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Received: 14 July 2023 Revised: 14 August 2023 Accepted article published: 16 August 2023 Published online in Wiley Online Library:
(wileyonlinelibrary.com) DOI 10.1002/ps.7728
Predicting the unpredictable: the regulatory
nature and promiscuity of herbicide cross
resistance
Lucas K Bobadilla and Patrick J Tranel
*
Abstract
The emergence of herbicide-resistant weeds is a significant threat to modern agriculture. Cross resistance, a phenomenon where
resistance to one herbicide confers resistance to another, is a particular concern owing to its unpredictability. Nontarget-site (NTS)
cross resistance is especially challenging to predict, as it arises from genes that encode enzymes that do not directly involve the
herbicide target site and can affect multiple herbicides. Recent advancements in genomic and structural biology techniques could
provide new venues for predicting NTS resistance in weed species. In this review, we present an overview of the latest
approaches that could be used. We discuss the use of genomic and epigenomics techniques such as ATAC-seq and DAP-
seq to identify transcription factors and cis-regulatory elements associated with resistance traits. Enzyme/protein structure
prediction and docking analysis are discussed as an initial step for predicting herbicide binding affinities with key enzymes
to identify candidates for subsequent in vitro validation. We also provide example analyses that can be deployed toward elu-
cidating cross resistance and its regulatory patterns. Ultimately, our review provides important insights into the latest scien-
tific advancements and potential directions for predicting and managing herbicide cross resistance in weeds.
© 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Keywords: cross resistance; herbicide metabolism; cis-regulatory elements; transcription factors
1 INTRODUCTION
Herbicide resistance is a phenomenon that has been documented
during the last decades as the main challenge for weed manage-
ment. Multiple cases of point mutation at the herbicide site-of-action
(SoA, target-site resistance) have been identified and can provide a
high resistance level.
1
However, a plant can become resistant with-
out any mutation at the herbicide site of action; this resistance type
is referred to as nontarget-site resistance (NTS).
2
For the last several
years, unraveling the mechanisms of NTS herbicide resistance has
been a primary goal for weed scientists. However, only recently
have significant advances been made toward identifying key
enzyme players, mainly as a consequence of the advent of weed
genomic resources.
3,4
In many cases, NTS-resistant weed populations are identified as
cross resistant, able to survive herbicides from the same and dif-
ferent SoA groups. These populations are challenging to study
owing to the potential involvement of numerous genes for NTS
resistance traits.
5–7
For example, even when a major player in
the detoxification of a herbicide is identified, it is typically
unknown if the same encoded enzyme can reduce the toxicity
of other herbicides or if cross resistance is caused by other genes.
From a practical standpoint, the unpredictable nature of cross
resistance makes it difficult to select the best herbicide combina-
tion to mix or rotate to mitigate resistance evolution.
8
Conse-
quently, NTS herbicide resistance is viewed as one of the greatest
threats to the sustainability of chemical weed management. But
what if it is possible to make the unpredictable predictable?
We believethat recent scientific advances have now made it possi-
ble to begin systematically unravelling the genetic underpinnings
of NTS resistance and cross resistance.
Current genomics resources for weeds, several of which have been
achieved by the International Weed Genomics Consortium, are
growing and will serve as the basis for prediction tools for better
weed management decisions. However, weed scientists still lack
important datasets such as regulome and epigenome maps. Such
datasets would allow weed scientists to understand complex adap-
tation patterns in weeds, such as NTS resistance and climate change
adaptation. The present review aims to shed light on new frontiers
for weed scientists regarding the regulatory machinery of cros s resis-
tance, how this information could be generated and how it could be
used to build prediction models based on gene regulatory networks.
2 MECHANISMS OF HERBICIDE CROSS
RESISTANCE
The primary chemical strategy for managing herbicide resistance
is to use a different herbicide. However, this strategy can be
*Correspondence to: PJ Tranel, Department of Crop Sciences, University of
Illinois, 1201 W Gregory Dr, Urbana, IL 61801, USA. E-mail: tranel@illinois.edu
Department of Crop Sciences, University of Illinois, Urbana, IL, USA
© 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
1
rendered ineffective by multiple resistance and cross resistance.
The Herbicide Resistance Action Committee (www.hracglobal.
com) considers multiple herbicide resistance to be the existence
of two or more distinct mechanisms, each of which provides resis-
tance to separate herbicides. By contrast, cross resistance is
defined as the presence of a single mechanism that confers resis-
tance to different herbicides.
In some cases, the distinction between multiple and cross resis-
tance is clear: a plant that has increased expression of an enzyme
that can detoxify one herbicide and a target-site mutation confer-
ring resistance to another herbicide would be an example of mul-
tiple resistance, whereas a plant that has increased expression of
an enzyme that can detoxify multiple herbicides would be an
example of cross resistance. However, with an increased appreci-
ation for the complexity of herbicide resistance in some cases, it is
becoming apparent that the distinction between multiple and
cross resistance is not always clear. For example, in cases where
resistance to a single herbicide is mediated by increased expres-
sion of two or more enzymes working together to detoxify the
herbicide, and they are able to singularly or jointly detoxify
another herbicide, should that be considered multiple resistance
or cross resistance? Does the classification of this case change if
the two or more enzymes are independently regulated or if they
are regulated by the same transcription factor? If a plant was
found to be resistant to two different herbicides as a result of
increased expression of two different enzymes, each of which
acted on only one of the herbicides, presumably the plant would
be considered to have multiple resistance. But if it was later deter-
mined that the two enzymes were upregulated due to a single
genetic change (e.g., increased expression of a transcription fac-
tor), should the plant be reclassified as being cross resistant? For
the purpose of this review, we define cross resistance as resis-
tance to herbicides other than the one that selected the
resistance.
There are several mechanisms by which cross resistance can
occur, and they differ in their complexity and predictability
(Table 1). The simplest is target-site cross resistance;
9
if a herbicide
selects for a mutation that reduces binding affinity between the
herbicide and its target site, the same mutation often also will
confer resistance to other herbicides that have the same target
site. For example, mutations in the gene encoding acetolactate
synthase often confer cross resistance to multiple herbicides that
target this enzyme. This type of cross resistance is, at some level,
predictable because it affects only herbicides with the same bind-
ing site. NTS resistance, by contrast, is less predictable and often
occurs as a result of increased expression of one or more genes,
which results in less herbicide reaching the site of action or allows
the plant to ameliorate the effects of herbicide inhibition of the
target site.
1
NTS is less predictable than TS resistance because
(i) a single gene can affect herbicides across different SoA groups
and (ii) it can involve multiple genes, each potentially affecting
different herbicides (Table 1). Although NTS resistance potentially
could be caused by any of a vast array of genes, the end result
often is increased metabolism (detoxification) of the herbicide.
Because herbicide metabolism is such an important component
of NTS, it is the focus of this review. However, several of the
approaches which we discuss also could be applicable to other
NTS resistance mechanisms.
Herbicide metabolism can be divided into three phases
1,10,11
:
Phase 1 involves the introduction of small functional groups on
herbicide molecules through catalysis by key enzymes such as
cytochrome P450s (CYP450s); Phase 2 involves the conjugation
of catalyzed herbicides to common plant metabolites via
transferases such as glutathione-S-transferases (GSTs) and
UDP-glycosyltransferases (UGTs); and Phase 3 involves the
sequestration of metabolites into vacuoles and/or cell wall mate-
rial, potentially through ATP-binding cassette (ABC) transporters.
Cross resistance may be caused by a single enzyme or by the
combination of multiple ones participating in any of the three
phases of herbicide metabolism. For example, single CYP450s
and GSTs have been shown to metabolize herbicides from multi-
ple SoA groups.
12–17
Additionally, multiple genes encoding differ-
ent enzymes can be co-expressed (and potentially co-regulated),
where each enzyme could play a role in the detoxification process
of multiple herbicides. For instance, a study identified that expres-
sion of a GST and ABC transporter were co-induced by multiple
Table 1. Some potential mechanisms of herbicide cross resistance and their predictability
Mechanism Number of genes Can confer resistance to: Example Predictability
Site of action 1 Herbicides with the same
site of action
A mutation in the gene encoding acetolactate
synthase selected by one Group 2 herbicide confers
cross resistance to other Group 2 herbicides.
High
Detoxification
enzyme
1 Herbicides with similar
functional groups
A cytochrome P450 that can detoxify a Group 1
herbicide is also able to detoxify a Group 2 herbicide.
Shared genomic
location
>1 Potentially any herbicide Genes for two cytochrome P450s are located near each
other on the same chromosome: selection for
increased expression of one (e.g., by chromatin
remodeling) results in increased expression of the
other. Each cytochrome P450 can detoxify different
herbicides.
Shared gene‐
expression
network
>1 Potentially any herbicide A cytochrome P450 and a GST are in a shared
regulatory network. Selection for increased
expression of one (e.g., by increased expression of a
transcription factor) results in increased expression
of the other due to a shared transcription factor
binding site. The P450 and GST can detoxify
different herbicides.
Low
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herbicides from different SoA groups.
18
The multigenic nature of
NTS cross resistance also has been suggested by several transcrip-
tomics studies, which typically reveal combinations of CYP450s,
GSTs, ABC transporters and UGTs being constitutively over-
expressed in resistant biotypes.
19
Another example of how co-
expressed genes could lead to cross resistance was suggested
by a previous study in which a multiple-resistant Amaranthus
tuberculatus population contained genomic hotspots of differen-
tially expressed genes.
6
If such a cluster of differentially expressed
genes included multiple NTS genes, selection by a herbicide for
increased expression of one could lead to cross resistance to
herbicides conferred by another.
Selection for NTS cross resistance is often observed in fields
where an indirect selection occurs (e.g., cross resistance had
already evolved to a herbicide never sprayed in that field). This
phenomenon also was observed in antibiotic resistance, where
>50% of resistance cases that show cross resistance evolved
under a single antibiotic pressure.
20
As mentioned previously,
multiple enzymes and other molecules are known to have some
involvement in NTS metabolic resistance; however, their
co-induction and regulation still require elucidation. Additionally,
phytohormones related to plant stress, including salicylic acid and
jasmonic acid, need to be further examined regarding their
involvement in metabolic resistance and how they can affect
the co-regulation and co-expression of key NTS genes.
21–24
Further molecular structural characterization and herbicide/
enzyme affinity estimations also are needed to identify metabolic
potentials to avoid cross resistance to novel herbicides. Although
it is well-known that enzyme promiscuity can account for herbi-
cide cross resistance, a recent study confirmed that co-regulation
of NTS genes also can lead to cross resistance.
25
There is much to
be learned from investigation of how NTS genes are regulated,
26
and it is our opinion that exploration of expression networks
involving NTS genes will be necessary to more fully predict NTS
cross resistance.
3 APPROACHES FOR IDENTIFICATION OF
REGULATORY ELEMENTS AND
CROSS-RESISTANCE PREDICTION
A fundamental question in biology is how complex gene expres-
sion patterns are regulated. Very few NTS resistance studies inves-
tigate the regulatory nature of NTS genes and their co-expression
patterns. This leaves questions open about the involvement of
master regulators, such as cis-regulatory elements and transcrip-
tion factors. Identifying these key regulators is essential, so predic-
tions about cross resistance based on expression profiles can be
inferred. To achieve this, weed scientists must not only collect
gene expression data, but also generate epigenomic and
transcription-factor-binding-site data to establish the foundation
for constructing regulatory networks for cross resistance. Further-
more, developing predictive affinity models for key enzymes is
essential for identifying major genes encoding enzymes that
could potentially metabolize herbicide molecules. To validate
the accuracy of the affinity prediction, the generation of metabo-
lomics and in vitro validation assays is necessary (Fig. 1). These sets
of experiments would form the basis of a systems biology
approach to characterize metabolic cross resistance.
27
By inte-
grating these layers into the constructed regulatory networks,
one could determine which enzymes are co-regulated in a
specific weed species, and utilize their enzyme affinity and
metabolomics information to provide better-informed herbicide
recommendations. In the following sections, we discuss several
approaches and analyses that could be employed to achieve
this goal.
3.1 Herbicide and enzyme affinity predictions
The identification of key enzymes or proteins involved in herbi-
cide metabolism that can metabolize other herbicides is essential
for predicting cross resistance. However, predicting whether an
enzyme can metabolize a particular molecule can be challenging
and requires experimental validation. In vitro assays, such as
microsomal, recombinant enzyme and radio-labeled substrate
assays, can evaluate an enzyme's ability to metabolize a specific
molecule.
28
These assays have been widely used for drug–
enzyme interactions and molecule discoveries, but can be chal-
lenging and require specialized measurement equipment, such
as mass spectrometry.
29,30
Alternatively, protein structures can be used to predict the affin-
ity of a specific molecule for an enzyme. The gold standard for
identifying an enzyme/protein structure is via crystallography.
However, this method can be challenging, time-consuming and
unsuitable for high-throughput analysis.
31
Instead, molecule
docking using homology structure prediction can, as a starting
point, help identify how a specific molecule, such as a CYP450 or
a GST, could bind to a herbicide molecule.
Recent advances in artificial intelligence and machine-
learning algorithms, such as AlphaFold2 and the Google appli-
cation, ProteInfer, have made protein structure and function
prediction via homology and computational modeling much
more accessible.
32,33
Once the protein structure has been pre-
dicted, docking analysis combined with binding pocket predic-
tions can be used to predict the potential affinity between the
enzyme and multiple herbicides. In fact, some of the earliest
enzyme-docking predictions based on protein structure were
made using the D1 protein and photosystem II inhibitor herbi-
cides.
34
Herbicide-protein affinity predictions can be imple-
mented into a herbicide-resistance gene regulatory network
and used to select candidates for prediction validation via
in vitro or in vivo assays (Fig. 1). The predictions also could gen-
erate a database focused on herbicide–enzyme binding affinity
and enzyme structure to help weed scientists make better deci-
sions regarding weed management. With time, these initial
predictions should be curated via in vivo and in vitro validations
to ensure data quality.
A few recent studies in weed science have utilized protein struc-
ture prediction with docking simulations. For instance, Ha et al.
12
applied a docking analysis for herbicide affinity identification
using the identified CYP81A in Echinochloa phyllopogon and its
rice homologs. GSTs and UGTs also should be analyzed for their
affinity with herbicide molecules. Plant GSTs contain N-terminal
and C-terminal structures, and the C-terminal located at the cata-
lytic region is highly variable regarding its sequence, making plant
GSTs capable of accepting much larger and more diverse
substrates than those of mammals.
35
In order to illustrate the approach for herbicide/enzyme affinity,
we predicted the structure of CYP81A10v7 via AlphaFold2, which
was previously identified as the culprit for cross resistance in
Lolium rigidum,
13
and ran a docking simulation analysis for some
of the herbicides identified in the same study that could be
metabolized by CYP81A10v7. Glyphosate also was included to
illustrate how a molecule not metabolized by this specific
CYP450 would behave compared to the other herbicide
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molecules. Briefly, we ran AlphaFold2
32
using the monomer
model and used the CYP81A10v7 protein sequence as input.
Ligand-binding pocket predictions were made using P2Rank to
obtain the coordinates around heme binding and for docking
prediction.
36
The heme co-factor molecule was added into
the CYP450 structure inside the predicted binding pocket. We
then used the generated structure as the receptor in Autodock
Vina to predict the binding affinity at the identified binding
pocket with the herbicides chlorsulfuron, atrazine, mesotrione,
trifluralin, and glyphosate.
37–41
As expected, multiple docking
modeswithstrongaffinity (average of ∼−7.2 kcal mol
−1
)were
identified for the herbicides that CYP81A10v7 previously
was identified to be able to metabolize, with all docking within
the heme-binding region with a similar pattern (Fig. 2).
However, a lower affinity was identified for glyphosate
(−2.323 kcal mol
−1
), indicating that this specific CYP450 would
probably not be able to hydroxylate glyphosate as efficiently as
the other herbicides.
Databases also are available for some enzymes that could be
used as a starting point to build binding predictions. For instance,
the Cazy database (http://www.cazy.org) contains enzyme struc-
ture and binding information for carbohydrate-active enzymes
such as UGTs. For CYP450s, a recent database named PCPD was
created, which contains information about structure and ligand
docking for 181 plant CYP450s.
39
These databases could be used
as an initial step and as a repository for future weed species'
CYP450 structure and ligand information.
Even though AlphaFold2 is very good at predicting protein
structure, there are still limitations with complex proteins and in
building the correct conformation of some regions of the
Figure 1. Novel applications for the identification of regulatory elements and chromatin structure. DAP-seq can be used as a high-throughput tool for
genome-wide identification of transcription factor binding sites. In parallel, ATAC-seq and RNA-seq could be applied to identify accessible chromatin
and novel enhancers in different herbicide-resistant biotypes. Once these data are generated, they can be applied to build gene regulatory network
models for nontarget-site (NTS) resistance that, in conjunction with metabolomics and herbicide/enzyme affinity studies, could be used to predict
chances of cross resistance and apply better herbicide-resistance management decisions. Created with BioRender.com.
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structure. However, the field of protein structure prediction and
molecular docking is rapidly evolving, which may provide more
precise and better predictions in the near future. Despite the lim-
itations, protein structure prediction and docking analysis can
serve as a quick first step for identifying the cross-resistance
potential of some enzymes and narrowing down candidates for
further validation. In conjunction with gene expression informa-
tion, one can build a regulatory network to identify key NTS
enzymes that are co-expressed and the array of herbicides that
can be metabolized by co-expressed enzymes.
3.2 Discovery and characterization of regulatory
elements
The elucidation of how gene expression is regulated in
response to abiotic stress and other developmental cues in
plants requires identifying cis-regulatory elements (CREs) and
transcription factor (TF) binding sites in their native chromatin
environment.
CREs are noncoding regions of DNA that play a major role in reg-
ulating the transcription of nearby genes. CREs are essential com-
ponents of gene regulatory networks that govern processes such
as cell differentiation, fate determination, and responses to biotic
and abiotic stressors.
42–44
These elements consist of short DNA
motifs, typically 4–30 bp in length, which serve as binding sites
for sequence-specific transcription factors and modulate gene
expression by facilitating TF binding and transcriptional activa-
tion. TFs are proteins that can regulate and modulate gene
expression by binding to specific CREs controlling the transcrip-
tion of genes into mRNA molecules. They interact with other reg-
ulatory proteins, such as co-activators and co-repressors, to
activate or repress gene expression.
44
The identification of CREs can help to identify the regulatory
nature of duplicated genes and identify master regulators within
genomic hotspots (Fig. 3). For instance, CYP701 enzymes encoded
from duplicated genes in rice significantly differ in their hydroxyl-
ation capabilities, where gene duplication favors drift to the pro-
miscuity of one of the paralogs and negative selection on the
other.
45
Thus, identifying CREs of duplicated genes and compar-
ing them can lead to identifying genes that can encode enzymes
that are more promiscuous regarding metabolic capabilities.
One of the most used methodology for identifying in vivo CREs and
TF binding sites of interest is chromatin immunoprecipitation-
sequencing (ChIP-seq).
46
However, this method requires either
antibodies for the TFs or transgenic plants expressing epitope-
tagged TFs, which makes it impractical for high-throughput
analysis.
An alternative technique becoming increasingly attractive
for genome-wide characterization of CREs and TFs in plants is
the assay for transposase-accessible chromatin sequencing
(ATAC-seq).
47
This method utilizes a hyperactive transposase
enzyme to insert sequencing adapters into regions of open
chromatin, allowing the identification of open chromatin
regions, DNA accessibility, and transcription factor binding sites
in the genome. ATAC-seq is widely used to study epigenetic
changes associated with various biological processes, such as
development, differentiation and environmental stimuli.
48
Figure 2. Herbicide/enzyme affinity prediction. The protein structure of cytochrome P450 81A (CYP81A) from Lolium rigidum was predicted using Alpha-
Fold2. The predicted structure was used in docking analysis for herbicides previously identified to be metabolized by CYP81A. The far-right panel shows
the average docking distance (in Å) from the heme co-factor.
Figure 3. (a) Differential regulatory machinery in gene duplication
events. In the case of an interspersed duplication, the duplicated gene
can contain a different core promoter and cis-regulatory elements that
could modulate its expression. A similar situation could occur with a tan-
dem duplication event in which a novel cis-regulatory element could
modulate both or only one of the duplicated genes. (b) Genomic hotspot
containing a cluster of differentially expressed genes related to nontarget-
site (NTS) resistance. The hypothetical regulatory model indicates a distal
cis-regulatory element that enhances the expression of multiple NTS
genes. Created with BioRender.com.
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For ATAC-seq, Tn5 transposase is used to cut open chromatin,
leaving a staggered nick, followed by ligation of high-throughput
sequencing adapters to these regions. The nick is then repaired,
leaving a 9-bp duplication, and pair-end sequencing is performed
to ensure good coverage and unique alignments at the acquired
open regions.
49
ATAC-seq is now routinely being used for the
identification of CREs and motifs in animal and model plant
genomes, including at the level of single cells.
50
However, plants
pose an additional layer of complexity as a result of the Tn5 trans-
posase used in ATAC-seq being fully capable of accessing mito-
chondrial and chloroplast genomes, leading to a sequestration
of transposase and lower sequence output. To address this chal-
lenge, multiple methods have been developed.
51
DNA affinity purification sequencing (DAP-seq) is a method that
allows for the identification of DNA-binding proteins, transcrip-
tion factors and protein–DNA interactions by using recombinant
proteins to capture specific DNA sequences in vitro, followed by
high-throughput sequencing of the purified DNA. It is commonly
used to study the regulatory networks of transcription factors and
other DNA-binding proteins and to identify novel DNA-binding
proteins.
52
In order to conduct DAP-seq, expression vectors are created and
expressed in vitro to yield an affinity tag, such as HaloTag, fused to
TFs. To create a genomic DNA library, plant tissues are sonicated
to fragment the genomic DNA, and sequencing adapters are
ligated to the resulting DNA fragments. The HaloTag-TF is then
incubated with the DNA library, and the resulting TF/DNA com-
plexes are purified using HaloTag ligand-conjugated magnetic
beads. The unbound DNA is removed through washing, and
the TF-bound DNA is eluted and amplified via PCR for library
construction. This approach allows for consistent and precise
TF binding site determination and also can be adapted to exam-
ine the effect of DNA modificationsonTFbindingviathe
ampDAP-seq approach.
52
The choice between ATAC-seq and DAP-seq depends on the sci-
entific question being addressed. ATAC-seq is preferred when
investigating changes in chromatin accessibility and regulatory
regions in response to various stimuli or biological conditions.
DAP-seq, on the other hand, is useful when identifying DNA-
binding proteins, characterizing TF binding sites, or elucidating
regulatory networks.
44
ATAC-seq provides more efficient ways
for identifying CREs for a multitude of species and even different
cell types. However, without previous knowledge of TF sequence
motifs, regions identified from ATAC-seq cannot be validated. This
gap can be filled by DAP-seq to identify TF binding sites and serve
as a basis for ATAC-seq data interpretation (Fig. 1).
Recently, a new method for characterizing the cistrome
(complete set of regulatory elements in a genome) and chromatin
profiling, called MNase-defined cistrome-occupancy analysis
(MOA-seq), was proposed as a one-step assay for identifying puta-
tive TF-binding sites within accessible chromatin regions.
53
How-
ever, MOA-seq has only been tested for maize, whereas DAP-seq
and ATAC-seq have been conducted for multiple species. Another
point to consider is distal CREs that can affect their target genes
via chromatin interaction.
44,54
Genome-wide studies utilizing
chromosome conformation capture approaches showed that
chromatin regions with similar epigenomic landscapes could
physically interact via phase separation.
55
Studies utilizing
high-throughput chromosome conformation capture (HI-C) tech-
nologies can be conducted to elucidate this gene expression
regulation via distal chromatin interaction.
56–58
For instance, a
study in rice utilized HI-C technology to identify gene chromatin
loops and enhancer activities that could modulate gene expres-
sion.
59
Recent reference genomes are now routinely assembled
accompanied by HI-C, facilitating the identification of distal regu-
latory elements.
60
This approach is being used by the Interna-
tional Weeds Genomics Consortium to assemble the reference
genomes from multiple weed species.
61
These approaches have been applied to build cistromes and to
characterize chromatin states during different physiological
stages and stresses. For example, the cistrome was built for
Arabidopsis, where 1812 TFs and their binding sites were
characterized.
52
DAP-seq was used to characterize MADS-box
transcription factors in apple trees to investigate dormancy regu-
lation.
62
In studies on abiotic stresses, an interesting approach
involved combining ATAC-seq and RNA-seq to understand the
dynamics of chromatin accessibility in apple trees in response to
drought.
63
Another study also used the same approach to investi-
gate heat stress adaptation in rice, where available ATAC-seq and
RNA-seq data were used to identify key TFs for heat stress
responses.
64
In both DAP-seq and ATAC-seq experiments, careful
experimental design, appropriate controls, validation using alter-
native methods and rigorous data analyses are crucial to address
and mitigate potential pitfalls that can lead to, for example, false
positives.
In order to demonstrate a potential application of these data-
sets, we predicted the regulatory similarities of all CYP450s within
the Amaranthus tuberculatus genome. CYP450s were identified
through BLAST and hidden-Markov approaches to identify their
conserved domains, as described previously.
65
We utilized avail-
able cistrome and motif databases from Arabidopsis thaliana to
identify potential regulators and their similarities of each
CYP450.
52,66
We extracted the promoter region of each CYP450s
and performed a regulatory enrichment prediction analysis based
on motifs extracted from the database.
67
Briefly, the regulation
prediction tool of PlantRegMap was used to identify enriched
motifs. Extracted promoter sequences from each of 185 CYP450s
identified in the A. tuberculatus genome were used as input. The
promotor region was defined as from 2000 bp before to 100 bp
after the transcription start, and we used a binding site prediction
threshold of 0.05.
Of the 185 CYP450s, 54 contained enriched motifs for 16 TFs
(Fig. 4). CYP450s containing motifs from the enriched TFs were
grouped to identify those with putative similar regulatory pat-
terns. Further analyses of each TF revealed they were members
of the families CPP, NAC, TCP and MIKC MADS-box. Results indi-
cated that MIKC MADS-box TF were the major regulators of
CYP450s in A. tuberculatus, consistent with their role as regulators
of CYP450s in other species.
68,69
These methods could be useful for weed scientists to improve
our understanding of the regulatory nature of NTS resistance.
Identifying commonalities in the co-regulation of NTS genes
could help to identify master regulators for herbicide stress
responses and mechanisms of enhanced metabolic resistance. It
is important to note that our example used the A. thaliana motif
database which, even though a model organism, will not perfectly
represent all the potential TF binding sites in A. tuberculatus.In
the future, weed scientists should build regulome and motif
database within their species of interest for more precise predic-
tions. Also, predicting gene regulation solely based on the pres-
ence of a motif in a promoter sequence can lead to false
positives, and other factors such as chromatin structure and
modulation will need to be considered as well. Another impor-
tant factor to consider is that promoter sequences can contain
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multiple overlapping motifs, making it difficult to determine
which motif is responsible for the observed regulatory effect.
Lastly, gene regulation often involves the combinatorial action
of multiple regulatory elements. Predicting the effects of indi-
vidual motifs may not provide the full regulatory landscape of
a certain gene. Nevertheless, the example that we presented
illustrates the potential applications which can be done with
current available resources and how they could be further
developed.
Together with enzyme affinity predictions and metabolomics
studies, this system biology approach could provide a foundation
for predicting cross resistance and understanding NTS resistance
in weeds. Beyond the scope of characterizing NTS cross-resistance
regulation, this approach could also be used by weed scientists to
investigate weediness traits and species adaptation to climate
change, among other applications.
3.3 Gene regulatory networks
Gene regulatory networks (GRNs) are complex systems of inter-
acting genes and their regulatory elements, such as TFs and
CREs, that determine when and where genes are expressed in
an organism.
70
GRNs provide a systems biology perspective that
investigates the interactions among different components of
the plant system, including genes, proteins, metabolites and
environmental factors, to understand the underlying mecha-
nisms governing plant growth, development and response to
stress.
71,72
Studying GRNs is critical for comprehending how genes work
together to produce complex phenotypes, such as herbicide
stress responses, and how alterations in these networks can result
in resistance. To understand NTS resistance, GRNs can be
developed by collecting transcriptomics data to build a gene
co-expression network. Weighted co-expression analysis can
examine the global expression patterns for an NTS resistance phe-
notype.
73
Directionality can then be added to the co-expression
network to indicate which genes regulate other genes using
previously described methods for characterizing CREs and TFs.
This resource can serve as the core structure of the GRN, which
can later be incremented with other data types such as metabolo-
mics, proteomics and herbicide/enzyme affinity assays.
NTS resistance is an evolutionary phenomenon that can change
the topology of the herbicide response GRN. Understanding how
Figure 4. Regulatory prediction analysis via motif enrichment for Amaranthus tuberculatus cytochrome P450s (CYP450s). (a) All enriched transcription
factors (based on the Arabidopsis thaliana transcription factor database) and their respective families. Ribbons indicate the number of CPY450s predicted
to be regulated by that specific transcription factor. Ribbons are color-coded according to the transcription factor family. (b) Dendrogram of CPY450s in
the A. tuberculatus genome clustered according to their regulatory similarities, indicating groups of CYP450s that might be co-regulated.
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the evolution of a trait affects a GRN is critical to predicting
how this evolution could occur.
74
These evolutionary patterns
can be used to identify subnetworks and understand resistance
mechanisms and their regulation. This systems biology approach
could help to predict novel venues for resistance, allowing weed
scientists to build predictive models for novel resistance types.
Generating data on regulatory elements, for example with
ATAC-seq, can help compare GRNs and similar NTS resistance
phenotypes across species to identify commonalities across mul-
tiple weed species.
The application of GRNs to understand abiotic stress has been
proposed numerous times.
75,76
For example, a study used RNA-
seq, ChIP-seq and bisulfite sequencing to study the GRN varia-
tions of barley under abiotic stress.
77
Selection and evolution are
critical elements for the stability of a GRN, and abiotic stresses
can serve as significant variables in it. Herbicides can exert strong
selection pressure, making them optimal candidates for testing
variabilities in GRN topologies in an abiotic stress context. Besides
the application towards understanding the regulatory nature of
herbicide resistance, GRNs also could be applied to expand our
knowledge of how herbicide safeners increase crop tolerance to
herbicides.
4 PULLING IT ALL TOGETHER
The Herbicide Resistance Action Committee has grouped herbi-
cides by mode-of-action, which makes it easy to select herbi-
cides for the purpose of mitigating TS herbicide resistance. We
envision an app that would utilize much more extensive data-
sets, such as those described in this review, to guide herbicide
selection also for mitigating NTS herbicide resistance. To mini-
mize the chance of herbicide-resistance evolution, herbicide
mix or rotation partners to manage any given weed species
should not:
(1) Have the same target site.
(2) Be metabolized by the same enzyme.
(3) Be metabolized by enzymes that are different but share a
common regulatory element.
(4) Be metabolized by enzymes that are different but are proxi-
mal in the genome.
Unsurprisingly, obtaining such datasets is not trivial; however, it
is feasible. We also acknowledge that, as stated before, our review
is focused on metabolic resistance despite our awareness of other
NTS resistance mechanisms. For example, we mentioned but did
not discuss cross resistance that could arise from acquired ability
to mitigate toxicity arising from herbicidal inhibition of its target
site (e.g. elevated antioxidant activity). Perfect prediction of cross
resistance will likely never be possible, but the steps and
approaches described herein would be a significant advance.
We also fully acknowledge that much of what we are proposing
is model-based, and validation of modeled outcomes is required.
Nevertheless, generation of such datasets also could serve as the
starting point for implementing multi-scale models to under-
stand weed adaptation and herbicide-resistance evolution.
For instance, the CROPS In silico initiative focuses on using
multi-scale modeling for crop improvement and generating
new hypotheses for targeted engineering.
78
Although these
goals may seem utopian, weed scientists and others must work
together towards them to increase our predictability for the evo-
lution and adaptation of pests.
5 CONCLUSIONS
Weed and pest management scientists are delving into genomics
by creating reference genomes for multiple species. Moreover,
researchers could utilize protein structural computational model-
ing to develop prediction models for pesticide affinity with key
enzymes involved with NTS resistance. As a result, researchers
would be able to anticipate cross resistance and better under-
stand the regulatory nature of NTS resistance. For a more compre-
hensive understanding, researchers should prioritize generating
high-quality data to identify the regulation of crucial genes such
as CYP450s and GSTs by characterizing TFs and CREs. Investment
and time towards those analyses will allow weed scientists to
build predictive tools for herbicide management and rotation
recommendations based on cross-resistance potential. Advance-
ment in this area will enable weed scientists to elucidate the
unpredictability and promiscuity of NTS resistance and provide
genomics-based management recommendations to reduce the
occurrence of cross resistance.
AUTHOR CONTRIBUTIONS
LKB and PJT were involved in the conceptualization, writing and
editing of this paper.
ACKNOWLEDGEMENTS
This work was partially supported by the USDA National Institute
of Food and Agriculture (grant no. 2020-67013-31854).
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
Analyses conducted in this paper used publicly available data.
REFERENCES
1 Gaines TA, Duke SO, Morran S, Rigon CAG, Tranel PJ, Küpper A et al.,
Mechanisms of evolved herbicide resistance. J Biol Chem 295:
10307–10330 (2020).
2 Rigon CA, Gaines TA, Küpper A and Dayan FE, Metabolism-based her-
bicide resistance, the major threat among the non-target site resis-
tance mechanisms. Outlooks Pest Manage 31:162–168 (2020).
3 Maroli AS, Gaines TA, Foley ME, Duke SO, DoğramacıM, Anderson JV
et al., Omics in weed science: a perspective from genomics, tran-
scriptomics, and metabolomics approaches. Weed Sci 66:681–695
(2018).
4 Tranel PJ and Horvath DP, Molecular biology and genomics: new tools
for weed science. Bioscience 59:207–215 (2009).
5 Bobadilla LK, Giacomini DA, Hager AG and Tranel PJ, Characterization
and inheritance of dicamba resistance in a multiple-resistant water-
hemp (Amaranthus tuberculatus) population from Illinois. Weed Sci
70:4–13 (2022).
6 Giacomini DA, Patterson EL, Küpper A, Beffa R, Gaines TA and Tranel PJ,
Coexpression clusters and allele-specific expression in metabolism-
based herbicide resistance. Genome Biol Evol 12:2267–2278 (2020).
7 Moretti ML, Bobadilla LK and Hanson BD, Cross-resistance to diquat in
glyphosate/paraquat-resistant hairy fleabane (Conyza bonariensis)
and horseweed (Conyza canadensis) and confirmation of 2,4-D resis-
tance in Conyza bonariensis.Weed Technol 35:554–559 (2021).
8 Yanniccari M, Gigón R and Larsen A, Cytochrome P450 herbicide
metabolism as the main mechanism of cross-resistance to ACCase-
and ALS-inhibitors in Lolium spp. populations from Argentina: a
molecular approach in characterization and detection. Front Plant
Sci 11:600301 (2020).
www.soci.org LK Bobadilla, PJ Tranel
wileyonlinelibrary.com/journal/ps © 2023 The Authors.
Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Pest Manag Sci 2023
8
15264998, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ps.7728, Wiley Online Library on [30/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
9 Murphy BP and Tranel PJ, Target-site mutations conferring herbicide
resistance. Plan Theory 8:382 (2019).
10 Jugulam M and Shyam C, Non-target-site resistance to herbicides:
recent developments. Plan Theory 8:417 (2019).
11 Suzukawa AK, Bobadilla LK, Mallory-Smith C and Brunharo CA, Non-tar-
get-site resistance in Lolium spp. globally: a review, front.Plant Sci
11:609209 (2020).
12 Ha W, Yamaguchi T, Iwakami S, Sunohara Y and Matsumoto H, Compar-
ison of herbicide specificity of CYP81A cytochrome P450s from rice
and a multiple-herbicide resistant weed, Echinochloa phyllopogon.
Pest Manage Sci 78:4207–4216 (2022).
13 Han H, Yu Q, Beffa R, González S, Maiwald F, Wang J et al., Cytochrome
P450 CYP81A10v7 in Lolium rigidum confers metabolic resistance to
herbicides across at least five modes of action. Plant J 105:79–92
(2021).
14 Torra J, Montull JM, Taberner A, Onkokesung N, Boonham N and
Edwards R, Target-site and non-target-site resistance mechanisms
confer multiple andcross-resistance to ALS andACCase inhibiting her-
bicides in Lolium rigidum from Spain. Front Plant Sci 12:625138 (2021).
15 Reade JP, Milner LJ and Cobb AH, A role for glutathione S-transferases
in resistance to herbicides in grasses. Weed Sci 52:468–474 (2004).
16 Franco-Ortega S, Goldberg-Cavalleri A, Walker A, Brazier-Hicks M,
Onkokesung N and Edwards R, Non-target site herbicide resistance
is conferred by two distinct mechanisms in black-grass (Alopecurus
myosuroides). Front Plant Sci 12:636652 (2021).
17 Hu T, He S, Yang G, Zeng H, Wang G, Chen Z et al., Isolation and char-
acterization of a rice glutathione S-transferase gene promoter regu-
lated by herbicides and hormones. Plant Cell Rep 30:539–549 (2011).
18 Pang S, Duan L, Liu Z, Song X, Li X and Wang C, Co-induction of a
glutathione-S-transferase, a glutathione transporter and an ABC
transporter in maize by xenobiotics. PLoS One 7:e40712 (2012).
19 Giacomini DA, Gaines T, Beffa R and Tranel PJ, Optimizing RNA-seq
studies to investigate herbicide resistance. Pest Manage Sci 74:
2260–2264 (2018).
20 Lázár V, Nagy I, Spohn R, CsörgőB, Györkei Á, Nyerges Á et al., Genome-
wide analysis captures the determinants of the antibiotic cross-
resistance interaction network. Nat Commun 5:4352 (2014).
21 Khatami SA, Barmaki M, Alebrahim MT and Bajwa AA, Salicylic acid pre-
treatment reduces the physiological damage caused by the herbi-
cide mesosulfuron-methyl+ iodosulfuron-methyl in wheat (Triticum
aestivum). Agronomy 12:3053 (2022).
22 Khatooni M, Karimmojeni H, Zali AG and Tseng T-M, Salicylic acid
enhances tolerance of Valeriana officinalis L. to bentazon herbicide.
Ind Crops Prod 177:114495 (2022).
23 Ma LY, Zhang SH, Zhang JJ, Zhang AP, Li N, Wang XQ et al., Jasmonic
acids facilitate the degradation and detoxification of herbicide
isoproturon residues in wheat crops (Triticum aestivum). Chem Res
Toxicol 31:752–761 (2018).
24 Kaya A and Doganlar ZB, Exogenous jasmonic acid induces stress
tolerance in tobacco (Nicotiana tabacum) exposed to imazapic.
Ecotoxicol Environ Saf 124:470–479 (2016).
25 Suda H, Kubo T, Yoshimoto Y, Tanaka K, Tanaka S, Uchino A et al., Tran-
scriptionally linked simultaneous overexpression of P450 genes for broad-
spectrum herbicide resistance. Plant Physiol 192:3017–3029 (2023).
26 Wang J, Lian L, Qi J, Fang Y, Nyporko A, Yu Q et al., Metabolic resistance
to acetolactate synthase inhibitors in Beckmannia syzigachne: identi-
fication of CYP81Q32 and its transcription regulation. Plant J 115:
317–334 (2023).
27 Veenstra TD, Omics in systems biology: current progress and future
outlook. Proteomics 21:2000235 (2021).
28 Rao VS, Srinivas K, Sujini GN and Kumar GN, Protein-protein interaction
detection: methods and analysis. Int J Proteomics 2014:147648
(2014).
29 Kato H, Computational prediction of cytochrome P450 inhibition and
induction. Drug Metab Pharmacokinet 35:30–44 (2020).
30 Olofsson SK and Cars O, Optimizing drug exposure to minimize selec-
tion of antibiotic resistance. Clin Infect Dis 45:129–136 (2007).
31 Maveyraud L and Mourey L, Protein X-ray crystallography and drug dis-
covery. Molecules 25:1030 (2020).
32 Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O et al.,
Highly accurate protein structure prediction with AlphaFold. Nature
596:583–589 (2021).
33 Sanderson T, Bileschi ML, Belanger D and Colwell LJ, ProteInfer, deep
neural networks for protein functional inference. eLife 12:e80942
(2023).
34 Sobolev V and Edelman M, Modeling the quinone-B binding site of
the photosystem-II reaction center using notions of complemen-
tarity and contact-surface between atoms. Proteins 21:214–225
(1995).
35 Nianiou-Obeidat I, Madesis P, Kissoudis C, Voulgari G, Chronopoulou E,
Tsaftaris A et al., Plant glutathione transferase-mediated stress toler-
ance: functions and biotechnological applications. Plant Cell Rep 36:
791–805 (2017).
36 Krivák R and Hoksza D, P2Rank: machine learning based tool for rapid
and accurate prediction of ligand binding sites from protein struc-
ture. J Cheminf 10:39 (2018).
37 Trott O and Olson AJ, AutoDock Vina: improving the speed and accu-
racy of docking with a new scoring function, efficient optimization,
and multithreading. J Comput Chem 31:455–461 (2010).
38 Eberhardt J, Santos-Martins D, Tillack AF and Forli S, Autodock vina
1.2.0: new docking methods, expanded force field, and python bind-
ings. J Chem Inf Model 61:3891–3898 (2021).
39 Wang H, Wang Q, Liu Y, Liao X, Chu H, Chang H et al., PCPD: plant
cytochrome P450 database and web-based tools for structural
construction and ligand docking. Synth Syst Biotechnol 6:102–109
(2021).
40 Matthews S, Belcher JD, Tee KL, Girvan HM, McLean KJ, Rigby SE et al.,
Catalytic determinants of alkene production by the cytochrome
P450 peroxygenase OleTJE. J Biol Chem 292:5128–5143 (2017).
41 Hammerer L, Winkler CK and Kroutil W, Regioselective biocatalytic
hydroxylation of fatty acids by cytochrome P450s. Catal Lett 148:
787–812 (2018).
42 Galli M, Feng F and Gallavotti A, Mapping regulatory determinants in
plants. Front Genet 11:591194 (2020).
43 Marand AP, Zhang T, Zhu B and Jiang J, Towards genome-wide predic-
tion and characterization of enhancers in plants. Biochim Biophys
Acta, Gene Regul Mech 1860:131–139 (2017).
44 Schmitz RJ, Grotewold E and Stam M, Cis-regulatory sequences in
plants: their importance, discovery, and future challenges. Plant Cell
34:718–741 (2022).
45 Werck-Reichhart D, Promiscuity, a driver of plant cytochrome P450
evolution? Biomolecules 13:394 (2023).
46 Park PJ, ChIP–seq: advantages and challenges of a maturing technol-
ogy. Nat Rev Genet 10:669–680 (2009).
47 Grandi FC, Modi H, Kampman L and Corces MR, Chromatin accessibility
profiling by ATAC-seq. Nat Protoc 17:1518–1552 (2022).
48 Bajic M, Maher KA and Deal RB, Identification of open chromatin regions
in plant genomes using ATAC-seq, in Plant Chromatin Dynamics.
Methods in Molecular Biology, Chromatin Dynamics, ed. by Bemer M
and Baroux C. Methods in Molecular, Humana Press, New York, Vol.
1675, pp. 183–201 (2018).
49 Li Z, Schulz MH, Look T, Begemann M, Zenke M and Costa IG, Identifi-
cation of transcription factor binding sites using ATAC-seq. Genome
Biol 20:1–21 (2019).
50 Marand AP and Schmitz RJ, Single-cell analysis of cis-regulatory ele-
ments. Curr Opin Plant Biol 65:102094 (2022).
51 Lu Z, Hofmeister BT, Vollmers C, DuBois RM and Schmitz RJ, Combining
ATAC-seq with nuclei sorting for discovery of cis-regulatory regions
in plant genomes. Nucleic Acids Res 45:e41 (2017).
52 O'Malley RC, Huang SC, Song L, Lewsey MG, Bartlett A, Nery JR et al.,
Cistrome and epicistrome features shape the regulatory DNA land-
scape. Cell 165:1280–1292 (2016).
53 Savadel SD, Hartwig T, Turpin ZM, Vera DL, Lung P-Y, Sui X et al., The
native cistrome and sequence motif families of the maize ear. PLoS
Genet 17:e1009689 (2021).
54 Louwers M, Bader R, Haring M, van Driel R, de Laat W and Stam M, Tis-
sue- and expression level–specific chromatin looping at maize b1
epialleles. Plant Cell 21:832–842 (2009).
55 Ricci WA, Lu Z, Ji L, Marand AP, Ethridge CL, Murphy NG et al., Wide-
spread long-range cis-regulatory elements in the maize genome.
Nat Plants 5:1237–1249 (2019).
56 Forcato M, Nicoletti C, Pal K, Livi CM, Ferrari F and Bicciato S, Compar-
ison of computational methods for Hi-C data analysis. Nat Methods
14:679–685 (2017).
57 Fraser J, Williamson I, Bickmore WA and Dostie J, An overview of
genome organization and how we got there: from FISH to Hi-C.
Microbiol Mol Biol Rev 79:347–372 (2015).
58 Van Berkum NL, Lieberman-Aiden E, Williams L, Imakaev M, Gnirke A,
Mirny LA et al., Hi-C: a method to study the three-dimensional archi-
tecture of genomes. J Visualized Exp 39:e1869 (2010).
Herbicide cross resistance www.soci.org
Pest Manag Sci 2023 © 2023 The Authors.
Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
wileyonlinelibrary.com/journal/ps
9
15264998, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ps.7728, Wiley Online Library on [30/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
59 Dong Q, Li N, Li X, Yuan Z, Xie D, Wang X et al., Genome-wide Hi-C anal-
ysis reveals extensive hierarchical chromatin interactions in rice.
Plant J 94:1141–1156 (2018).
60 Lightfoot DJ, Jarvis DE, Ramaraj T, Lee R, Jellen EN and Maughan PJ,
Single-molecule sequencing and Hi-C-based proximity-guided
assembly of amaranth (Amaranthus hypochondriacus) chromo-
somes provide insights into genome evolution. BMC Biol 15:74
(2017).
61 Montgomery JS, Morran S, MacGregor DR, McElroy JS, Neve P, Neto C
et al., The International Weed Genomics Consortium: Community
resources for weed genomics research. bioRxiv:2023–07 (2023).
doi:10.1101/2023.07.19.549613
62 da Silveira FV, Severing E, Lai X, Estevan J, Farrera I, Hugouvieux V et al.,
Unraveling the role of MADS transcription factor complexes in apple
tree dormancy. New Phytol 232:2071–2088 (2021).
63 Wang S, He J, Deng M, Wang C, Wang R, Yan J et al., Integrating ATAC-
seq and RNA-seq reveals the dynamics of chromatin accessibility
and gene expression in apple response to drought. Int J Mol Sci 23:
11191 (2022).
64 Qiu F, Zheng Y, Lin Y, Woldegiorgis ST, Xu S, Feng C et al., Integrated
ATAC-seq and RNA-seq data analysis to reveal osbZIP14 function in
rice in response to heat stress. Int J Mol Sci 24:5619 (2023).
65 Li Y and Wei K, Comparative functional genomics analysis of cyto-
chrome P450 gene superfamily in wheat and maize. BMC Plant Biol
20:93 (2020).
66 Jin J, Tian F, Yang D-C, Meng Y-Q, Kong L, Luo J et al., PlantTFDB 4.0:
toward a central hub for transcription factors and regulatory interac-
tions in plants. Nucleic Acids Res 45:D1040–D1045 (2017).
67 Tian F, Yang D-C, Meng Y-Q, Jin J and Gao G, PlantRegMap: charting
functional regulatory maps in plants. Nucleic Acids Res 48:D1104–
D1113 (2020).
68 Fei X, Shi Q, Qi Y, Wang S, Lei Y, Hu H et al., ZbAGL11, a class D MADS-
box transcription factor of Zanthoxylum bungeanum, is involved in
sporophytic apomixis. Hortic Res 8:23 (2021).
69 GreenupAG,SasaniS,OliverSN,TalbotMJ,DennisES,HemmingMNet al.,
ODDSOC2 is a MADS box floral repressor that is down-regulated by ver-
nalization in temperate cereals. Plant Physiol 153:1062–1073 (2010).
70 Krouk G, Lingeman J, Colon AM, Coruzzi G and Shasha D, Gene
regulatory networks in plants: learning causality from time and per-
turbation. Genome Biol 14:1–7 (2013).
71 Cramer GR, Urano K, Delrot S, Pezzotti M and Shinozaki K, Effects of abi-
otic stress on plants: a systems biology perspective. BMC Plant Biol
11:163 (2011).
72 Kitano H, Systems biology: a brief overview. Science 295:1662–1664 (2002).
73 Langfelder P and Horvath S, WGCNA: an R package for weighted corre-
lation network analysis. BMC BMC Bioinf 9:1–13 (2008).
74 Jones DM and Vandepoele K, Identification and evolution of gene reg-
ulatory networks: insights from comparative studies in plants. Curr
Opin Plant Biol 54:42–48 (2020).
75 Castelán-Muñoz N, Herrera J, Cajero-Sánchez W, Arrizubieta M, Trejo C,
García-Ponce B et al., MADS-box genes are key components of
genetic regulatory networks involved in abiotic stress and plastic
developmental responses in plants. Front Plant Sci 10:853 (2019).
76 Hong-bo S, Plant gene regulatory network system under abiotic stress.
Acta Biol Szeged 50:1–9 (2006).
77 Xu Q, Huang S, Guo G, Yang C, Wang M, Zeng X et al., Inferring regula-
tory element landscapes and gene regulatory networks from inte-
grated analysis in eight hulless barley varieties under abiotic stress.
BMC Genomics 23:1–10 (2022).
78 Marshall-Colon A, Long SP, Allen DK, Allen G, Beard DA, Benes B et al.,
Crops in silico: generating virtual crops using an integrative and
multi-scale modeling platform. Front Plant Sci 8:786 (2017).
www.soci.org LK Bobadilla, PJ Tranel
wileyonlinelibrary.com/journal/ps © 2023 The Authors.
Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Pest Manag Sci 2023
10
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