Genomic and metabolomic diversity within a familial population of Aspergillus flavus.

Aspergillus flavus is an agriculturally significant micro-fungus having potential to contaminate food and feed crops with toxic secondary metabolites such as aflatoxin (AF) and cyclopiazonic acid (CPA). Research has shown A. flavus strains can overcome heterokaryon incompatibility and undergo meiotic recombination as teleomorphs. Although evidence of recombination in the AF gene cluster has been reported, the impacts of recombination on genotype and metabolomic phenotype in a single generation are lacking. In previous studies, we paired an aflatoxigenic MAT1-1 A. flavus strain with a non-aflatoxigenic MAT1-2 A. flavus strain that had been tagged with green fluorescent protein and then 10 F1 progenies (a mix of fluorescent and non-fluorescent) were randomly selected from single-ascospore colonies and broadly examined for evidence of recombination. In this study, we determined four of those 10 F1 progenies were recombinants because they were not vegetatively compatible with either parent or their siblings, and they exhibited other distinctive traits that could only result from meiotic recombination. The other six progenies examined shared genomic identity with the non-aflatoxigenic, fluorescent, and MAT1-2 parent, but were metabolically distinct. This study highlights phenotypic and genomic changes that may occur in a single generation from the outcrossing of sexually compatible strains of A. flavus.

Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning.

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.

Gradient boosting machine learning model to predict aflatoxins in Iowa corn.

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.

Genes and genetic mechanisms contributing to fall armyworm resistance in maize.

Maize (Zea mays L.) is a crop of major economic and food security importance globally. The fall armyworm (FAW), Spodoptera frugiperda, can devastate entire maize crops, especially in countries or markets that do not allow the use of transgenic crops. Host-plant insect resistance is an economical and environmentally benign way to control FAW, and this study sought to identify maize lines, genes, and pathways that contribute to resistance to FAW. Of the 289 maize lines phenotyped for FAW damage in artificially infested, replicated field trials over 3 years, 31 were identified with good levels of resistance that could donate FAW resistance into elite but susceptible hybrid parents. The 289 lines were genotyped by sequencing to provide single nucleotide polymorphism (SNP) markers for a genome-wide association study (GWAS), followed by a metabolic pathway analysis using the Pathway Association Study Tool (PAST). GWAS identified 15 SNPs linked to 7 genes, and PAST identified multiple pathways, associated with FAW damage. Top pathways, and thus useful resistance mechanisms for further study, include hormone signaling pathways and the biosynthesis of carotenoids (particularly zeaxanthin), chlorophyll compounds, cuticular wax, known antibiosis agents, and 1,4-dihydroxy-2-naphthoate. Targeted metabolite analysis confirmed that maize genotypes with lower levels of FAW damage tend to have higher levels of chlorophyll a than genotypes with high FAW damage, which tend to have lower levels of pheophytin, lutein, chlorophyll b and ß-carotene. The list of resistant genotypes, and the results from the genetic, pathway, and metabolic study, can all contribute to efficient creation of FAW resistant cultivars.

Gradient boosting and bayesian network machine learning models predict aflatoxin and fumonisin contamination of maize in Illinois - First USA case study.

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.

Flavonoids Modulate Aspergillus flavus Proliferation and Aflatoxin Production.

Aflatoxins are carcinogenic mycotoxins produced by Aspergillus flavus. They contaminate major food crops, particularly corn, and pose a worldwide health concern. Flavonoid production has been correlated to resistance to aflatoxin accumulation in corn. The effects of flavonoids on fungal proliferation and aflatoxin production are not well understood. In this study, we performed bioassays, fluorescence and scanning electron microscopy, and total antioxidant analysis to determine the effects of three flavonoids (apigenin, luteolin, and quercetin) on proliferation and aflatoxin production in A. flavus NRRL 3357. Results showed that concentrations of apigenin and luteolin modulated fungal proliferation and aflatoxin production in a dose-dependent manner, leading to inhibition or promotion of proliferation and toxin production. Microscopy studies of fungi exposed to flavonoids showed mycelial cell wall disruption, abnormal cell wall invaginations, and tears. Fluorescent enhancement of apigenin and luteolin using Naturstoff reagent A showed that these chemicals localized in sphere-like structures on the mycelia surface. Fungi exposed to low concentrations of apigenin, luteolin, and quercetin lowered the total antioxidant capacity in the environment compared to controls. Our results indicate that flavonoids disrupt cell wall integrity and may localize in vesicle-like structures. We hypothesize that flavonoids could act as potential signaling molecules at low concentrations and change the oxidative state of the microenvironment, either or both of which may lead to reduction of fungal proliferation and aflatoxin production.

Flavonoids Modulate the Accumulation of Toxins From Aspergillus flavus in Maize Kernels.

Aspergillus flavus is an opportunistic fungal pathogen capable of producing aflatoxins, potent carcinogenic toxins that accumulate in maize kernels after infection. To better understand the molecular mechanisms of maize resistance to A. flavus growth and aflatoxin accumulation, we performed a high-throughput transcriptomic study in situ using maize kernels infected with A. flavus strain 3357. Three maize lines were evaluated: aflatoxin-contamination resistant line TZAR102, semi-resistant MI82, and susceptible line Va35. A modified genotype-environment association method (GEA) used to detect loci under selection via redundancy analysis (RDA) was used with the transcriptomic data to detect genes significantly influenced by maize line, fungal treatment, and duration of infection. Gene ontology enrichment analysis of genes highly expressed in infected kernels identified molecular pathways associated with defense responses to fungi and other microbes such as production of pathogenesis-related (PR) proteins and lipid bilayer formation. To further identify novel genes of interest, we incorporated genomic and phenotypic field data from a genome wide association analysis with gene expression data, allowing us to detect significantly expressed quantitative trait loci (eQTL). These results identified significant association between flavonoid biosynthetic pathway genes and infection by A. flavus. In planta fungal infections showed that the resistant line, TZAR102, has a higher fold increase of the metabolites naringenin and luteolin than the susceptible line, Va35, when comparing untreated and fungal infected plants. These results suggest flavonoids contribute to plant resistance mechanisms against aflatoxin contamination through modulation of toxin accumulation in maize kernels.

An epigenetic pathway in rice connects genetic variation to anaerobic germination and seedling establishment.

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.

Plant Bio-Wars: Maize Protein Networks Reveal Tissue-Specific Defense Strategies in Response to a Root Herbivore.

In this study we examined global changes in protein expression in both roots and leaves of maize plants attacked by the root herbivore, Western corn rootworm (WCR, Diabrotica virgifera virgifera). The changes in protein expression Are indicative of metabolic changes during WCR feeding that enable the plant to defend itself. This is one of the first studies to look above- and below-ground at global protein expression patterns of maize plants grown in soil and infested with a root herbivore. We used advanced proteomic and network analyses to identify metabolic pathways that contribute to global defenses deployed by the insect resistant maize genotype, Mp708, infested with WCR. Using proteomic analysis, 4878 proteins in roots and leaves were detected and of these 863 showed significant changes of abundance during WCR infestation. Protein abundance patterns were analyzed using hierarchical clustering, protein correlation and protein-protein interaction networks. All three data analysis pipelines showed that proteins such as jasmonic acid biosynthetic enzymes, serine proteases, protease inhibitors, proteins involved in biosynthesis and signaling of ethylene, and enzymes producing reactive oxygen species and isopentenyl pyrophosphate, a precursor for volatile production, were upregulated in roots during WCR infestation. In leaves, highly abundant proteins were involved in signal perception suggesting activation of systemic signaling. We conclude that these protein networks contribute to the overall herbivore defense mechanisms in Mp708. Because the plants were grown in potting mix and not sterilized sand, we found that both microbial and insect defense-related proteins were present in the roots. The presence of the high constitutive levels of reduced ascorbate in roots and benzothiazole in the root volatile profiles suggest a tight tri-trophic interaction among the plant, soil microbiomes and WCR-infested roots suggesting that defenses against insects coexist with defenses against bacteria and fungi due to the interaction between roots and soil microbiota. In this study, which is one of the most complete descriptions of plant responses to root-feeding herbivore, we established an analysis pipeline for proteomics data that includes network biology that can be used with different types of "omics" data from a variety of organisms.

A Maize Inbred Exhibits Resistance Against Western Corn Rootwoorm, Diabrotica virgifera virgifera.

Insect resistance against root herbivores like the western corn rootworm (WCR, Diabrotica virgifera virgifera) is not well understood in non-transgenic maize. We studied the responses of two American maize inbreds, Mp708 and Tx601, to WCR infestation using biomechanical, molecular, biochemical analyses, and laser ablation tomography. Previous studies performed on several inbreds indicated that these two maize genotypes differed in resistance to pests including fall armyworm (Spodoptera frugiperda) and WCR. Our data confirmed that Mp708 shows resistance against WCR, and demonstrates that the resistance mechanism is based in a multi-trait phenotype that includes increased resistance to cutting in nodal roots, stable root growth during insect infestation, constitutive and induced expression of known herbivore-defense genes, including ribosomal inhibitor protein 2 (rip2), terpene synthase 23 (tps23) and maize insect resistance cysteine protease-1 (mir1), as well high constitutive levels of jasmonic acid and production of (E)-ß-caryophyllene. In contrast, Tx601 is susceptible to WCR. These findings will facilitate the use of Mp708 as a model to explore the wide variety of mechanisms and traits involved in plant defense responses and resistance to herbivory by insects with several different feeding habits.

Ethylene Contributes to maize insect resistance1-Mediated Maize Defense against the Phloem Sap-Sucking Corn Leaf Aphid.

Signaling networks among multiple phytohormones fine-tune plant defense responses to insect herbivore attack. Previously, it was reported that the synergistic combination of ethylene (ET) and jasmonic acid (JA) was required for accumulation of the maize insect resistance1 (mir1) gene product, a cysteine (Cys) proteinase that is a key defensive protein against chewing insect pests in maize (Zea mays). However, this study suggests that mir1-mediated resistance to corn leaf aphid (CLA; Rhopalosiphum maidis), a phloem sap-sucking insect pest, is independent of JA but regulated by the ET-signaling pathway. Feeding by CLA triggers the rapid accumulation of mir1 transcripts in the resistant maize genotype, Mp708. Furthermore, Mp708 provided elevated levels of antibiosis (limits aphid population)- and antixenosis (deters aphid settling)-mediated resistance to CLA compared with B73 and Tx601 maize susceptible inbred lines. Synthetic diet aphid feeding trial bioassays with recombinant Mir1-Cys Protease demonstrates that Mir1-Cys Protease provides direct toxicity to CLA. Furthermore, foliar feeding by CLA rapidly sends defensive signal(s) to the roots that trigger belowground accumulation of the mir1, signifying a potential role of long-distance signaling in maize defense against the phloem-feeding insects. Collectively, our data indicate that ET-regulated mir1 transcript accumulation, uncoupled from JA, contributed to heightened resistance to CLA in maize. In addition, our results underscore the significance of ET acting as a central node in regulating mir1 expression to different feeding guilds of insect herbivores.

Caterpillar attack triggers accumulation of the toxic maize protein RIP2.

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.