An endogenous defensive protein in maize that encodes a Cys protease, provides enhanced resistance to phloem sap-consuming pests through the action of an ethyle

Summary: Some plant‐derived anti‐herbivore defensive proteins are induced by insect feeding, resist digestion in the caterpillar gut and are eliminated in the frass. We have identified several maize proteins in fall armyworm (Spodoptera frugiperda) frass that potentially play a role in herbivore defense. Furthermore, the toxicity of one of these proteins, ribosome‐inactivating protein 2 (RIP2), was assessed and factors regulating its accumulation were determined. To understand factors regulating RIP2 protein accumulation, maize (Zea mays) plants were infested with fall armyworm larvae or treated with exogenous hormones. The toxicity of recombinant RIP2 protein against fall armyworm was tested. The results show that RIP2 protein is synthesized as an inactive proenzyme that can be processed in the caterpillar gut. Also, caterpillar feeding, but not mechanical wounding, induced foliar RIP2 protein accumulation. Quantitative real‐time PCR indicated that RIP2 transcripts were rapidly induced (1 h) and immunoblot analysis indicated that RIP2 protein accumulated soon after attack and was present in the leaf for up to 4 d after caterpillar removal. Several phytohormones, including methyl jasmonate, ethylene, and abscisic acid, regulated RIP2 protein expression. Furthermore, bioassays of purified recombinant RIP2 protein against fall armyworm significantly retarded caterpillar growth. We conclude that the toxic protein RIP2 is induced by caterpillar feeding and is one of a potential suite of proteins that defend maize against chewing herbivores.

Introduction: Aflatoxin (AFL), a secondary metabolite produced from filamentous fungi, contaminates corn, posing significant health and safety hazards for humans and livestock through toxigenic and carcinogenic effects. Corn is widely used as an essential commodity for food, feed, fuel, and export markets; therefore, AFL mitigation is necessary to ensure food and feed safety within the United States (US) and elsewhere in the world. In this case study, an Iowa-centric model was developed to predict AFL contamination using historical corn contamination, meteorological, satellite, and soil property data in the largest corn-producing state in the US.Methods: We evaluated the performance of AFL prediction with gradient boosting machine (GBM) learning and feature engineering in Iowa corn for two AFL risk thresholds for high contamination events: 20-ppb and 5-ppb. A 90%–10% training-to-testing ratio was utilized in 2010, 2011, 2012, and 2021 (n = 630), with independent validation using the year 2020 (n = 376).Results: The GBM model had an overall accuracy of 96.77% for AFL with a balanced accuracy of 50.00% for a 20-ppb risk threshold, whereas GBM had an overall accuracy of 90.32% with a balanced accuracy of 64.88% for a 5-ppb threshold. The GBM model had a low power to detect high AFL contamination events, resulting in a low sensitivity rate. Analyses for AFL showed satellite-acquired vegetative index during August significantly improved the prediction of corn contamination at the end of the growing season for both risk thresholds. Prediction of high AFL contamination levels was linked to aflatoxin risk indices (ARI) in May. However, ARI in July was an influential factor for the 5-ppb threshold but not for the 20-ppb threshold. Similarly, latitude was an influential factor for the 20-ppb threshold but not the 5-ppb threshold. Furthermore, soil-saturated hydraulic conductivity (Ksat) influenced both risk thresholds.Discussion: Developing these AFL prediction models is practical and implementable in commodity grain handling environments to achieve the goal of preventative rather than reactive mitigations. Finding predictors that influence AFL risk annually is an important cost-effective risk tool and, therefore, is a high priority to ensure hazard management and optimal grain utilization to maximize the utility of the nation’s corn crop.

Rice production is shifting from transplanting seedlings to direct sowing of seeds. Following heavy rains, directly sown seeds may need to germinate under anaerobic environments, but most rice (Oryza sativa) genotypes cannot survive these conditions. To identify the genetic architecture of complex traits, we quantified percentage anaerobic germination (AG) in 2,700 (wet-season) and 1,500 (dry-season) sequenced rice genotypes and performed genome-wide association studies (GWAS) using 693,502 single nucleotide polymorphisms. This was followed by post-GWAS analysis with a generalized SNP-to-gene set analysis, meta-analysis, and network analysis. We determined that percentage AG is intermediate-to-high among indica subpopulations, and AG is a polygenic trait associated with transcription factors linked to ethylene responses or genes involved in metabolic processes that are known to be associated with AG. Our post-GWAS analysis identified several genes involved in a wide variety of metabolic processes. We subsequently performed functional analysis focused on the small RNA and methylation pathways. We selected CLASSY 1 (CLSY1), a gene involved in the RNA-directed DNA methylation (RdDm) pathway, for further analyses under AG and found several lines of evidence that CLSY1 influences AG. We propose that the RdDm pathway plays a role in rice responses to water status during germination and seedling establishment developmental stages.

Mycotoxin contamination of corn results in significant agroeconomic losses and poses serious health issues worldwide. This paper presents the first report utilizing machine learning and historical aflatoxin and fumonisin contamination levels in-order-to develop models that can confidently predict mycotoxin contamination of corn in Illinois, a major corn producing state in the USA. Historical monthly meteorological data from a 14-year period combined with corresponding aflatoxin and fumonisin contamination data from the State of Illinois were used to engineer input features that link weather, fungal growth, and aflatoxin production in combination with gradient boosting (GBM) and bayesian network (BN) modeling. The GBM and BN models developed can predict mycotoxin contamination with overall 94% accuracy. Analyses for aflatoxin and fumonisin with GBM showed that meteorological and satellite-acquired vegetative index data during March significantly influenced grain contamination at the end of the corn growing season. Prediction of high aflatoxin contamination levels was linked to high aflatoxin risk index in March/June/July, high vegetative index in March and low vegetative index in July. Correspondingly, high levels of fumonisin contamination were linked to high precipitation levels in February/March/September and high vegetative index in March. During corn flowering time in June, higher temperatures range increased prediction of high levels of fumonisin contamination, while high aflatoxin contamination levels were linked to high aflatoxin risk index. Meteorological events prior to corn planting in the field have high influence on predicting aflatoxin and fumonisin contamination levels at the end of the year. These early-year events detected by the models can directly assist farmers and stakeholders to make informed decisions to prevent mycotoxin contamination of Illinois grown corn.

Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to the agricultural industry worldwide. Historical aflatoxin (AFL) and fumonisin (FUM) mycotoxin contamination data of corn, daily weather data, satellite data, dynamic geospatial soil properties, and land usage parameters were modeled to identify factors significantly contributing to the outbreaks of mycotoxin contamination of corn grown in Illinois (IL), AFL >20 ppb, and FUM >5 ppm. Two methods were used: a gradient boosting machine (GBM) and a neural network (NN). Both the GBM and NN models were dynamic at a state-county geospatial level because they used GPS coordinates of the counties linked to soil properties. GBM identified temperature and precipitation prior to sowing as significant influential factors contributing to high AFL and FUM contamination. AFL-GBM showed that a higher aflatoxin risk index (ARI) in January, March, July, and November led to higher AFL contamination in the southern regions of IL. Higher values of corn-specific normalized difference vegetation index (NDVI) in July led to lower AFL contamination in Central and Southern IL, while higher wheat-specific NDVI values in February led to higher AFL. FUM-GBM showed that temperature in July and October, precipitation in February, and NDVI values in March are positively correlated with high contamination throughout IL. Furthermore, the dynamic geospatial models showed that soil characteristics were correlated with AFL and FUM contamination. Greater calcium carbonate content in soil was negatively correlated with AFL contamination, which was noticeable in Southern IL. Greater soil moisture and available water-holding capacity throughout Southern IL were positively correlated with high FUM contamination. The higher clay percentage in the northeastern areas of IL negatively correlated with FUM contamination. NN models showed high class-specific performance for 1-year predictive validation for AFL (73%) and FUM (85%), highlighting their accuracy for annual mycotoxin prediction. Our models revealed that soil, NDVI, year-specific weekly average precipitation, and temperature were the most important factors that correlated with mycotoxin contamination. These findings serve as reliable guidelines for future modeling efforts to identify novel data inputs for the prediction of AFL and FUM outbreaks and potential farm-level management practices.