Pathways and genes in common with other larval insect pest
resistance studies
A similar study was done previously by this group on corn earworm
(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 (Ostrinia nubilalis ) and western corn
rootworm (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
Supplemental Figure 1: QQ plot of association values calculated
with the General Linear Model (GLM; 1a) and Mixed Linear Model (MLM;
1b).
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.
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.
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.
Supplemental Table 3: The MLM association scores for p
< 0.05 associated with Fall Armyworm (FAW) damage levels.
Supplemental Table 4 : The pathways associated
(p<0.05) with Fall Armyworm (FAW) damage levels.
Supplemental Table 5: Averaged data for the metabolites
analyzed, clusters summary statistics and PCA variance contribution
information.
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