Progress report for GNE24-335

GNE24-335 (project overview)

Project Type: Graduate Student

Funds awarded in 2024: $14,935.00

Projected End Date: 10/31/2026

Grant Recipient: The Pennsylvania State University

Region: Northeast

State: Pennsylvania

Graduate Student:

Chelsea Newbold

Faculty Advisor:

Dr. Sharifa Crandall

The Pennsylvania State University

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Project Information

Summary:

Drought events in the Northeastern United States are projected to become more frequent putting crops at greater risk of yield loss and increased
mycotoxin contamination. Drought stress can directly affect maize (Zea mays) production, reducing kernel set and ear size. An increase in
maize susceptibility to disease that involves hot and dry conditions, favor pathogenic and mycotoxigenic fungi from the genus Fusarium, the causal
agents of stalk and ear rots (F. verticillioides and F. subglutinans). These fungi also produce mycotoxins which put human and livestock health at risk. To reduce disease and mycotoxin contamination from Fusarium pathogens, transgenic Bt-maize is used, which reduces pest pressure and
opportunities for infection of ears and stalks. However, previous studies have demonstrated that Fusarium pathogens and mycotoxin producers can colonize maize leaves asymptomatically and produce mycotoxins even when no disease is visually present and therefore can potentially contribute to the contamination of silage for feed. Silage is a fermented feed product made from chopped ground stalk, ear, and leaf tissue
and is at high risk of mycotoxin contamination from Fusarium when infection is high in harvested plant material. It is unclear how drought stress shapes Fusarium spp. in maize leaves, especially pathogenic and mycotoxigenic species. This is
critically important for regional maize silage producers who may be disproportionately
affected throughout the Northeast by changes in foliar Fusarium spp. communities. This research aims to determine how drought stress shapes foliar pathogenic and mycotoxigenic Fusarium spp. in maize.

Project Objectives:

1. To determine the interaction between host development, drought stress events, and
Fusarium spp. fungal communities of maize.

a. Assess how the Fusarium spp. community of maize leaves changes across drought exposure events pre- and post-flowering.

b. Assess the effect of drought exposure pre- and post-flowering on the functional composition of Fusarium spp. communities.

c. Assess the effect of drought exposure pre- and post-flowering on Fusarium spp. communities at harvest for silage.

2. To reveal the interaction between drought stress events and pathogenic and mycotoxigenic Fusarium spp. in maize at harvest for silage.

3. To quantify the effect of host genotype and drought on the foliar Fusarium spp. community.

Introduction:

The purpose of this project is to understand the ability of pre- and post-flowering drought stress events to shape the Fusarium spp. community of maize leaves, with a focus on maize pathogens and mycotoxin-producing species. Drought is a serious concern for maize (Zea mays) production in the Northeast⁵⁰. Climate projections suggest regional drought events will become more frequent which is a problem for maize farmers, but also the regional economy^7,53,57. When a maize plant experiences drought stress through various growth stages, it results in significant yield loss^20,40, increased disease severity²⁴, and mycotoxin contamination in silage and grain^27,35. Silage is a critical feed source in the dairy industry, which plays a vital role in Northeast agriculture and economy. Northeastern states like New York and Pennsylvania, are top dairy producers in the US, where 70% of feed comes from on-farm or regional maize production^9,54. Management for disease and mycotoxin contamination largely focusses on conventional growers with the use of Bt-maize varieties (transgenic maize that produces a protein toxic to arthropod pests originally found in the soil bacterium Bacillus thuringiensis¹⁶) against pests, and the application of pesticides and fungicides. However, these approaches are not viable for organic producers and increased tolerance of pests to Bt-maize and fungicide resistance development concerns mean that long term sustainable management requires new tools and an understanding of how environmental factors, like drought, shape pathogen communities^3,5,15,51.

Drought events are known to increase pathogen colonization and mycotoxin levels in seed and silage^27,28. Mycotoxins are metabolites produced by some fungi that are toxic to humans and other animals when ingested³⁵. For example, of regional importance are the mycotoxins deoxynivalenol (DON) and the fumonisins, produced by the stalk and ear rot pathogens Fusarium graminearum and F. verticillioides respectively. Ingestion of DON causes vomiting, immune dysfunction, and organ damage, which can lead to death, poor development, and reduced meat quality in livestock^11,17. Fumonisins are also associated with esophageal and liver cancers in humans^14,29. Increased seasonal drought events and potential impacts on yield and mycotoxin contamination require a greater understanding of how drought stress events across host development shape the Fusarium spp. community in maize.

Drought events and their effects on maize are characterized by their timing in maize development, duration, and intensity, where timing can have the greatest negative effect on grain and silage quality. For example, drought exposure is considered the most dangerous to production two weeks before and after pollination, when water deficit can cause poor fertilization, leading to reduced kernel count and improper kernel development⁴. Drought events can also shape the quality of silage. Silage fermentation relies on a balance of fiber, water, starch, and protein in maize biomass, which drive the growth of aerobic bacteria responsible for fermentation^26,59. Drought stress at vegetative development has been shown to decrease shoot biomass and water content, and increase simple sugars of pre-fermented silage, and decrease protein and fiber post ferment, resulting in a lower nutrient dense feed^31,59.

Drought stress also increases plant susceptibility to disease at a chemical and physiological level, favoring pathogens that thrive under dry and hot conditions, such as F. verticillioides^32,36,47. In maize, drought increases stalk and ear rot severity and incidence by Fusarium spp. pathogens and mycotoxin contamination of seed and silage^33,41,48. Drought shapes the pathogenic and mycotoxigenic taxa in maize stalks and ears during periods of drought pre- and post-flowering^42,45. However, research has yet to address how drought shapes colonization of maize leaves by these important taxa. This is especially critical because leaf tissue contributes biomass to silage, and subsequent pathogenic and mycotoxigenic Fusarium spp. colonization^18,25,55,56. It is known that drought directly effects leaf physiology, increasing stomatal density creating greater opportunity for fungal colonization suggesting that drought timing in host development may contribute to establishment of Fusarium spp.⁴⁷. Previous work in sorghum leaf fungal communities has demonstrated that drought pre-flowering increases Fusarium taxa and pathogen abundance more than drought post flowering, suggesting that drought timing may shape foliar maize colonization by Fusarium spp.¹². Extreme weather events, such as drought have increased across maize producing regions of the Northeast and are projected to continue to increase into the future^50,53. It is critical to the success of sustainable maize and livestock production that we learn, test, and inform growers on how these drought events will shape the pathogenic and mycotoxigenic Fusarium spp. of maize. I hypothesize that drought stress pre- and post-flowering will have differing effects on pathogenic and mycotoxigenic Fusarium spp. colonization of maize leaves effects because of drought on leaf physiology.

Research

Materials and methods:

To investigate the effect of pre-and post-flowering drought events on Fusarium spp. of maize leaves an environmentally manipulated field experiment will be performed. The experiment will characterize Fusarium spp. communities of maize exposed to drought conditions during vegetative growth pre-flowering, and separately during reproductive growth post-flowering. I will specifically test how these events shape the Fusarium spp. communities at plant maximum water content when maize would be typically harvested for silage by farmers in the Northeast. The experiment will take place at the Plant Biology Farm at the Russell E. Larson Agricultural Research Center at Rock Springs, PA using top -open hoop houses and precipitation elimination measures during experimental drought events.

Financial support is needed from this grant; I will hire an undergraduate student to assist in the field and laboratory. In the field, the student will support crop development assessments and assist Chelsea with drought stress testing, sample collection, weed removal, and harvesting at the end of the season. In the lab, the student will support sample processing, DNA extractions and quality testing, and help prepare samples for sequencing. The student will leave this project with the skills and knowledge necessary to plan and problem solve independent research projects in the future.

To address all objectives of this project three 36 ft x 25 ft open-top hoop houses will each be tilled, fertilized in furrow at time of planting, and sewn with two varieties of maize seed in two equidistant strips of approximately 10 rows each (36 ft x 10 ft). Each hoop house will be equipped with drip tape irrigation, and manual or electronic top covers to prevent or allow natural precipitation event exposure.

Hoop House 1 Pre-Flowering Drought Conditions (PFDC). Plants will be irrigated until they reach phenological stage V4, or four leaves and leaf collars are visible. This will ensure that plants are established before drought exposure. At V4 irrigation will stop, and a top cover will be set in place to generate drought until the plant reaches phenological tasseling stage. At tasseling irrigation will continue and the cover lifted. Irrigation and precipitation exposure will persist until the final sampling date, then cease.

Hoop House 2 Post-Flowering Drought Conditions (PTFDC). Plants will be irrigated until they reach phenological stage R1, or when silks have dried. This will ensure plants have finished flowering. At R1 irrigation will stop and a top cover will be set in place to generate drought conditions that will persist until the final sampling date, then cease.

Hoop House 3 Control Conditions (CC): The hoop house will be divided crosswise in the middle, where one side is continuous irrigation and precipitation exposure (negative control), and the other is complete drought (positive control). The side with irrigation and precipitation exposure will be left open and irrigated throughout the sampling period. The side under complete drought will be covered manually with plastic sheeting to prevent precipitation exposure, and irrigation will be turned off after plants reach V4.

Assessment of Drought Status: To determine drought status in maize, assessments of leaf relative water content (LRWC) and soil moisture content (SMC) will be compared to the negative control. A significant decrease between LRWC and SMC between the treatment and control will determine that drought establishment was reached. The LRWC will be determined using a protocol developed by Zhan and Lynch (2015). Soil moisture content will be determined via a gravimetric method in which soil cores (5 mm deep) will be collected and immediately weighed, then dried for 72 hours and weighed again. The total water content will be determined as the difference between these two measurements. Drought status will be measured weekly. The LRWC measurements will be collected from 5 randomly chosen plants across each treatment and variety and treated as replicates. Soil water content will be quantified across each variety and treatment from 5 soil cores sampled in a w-shape from each site and treated as replicates. Mean comparisons will be determined using Wilcox rank sum in R with a significance threshold of 0.05.

All objectives for this project will be assessed from sequencing data gathered through a randomized complete block design with three biological replicates. From the PFDC and PRFDC hoop houses leaf sampling will occur at the time of drought initiation, when drought stress is reached, when drought conditions are ceased, and at the time of harvest for silage. From the CC hoop house, samples will be collected from each control section at the same time as the sampling of treatment hoop houses.

Tissue Sampling Approach: Leaf sampling will occur in triplicate (n=3) for each sampling time point. For each replicate, leaf tissue will be pooled from 2 randomly selected rows, 5 plants per row via reductive randomization, per variety at each sampling time point. Sampling of each plant will follow in a canopy-stratified manner, where the collection will occur from two asymptomatic leaves from the top canopy, middle, and bottom. Leaves will be visually assessed for symptoms of disease (wilting, necrosis, discoloration, sporulating lesions, etc.), before sampling. Sampling will occur semi-destructively, in which a whole punch will be used to remove 12 discs, 6 from each side of the bulk leaf along the midvein, from each leaf.

The above methods and sampling descriptions are reflected in the attached Figure 1.

Leaf discs will be stored at 4^oC during and after field sampling to reduce the growth of fungal colonizers that could negatively mislead sequencing analyses. Within 5 days of sampling leaves will be surface disinfested using a modified approach from Schulz et al. (1993) in which discs are first rinsed in sterile water, followed by a series of ethanol, bleach, and sterile water baths, then dried and stored at -20^oC. Discs will be cryogenically frozen in liquid nitrogen and pulverized with a mortar and pestle. Approximately 100 mg of tissue from each sample will be used for DNA extraction. Extraction of whole DNA will be performed using the DNeasy Plant Pro Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. Nucleic acid concentration and purity will then be measured using Nanodrop or Qubit based quantification.

To characterize the Fusarium taxa from my samples a metagenomic approach will be used with the Illumina MiSeq platform. The TEF 1-α (translation elongation factor 1-alpha) region will be sequenced. The application of Illumina MiSeq technology for TEF 1-α was previously described by Cobo-Diaz et al. (2019) and Boutigny et al. (2019) to be effective for characterizing mock and naturally occurring Fusarium communities in cereal tissues and bulk soil, which demonstrate the effectiveness of this approach for my system. Library preparation and sequencing will be performed by MR DNA (Molecular Research, Shallowater, TX, USA), via modified protocols from Cobo-Diaz et al. (2019) and Torres-Cruz et al. (in preparation) with primers Fa_150 (5’-CCGGTCACTTGATCTACCAG-3’) and Ra-2 (5’-ATGACGGTGACATAGTAGCG-3’)¹⁰.

Sequences will be processed and filtered through the publicly available bioinformatics pipeline DADA2 v.1.26.0⁹, adapted specifically for fungi⁴³. In brief, primers will be removed, sequences trimmed and filtered, dereplicated, forward and reverse reads merged, chimeric sequences removed, and taxonomy assigned using a generated database of TEF 1-α sequences collected from NCBI (National Center for Biotechnology Information) and FUSARIUM-ID⁵² based on Fusarium species observed regularly in maize in the literature.

To build the comprehensive database of Fusarium targeted TEF 1-α sequences for use in characterizing Fusarium communities a literature search was conducted, and a preliminary Fusarium-specific TEF 1-α database was developed (~178 sequences). The database was then run against a sample dataset, identifying fourteen unique common Fusarium endophyte species, but could not characterize 90% of reads beyond the genus level. To address this issue, an existing database from the literature (Cobo-Díaz et al., 2019) was then applied to the same sample dataset; both databases were then compared for their ability to capture known Fusarium endophyte diversity and characterize mycotoxigenic and pathogenic Fusarium species of maize. A phylogenetic tree and accompanying analyses will then be used to characterize unidentified reads using alignments of the data and the two databases. Testing two databases will help determine the optimal database for use during the species assignment and analysis portions of this study, and the development of code for phylogenetic tree creation and analysis will provide a pipeline for future tree creation and species assignment in this project.

Literature Search

The literature search used two sources for literature gathering: Google Scholar” and “Web of Science.” The terms used for Web of Science were “Fusarium spp. from corn leaves” (19 results), “Fusarium spp. from corn” (365 results), “Fusarium endophytes of corn” (72 results), and “Fusarium of maize” (4,916 results). Web of Science was searched on August 20th, September 4th, and December 9th, 2024. The same terms were used in Google Scholar; “Fusarium spp. from corn leaves” (44,500 results), “Fusarium spp. from corn” (91,900 results), “Fusarium endophytes of corn” (19,700 results), and “Fusarium of maize” (343,000 results). Google Scholar was searched July (dates unrecorded), August 19th, 20th, September 2nd, 3rd, 4th, and December 2nd, 5th, and 9th, 2024. The top 100 citations from Google Scholar and Web of Science were reviewed using abstracts and titles, and papers relevant to the key terms were examined in more detail (e.g., methods, results, and figures). The names of Fusarium species were gathered manually from 11 papers ranging in publication date from the 1970s to the 2020s. Papers included lists or figures of Fusarium characterized through morphology in culture and/or amplicon sequencing. Species names as published in their referenced literature, current names from Mycobank (Mycobank.com, SimpleSearch tool), Fusarium species complex, source citations, host type, and geographic origin of the reference study were recorded in an Excel spreadsheet (Table 1).

The resulting reference list of potential Fusarium spp. colonizers included 33 unique species, 5 species complexes, 4 hosts (corn/ sweet corn, sorghum, millet, barley), and 5 countries (United States, Mexico, Nigeria, Japan, and Germany).

Database Development

To develop the initial database five reference isolate sequences of the partial or whole translation elongation factor 1-alpha region (EF 1-α) were collected per species identified in the literature search (Table 1) using Fusarium-ID (FUSARIUM-ID: http://isolate.fusariummdb.org/) and the National Center for Biotechnology Information’s nucleotide database (NCBI: http://www.ncbi.nlm.nhi.gov/). Sequences were pooled in a text document in the format >Kingdom;Phylum;Class;Order;Family,Genus,Species,TEF 1-α sequence 5’ to 3’ and saved as an .sa shared archive) formatted file, for use in the R ( R statistical software, v. 4.2.3 (2023-10-11), R Core Team) package Phyloseq (McMurdie and Holmes, 2013). The initial database included ~165 sequences.

The database was further expanded to include 13 additional TEF 1-α sequences gathered by a previous student in the Kuldau lab (Hussein Karemera et al., 2022, unpublished). These sequences represented thirteen Fusarium species isolated from various maize leaf parts (midrib, base, and blade). Sequences were previously generated using Sanger sequencing of the EF1-alpha region (EF1 5’-ATGGGTAAGGAGGACAAGAC-3’ and EF2 5’-GGAAGTACCAGTGATCATGTT-3’), and species assignment made using the Basic Local Alignment Search Tool (BLAST) network service of NCBI and Fusarium-ID with 98% sequence identity (O'Donnell et al., 1998). This resulted in a final database of ~178 TEF 1-α sequences.

Database Testing

A dataset of TEF 1-α sequences from a previous fungal endophyte study conducted by the authors of this project was used to test the database (unpublished dataset). This dataset consisted of sequences gathered through amplicon metabarcoding of whole DNA extracted from 83 maize leaf samples, targeting the internal transcribed spacer (ITS1, universal fungal identifier) and TEF 1-α (Fusarium species target) regions. The use of ITS1 was applied for whole community analysis in the previous study and preliminarily for identification of Fusarium in the samples; F. solani, F. sambucinum, F. oxysporum, F. merismoides, F. graminearum, F. fujikuroi, and F. equisiti. Amplicon sequencing of TEF 1-α was conducted using primers Fa_150 (5’-ACACTGACGACATGGTTCTACA CCGGTCACTTGATCTACCAG-3’) and Ra-2 (5’-TACGGTAGCAGAGACTTGGTCT ATGACGGTGACATAGTAGCG-3’) and resulted in 5,872,973 reads, 34,547 per sample, which were processed, and taxa assigned through the DADA2 pipeline (Callahan et al., 2016; Cobo-Díaz et al., 2019; Rolling et al., 2022), resulting in 827 unique amplicon sequence variants (ASVs), 726 Fusarium-specific ASVs, and 14 species.

A second database from Cobo-Díaz et al. (2019) containing 109 Fusarium spp. and closely related genera including Geejayessia, Rectifusarium, Neocosmospora and Bisifusarium) and closely associated taxa to the Nectriaceae family, such as Fusicolla and Ilyonectria, composed of 273 unique TEF 1-α sequences was then applied to the sample dataset. This database was not available as a direct download for use in the R package Phyloseq, so the database was generated manually using NCBI nucleotide database reference codes. The reference codes for each 273 species were manually searched, and sequences were compiled along with amplicon length and taxonomic information into an Excel spreadsheet and then transferred into a .sa (shared archive) formatted file for use in Phyloseq. Taxa were again assigned through the DADA2 pipeline. Which resulted in 978 unique amplicon sequence variants (ASVs), 81 Fusarium-specific ASVs, and 7 species.

To address Objective 1A (Assess how the Fusarium spp. community of maize leaves changes across drought exposure events pre- and post-flowering) Fusarium community α-diversity and β-diversity will be compared for the two drought treatments and the controls for each sampling event. For α-diversity three metrics will be applied; Shannon’s diversity, observed diversity, and Simpson’s diversity to account for both unique taxa and their richness across treatments. A Wilcox Rank sum will be applied in R with a significance level of 0.05 to compare treatment diversity metrics to the negative and positive controls at each sampling point. For β-diversity taxa abundance will be transformed into a distance matrix and normalized, then a PERMANOVA used to assess the community similarity of treatments and controls across sampling. The PERMANOVA will be conducted in R with a significance level of 0.05.

To address Objective 1B (Assess the effect of drought exposure pre- and post-flowering on the functional composition of Fusarium spp. communities) taxa will be manually assigned functional groups through literature review of maize and other cereal crops. Functional assignment will be modified from Nguyen et al. (2016) with categories; commensal (no described positive or negative relationship), saprophyte (described as saprophytic or found only in stover or litter), pathogen (known parasite with disease-causing capacity), and multifunction (multiple functions described in the literature). Functional α-diversity will be quantified via Shannon’s diversity, observed diversity, and Simpson’s diversity. And diversity between the drought treatment and control compared in R with Wilcox Rank sum and a significance level of 0.05. For β-diversity functional groups will be transformed into a distance matrix and normalized, then a PERMANOVA used to assess the community functional similarity between treatments and controls across sampling. The PERMANOVA will be conducted in R with a significance level of 0.05.

To address Objective 1C (Assess the effect of drought exposure pre- and post-flowering on Fusarium spp. communities at harvest for silage), as in methods for objective 1a Fusarium community a-diversity and b-diversity will be compared for the two drought treatments, however only for the final sampling time, between one another as well as the between treatments and controls. Community α-diversity will be quantified as before, diversity compared between the two drought treatments and the controls. And β-diversity performed as previously described.

To address Objective 2 (To reveal the interaction between drought stress events and pathogenic and mycotoxigenic Fusarium spp. in maize at harvest for silage), the parasite functional assignment generated in objective 1b will be subset from the bulk dataset. Mycotoxigenic species will get a new designation as mycotoxin producers, regardless of whether they were collected in the parasite functional group or not. These two new datasets will be used to assess both α-diversity and β-diversity as previously discussed in objective 1c, between treatments and controls at the final sampling point.

To address Objective 3 (To quantify the effect of host genotype and drought on the foliar Fusarium spp. community) the dataset will be subset by variety for both drought treatments and controls, and methods used for α-diversity and β-diversity described in objective 1a applied to assess differences in Fusarium communities between varieties for pre-flowering drought and controls and between post-flowering drought and controls across sampling.

Research results and discussion:

The curated database of 33 species and 178 partial or complete TEF 1-α sequences was used to characterize Fusarium spp. in a sample dataset representing 46 maize leaf samples and 5,872,973 reads. The resulting assignment identified 726 Fusarium-specific ASVs, and 14 unique species (90% ASVs unidentified); F. fujikuroi (average percent of sequences per sample, 0.06%), F. graminearum (0.21%), F. acumunatum (avg. 0.75%), F. armeniacum (0.9%), F. avenaceum (0.39%), F. graminearum (0.21%), F. ipomoea (1.32%). F. commune (0.98%), F. sporotrichioides (1.76%), F. equiseti (1.42%), F. compactum (1.46%), F. venenatum (2.45%), F. subglutinans (avg. 1.54%), and F. culmorum (2.26%).

The Cobo-Díaz et al. (2019) database of 109 Fusarium spp. and closely related genera to Fusarium and the Nectriaceae family, included 273 unique TEF 1-α sequences, was then used to characterize Fusarium spp. in the sample dataset. The resulting assignment identified 81 Fusarium-specific ASVs, and 7 unique species (90% ASVs unidentified); F. graminearum (average percent of sequences per sample, 2.41%), F. acumunatum (avg. 1.66%), F. lacertarum (0.58%), F. avenaceum (0.73%), F. proliferatum (0.76%), F. sporotrichioides (2.46%), and Neonectria lugdunensis (1.42%).

Preliminary results of the developed Fusarium TEF 1-α database indicated that the database captured common Fusarium endophytes but was unable to identify most Fusarium TEF 1-α ASVs beyond the genus level (Fig. 2A). This is especially important since the database was unable to capture F. verticillioides, a common endophyte and one of the economically important mycotoxin producing pathogens of interest to this project. To address this limitation, an existing database developed by Cobo-Díaz et al. (2019) was then applied to the sample dataset, and results between the two databases were compared. Applying the Cobo-Díaz et al. (2019) database was intended to provide an alternative database that captured a greater variety of Fusarium species, not specific to maize, unassigned members of Fusarium species complexesand closely related taxa in the Nectriaceae family.

The Cobo-Díaz et al. and curated database resulted in the identification of 7 species: 6 Fusarium and 1 Neonectria, and 14 Fusarium species, respectively (Fig. 2). The Cobo-Díaz et al. database produced significantly fewer Fusarium assigned ASVs (n = 81, T-test, p < 0.05) than the curated database (n=726) and had an overall reduction in unidentified ASVs (unidentified =20%, Fig. 2B) from the curated database (unidentified =90%, Fig. 2A). The fewer number ofASVs found with the Cobo-Díaz et al. database may suggest improved Fusarium assignment due to the increase in TEF 1-α diversity within the database. The curated database may have lacked enough diversity of Fusarium and Nectriaceae TEF 1-α sequences for accurate Fusarium assignment, resulting in an over-representation of Fusarium ASVs. However, the Cobo-Díaz et al. database was still unable to capture F. verticillioides.

Moving forward, the two databases will be combined and duplicate sequences removed. The final database and the sample Fusarium TEF 1-α dataset will be aligned, and a phylogenetic tree will be developed using the consensus of trees created with a Markov chain Monte Carlo (MCMC) algorithm, as described by Cobo-Díaz et al. (2019). The goal of the tree and the use of posterior probability through the MCMC will allow for species-level assignment of ASVs which were unidentified through the database and Phyloseq program alone.

The resulting database and MCMC approach will be applied as part of the data processing pipeline for sequences generated for this project in the coming season.

Species Name at Time of Publication	Revised name as of 2024	Species Complex	Sourced Article	Host	Geography
F. acuminatum	F. acuminatum	FTSC	Leslie et al., 1990; Parikh et al., 2018	Corn	United States
F. chlamydosporum	F. chlamydosporum	FCSC	Leslie et al., 1990	Corn	United States
F. compactum	F. compactum	FIESC/FCAMSC	Leslie et al., 1990	Corn	United States
F. equiseti	F. equiseti	FIESC/FCAMSC	Leslie et al., 1990; Goertz et al., 2010; Uegaki et al., 2012; Parikh et al., 2018	Corn	United States, Japan
F. graminearum	F. graminearum	FSAMSC/FGSC	Abbas et al., 1988; Leslie et al., 1990; Goertz et al., 2010; Parikh et al., 2018; Luis et al., 2023	Corn	United States, Mexico
F. merismoides	Fusicolla merismoides	none	Leslie et al., 1990	Corn	United States
F.moniliforme	F. verticillioides	FFSC	Kommedahl et al., 1979; Abbas et al., 1988; Leslie et al., 1990; Nirenberg and O'Donnell, 1998; Jurjevic et al., 2005; Uegaki et al., 2012; Parikh et al., 2018	Corn	United States, Japan
F. oxysporum	F. oxysporum	FOSC	Kommedahl et al., 1979; Abbas et al., 1988; Leslie et al., 1990; Goertz et al., 2010; Parikh et al., 2018	Corn	United States, Germany
F. proliferatum	F. proliferatum	FFSC	Abbas et al., 1988; Leslie et al., 1990; Goertz et al., 2010; Uegaki et al., 2012; Vismer et al., 2019	Corn, Sorghum, Millet	United States, Germany, Nigeria, Japan
F. semitectum	F. incarnatum	FIESC/FCAMSC	Leslie et al., 1990; Luis et al., 2023	Corn	United States, Mexico
F. solani	Neocosmospora solani	none	Kommedahl et al., 1979; Leslie et al., 1990; Parikh et al., 2018	Corn	United States
F. subglutinans	F.subglutinans	FFSC	Abbas et al., 1988; Nirenberg and O'Donnell, 1998; Leslie et al., 1990; Goertz et al., 2010	Corn	United States, Germany
F. temperatum	F. temperatum	FFSC	Ridout et al., 2016	Swet Corn	United States
F. cerealis	F. cerealis	FSAMSC/FGSC	Goertz et al., 2010; Cummings et al., 2017;	Corn, Barley	United States
F. avenaceum	F. avenaceum	FCOSC	Goertz et al., 2010; Parikh et al., 2018	Corn	Germany, United States
F. culmorum	F. culmorum	FSAMSC/FGSC	Goertz et al., 2010; Parikh et al., 2018	Corn	Germany, United States
F. poae	F. poae	FSAMSC/FGSC	Goertz et al., 2010	Corn	Germany
F. sporotrichioides	F. sporotrichioides	FSAMSC/FGSC	Goertz et al., 2010; Parikh et al., 2018; Luis et al., 2023	Corn	Germany, United States, Mexico
F. tricinctum	F. tricinctum	FTSC	Kommedahl et al., 1979; Goertz et al., 2010	Corn	Germany, United States
F. venenatum	F. venenatum	FSAMSC/FGSC	Goertz et al., 2010	Corn	Germany
F. pseudonygamai	F. pseudonygamai	FFSC	Nirenberg and O'Donnell, 1998; Jurjevic et al., 2005; Vismer et al., 2019	Corn, Sorghum, Millet	Nigeria, Germany, United States
F. redolens	F. redolens	FRSC	Parikh et al., 2018	Corn	United States
F. fujikuroi	F. fujikuroi	FFSC	Uegaki et al., 2012; Parikh et al., 2018; Luis et al., 2023	Corn	United States, Japan, Mexico
F. roseum	F. sambucinum	FSAMSC/FGSC	Kommedahl et al., 1979	Corn	United States
F. asiaticum	F. asiaticum	FSAMSC/FGSC	Uegaki et al., 2012	Corn	Japan
F. irregulare	F. irregulare	FIESC/FCAMSC	Luis et al., 2023	Corn	United States, Mexico
F. sulawesiense	F. sulawesiense	FIESC/FCAMSC	Luis et al., 2023	Corn	United States, Mexico
F. pernambucanum	F. pernambucanum	FIESC/FCAMSC	Luis et al., 2023	Corn	United States, Mexico
F. nanum	F. nanum	FIESC/FCAMSC	Luis et al., 2024	Corn	United States, Mexico
F. ipomoeae	F. ipomoeae	FIESC/FCAMSC	Luis et al., 2025	Corn	United States, Mexico
F.acuminatum	F. acuminatum	FTSC	Luis et al., 2025	Corn	United States, Mexico
F. luffae	F. luffae	FIESC/FCAMSC	Luis et al., 2025	Corn	United States, Mexico
F. hainanense	F. hainanense	FIESC/FCAMSC	Luis et al., 2025	Corn	United States, Mexico

Table 1. Fusarium spp. reference list created from a literature search of 100 top papers from Google Scholar and Web of Science on Fusarium colonization of maize. Name revisions are based on current species names listed on MycoBank, species complexes are based on those described in Fusarium-ID, and geography notes in what country the isolation or characterization of the species was made based on the cited literaure in the sourced article column.

Figure 2. Percent prevalence of Fusarium spp. and like species assigned amplicon sequence variants (ASVs) across 46 maize leaf samples, colored by species, for both the (A) developed database and the (B) Cobo-Díaz et al. (2019) database.

Citation List

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Participation Summary

Education & Outreach Activities and Participation Summary

Participation Summary:

Education/outreach description:

The results of this project will be shared with a wide range of stakeholders, including fellow researchers, PSU extension educators, producers, non-profits such as Pasa – PA sustainable agriculture and industry representatives. Sharing contributes greatly to foundational pathogen ecology, informs risk models for mycotoxin contamination of silage, and helps elucidate the impacts of seasonal uncertainty and drought stress in maize. With these results, I hope to contribute a greater understanding of the interactions between maize, Fusarium, and the environment. This work can be translated directly toward recommendations used by extension educators and to producer decisions regarding mycotoxin risk in silage and the implementation of pathogen and mycotoxin management approaches. Therefore, communicating the findings of this project with extension educators and producers is important to the impact of this project’s findings.

It is my goal to make the findings, data, and methods of this work easily accessible and approachable to fellow students and researchers across scientific fields. By doing so this work can directly contribute to future studies and continue to build on our understanding of maize, Fusarium spp., and climate. As such I will make the raw and filtered data, research methods, data analysis, and results interpretation available through the open access data sharing platform GitHub. And submit sequences generated from this work to the National Center for Biological Technology Information (NCBI) database.

Specific outreach goals: It is also my goal to communicate project results in a succinct and approachable manner that reaches the greatest number of stakeholders in the Northeast, especially producers. Therefore, I will publish a research review with Crop Protection Network (https://cropprotectionnetwork.org/), a summary that describes the main findings of my work. The Crop Protection Network is a multi-state and international partnership of university and provincial extension specialists, and public and private agents that provide critical information related to the protection of row crops including maize across the United States. Their website and publication network will allow me to connect with Pennsylvania and other Northeastern maize growers through a trusted and non-biased source and are optimal for the broad distribution of my research findings.

To communicate my work further with researchers, extension educators, and industry representatives in the Northeast the results of my project will be presented to audiences at the APS (American Phytopathological Society) Northeastern division meeting in 2026. The attendees of this regional meeting specialize in plant pathology and have an interest in understanding how environmental stress shapes pathogen ecology. Therefore, my work will contribute novel information on the interaction of drought stress timing, the Fusarium spp. community and pathogenic and mycotoxigenic Fusarium species in maize. I will also present at the Pasa – PA Sustainable Agriculture meeting where there are many growers and livestock producers who are interested in learning more about the implications of drought-stress on crops and willing to discuss sustainable solutions which will be useful for extending the impact of these results into the future.

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Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy.