The Maize Genetics and Genomics Database (MaizeGDB) is the community resource for maize researchers, offering a suite of tools, informatics resources, and curated data sets to support maize genetics, genomics, and breeding research. Here, we provide an overview of the key resources available at MaizeGDB, including maize genomes, comparative genomics, and pan-genomics tools. This review aims to familiarize users with the range of options available for maize research and highlights the importance of MaizeGDB as a central hub for the maize research community. By providing a detailed snapshot of the database's capabilities, we hope to enable researchers to make use of MaizeGDB's resources, ultimately assisting them to better study the evolution and diversity of maize.

Pan-genomes, encompassing the entirety of genetic sequences found in a collection of genomes within a clade, are more useful than single reference genomes for studying species diversity. This is especially true for a species like Zea mays, which has a particularly diverse and complex genome. Presenting pan-genome data, analyses, and visualization is challenging, especially for a diverse species, but more so when pan-genomic data is linked to extensive gene model and gene data, including classical gene information, markers, insertions, expression and proteomic data, and protein structures as is the case at MaizeGDB. Here, we describe MaizeGDB's expansion to include the genic subset of the Zea pan-genome in a pan-gene data center featuring the maize genomes hosted at MaizeGDB, and the outgroup teosinte Zea genomes from the Pan-Andropoganeae project. The new data center offers a variety of browsing and visualization tools, including sequence alignment visualization, gene trees and other tools, to explore pan-genes in Zea that were calculated by the pipeline Pandagma. Combined, these data will help maize researchers study the complexity and diversity of Zea, and to use the comparative functions to validate pan-gene relationships for a selected gene model.

Cover crops are plants seeded before or after cash crops to improve soil health, reduce weed pressure, and prevent erosion. Cover crops also produce various antimicrobial secondary metabolites (i.e., glucosinolates, quercetin), yet the role of cover crops in moderating the population of human pathogens in the soil has rarely been investigated. This study aims to determine the antimicrobial capacity of three cover crop species to reduce the population of generic Escherichia coli (E. coli) in contaminated agricultural soil. Four-week-old mustard greens (Brassicajuncea), sunn hemp (Crotalaria juncea), and buckwheat (Fagopyrum esculentum) were mixed into autoclaved soil and inoculated with rifampicin-resistant generic E. coli to achieve a starting concentration of 5 log CFU/g. The surviving microbial populations on days 0, 4, 10, 15, 20, 30, and 40 were enumerated. All three cover crops significantly reduced the population of generic E. coli compared to the control (p < 0.0001), particularly between days 10 and to 30. Buckwheat resulted in the highest reduction (3.92 log CFU/g). An inhibitory effect (p < 0.0001) on microbial growth was also observed in soils containing mustard greens and sunn hemp. This study provides evidence for the bacteriostatic and bactericidal effect of particular cover crops. More research regarding the secondary metabolites produced by certain cover crops and their potential as a bio mitigation strategy to improve on-farm produce safety is warranted.

Protein structure prediction tools have significantly reduced the time and cost to generate protein structures and accelerated protein discovery and design. However, plant proteins are underrepresented in sequence and structural datasets used to train these programs. To quantify the downstream impact of this deficiency, we benchmarked five structure-prediction programs (AlphaFold 2, AlphaFold 3, ESMFold, Boltz-1, and Boltz-2) across 417 well-characterized Zea mays genes. These “classical” genes represent a set of well-studied genes with known genetic and phenotypic effects. We generated structures for each gene using these programs and compared how sequence, structural, and evolutionary conservation impacted the structures’ confidence and geometric features. Proteins lacking conserved sequence and/or structural domains had on average 25% to 43% lower confidence scores than proteins having both domains. Proteome-wide phylostratigraphy revealed that species-specific proteins had substantially lower confidence scores than proteins conserved amongst angiosperms and Eukaryotes. Boltz-1 and ESMFold structures had the highest occurrence of structures with severe geometry issues, including overlapping atoms and unlikely bond angles. We also compared computational and experimental alignments of Arabidopsis, maize, rice, wheat, and soybean proteins from the Protein Data Bank and identified structures showing incongruences with experimental data. This study challenges the assertion that protein folding has been completely ‘solved’, and urges more investigation into benchmarking and standardized evaluation frameworks to improve model performance and assessment in agricultural crops.

Plant pathogenic fungi secrete small proteins known as effectors which help overcome the plant defense response and cause disease. The concept of effector-triggered immunity in plants evolved from the “gene for gene hypothesis” which describes plant resistance or susceptibility to plant pathogens based on interspecies protein-protein interactions (PPIs) between plant-derived resistance (R) genes and pathogen-derived avirulence (Avr) effector genes. Understanding the molecular interactions mediating these host-pathogen interactions in effector-triggered immunity is thus essential to managing fungal disease. In silico methods of predicting interspecies PPIs have been heavily studied to identify target genes for crop resistance. But conventional sequence-based homology methods (i.e., interlog, domain-based inference) for predicting interspecies PPIs are not as powerful as methods that also incorporate structural homology. The objective of this study was to develop a computational workflow to predict PPIs between pathogenic fungi and their cereal hosts by leveraging recent advances in artificial intelligence and structural biology. This workflow proposes the use of a generative model, RFdiffusion, to predict the structure of truncated segments of proteins likely to bind to query effector proteins. The binder structures were filtered based on the number of contacts at the effectors’ known binding residues. Acceptable structures were then input into FoldSeek to search the host proteome for host proteins containing similar sub-structures. Experimentally-validated PPIs between rice ( Oryzae sativa cv. ‘Japonica’) and rice blast fungus ( Magnaporthe oryzae ) were used for workflow validation. The effects of binder length and the binding residues’ mode of action (i.e., residues at active/substrate recognition sites) on the binder quality and presumptive host protein matches were explored. Ultimately, 11 out of 14 experimentally validated PPIs were recovered computationally, indicating a high recall (>78%) for the workflow. The shorter binders recovered most of the PPIs, but may have produced the most false positives, as functional analyses revealed that these host proteins displayed a wide variety of functions. These findings emphasize that subject matter expertise is still required to decipher the prediction results. Yet, this framework for elucidating interactions between fungal pathogens and host proteins could provide valuable insight into mechanisms of susceptibility or resistance at a scale friendly to limited computational resources, and facilitate the development of control strategies that reduce crop diseases.

Ultraviolet-C (UV-C) light-assisted water treatment systems are an increasingly investigated alternative to chemical sanitizers for agricultural surface water decontamination. However, the relatively high concentration of particulate matter in surface water is a major challenge to expanding its application in the production of fresh produce. The objective of this project was to test the efficacy of two commercial UV-C devices to reduce the microbial risk of agricultural water in order to develop a web application to assist growers in decision-making related to the on-farm implementation of UV-C technologies for agricultural water treatment. An on-farm study using three agricultural water sources was performed to determine the microbial reduction efficacy of a low power, low flow (LP/LF; 1-9 gallons per minute (GPM), 1.34-gallon capacity) and a high powered, high flow (HP/HF; 1-110 GPM, 4.75-gallon capacity) device at flow rates of 6, 7, and 9 GPM. A threshold of 30% UVT for the HP/HF device was observed, wherein lower water transmissibility significantly impacted microbial inactivation. Although less effective at lower %UVT, the LP/LF device costs less to install, maintain, and operate. The observations were used to design an online tool for growers to calculate the predicted reduction of generic Escherichia coli using either device based on the %UVT of their water source. However, because this study utilized an exploratory and proof-of-concept approach, the experimental flow rates were limited to reflect the capacities of the smaller unit (9 GPM) for direct comparison to the larger unit. Thus, the preliminary model and tool are largely limited to the experimental conditions. Yet, these results of this study demonstrate the utility of UV-C light in reducing the microbial risk of agricultural water, and future studies using different UV-C devices and higher flow rates will expand the use of the decision-making tool.