Nicolli, C. | bioRxiv Science

bioRxiv2024

Receptor-ligand interactions in plant inmate immunity revealed by AlphaFold protein structure prediction

One of the common mechanisms to trigger plant innate immunity is recognition of pathogen avirulence gene products directly by products of major resistance (R) genes in a gene for gene manner. In the USA, the R genes, Pik-s, PiKh/m, and Pi-ta, Pi-39(t), and Ptr genes have been effectively deployed to prevent the infections of M. oryzae races, IB49, and IC17 for some time. Pi-9 is only recently being deployed to provide overlapped and complimentary resistance to Magnaporthe oryzae races IB49, IC17 and IE1k in the USA. Pi-ta, Pi-39(t), Pi9 are major nuclear binding site-leucine rich (NLR) proteins, and Ptr is an atypical R protein with 4 armadillo repeats. AlphaFold is an artificial intelligence system that predicts a protein 3D structure from its amino acid sequence. Here we report genome sequence analyses of the effectors and avirulence (AVR) genes, AVR-Pita and AVR-Pik, and AVR-Pi9, in 3 differential M. oryzae races. Using AlphaFold 2 and 3 we find strong evidence of direct interactions of products of resistance genes Pi-ta and Pik with M. oryzae avirulence (AVR) genes, AVR-Pita and AVR-Pik respectively. We also found that AVR-Pita interacts with Pi-39(t) and Ptr, and Pi9 interacts with both AVR-Pi9 and AVR-Pik. Validation of direct interactions of two pairs of R and AVR proteins supported a direct interaction mechanism of plant innate immunity. Detecting interaction of both Ptr and Pi39(t) with AVR-Pita, and Pi-9 with both AVR-Pi9 and AVR-Pik, revealed a new insight into recognition of pathogen signaling molecules by these host R genes in triggering plant innate immunity.

molecular biology↗

bioRxiv2023

Uncovering the Environmental Conditions Required for Phyllachora maydis Infection and Tar Spot Development on Corn in the United States for Use as Predictive Models for Future Epidemics

Phyllachora maydis is a fungal pathogen causing tar spot of corn (Zea mays L.), a new and emerging, yield-limiting disease in the United States. Since being first reported in Illinois and Indiana in 2015, P. maydis can now be found across much of the corn growing of the United States. Knowledge of the epidemiology of P. maydis is limited but could be useful in developing tar spot prediction tools. The research presented here aims to elucidate the environmental conditions necessary for the development of tar spot in the field and the creation of predictive models to anticipate future tar spot epidemics. Extended periods (30-day windowpanes) of moderate ambient temperature were most significant for explaining the development of tar spot. Shorter periods (14- to 21-day windowpanes) of moisture (relative humidity, dew point, number of hours with predicted leaf wetness) were negatively correlated with tar spot development. These weather variables were used to develop multiple logistic regression models, an ensembled model, and two machine learning models for the prediction of tar spot development. This work has improved the understanding of P. maydis epidemiology and provided the foundation for the development of a predictive tool for anticipating future tar spot epidemics.

plant biology↗