific Reports
DO - 10.1038/s41598-026-52604-6
UR - https://hal.inrae.fr/hal-05750047v1
AB - Quantitative disease resistance in plants emerges from complex interactions between host tissues and pathogen growth dynamics, producing a spectrum of phenotypic responses. In plant-fungal interactions, disease is most visibly expressed through lesions that vary in number, size, shape, and color, collectively defining a lesion profile. For Cochliobolus heterostrophus, a fungus causing Southern Corn Leaf Blight of maize (Zea mays ssp. mays), we show that infection on different maize genotypes produces strikingly different lesion profiles. However, it remains unclear whether such macroscopic variation in lesion profiles corresponds to consistent differences in the three-dimensional organization of pathogen colonization within host tissue. We therefore examined variation in the three-dimensional structure of C. heterostrophus-infection networks across host genotypes representing four lesionprofile classes. Using light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue. In this dataset, network depth was similar across genotypes, whereas network morphology (shape and density), spatial association with vascular bundles, hyphal segment length, and branching frequency varied. Notably, genotypes with similar quantitative resistance levels sometimes exhibited distinct patterns of fungal colonization, suggesting that comparable resistance can arise from different underlying infection dynamics. These findings indicate that lesion profiles may not uniquely predict infection network structure and highlight the utility of three-dimensional network metrics for describing variation that likely reflects multiple underlying host and pathogen processes. This multi-scale framework provides tools for linking macroscopic disease phenotypes with microscopic infection processes in quantitative disease resistance.
SN - 2045-2322
ER -