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\begin{document}

\title{Genes and genetic mechanisms contributing to fall armyworm resistance in
maize}



\author[1]{Marilyn L. Warburton}%
\author[1]{Sandra Woolfolk}%
\author[1]{Jessie Smith}%
\author[1]{Leigh Hawkins}%
\author[1]{Lina Castano-Duque}%
\author[1]{Matthew Lebar}%
\author[1]{William Williams}%
\affil[1]{USDA ARS}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
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 289 maize lines phenotyped for FAW
damage in artificially infested, replicated field trials over three
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 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 also
tend to have lower levels of pheophytin, lutein, chlorophyll b and
\selectlanguage{greek}β-\selectlanguage{english}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.%
\end{abstract}%



\sloppy


To the Editors of the Plant Genome:

Our collaborative research project on Genome-wide Association Study of
Fall Armyworm resistance in maize, followed by metabolic pathway
analysis, has resulted in the manuscript presented here. This is the
first work of its kind on this trait in maize, and the second on
insects, and we believe it will be useful to plant breeders working on
insect resistance, as well as geneticists and physiologists working to
decipher complex traits in plant populations. We hope the reviewers and
editors of The Plant Genome agree, and we look forward to hearing your
suggestions on further improvement of our paper.

Sincerely yours, Marilyn Warburton and co-authors.



\textbf{Core ideas:}

\begin{itemize}
\tightlist
\item
  GWAS and metabolic pathway analysis identified genes and pathways
  associated with Fall Armyworm resistance.
\item
  31 maize lines with good resistance to Fall Armyworm were identified
  that can be useful donor lines.
\item
  Pathways identified may provide evidence of new resistance mechanisms
  for direct selection or new research avenues.
\item
  Measurement of targeted metabolites confirm pathway analysis results
  and provide further avenues of new research.
\end{itemize}

\textbf{Genes and genetic mechanisms contributing to fall armyworm
resistance in maize}

Marilyn L. Warburton\textsuperscript{1}, Sandra W.
Woolfolk\textsuperscript{2}, J. Spencer Smith\textsuperscript{2}, Leigh
K. Hawkins\textsuperscript{2}, Lina Castano-Duque\textsuperscript{3},
Matthew D. Lebar\textsuperscript{3}, and W. Paul
Williams\textsuperscript{2}

\textsuperscript{1}USDA ARS Plant Germplasm Introduction and Testing
Research Unit, Pullman, WA

\textsuperscript{2}USDA ARS Corn Host Plant Resistance Research Unit,
Mississippi State, MS

\textsuperscript{3}USDA ARS Food and Feed Safety Research Unit, New
Orleans, LA

Abbreviations: BLUE (Best Linear Unbiased Estimator); CIMMYT
(International Maize and Wheat Improvement Center, in Spanish); CHPRRU
(Corn Host Plant Resistance Research Unit); FAW (fall armyworm); GBS
(genotyping by sequencing); GLM (general linear model); GWAS
(genome-wide association study); MAF (minor allele frequency); MLM
(mixed linear model); PAST (pathway analysis study tool); QTL
(quantitative trait loci).

Abstract

Maize (\emph{Zea mays} L.) is a crop of major economic and food security
importance globally. The fall armyworm (FAW), \emph{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 289 maize lines
phenotyped for FAW damage in artificially infested, replicated field
trials over three 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
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 also tend to have lower levels of pheophytin, lutein, chlorophyll
b and \selectlanguage{greek}β-\selectlanguage{english}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.

Introduction

Maize (\emph{Zea mays} L.) provides food, feed, and industrial
ingredients to the economy of the United States, worth many billions of
dollars annually. It is a major staple food crop globally as well, and
total grain production was 1,266 million tons in 2019 (World Data Atlas,
Knoema.com). The fall armyworm (FAW), \emph{Spodoptera frugiperda} (J.
E. Smith) (Lepidoptera: Noctuidae), originally evolved with maize in the
tropical and subtropical regions of the Americas, and although it feeds
on a broad range of host plants, it prefers gramineous plants including
maize, and can devastate a maize crop in a short time (Overton et al.,
2021). It has now spread to almost every country of the Americas,
Africa, and Asia as an invasive and destructive species (Goergen et al.,
2016; FAO 2021). This is of particular concern in Africa, where many
countries do not allow the use of transgenic crops, removing some insect
control options from farmers, but new genetic editing techniques may be
more acceptable to the public (Turnbull et al., 2021).

Native resistance, or host-plant insect resistance conferred by
naturally occurring maize genes, is an economical and environmentally
benign way to control FAW. Maize lines containing native resistance
genes were created in the 1970s through 1990s by the International Maize
and Wheat Improvement Center (CIMMYT) in Mexico (Mihm et al., 1988). The
USDA-ARS Corn Host Plant Resistance Research Unit developed and released
several resistant temperate maize inbred lines; these include Mp496,
Mp701-708, Mp713, Mp714, and Mp716 (Scott and Davis, 1981; Scott et al.,
1982; Williams and Davis, 1980, 1982, 1984, 2000, 2002; Williams et al.,
1990). Although high levels of FAW infestation are better controlled by
transgenic hybrids containing the Bt gene than conventional hybrids with
native resistance, both transgenic and conventionally bred resistant
maize hybrids are significantly less damaged by FAW feeding than
non-resistant commercial hybrids (Williams et al., 1997).

Native resistance to insects in maize is generally quantitative in
nature, governed by many genes, rather than a single gene conferring
most or all of the resistance (McMullen et al., 2009). Quantitative
trait loci (QTL) for FAW resistance in maize have been identified
including one with a very large phenotypic effect in bin 9.03 (Brooks et
al., 2007; Womack et al., 2018; 2020). Genetic mechanisms causing FAW
resistance in maize can be due to morphological features such as
structural barriers (waxes or tougher cell walls); antibiosis, or the
creation of metabolites toxic or off-putting to the insects; or hormones
that call insect predators (McMullen et al., 2009). A previous study by
Davis et al. (1998) found that resistance in young plants was linked to
thickness of the cuticle and the epidermal cell wall complex, which was
nearly twice as thick in resistant than susceptible plants and contained
more hemicellulose. In addition, the inner whorl tissue from the
resistant inbreds were tougher than in the susceptible, and resistant
hybrids completed the transition from juvenile to adult tissues (which
are less palatable to insects) sooner than susceptible hybrids. FAW
larvae feeding on maize plants in the whorl stage of growth feed
primarily on the yellow-green portion of whorl leaves. Davis et al.
(1999) compared the sizes of larvae fed the yellow-green leaves of
resistant or susceptible hybrids, compared to the outer (green) or
innermost (yellow-white) portions of the leaves. Larvae feeding on the
susceptible hybrid were larger than those feeding on the resistant, and
larvae feeding on the yellow-green leaves in resistant (and to a lesser
extent, susceptible) plants were smaller compared to those feeding on
other portions within the whorl. Thus, it is hypothesized that there are
antibiosis compounds localized there, especially in resistant plants,
that protect the growing point of the seedling plants (Williams et al.,
1998).

Both QTL mapping and association mapping may identify genes and genomic
regions associated with a trait of interest. A QTL mapping experiment
will likely identify very long genomic regions associated with the trait
and will only find the alleles that happen to be segregating in the two
parents of the mapping population. Conversely, a genome-wide association
study (GWAS) will identify far more regions associated with a trait,
often precisely enough to narrow down to one or a few genes influencing
the trait, but rarely identifies large effect genes for highly
quantitative traits, and many genes may be missed (Flint-Garcia et al.,
2005). A metabolic pathway analysis of the output of a GWAS study may
remedy this, and may identify more useful genes than the GWAS, and these
genes will be grouped into pathways of known function (Tang et al.,
2017; Thrash et al., 2022; 2021). The spread of FAW outside its native
range has increased the importance of identifying genes and regions of
the maize genome contributing resistance to FAW both to facilitate
breeding of non-GMO lines with FAW resistance, and to identify genes as
candidates for gene editing. Thus, this study was undertaken to dissect
the genetic basis and physiological mechanisms of resistance to FAW in
maize. The objectives of this study were to identify resistant maize
lines and SNPs associated with FAW resistance via GWAS; to use this
information to identify metabolic pathways associated with FAW
resistance via PAST; validate the presence of one or more of these
metabolites in resistant vs. susceptible plants; to integrate these
pathways into specific mechanisms used by maize plants as protection
from FAW; and to compare these results to previous genomic and
metabolomic studies of insect resistance in maize.

Materials and Methods

\textbf{Germplasm and data generation}

A panel of 289 diverse maize inbred lines was used for this study
(Supplemental Table 1). The panel included both FAW resistant and
susceptible lines from various breeding programs, some of which formed
association panels published previously (Warburton et al., 2013;
Flint-Garcia et al., 2005). These lines were genotyped and phenotyped as
inbreds. They were grown at the R.R. Foil Plant Science Research Center,
Mississippi State, Mississippi (MS) in 2019, 2020, and 2021. Seeds were
planted in 4 m single-row plots with 0.97 m spacing between rows and
thinned to 20 plants plot\textsuperscript{-1}. The experiment was
planted in a randomized complete block design with two replications in
2019 and 2020 and three replications in 2021. Standard production
practices were followed, and supplemental irrigation was applied as
needed.

The fall armyworm neonate larvae used in this study were obtained from
the laboratory colony at the insect rearing facility of the Corn Host
Plant Resistance Research Unit (CHPRRU), USDA-ARS, Mississippi State.
The larvae were reared using artificial diet following the procedure
described by Davis (1989). Within 24 hours following the egg hatch,
first instar larvae (neonates) were mixed with corn cob grits. The
mixture was dispensed into the maize whorl utilizing a mechanical
applicator (Wiseman et al., 1980). Each individual plant was infested
with approximately 40 FAW neonates at the mid-whorl stage (V-7 of maize
growth stage). All plants within experimental plots were rated
individually to determine the leaf feeding damage caused by FAW. The
damage was visually rated at 7 days and 14 days after infestation. The
7-day and 14-day leaf feeding ratings were conducted on a 0 (no visible
leaf feeding damage) to 9 scale according to Davis et al. (1992;
Supplemental Table 2). The 7-day ratings were conducted by one person
and the 14-day ratings were conducted independently by two people.

The mean rating for each entry was calculated as an adjusted mean (Best
Linear Unbiased Estimator; BLUE) using mixed model methodology in the
PROC GLIMMIX procedure of SAS 9.4 (SAS Institute, Cary, NC) and can be
found in Supplemental Table 1. For the 7-day ratings, the individual
plants within a plot were treated as subsamples. Plot means were
calculated within years treating genotype as a fixed effect and
replication and genotype by replication as random effects. To combine
the 7-day ratings over years, genotype was treated as a fixed effect and
year, replication nested within year, year by genotype interaction, and
genotype by replication nested in year were all treated as random
effects. For the 14-day ratings two sets of individual plant ratings,
one set per rater, were used as subsamples for each plot. Plot means
were calculated similarly to the 7-day ratings, but additional model
terms were added to account for the variance due to different raters.
Within years, genotype was treated as a fixed effect and replication,
rater, rater by replication, genotype by replication, and genotype by
rater by replication were all treated as random effects. When the data
was combined across three years, genotype was treated as a fixed effect
and year, replication nested in year, rater, rater by replication nested
in year, year by genotype interaction, year by rater interaction,
genotype by replication nested in year, and rater by genotype by
replication nested in year were all treated as random effects.

\textbf{GWAS and pathway analyses}

Genotyping of the inbred lines in the panel was done via Genotype by
Sequencing (GBS) according to Elshire et al. (2011). Briefly, SNPs were
extracted from raw GBS data using the Java pipeline for GBS
Bioinformatics (Glaubitz et al. 2014). SNPs were aligned against the B73
reference genome, version 4. The GBS marker dataset was imputed and
filtered by removing SNPs with a minor allele frequency (MAF)
\textless{} 6\% and any SNP with \textgreater{} 2 alleles, resulting in
a total of 1,105,817 SNPs. Useful GBS data was successfully obtained
from 281 of the inbred lines, which were used in the GWAS. A subset
consisting of 8,000 SNPs with a low missing data rate (\textless{}
7.5\%) and a balanced allele frequency (MAF \textgreater{} 40\%) was
extracted for calculation of population sub-structure (Q matrix) and
linkage disequilibrium, and 148,000 unimputed SNPs were used to
calculate the K matrix. K, Q, and LD were calculated in the same manner
reported in Warburton et al., (2015). The software package TASSEL 3.0
(Bradbury et al., 2007) was used to perform the GWAS using the BLUE
values of the 7-day and 14-day FAW ratings within and across years. The
General Linear Model (GLM) was run, as well as the Mixed Linear Model
(MLM; Yu et al., 2006) using three subpopulations (Q matrix) and the K
matrix. To correct for multiple comparisons, an adjusted
Bonferroni-corrected threshold (i.e., P [?] 1/N, where N is total number
of genome-wide SNPs) was used for declaring the significance of GWAS
associations (Benjamini and Hochberg 1995).

The Pathway Association Studies Tool (PAST; Thrash et al., 2020a) was
used to perform a metabolic pathway analysis, as was first described in
Tang et al. (2015). Linkage disequilibrium values between each marker
and the 50 closest up- and downstream SNPs, and the SNP-trait
association values for significance (p), correlation
(R\textsuperscript{2} or the proportion of the phenotypic variation
accounted for), and allele effect as calculated by TASSEL were used by
the PAST program to calculate the running enrichment score for all
annotated maize pathways. A running enrichment score measures the
probability that each pathway is associated with FAW resistance. The
PAST program assigned each SNP to an annotated gene, based on user
defined linkage disequilibrium and physical distance values
of\emph{r\textsuperscript{2} \textgreater{}} 0.8 and \emph{+} 1 Kb,
respectively. Each gene was assigned to a pathway using the gene
annotation files in MaizeCyc (Monaco et al. 2013). Only pathways with
five or more mapped genes (298 pathways) were considered in the
analysis. More detail of the pathway analysis can be found in Warburton
et al., (2017) and Li et al., (2019).

\textbf{Targeted metabolite confirmation analysis}

To test differential production of metabolites identified via PAST,
leaves of the 31 most resistant lines in the study and 28 of the most
susceptible lines based on the 7-day visual ratings from 2019 and 2020
were collected in 2021. Best and worst lines are indicated in
Supplemental Table 1 in green or red highlight, respectively. Leaf
tissue was collected from 10 plants of two replications of each genotype
after the 14-day damage rating scores had been collected. Leaves from
each plot of each selected genotype (except Mp704 and Mp707, which had
too few live plants in the field) were harvested and flash frozen in
liquid nitrogen and then used in a confirmation study whereby
carotenoids and chlorophylls were extracted and analyzed for
preferential abundance between the selected lines. This was done as
previously described (Wojdy\selectlanguage{polish}ł\selectlanguage{english}o et al., 2021) with the modification that
magnesium carbonate (MgCO3, 50 mg) was added to lyophilized leaf samples
(500 mg) to prevent isomerization. The samples were then extracted with
5 ml hexane:acetone:methanol (2:1:1, v/v/v) containing 1\% BHT for 1 h
in the dark on an orbital shaker (5000 rpm). The extracts were filtered
through cotton plugs and the filtrates were concentrated via speedvac
(Savant, Thermo Scientific). Each extract was re-dissolved in methanol
(1 ml) and particulates were removed via centrifuge.

For carotenoid and chlorophylls analysis, the re-dissolved, centrifuged
extracts were analyzed on a Waters Acquity UPLC system coupled to a
Waters Xevo G2 XS Quadrupole Time-of-Flight (QTOF) mass spectrometer
(MS). The QTOF MS was equipped with a Z-spray ionization source running
in ESI+ mode using MassLynx 4.2 software with the following settings:
source temperature: 100 \selectlanguage{ngerman}°C, desolvation temperature: 250 °C, desolvation
gas flow: 600 L/h, cone gas flow: 50 L/h, capillary voltage: 3.0 kV,
sampling cone voltage: 40 V. Analyses were performed in sensitivity and
continuum mode, with a mass range of m/z 50--1200 and a scan time of 0.1
s. A data-independent acquisition method with elevated collision energy
(MSE) was used with 6 eV low energy and a high energy ramp from 15\selectlanguage{english}-45
eV. Separation was performed on a Waters BEH C18 1.7 \selectlanguage{greek}µ\selectlanguage{english}m, 2.1 x 50 mm
column with the following gradient solvent system: (0.5 ml/min, solvent
A: 0.1\% formic acid in water; solvent B: 7:3 acetonitrile:methanol):
75\% B (0.0-0.6 min.), gradient to 95.1\% B (0.6-6.5 min.), gradient to
100\% B (6.5-13.6 min.), 100\% B (13.6-14.6 min.), then column
equilibration to 5\% B (14.6-17.5 min.). Data were analyzed on Waters
UNIFI 1.9.4 software using the ``Quantify Assay Tof 2D'' analysis method
with lock mass (leucine-enkephalin) corrected by UNIFI. Analytical
standards (lutein and zeaxanthin) were purchased from ChromaDex and used
to confirm identity of analytes and generate standard curves. Lutein and
zeaxanthin are reported in \selectlanguage{greek}µ\selectlanguage{english}g carotenoid/g leaf sample. Relative amounts
of chlorophyll a, chlorophyll b, and pheophytin b are reported as
intensity counts/g leaf sample.

Normality of distribution on the metabolite levels was tested using
QQplots and by performing a Shapiro-wilk test in R (Team, 2015). To
achieve normality, metabolite content levels were transformed using log
base 10. Normalized metabolite data and FAW ratings for day 7 and 14
from the 2021 field season were the input variables used to perform a
correlation analysis using hierarchical clustering with 0.095 confidence
intervals and 0.05 cut-off p-value in R. The data was scaled then used
to generate a dendrogram by applying the Euclidean distance method and
clustered by using the complete agglomeration method in R. Principal
component analysis was performed using the same input variables used for
the dendrogram. The first two PCs were used to create a PCA plot and
variance by variable and individual was extracted from the analysis.
Transformed and scale variables were plotted in relation to the cluster
identity.

Results and Discussion

\textbf{Variation for FAW damage in the GWAS panel}

There was a wide range of variation in the damage ratings on the 289
inbred lines in the study, from a low of 2.01 to a high of 7.65 over the
three years (Supplemental Table 1). The data over the three years of the
study were well correlated, with correlations of 0.68 (2019 vs. 2021),
0.70 (2020 vs. 2021) and 0.72 (2019 vs. 2020). The 7-day ratings were
also correlated with the 14-day ratings (0.94); (data not shown). The 31
most resistant inbred lines are highlighted in Supplemental Table 1 in
green and include many lines from the USDA ARS CHPRRU (19 breeding lines
and released inbred lines designated Mp); CIMMYT (10 released inbreds
designated CML); and NCSU (1 inbred designated NC). Considering the
large effect QTL identified in some of the CPHRRU lines in previous
mapping studies (Brooks et al., 2007; Womack et al., 2018; 2020), and
the strong resistance demonstrated by these lines in this study (all had
average damage ratings of less than 4.0 over the three years), these
lines would make excellent donor lines for introgression breeding of FAW
resistance into elite but susceptible maize lines. The range of
phenotypic variation was also sufficient for a successful GWAS study.

\textbf{Genes associated with FAW damage ratings}

When the GLM was used for association analysis, the observed -Log10(p
values) were higher than the expected, as can be seen in the QQ plot in
Supplemental Fig. 1a. The observed values were much closer to the
expected when the MLM was used (Supplemental Fig. 1b), and thus the MLM
results are presented here and were used in the subsequent pathway
analysis. Because of missing data of some SNPs, several of the most
significant SNPs ended up comparing only one or two individuals with one
allele against one or more hundred with the other allele. To avoid this
statistically risky analysis, the Minor Allele Frequency was increased
to 0.09 and the GWAS run again with 940,585 remaining SNPs. Only results
of this second analysis are presented here and were used in the pathway
analysis. Setting a significance threshold of 1/n, there were 3 SNPs,
corresponding to 2 linked loci, significantly associated at p
\textless{} 1.06 x 10\textsuperscript{-6} with the data for FAW damage
at 7 days after infestation, and 12 SNPs, corresponding to 5 linked
loci, with the data at 14 days (Table 1). There was very good
correspondence with the SNPs found to be associated in the 7-day and
14-day ratings. All 3 significantly associated SNPs in the 7-day rating
analysis were associated in the 14-day analysis at the p \textless{}
10\textsuperscript{-5} or better, and all but one of the 12 SNPs
significantly associated in the 14-day rating analysis were associated
in the 7-day analysis at the p \textless{} 10\textsuperscript{-5} or
better. The table of MLM association scores for p \textless{} 0.05 can
be found in Supplemental Table 3. Associations that are better than the
Bonferroni corrected threshold are highlighted in yellow.

\textbf{Table 1:} The 15 SNPs (listed by chromosome (Chr) and position
along the chromosome) significantly (p \textless{} 1.06 x
10\textsuperscript{-6}) associated with Fall Armyworm (FAW) damage
levels measured at 7 and at 14 days after infestation were in or closely
linked to 7 genes. Association values (degrees of freedom (df), F
values, p values, and Error degrees of freedom (Error df) as well as
correlations (Marker R\textsuperscript{2}), gene identifications
(``Linked gene'') and annotations (``Gene name or function'') are noted.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\textbf{7 Day FAW damage rating} & \textbf{7 Day FAW damage rating} &
\textbf{7 Day FAW damage rating} & \textbf{7 Day FAW damage rating} & &
& & &\tabularnewline
\midrule
\endhead
\textbf{Chr} & \textbf{Position} & \textbf{df} & \textbf{F} & \textbf{p}
& \textbf{Error df} & \textbf{Marker R\textsuperscript{2}} &
\textbf{Linked gene} & \textbf{Gene name or function}\tabularnewline
1 & 46380400 & 2 & 16.277 & 2.30E-07 & 252 & 0.041 & Zm00001d028785 &
Not annotated\tabularnewline
6 & 85713720 & 1 & 29.505 & 7.41E-07 & 76 & 0.100 & Zm00001d036360 &
cyc3 - cyclin3\tabularnewline
6 & 85715213 & 2 & 14.574 & 1.03E-06 & 255 & 0.037 & &\tabularnewline
\textbf{14 Day FAW damage rating} & \textbf{14 Day FAW damage rating} &
\textbf{14 Day FAW damage rating} & \textbf{14 Day FAW damage rating} &
& & & &\tabularnewline
\textbf{Chr} & \textbf{Position} & \textbf{df} & \textbf{F} & \textbf{p}
& \textbf{Error df} & \textbf{Marker R\textsuperscript{2}} &
\textbf{Linked gene} & \textbf{Gene name or function}\tabularnewline
4 & 15980038 & 2 & 18.029 & 6.54E-08 & 201 & 0.058 & Zm00001d049099 &
pdi1 - protein disulfide isomerase1\tabularnewline
4 & 15980032 & 2 & 18.029 & 6.54E-08 & 201 & 0.058 & &\tabularnewline
1 & 177103292 & 2 & 16.568 & 2.08E-07 & 216 & 0.046 & Zm00001d031097 &
Not annotated\tabularnewline
8 & 22544824 & 2 & 16.246 & 2.29E-07 & 261 & 0.039 & Zm00001d008850 &
LEA membrane bound glycoprotein\tabularnewline
3 & 139450676 & 1 & 31.336 & 7.46E-07 & 59 & 0.123 & Zm00001d041826 &
Nucleus associated protein kinase domain containing\tabularnewline
3 & 139450701 & 1 & 31.336 & 7.46E-07 & 59 & 0.123 & &\tabularnewline
3 & 139450683 & 1 & 31.336 & 7.46E-07 & 59 & 0.123 & &\tabularnewline
3 & 139450697 & 1 & 31.336 & 7.46E-07 & 59 & 0.123 & &\tabularnewline
3 & 139450106 & 1 & 25.460 & 9.14E-07 & 236 & 0.033 & &\tabularnewline
3 & 139450107 & 1 & 25.460 & 9.14E-07 & 236 & 0.033 & &\tabularnewline
3 & 139450108 & 1 & 25.460 & 9.14E-07 & 236 & 0.033 & &\tabularnewline
5 & 191019633 & 2 & 14.932 & 8.27E-07 & 227 & 0.040 & Zm00001d017264 &
cell wall-associated receptor kinase-like 8\tabularnewline
\bottomrule
\end{longtable}

The 15 SNPs associated with FAW damage levels were in or closely linked
to 7 genes (Table 1), two of which were not annotated. The other five
included a cyclin with kinase activity to promote cell division (cyc3,
Zm00001d036360); a protein disulfide isomerase member of the thioredoxin
superfamily involved in starch synthesis (pdi1, Zm00001d049099); a late
embryogenesis abundant (LEA) hydroxyproline-rich member of the
glycoprotein family that is associated with the plasma membrane
(Zm00001d008850); a protein kinase domain containing protein located in
the nucleus (Zm00001d041826); and a cell wall-associated receptor
kinase-like 8 (Zm00001d017264). Three of these genes (Zm00001d028785,
Zm00001d041826, and Zm00001d049099) were previously found to increase
expression following exposure to fungal pathogens (Hoopes et al., 2019
and Swart et al., 2017). Although none of the genes were part of
annotated maize pathways, one (Zm00001d049099) is a thioredoxin, and the
thioredoxin pathway was found to be associated with FAW damage levels
(see below).

\textbf{Pathways associated with FAW damage levels}

The pathway analysis was run in four separate tests on FAW damage.
Pathways associated with reduced FAW damage scores at the p \textless{}
0.05 level were identified in the 7-day and 14-day ratings datasets, and
pathways associated with increased FAW damage scores were run on the
ratings datasets collected at 7 and 14 days. Following these pathway
analyses, 31 pathways were associated with increased FAW damage: 26 with
the 7-day ratings, 18 with the 14-day ratings, and 5 in common. Another
42 pathways were associated with decreased FAW damage: 29 with the 7-day
ratings, 22 with the 14-day ratings, and 9 in common (Supplemental Table
4). There were no common pathways between the analyses for increased
damage vs. decreased damage, but three related pathways were found
between the increased and decreased analyses. Triacylglycerol
biosynthesis was associated with reduced FAW damage, while
triacylglycerol degradation was associated with increased damage;
phospholipid biosynthesis was associated with increased damage, and
phospholipid conversion to another compound was associated with reduced
damage. On the other hand, UDP-\selectlanguage{greek}β-\selectlanguage{english}L-arabinose biosynthesis was associated
with both increased and decreased damage (via different pathways).

Some pathways share genes, and one or both pathways may become
significantly associated with reductions in FAW damage scores. Which
causes the reduction (or if both do) may not be readily apparent. For
example, the top pathway for damage scores collected on both 7 and 14
days is the sporopollenin precursors biosynthesis pathway (32 genes
total), at p \textless{} 0.001 and 0.006, respectively, but the suberin
biosynthesis pathway (55 genes total) is also significantly associated
with a reduction in FAW damage rated at 14 days, at p \textless{} 0.05,
and the two pathways have 26 genes in common. The cutin biosynthesis
pathway (42 genes) is significantly associated with reduced damage at 7
days, and cutin and sporopollenin precursors biosynthesis pathways also
have 26 genes in common. However, suberin biosynthesis is not associated
with reduced damage at 7 days and cutin is not associated with reduced
damage at 14 days. All three pathways could plausibly be associated, but
sporopollenin precursors (which are carotenoids and carotenoid esters)
may have better statistical evidence for association. Most pathways with
shared genes create related compounds, such as homoserine and
L-homoserine biosynthesis. Most pathways contained several genes that
jointly contributed to association with the trait. Few pathways had only
one gene contributing to the trait and these, such as acyl-CoA
hydrolysis, generally had only a few genes in the pathway so statistical
evidence that they are actually associated is lower.

Looking at the 31 pathways associated at a more stringent significance
threshold p \textless{} 0.02 (Table 2), mechanisms that may be
highlighted by the current study as involved with FAW feeding resistance
include the biosynthesis of carotenes including zeaxanthin and other
pathways that also utilize geranylgeranyl pyrophosphate, including
ent-kaurene and chlorophyll (Fig. 1). These are highly associated with
FAW damage levels, both increasing and decreasing. Other mechanisms of
resistance may include hormone signaling, as the IAA and sterol
biosynthesis (of which brassinosteroids are a subset) as well as the
ent-kaurene biosynthesis pathway (which can be converted to
gibberellins, Fig. 1) were all identified as associated with damage
levels. Reactions associated with FAW damage (Table 2) include the
S-adenosyl-L-methionine cycle, which was associated with increased
damage; acyl-CoA hydrolysis (decreasing damage); and thioredoxins and
glutathione redox reaction (related reactions, associated with decreased
damage). The production of lipids and waxes, including cuticular wax,
phosphatidylethanolamine, and phospholipids (compounds in biological
membranes) and the essential oil linalool and \selectlanguage{greek}β-\selectlanguage{english}caryophyllene), all
associate with increased levels of damage. The biosynthesis of several
other compounds is associated with decreased FAW feeding damage,
including ascorbate, flavin or riboflavin, myo-inositol and phytate, and
the amino acid cysteine. Some compounds associated with an increase in
damage ratings include ammonia, 1,4-dihydroxy-2-naphthoate, the phenolic
compound coumarin, and the amino acids homoserine, methionine, and
homocysteine.

\textbf{Figure 1} : Many of the pathways associated with Fall Armyworm
leaf damage rating scores were in the carotenoid biosynthesis pathway or
pathways branching off from or downstream of it. Pathways significantly
associated with damage scores are circled in red (solid line circles p
\textless{} 0.02; dashed line p \textless{} 0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Table 2:} The 31 pathways associated (p \textless{} 0.02) with
Fall Armyworm leaf damage rating scores at 7 and 14 days after
infestation, including pathways that increased and that decreased
resistance in maize plants. Pathway identification (Pathway ID) from
MaizeCyc, the pathway name, and association values (p and q value), the
number of genes in the pathway (\# genes) and notes about each pathway
are included in the table.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Decreasing Damage at 14 days after infestation} &
\textbf{Decreasing Damage at 14 days after infestation} & & &
&\tabularnewline
\midrule
\endhead
\textbf{Pathway ID} & \textbf{Pathway name} & \textbf{p value} &
\textbf{q value} & \textbf{\# genes} & \textbf{Pathway
notes}\tabularnewline
PWY-6733 & sporopollenin precursors biosynthesis & 0.0064 & 0.8666 & 32
& Sporopollenin precursors are carotenoids and carotenoid
esters\tabularnewline
R-ZMA-1119348.1 & Ent-kaurene biosynthesis & 0.0066 & 0.8666 & 4 &
Ent-kaurene is formed from geranylgeranyl-PP, which is also a precursor
to carotenoids\tabularnewline
R-ZMA-1119516.1 & Trehalose biosynthesis I & 0.0107 & 0.8666 & 5 &
Biosynthesis of a non-typical sugar\tabularnewline
PWY-5148 & acyl-CoA hydrolysis & 0.0180 & 0.8666 & 6 & Acyl-CoA~is a
group of~coenzymes~that metabolize~fatty acids\tabularnewline
\textbf{Increasing Damage at 14 days after infestation} &
\textbf{Increasing Damage at 14 days after infestation} & & &
&\tabularnewline
\textbf{Pathway ID} & \textbf{Pathway name} & \textbf{p value} &
\textbf{q value} & \textbf{\# genes} & \textbf{Pathway
notes}\tabularnewline
CAROTENOID-PWY & superpathway of carotenoid biosynthesis & 0.0017 &
0.7682 & 6 & Carotenoids have photoprotective, antioxidant and
pigmentation properties; are hormone precursors, can attract insect
pollinators and indicate ripeness.\tabularnewline
PWY-5791 & 1,4-dihydroxy-2-naphthoate biosynthesis II & 0.0049 & 0.9173
& 10 & 1,4-dihydroxy-2-naphthoic acid is an converted to menaquinone
(vitamin K2)\tabularnewline
PWY-5944 & zeaxanthin biosynthesis & 0.0078 & 0.9173 & 5 & Zeaxanthin~is
one of the most common carotenoids in nature\tabularnewline
CHLOROPHYLL-SYN & 3,8-divinyl-chlorophyllide~a~biosynthesis I & 0.0108 &
0.9173 & 12 & This component is an important intermediate in the
biosynthesis of all chlorophylls\tabularnewline
PWY-282 & cuticular wax biosynthesis & 0.0118 & 0.9173 & 12 & Cuticular
wax can increase resistance to leaf feeding insects\tabularnewline
PWY-5698 & allantoin degradation to ureidoglycolate II & 0.0137 & 0.9173
& 5 & Ureide metabolism in C3 and~C4 plants~to recycle N for growth
maintenance\tabularnewline
HOMOSERSYN-PWY & L-homoserine biosynthesis & 0.0177 & 0.9173 & 9 &
Biosynthesis of amino acid\tabularnewline
R-ZMA-1119374.1 & Abscisic acid biosynthesis & 0.0469 & 0.9173 & 5 & ABA
is involved in plant hormone signaling\tabularnewline
\textbf{Decreasing Damage at 7 days after infestation} &
\textbf{Decreasing Damage at 7 days after infestation} & & &
&\tabularnewline
\textbf{Pathway ID} & \textbf{Pathway name} & \textbf{p value} &
\textbf{q value} & \textbf{\# genes} & \textbf{Pathway
notes}\tabularnewline
PWY-6733 & sporopollenin precursors biosynthesis & 0.0012 & 0.3082 & 32
& Sporopollenin precursors are carotenoids and carotenoid
esters\tabularnewline
PWY3DJ-35471 & L-ascorbate biosynthesis (partial pathway) & 0.0013 &
0.3082 & 8 & Biosynthesis of vitamin C\tabularnewline
CYSTSYN-PWY & L-cysteine biosynthesis I & 0.0068 & 0.6644 & 10 &
Biosynthesis of amino acid\tabularnewline
R-ZMA-1119370.1 & Sterol biosynthesis & 0.0097 & 0.6644 & 5 & Sterols
are involved in plant hormone signaling\tabularnewline
HISTSYN-PWY & L-histidine biosynthesis & 0.0109 & 0.6644 & 8 &
Biosynthesis of amino acid\tabularnewline
R-ZMA-1119379.1 & Flavin biosynthesis & 0.0126 & 0.6644 & 6 & Flavins
are known antibiosis agents\tabularnewline
R-ZMA-1119378.1 & Myo-inositol biosynthesis & 0.0128 & 0.6644 & 4 &
Myo-inositol creates some secondary messengers and is a component of
structural lipids. Phytic acid is the principal storage form of
phosphorus in plants.\tabularnewline
R-ZMA-1119434.1 & Lipid-independent phytate biosynthesis & 0.0128 &
0.6644 & 4 & Related to Myo-inositol biosynthesis\tabularnewline
THIOREDOX-PWY & Thioredoxin pathway & 0.0147 & 0.6644 & 14 &
Thioredoxins sustain thiol-based redox homeostasis in plant tissues to
protect against insects.\tabularnewline
PWY-2301 & Myo-inositol biosynthesis & 0.0150 & 0.6644 & 8 &
Myo-inositol creates some secondary messengers and is a component of
structural lipids. Phytic acid is the principal storage form of
phosphorus in plants.\tabularnewline
R-ZMA-1119509.1 & Histidine biosynthesis I & 0.0160 & 0.6644 & 5 &
Biosynthesis of amino acid\tabularnewline
R-ZMA-1119437.1 & Glutathione redox reactions I & 0.0182 & 0.6933 & 6 &
Related to thioredoxin function\tabularnewline
\textbf{Increasing Damage at 7 days after infestation} &
\textbf{Increasing Damage at 7 days after infestation} & & &
&\tabularnewline
\textbf{Pathway ID} & \textbf{Pathway name} & \textbf{p value} &
\textbf{q value} & \textbf{\# genes} & \textbf{Pathway
notes}\tabularnewline
PWY-5041 & S-adenosyl-L-methionine cycle II & 0.0033 & 0.7935 & 6 &
S-adenosyl-methionine (SAM) is a~ubiquitous methyl donor and a precursor
in the biosynthesis of ethylene, polyamines, biotin, and
nicotianamine~in plants\tabularnewline
R-ZMA-1119486.1 & IAA biosynthesis I & 0.0075 & 0.7935 & 19 & IAA is
involved in plant hormone signaling\tabularnewline
R-ZMA-1119501.1 & S-adenosyl-L-methionine cycle & 0.0103 & 0.7935 & 5 &
S-adenosyl-methionine (SAM) is a~ubiquitous methyl donor and a precursor
in the biosynthesis of ethylene, polyamines, biotin, and
nicotianamine~in plants\tabularnewline
PWY-5669 & phosphatidylethanolamine biosynthesis I & 0.0130 & 0.7935 & 8
& Phospholids in biological membranes\tabularnewline
PWY-6275 & \selectlanguage{greek}β-\selectlanguage{english}caryophyllene biosynthesis & 0.0140 & 0.7935 & 5 &
\selectlanguage{greek}β-\selectlanguage{english}caryophyllene biosynthesis is a natural bicyclic~sesquiterpene~that is
a constituent of many~essential oils\tabularnewline
PWY-5176 & coumarin biosynthesis (via 2-coumarate) & 0.0162 & 0.7935 & 8
& Coumarins are known antibiosis agents and are upstream of flavonoid
synthesis\tabularnewline
PHOSLIPSYN2-PWY & superpathway of phospholipid biosynthesis II & 0.0164
& 0.7935 & 11 & Phospholipid component of biological
membranes\tabularnewline
METHIONINE-DEG1-PWY & L-methionine degradation I (to L-homocysteine) &
0.0172 & 0.7935 & 4 & Biosynthesis of amino acid\tabularnewline
PWY-6927 & Chlorophyll~\emph{a}~degradation II & 0.0177 & 0.7935 & 8 &
Breakdown of chlorophyll in plant cells\tabularnewline
\bottomrule
\end{longtable}

\textbf{Validation of metabolites in resistant vs. susceptible maize
lines}

Using 29 of the most resistant and 28 of the most susceptible maize
lines in this study, we sought correlations between the presence of
zeaxanthin, \selectlanguage{ngerman}ß-carotene, lutein, chlorophyll a, chlorophyll b, and
pheophytin b, with 7- and 14-day damage rating. The carotenoids were
measured because of the multiple carotenoid-related pathways identified
by PAST, as were the chlorophylls and pheophytins. Pheophytins are
identical in structure to chlorophylls but lack magnesium(II).
Chlorophylls can be degraded to pheophytins under low pH or
enzymatically. Along with chlorophylls and ß-carotene pheophytins are
also involved in photosynthesis (photosystem II). We were unable to find
measurable levels of zeaxanthin in the leaf samples, which is
unsurprising as this compound tends to be produced in maize grains. How
the biosynthesis pathway is then associated with reduced leaf feeding by
FAW is thus unknown. The four other metabolites measured showed
phenotypic variation among the maize lines tested, although about 80\%
of the lines showed low levels, with less than 300 \selectlanguage{greek}µ\selectlanguage{english}g/g of lutein, 2,000
\selectlanguage{greek}µ\selectlanguage{english}g/g of \selectlanguage{ngerman}ß-carotene, 3E6 counts/g of chlorophyll a, 2E6 counts/g of
chlorophyll b and 2E6 counts/g of pheophytin b (Supplemental Table 5).

Levels of these four metabolites were correlated with each other, and
some with damage ratings (Fig. 2A). Lutein and pheophytin b were
correlated with an increase in FAW damage, and chlorophyll a was
correlated with a decrease in FAW damage, in agreement with pathway
PWY-6927 (chlorophyll~a~degradation II) being associated with increased
FAW damage ratings at 7 days (and less significantly so at 14 days,
along with pathway PWY-5098, chlorophyll a degradation I), (Supplemental
Table 4). Chlorophyll b is positively correlated to ß-carotene, lutein
and pheophytin but negatively correlated with chlorophyll a. The
chlorophyll cycle converts a to b forms of chlorophyll via a step
through the intermediary 3,8-divinyl-chlorophyllide~a; thus, it makes
sense that a and b are negatively correlated and also that pathway
CHLOROPHYLL-SYN, which produces 3,8-divinyl-chlorophyllide~a, is
associated with increased damage in the PAST analysis. ß-carotene was
positively correlated to pheophytin and lutein, which were correlated to
FAW damage, but ß-carotene itself was not significantly correlated with
FAW damage scores. These metabolites appear to agree with the metabolic
pathway analysis that found carotenoids to be important in FAW
resistance but are not conclusive.

The wide distribution of phenotype in relation to metabolite content
made straightforward conclusions difficult. The cluster analysis (Fig.
2B) and PCA (Fig. 2C) of metabolites and FAW ratings showed six groups,
formed based on FAW rankings and also metabolite content. For example,
two of the clusters showed high levels of lutein content, one of which
were FAW-resistant and the other FAW-susceptible. For a clearer
breakdown of metabolite and resistance phenotype, Supplemental Fig. 2
shows the levels of each metabolite within each of the six clusters; it
is apparent that some of the clusters are highly significantly different
than the others for levels of that metabolite. Although the numbers of
lines within each cluster reduce the statistical power of the analysis,
it is likely that within the resistant lines, there is more than one
mechanism leading to resistance, but that levels of lutein, pheophytin,
and chlorophyll a in particular are almost certainly contributing to
resistance (or susceptibility). Although the effect of each metabolite
on resistance may not be enough to create resistant lines, they do help
to validate the results of the PAST metabolic pathway analysis.

\textbf{Figure 2} : Targeted metabolite analysis for lutein, \selectlanguage{greek}β-\selectlanguage{english}carotene,
chlorophyll a and b and pheophytin b. A) Correlation matrix, B)
dendrogram, and C) principal component analysis (PCA) of metabolite and
Fall Armyworm rating analysis. In the dendrogram, maize lines are
colored salmon (BEST) and blue (WORST) representing resistance
phenotype. In the dendrogram and PCA, clusters are represented with the
following colors, 1 = black, 2 = blue, 3 = yellow, 4 = green, 5 = purple
and 6 = red. In the correlation matrix graph, blue represents positive
correlation, red negative correlation and black ``X'' indicates that the
comparison was not significantly correlated with a p-value cut-off of
0.05. In the PCA, shape of points represents resistant phenotype, circle
(BEST) and triangle (WORST).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Pathways and genes in common with other larval insect pest
resistance studies}

A similar study was done previously by this group on corn earworm
(\emph{Helicoverpa zea} (Boddie)) ear-damage levels in maize (Warburton
et al., 2017), and five of the same or very closely related pathways
were identified in both studies. These include wax esters biosynthesis
II, simple coumarins biosynthesis, geranylgeranyl diphosphate (GGPP)
biosynthesis, the chlorophyll degradation pathway, ent-kaurene
biosynthesis I, and phospholipases biosynthesis. Wax esters are a
component of epicuticular wax, which is a physical barrier to insect
predation. Coumarins belong to the phenolics class of compounds, which
repel feeding insects. The production of GGPP is the first step leading
into the carotenoids and related pathways (Fig. 1). Phospholipases may
be expressed when plants are wounded, as during insect feeding, and
initiate production of important defense signaling molecules, such as
oxylipins and jasmonates (De Vleesschauwer et al., 2014). All these
mechanisms were important in FAW resistance as well and may form part of
the common defense mechanisms of maize plants against many larval
feeding insects.

Another recent study of the defense response of resistant maize lines to
European corn borer (\emph{Ostrinia nubilalis} ) and western corn
rootworm (\emph{Diabrotica virgifera virgifera} ), two insects whose
larvae are economically damaging. These two studies used transcriptomics
to identify changes in gene expression following feeding damage
(Pingault et al., 2021). They found significant gene expression changes
in a number of genes, some in common with the current study. These
include genes encoding, regulating, or modifying carotenoids, coumarins,
S-adenosyl-L-methionine, methionine, Acyl-CoA, glutathione, thioredoxin,
histidine, myo-inositol, flavin, sterol, trehalose, chlorophyll,
phospholipases, and phospholipids. While this list is too long and
varied to point to a specific common mechanism of resistance, it does
suggest that common mechanisms may exist, and may indicate where the
search may begin.

Acknowledgments

The Authors would like to thank Susan Wolf, Gerald Matthews and Carol
Carter-Wientjes for excellent technical and editorial help. Any mention
of trade names or commercial products is solely for the purpose of
providing specific information and does not imply recommendation or
endorsement by the USDA.

Conflict of Interest

The authors declare no conflict of interest.

ORCID

Marilyn L. Warburton: https://orcid.org/0000-0002-9542-9912

Lina Castano-Duque: https://orcid.org/0000-0001-9161-2907

W. Paul Williams: https://orcid.org/0000-0002-7827-3186

Matthew D. Lebar: https://orcid.org/0000-0003-4910-1438

Sandra W. Woolfolk: https://orcid.org/0000-0001-7025-9745

Supplemental Material

\textbf{Supplemental Figure 1:} QQ plot of association values calculated
with the General Linear Model (GLM; 1a) and Mixed Linear Model (MLM;
1b).

\textbf{Supplemental Figure 2:} Box plots showing clusters and the
distribution of the amounts of metabolites and Fall Armyworm ratings.
Box-plot whiskers depict the maximum and minimum without outliers, and
the box depicts median, first and third quantiles distribution. Data
shown is transformed and scale to center around 0.

\textbf{Supplemental Table 1:} Fall Armyworm damage scores at 7 and 14
days after infestation for the panel of 289 diverse maize inbred lines
used for this study, including averages and standard deviations over
replications within years, and averaged over years.

\textbf{Supplemental Table 2:} Fall Armyworm damage score descriptions,
from the original rating scale of Davis et al., 1991, for 7-day and
14-day time points following infestation with Fall Armyworm (FAW)
neonates.

\textbf{Supplemental Table 3:} The MLM association scores for p
\textless{} 0.05 associated with Fall Armyworm (FAW) damage levels.

\textbf{Supplemental Table 4} : The pathways associated
(p\textless{}0.05) with Fall Armyworm (FAW) damage levels.

\textbf{Supplemental Table 5:} Averaged data for the metabolites
analyzed, clusters summary statistics and PCA variance contribution
information.

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