alleles. Missense mutations (13) were also identified, but phenotype predictions are not
possible in this case as the effect of these mutations on Cry1F susceptibility and resistance
requires functional testing. We document, for the first time, that a *SfABCC2* resistance allele
previously described in *S. frugiperda* from Brazil is present in Puerto Rico fall armyworm
populations. These data provide proof of concept for the power of HP targeted sequencing
for performing surveillance and field monitoring of well-established and novel candidate
*Bt* resistance alleles.

**2. Materials and Methods**

2.1. *S. frugiperda* Sample Collection and Genomic DNA Extraction

Adult moths were captured at three locations in Puerto Rico (Lajas, Salinas and Santa Isabel) using sex pheromone-baited traps [19] at sites near field and sweet/field corn plantings in order to optimize the capture efficiency of C-strain males. Collection details of the 41 *S. frugiperda* individuals tested are in Table S1. Captured moths were confirmed as *S. frugiperda* by visual inspection [20] and kept refrigerated until shipped to the University of Tennessee. Samples were stored at -20 °C until processed for purification of genomic DNA. Samples were named and numbered according to trapping location as PRL (Lajas), PRS (Salinas) and PRSI (Santa Isabel). A sample from the previously characterized Cry1F-resistant 456LSD4 strain of *S. frugiperda*, which originated from collections in Puerto Rico [21], was also included as positive control for detection of *SfABCC2mut* [8].

Genomic DNA was isolated from moth legs using the Pure Link Genomic DNA mini kit (Invitrogen) following manufacturer's protocols, and then quantified using a Nanodrop spectrophotometer (Thermo Scientific).

2.2. *TaqMan Genotyping for SfABCC2mut*

Detection of the *SfABCC2mut* allele using Taqman probes was performed as previously described [8]. Briefly, 10 µL (final volume) reactions included 10–20 ng of gDNA as template, a VIC-labeled probe specific to the *SfABCC2mut* allele (5' AAGCACATCGCCCACTT 3'), a FAM-labeled probe specific to the wild-type *SfABCC2* allele (5' CCAAGCACATCCCACTT 3'), and forward (5' TGGAGGCCGAAGAGAGACA 3') and reverse (5' AGGAGTTGACT-GACTTCATGTACCT 3') primers. Plates (Micro Amp Fast optical 96 well reaction plate, Applied Biosystems) were loaded in the Quant studio 6 Real Time PCR instrument (Ap-plied Biosystems) and amplified as follows: pre read stage at 60 °C for 30 s, hold stage at 95 °C for 10 min, PCR stage at 95 °C for 15 s and 60 °C for 1 min for 40 cycles, post read stage at 60 °C for 30 s. The fluorescence in each well was measured in the post read stage of the PCR, and the software generated an allelic discrimination plot based on the post amplification intensity of the fluorescent probes.

The frequency of the *SfABCC2mut1* allele or any mutation was determined using the Hardy-Weinberg equation with the formula: $F = (2 \times \text{ObsAa} + \text{Obsaa})/[2 \times (\text{ObsAA} + \text{ObsAa} + \text{Obsaa})]$, where “F” is the frequency of the “a” allele (*SfABCC2mut1*) and “Obs” the observed frequency of each of the three possible genotypes for the allele.

2.3. *Targeted DNA Amplification and Sequencing*

In early BLASTn searches of the *S. frugiperda* corn strain v3.1 genome from LepidoDB (https://bipaa.genouest.org/sp/spodoptera-frugiperda_pub/, accessed on 11 July 2019), we found that the *SfABCC2* gene was split between Scaffolds 11087 and 7154. These scaffolds were manually combined and the resulting *SfABCC2* gene sequence was used as a template to design TILING primers, using PCRTiler [22] at default settings allowing 50 bp overlap on each end and producing 180–200 bp final amplicon sizes (Table S2). The two approximately 1 Kbp intronic regions at the beginning of that gene sequence (Scaffold_11087) were not targeted. In a later version of the *S. frugiperda* corn strain genome (v6.0) from LepidoDB (accessed on 16 November 2020), the full-length *SfABCC2* gene appears in Scaffold 33. The initial *SfABCC2* gene assembly based on Scaffolds 11087 and 7154 used for TILING primer design did not include an inter-scaffold gap encompassing
nucleotides 7239–8976 of the *SfABCC2* gene assembly in the v6.0 genome. The TILING primers covered approximately 90% of the exonic regions of the assembled *SfABCC2* gene found in Scaffold 33 of the *S. frugiperda* corn strain v6.0 genome (see Figure S1 for a visual representation of typical coverage).

All TILING primers (238) were mixed into a single multiplex (119 overlapping targets) and PCR performed using the Hi-Plex targeted sequencing strategy, as previously described [17]. The resulting amplicons were pooled and sequenced on a HiSeqX device running a 2 × 150 bp configuration and merged to produce single, high quality reads using PEAR [23]. Demultiplexing was accomplished using the sample-specific barcode sequences incorporated during the Hi-Plex PCR amplification.

## 2.4. Genotype Assignment for SfABCC2mut Using a Targeted-Sequencing k-mer-Based Approach

To genotype the *SfABCC2mut* 2 bp insertion in the *SfABCC2* targeted sequencing data, a custom Python 3 script (dubbed Pgeno) was developed and is deposited in Github at https://github.com/pertandy/pgeno (accessed on 26 May 2021). This script scans each of the sequence reads for the known mutation by using 10 mer flanking sequences as a guide. To account for unknown variation in the flanking regions, each of the 10 mers was allowed up to two mismatches. Sequences with matching flanks were counted and samples assigned a *SfABCC2mut* genotype based on the proportion of the known 2 bp insertions. In order to reduce false-positive genotype assignment, we required at least 40X sequence coverage in the PCR targeted regions and >15% of the total number of matching reads to assign a genotype of hetero- or homozygous for the 2 bp insertion. The minimum sequence coverage refers to the number of high-quality reads covering a target region in the *SfABCC2* reference gene and depends on the efficiency of the primers in the multiplex. The primer efficiencies are highly reproducible and by over-sequencing the samples, most of the gene is covered at a high depth (up to 1000X for some targets). These parameters can be adjusted in the Pgeno script to increase or decrease the genotyping stringency.

## 2.5. Alignment, Variant Calling and Variant Analysis

The complete pipeline used for trimming, alignment, variant calling, and analysis using targeted *SfABCC2* sequences is summarized in Figure 1. Raw reads were quality trimmed at both ends and filtered for adapter sequences using BBDuk [24]. The quality of samples was checked before and after trimming/processing to confirm adapter and non-relevant (e.g., contaminants, primer artifacts) sequences were removed using FastQC [25]. The full-length *SfABCC2* gene interval was determined by aligning a *SfABCC2* cDNA from a Cry1F-susceptible *S. frugiperda* strain (GenBank accession number KY489760.1) to the *S. frugiperda* corn reference genome v6.0 [26] via the BLAST server hosted on LepidoDB. The full-length *SfABCC2* gene sequence was found included in Scaffold 33, and this reference gene sequence was used for further analyses.

<figure><img src="image_77.png" /><figcaption>**Figure 1.** Schematic pipeline of overall workflow used in this work from raw Fastq targeted sequencing reads to population genetics and phylogenetic analyses based on bi-allelic SNP variant calling.</figcaption></figure>
Reads were aligned to the *SfABCC2* reference gene using a Burrows-Wheeler aligner
algorithm (bwa-mem) with default options [27]. The mapping rate for all samples was
determined via SAMtools flagstat [28]. Only samples with a mapping rate of 80% or
higher were kept, which led to the removal of only three samples from further analysis.
We determined the count of DNA sequence reads across the *SfABCC2* gene using bam-
readcount (https://github.com/genome/bam-readcount, accessed on 20 June 2019). As
noted in 2.3, two regions (nucleotides ~138–1518 and ~7195–7858) within the *SfABCC2* gene
were not sequenced due to the TILING primers being designed based on an old assembly
(Scaffolds 11,087 and 7154 from *S. frugiperda* corn genome v3.1) of the gene. The second
region representing the inter-scaffold gap in the early gene assembly was filled with ‘Ns’,
precluding alignment of any reads to this region.

We performed alignment cleanup by sorting the bam file via SAMtools [28], deduplicating via Genome Analysis Tool Kit (GATK) version 4.1.20 [29] with MarkDuplicates and then adding read groups to each bam file via GATK's function addOrReplaceReadGroup. Variants were called via the Haplotypecaller of GATK with default parameters set based on the GATK Best Practice pipeline. The initial set included 2006 variants with a quality score greater than 40. After considering different filters based on depth and minor allele frequencies, the final filter applied included depth of coverage ≥ 8, each individual allele depth ≥2, alternate allele frequency ≥5%, and the allele being called in at least 25% of the samples (>10). This filter produced 1861 variants (1428 SNPs and 433 indels), over 92% of the unfiltered set. We further filtered this variant set to include only bi-allelic variants using the GATK SelectVariants function, which resulted in 1333 bi-allelic variants (1143 SNPs and 190 indels) that were used in all variant-based analysis.

The intron/exon boundaries in *SfABCC2* were determined by alignment to a *SfABCC2*
cDNA (GenBank accession number KY489760.1) using Exonerate with the est2genome
model option [30]. To confirm the accuracy of the intron/exon boundaries, we per-
formed the same alignment of the *SfABCC2* gene with three additional available cDNA
sequences from Cry1F-susceptible *S. frugiperda* (GenBank accession numbers MG387043.1,
MN399979.1 and KY646296.1). The same intron/exon boundaries were identified in each
generic feature file (GFF) generated by Exonerate. The consensus protein sequence from
aligning the SfABCC2 sequences derived from translating the *SfABCC2* cDNA sequences
above in CLC Genomics Workbench 21.0.4 (Figure S2) was used as a reference to locate
amino acid changes from mutations. This consensus SfABCC2 protein sequence differs
in two amino acid positions in the notation for mutations located to extracellular loop
4 region presented by Boaventura et al. [10].

Functional annotation was performed with SnpEff [31], and SnpSift was used to fil-
ter annotations to a tabular format [32]. The final filtered set of 1333 variants contained
17 missense mutations and 1 nonsense mutation. Compared to the unfiltered variant
set, the filtered set excluded one nonsense mutation because of both alternate allele fre-
quency and read depth criteria, and two missense mutations because of the alternate allele
frequency criterion.

We also computed PCA projections via PLINK [33], and visualized them in gg-
plot2 [34]. Given the highly polymorphic nature of the samples, we also determined
haplotypes for PCA and generated neighbor joining trees using the ShapeIT software [35].
Since no recombination maps are available for *S. frugiperda*, the recombination rate in
ShapeIT was set to 0.000023 cM/Mb based on previous research for *Spodoptera litura* [36].

Admixture model-based clustering was performed using FastStructure [37] to identify
genetic structure within the *SfABCC2* gene. FastStructure is a generative model-based
approach based on Hardy-Weinberg equilibrium assumptions between alleles and linkage
disequilibrium between genotyped loci. The FastStructure script structure.py was used to
determine *k*, the number of assumed populations or genetic groups that share a subset of
allele frequencies from *k* = 2 to 10. The choosek.py script in FastStructure chose *k* = 3 for
model complexity maximizing the marginal likelihood, and *k* = 4 for model components
to explain structure in the data. The final output of FastStructure was visualized using
`distruct.py` at *k* = 4.

**3. Results**

**3.1. Genotyping Results Using Taqman**

Based on Taqman genotyping, of the 41 *S. frugiperda* samples tested, 18 carried the *SfABCC2mut* allele, with nine being homozygous. Overall, these detections suggest an allele frequency of 0.3292 for *SfABCC2mut* in the analyzed samples, similar to previous estimations for the frequency of this allele in Puerto Rico [8]. The relative proportion of individuals carrying the *SfABCC2mut* allele at each location was higher in Lajas (53% carriers, including 33% homozygotes and 20% heterozygotes) followed by the Salinas (41% carriers, 8% homozygotes and 33% heterozygotes) and Santa Isabel (21% homozygotes) locations.

3.2. *Genotyping Results Using Targeted Sequencing*

Overall, the multiplexed TILING primers amplified 63% of the *SfABCC2* reference gene from the *S. frugiperda* v.6.0 corn genome, with 23 of the 25 predicted exons located in contig 33 being fully sequenced (Figure S1). The Hi-Plex strategy does not rely on equilibration of primer efficiencies (or any kind of primer optimization) and as such, some targets will amplify more efficiently than others (or fail completely) and thus receive more sequencing (or none at all). To accommodate these expected variations in template concentration, the samples were sequenced to high depth (x100 per target amplicon).

Of the 41 samples genotyped via targeted sequencing, 14 contain the *SfABCC2mut* GC insertion, with 8 scored as heterozygous and 6 as homozygous, resulting in a 0.2683 allele frequency. There was a 73% (30/41) concordance rate between the Taqman and the k-mer-based *SfABCC2mut* genotyping (Table 1). Five samples identified as susceptible by the k-mer-based genotyping were identified as heterozygotes (3) or homozygote resistant (2) by the Taqman assay. Furthermore, three heterozygous samples, based on k-mer-based genotyping, appeared as homozygous resistant in Taqman tests. Finally, two samples identified as homozygous resistant by the k-mer method were classified as homozygous susceptible by the Taqman assay. Examination of the assembled targeted sequences for the discrepant samples detected a number of SNPs in the *SfABCC2mut* site targeted by Taqman primers, which likely affected Taqman amplifications and therefore explain the observed discrepancies. In fact, of the 58 sites covered by the Taqman primers and probe, 19% were polymorphic in at least one of the 41 samples (data not shown).

3.3. *Variant Analysis Pipeline and Genotyping*

Functional annotation of variants detected using the bioinformatics pipeline shown
in Figure 1 detected *SfABCC2mut* (2 bp GC insertion) as a frameshift mutation (D740A).
There was a total of 18 samples in which the *SfABCC2mut* allele was detected using the
GATK variant and filtering described earlier (see Methods). Within these 18 samples, 12
were heterozygous and 6 homozygous for the mutation, displaying 100% concordance
with results from the k-mer-based genotyping approach. When a lower alternative allele
threshold (≥3) was utilized, some discrepancies occurred between GATK and the k-mer-
based genotyping (data not shown).

In addition to identifying the known *SfABCC2mut* allele, we also detected 374 coding
synonymous mutations (data not shown) and 23 nonsynonymous and frameshift mutations
in *SfABCC2* in the filtered dataset. After mutations already observed in at least one
*SfABCC2* cDNA from Cry1F-susceptible *S. frugiperda* were removed, 14 nonsynonymous
and 9 frameshift mutations (apart from *SfABCC2mut* being synonymous with D740A) were
identified as new potential Cry1F resistance alleles (Table 2 and Table S1).
<table><caption>**Table 1.** Concordance between results using Taqman and k-mer-based genotyping for the SfABCC2mut (GC insertion) allele, represented as the "r" allele. Samples with discrepant genotypes are highlighted in bold font.</caption><thead><tr><th>Sample Name</th><th>Taqman</th><th>K-mer</th></tr></thead><tbody><tr><td>PRL_164</td><td>SS</td><td>SS</td></tr><tr><td><strong>PRL_165</strong></td><td><strong>rr</strong></td><td><strong>Sr</strong></td></tr><tr><td>PRL_166</td><td>SS</td><td>SS</td></tr><tr><td>PRL_167</td><td>SS</td><td>SS</td></tr><tr><td>PRL_168</td><td>Sr</td><td>Sr</td></tr><tr><td><strong>PRL_169</strong></td><td><strong>rr</strong></td><td><strong>Sr</strong></td></tr><tr><td>PRL_170</td><td>Sr</td><td>Sr</td></tr><tr><td>PRL_171</td><td>Sr</td><td>Sr</td></tr><tr><td>PRL_172</td><td>rr</td><td>rr</td></tr><tr><td>PRL_173</td><td>rr</td><td>rr</td></tr><tr><td>PRL_174</td><td>SS</td><td>SS</td></tr><tr><td>PRL_175</td><td>SS</td><td>SS</td></tr><tr><td>PRL_176</td><td>SS</td><td>SS</td></tr><tr><td>PRL_177</td><td>rr</td><td>rr</td></tr><tr><td><strong>PRL_178</strong></td><td><strong>SS</strong></td><td><strong>SS</strong></td></tr><tr><td><strong>PRS_6</strong></td><td><strong>SS</strong></td><td><strong>SS</strong></td></tr><tr><td>PRS_7</td><td>Sr</td><td>Sr</td></tr><tr><td>PRS_8</td><td>SS</td><td>SS</td></tr><tr><td><strong>PRS_9</strong></td><td><strong>Sr</strong></td><td><strong>SS</strong></td></tr><tr><td>PRS_10</td><td>rr</td><td>rr</td></tr><tr><td>PRS_11</td><td>SS</td><td>SS</td></tr><tr><td><strong>PRS_12</strong></td><td><strong>Sr</strong></td><td><strong>SS</strong></td></tr><tr><td>PRS_13</td><td>SS</td><td>SS</td></tr><tr><td>PRS_14</td><td>SS</td><td>SS</td></tr><tr><td><strong>PRS_17</strong></td><td><strong>Sr</strong></td><td><strong>SS</strong></td></tr><tr><td><strong>PRS_18</strong></td><td><strong>SS</strong></td><td><strong>rr</strong></td></tr><tr><td>PRS_20</td><td>SS</td><td>SS</td></tr><tr><td>PRSI_1</td><td>SS</td><td>SS</td></tr><tr><td>PRSI_2</td><td>SS</td><td>SS</td></tr><tr><td>PRSI_4</td><td>SS</td><td>SS</td></tr><tr><td><strong>PRSI_5</strong></td><td><strong>SS</strong></td><td><strong>Sr</strong></td></tr><tr><td><strong>PRSI_6</strong></td><td><strong>rr</strong></td><td><strong>Sr</strong></td></tr><tr><td><strong>PRSI_7</strong></td><td><strong>rr</strong></td><td><strong>SS</strong></td></tr><tr><td>PRSI_8</td><td>SS</td><td>SS</td></tr><tr><td><strong>PRSI_9</strong></td><td><strong>SS</strong></td><td><strong>rr</strong></td></tr><tr><td><strong>PRSI_10</strong></td><td><strong>rr</strong></td><td><strong>SS</strong></td></tr><tr><td>PRSI_11</td><td>SS</td><td>SS</td></tr><tr><td>PRSI_12</td><td>SS</td><td>SS</td></tr><tr><td>PRSI_13</td><td>SS</td><td>SS</td></tr><tr><td>PRSI_14</td><td>SS</td><td>SS</td></tr><tr><td>PRSI_15</td><td>SS</td><td>SS</td></tr></tbody></table>

All missense (13) and nonsense (1) nonsynonymous mutations, except for an homozygous sample for T14M, were detected in heterozygous individuals. The most abundant missense mutations (10 individuals each, 24% of samples) were G1085E and S1209N, which occur in the C terminal intracellular region of SfABCC2 (Figure 2). The other common missense mutation was V414A (carried by 9 individuals, 22% of samples), which occurs in the loop connecting the first SfABCC2 transmembrane domain and ATP-binding cassette. Mutation P1079L was localized to a predicted splice site region. Only one missense mutation (Q788P, found in 1 sample) was localized to the fourth extracellular loop, a region hypothesized to be vital to Cry1F toxicity [10,11]. Missense mutations had a predicted moderate effect on the SfABCC2 protein, while the only nonsense mutation (E700*) was predicted to result in a truncated SfABCC2 protein encompassing up to the first ATP-binding cassette (Figure 2).
<table><caption>**Table 2.** Nonsynonymous and frameshift mutations identified by SnpEFF across 41 *S. frugiperda* *SfABCC2* bi-allelic variants. The nucleotide position in the *SfABCC2* gene reference (Position), the functional class of mutation (MISS = missense, NON = nonsense, and SHIFT = frameshift), the mutation with capitalized reference and alternative alleles, the corresponding amino acid change (Aa change), and the number of samples positive for that mutation (#Samples) are shown. Asterisks denote that the mutation leads to a premature stop codon. The *SfABCC2mut* allele is synonymous with D740A.</caption><thead><tr><th>Position</th><th>Class</th><th>Mutation</th><th>Aa Change</th><th>#Samples</th></tr></thead><tbody><tr><td>38</td><td>SHIFT</td><td>ggc/ggTGGTc</td><td>G13GG</td><td>1</td></tr><tr><td>40</td><td>SHIFT</td><td>acg/aATGGTGGTCATcg</td><td>T14NGGH *</td><td>1</td></tr><tr><td>41</td><td>MISS</td><td>aCg/aTg</td><td>T14M</td><td>4</td></tr><tr><td>42</td><td>SHIFT</td><td>gct/</td><td>A15 *</td><td>20</td></tr><tr><td>45</td><td>SHIFT</td><td>gtg/gGTCAACG</td><td>V16GQR *</td><td>4</td></tr><tr><td>48</td><td>SHIFT</td><td>ggc/CAggc</td><td>G17Q *</td><td>4</td></tr><tr><td>3421</td><td>SHIFT</td><td>gtc/gCATGtc</td><td>V210AC *</td><td>1</td></tr><tr><td>4077</td><td>MISS</td><td>Ctc/Ttc</td><td>L330F</td><td>2</td></tr><tr><td>4357</td><td>MISS</td><td>Ttt/Gtt</td><td>F398V</td><td>1</td></tr><tr><td>4540</td><td>MISS</td><td>gTg/gCg</td><td>V414A</td><td>9</td></tr><tr><td>6020</td><td>MISS</td><td>cAa/cGa</td><td>Q627R</td><td>1</td></tr><tr><td>6915</td><td>NON</td><td>Gaa/Taa</td><td>E700 *</td><td>1</td></tr><tr><td>7034</td><td>SHIFT</td><td>gat/gCGat</td><td>D740A *</td><td>18</td></tr><tr><td>7070</td><td>SHIFT</td><td>tgcatggtg/</td><td>CMV753</td><td>1</td></tr><tr><td>7120</td><td>MISS</td><td>gCt/gGt</td><td>A768G</td><td>3</td></tr><tr><td>9098</td><td>MISS</td><td>cAa/cCa</td><td>Q788P</td><td>1</td></tr><tr><td>9122</td><td>SHIFT</td><td>gga/GTAAgga</td><td>G793VR</td><td>8</td></tr><tr><td>9335</td><td>MISS</td><td>gCc/gTc</td><td>A822V</td><td>3</td></tr><tr><td>10491</td><td>SHIFT</td><td>gga/</td><td>G950</td><td>1</td></tr><tr><td>11708</td><td>MISS</td><td>cCg/cTg</td><td>P1079L</td><td>1</td></tr><tr><td>11726</td><td>MISS</td><td>gGa/gAa</td><td>G1085E</td><td>10</td></tr><tr><td>12507</td><td>MISS</td><td>Gaa/Aaa</td><td>E1195K</td><td>2</td></tr><tr><td>12748</td><td>MISS</td><td>aGt/aAt</td><td>S1209N</td><td>10</td></tr><tr><td>13285</td><td>MISS</td><td>Tcc/Acc</td><td>S1310T</td><td>1</td></tr></tbody></table>

<figure><img src="image_78.png" /><figcaption>**Figure 2.** Schematic representation (not drawn to scale) of *SfABCC2* topology on the cell membrane and location of detected nonsynonymous, nonsense and frameshift mutations in sequenced *S. frugiperda* samples from Puerto Rico. Amino acid numbers are indicated at the start and end of transmembrane domains and ATP-binding cassettes as predicted based on detection of conserved domains in BLAST searches. Distinct colors in boxes indicate the number of individuals carrying each mutation, as detailed in the color legend. The only nonsense mutation (E700 *) and all frameshift mutations are shown in boxes with bold borders, including the *SfABCC2mut* allele (D740A). TD = transmembrane domain.</figcaption></figure>
Frameshifts were comparatively more frequent than nonsynonymous mutations, with homozygous individuals detected for all the frameshift mutations for which more than one positive sample was detected. These frameshift mutations generated aberrant proteins and predicted premature stop codons, leading to truncated SfABCC2 proteins, which in homozygous individuals would correspond to a Cry1F-resistant phenotype [8,12]. The A15 mutation was the most common (49% of individuals), with a slightly lower allele frequency (0.3170) than *SfABCC2mut* (synonymous with D740A mutation). Only three samples (PRL_168, PRL_170, PRL_171) from the same location (Lajas) were heterozygous for both A15 and D740A. These samples could be resistant to Cry1F based on complementation of these two alleles.

3.4. *SfABCC2 Sequence Variation*

We also considered population-level diversity in *SfABCC2* sequences from Puerto Rico. Interestingly, a Principal Component Analysis (PCA) of all samples showed two distinct groups, one of them without *SfABCC2mut* in any individual (Figure 3A, right). Individuals homozygous for *SfABCC2mut* had the most compact cluster, suggesting more *SfABCC2* sequence variation within the SS compared to rr samples.

<figure><img src="image_79.png" /><figcaption>**Figure 3.** PCA projection plot of 1333 bi-allelic variants found within *SfABCC2* of 41 *S. frugiperda* samples and colored based on genotype for *SfABCC2mut* (A) and location (B). The *SfABCC2mut* genotype was determined as described in the Methods.</figcaption></figure>
To determine whether location was driving differentiation of the PC clusters observed given different frequencies of *SfABCC2mut* in the three Puerto Rico locations, we also plotted these samples colored by location (Figure 3B). No clusters correlated with sample location. Further, a logistic regression association test on PC1 vs. PC2 (Figure 4) generated eight SNPs with significant -log 10 *p*-value > 5 at positions 3874, 3936, 3944, 4085, 4097, 5485, 5788, and 13206. None of these variants, however, were predicted to have any functional effect change by SnpEff or corresponded to previously reported Cry1F-resistant alleles [8,10]. These data suggest that there may be barriers to Bt resistance gene flow, for example between host strains, even within the island of Puerto Rico.

<figure><img src="image_80.png" /><figcaption>**Figure 4.** Manhattan plot of SNPs associated with PC2 (differentiating clusters of Sr and rr genotyped samples) on the filtered bi-allelic dataset (1333 variants).</figcaption></figure>

To confirm this observation, we re-ran the PCA analysis without the *SfABCC2mut* homozygous individuals and observed the same clusters (data not shown). We next haplif-
fied our *SfABCC2* data using ShapeIt2 (1333 variants, see Methods) and ran FastStructure (Figure 5). Based on the targeted sequence data, we observed four populations, with the *SfABCC2mut* allele always associating with one population (purple color in Figure 5). Furthermore, even though there is little observed sharing of *SfABCC2* between the two primary PCA-based groups, admixture did occur between the *SfABCC2mut*-associated population and the three other populations. The frequency of the *SfABCC2mut* allele was highest in Lajas, indicating that this allele likely originated from this location and spread to other fall armyworm populations in Puerto Rico, as seen in the Structure plot. The observed moderate levels of admixture between the fall armyworms studied across all three Puerto Rican populations suggest that—contrary to the PCA results—the *SfABCC2mut* has the potential to spread to other subpopulations.
<figure><img src="image_81.png" /><figcaption>**Figure 5.** Genetic structure inferred by FastStructure on 82 haplotypes representing 41 samples at *k* = 4. Samples in pink text contain the *SfABCC2mut* allele.</figcaption></figure>

**4. Discussion**

Bioassay-based testing of F1 or F2 generations has traditionally been used to screen for resistance alleles in *S. frugiperda* to insecticidal proteins from *B. thuringiensis* produced in transgenic crops [5,14,38,39]. As an alternative, DNA-based screening methods provide higher sensitivity and are amenable to high-throughput applications and can considerably reduce overall labor and costs. Initial DNA-based resistance monitoring efforts for *S. frugiperda* focused on Taqman PCR targeting known mutations in the *SfABCC2* gene [8,9] and pooled population sequencing [10]. More recently, whole-genome resequencing was used to detect the presence of five reported resistance alleles in *S. frugiperda* populations from 12 geographic locations [40]. As a more cost-effective alternative to whole-genome sequencing, we provide proof of concept for the use of targeted highly multiplexed PCR for monitoring a known resistance allele (*SfABCC2mut*) and using bioinformatics to identify novel candidate *SfABCC2* resistance alleles. An advantage of the Cry1F-SfABCC2 system is that available knockout [12,13], mutational [11] and resistance [8,9] data in *S. frugiperda* support that a truncated SfABCC2 protein cannot function as Cry1F receptor. Thus, homozygote individuals for nonsense or frameshift mutations leading to truncated SfABCC2 proteins display a Cry1F-resistant phenotype, as observed for the *SfABCC2mut* resistance allele [8,9]. In addition, the amplification strategy of Hi-Plex allows the use of highly fragmented DNA, suggesting that poorly preserved samples, as is often the case for adult moths collected with pheromone traps, are amenable to analysis.

Both a k-mer-based and a bioinformatics pipeline provided the same genotyping results for a known mutation (*SfABCC2mut*). The genotypes of some samples differed between the Hi-Plex and Taqman methods. We observed that 19% of tested samples had polymorphisms around the priming/probe site for Taqman, which suggests an advantage of Hi-Plex over Taqman when dealing with highly polymorphic resistance genes. Most likely, the overlapping targets (and thus different priming sites) used in the TILING approach helps reduce the potential for erroneous genotypes and conclusions.

We found that among the individuals carrying the *SfABCC2mut* allele, 14.6% were homozygotes, which are expected to produce a truncated SfABCC2 and thus be resistant to Cry1F [8,9,12,13]. The allele frequency of *SfABCC2mut* (0.3292) was in line with previous screening efforts in fall armyworm from Puerto Rico [8,9]. Based on the Faststructure and PCA-based analysis, no clear selective sweep was observed. The higher relative frequency of *SfABCC2mut* suggests the allele may have emerged in *S. frugiperda* in or around Lajas. Our population-level analysis suggests that the genomic region around *SfABCC2mut* is
subject to both recombination and introgression, and that this occurs frequently, especially
between populations at Santa Isabel and Lajas. In contrast, we still observed two main
populations in Puerto Rico with statistically significant markers that do not associate with
known or candidate resistance alleles. Further work is needed to determine whether there
is at least one barrier to large-scale gene flow on the island, e.g., the well-established Corn
vs. Rice strains in *S. frugiperda*. It is important to consider that Lajas has had rice winter
nurseries (Univ. of Puerto Rico and Ricetech) for more than 30 years, while corn is the
most prevalent crop at Santa Isabel. Furthermore, organic corn is planted in Lajas, which
requires the frequent use of *B. thuringiensis* insecticides (e.g., Dipel) to control Lepidopteran
pests [41].

In addition to *SfABCC2mut*, a second Cry1F-resistant allele was previously described in *S. frugiperda* from Puerto Rico [9]. In this case, insertion of a highly repetitive sequence at the beginning of the *SfABCC2* gene resulted in aberrant splicing and resistance. This type of allele would not be detected in our current Hi-Seq strategy as our TILING primers were not designed to amplify the highly repetitive sequence. Addition of primers amplifying this region and re-sequencing may allow detection and estimation of frequency for this allele. However, the reported highly repetitive nature of the insert [9] may hinder successful detection.

Targeted sequencing combined with a custom bioinformatics pipeline allowed detect-
ing, in addition to 374 coding synonymous mutations, 23 nonsynonymous and frameshift
*SfABCC2* mutations as novel candidate Cry1F resistance alleles. A Cry1F-resistant phe-
notype similar to homozygous individuals for *SfABCC2mut* would be predicted for ho-
mozygous frameshift and nonsense mutations, as they would result in lack of a functional
*SfABCC2* protein [9,12,13]. Interestingly, some of the frameshift mutations were noticeably
frequent. Importantly, a number of individuals positive for these frameshift mutations
were homozygous samples predicted to produce truncated SfABCC2 proteins and thus
having a Cry1F-resistant phenotype similar to *SfABCC2mut* [8]. In addition, heterozygous
individuals could also be phenotypically resistant to Cry1F in cases of complementation
between alleles involving nonsense and frameshift mutations.

Significant gene flow between *S. frugiperda* populations from Brazil and Puerto Rico is not expected based on haplotype marker and meteorological observations [3]. In agreement with this observation, previous genotyping efforts did not detect *SfABCC2mut* among Brazilian *S. frugiperda* populations [9]. However, whole-genome comparisons suggest panmixia among *S. frugiperda* populations [42]. We did find two heterozygote individuals from Lajas (PRL_168 and PRL_164) carrying a GY deletion (DGY784D in our model, allele frequency 0.0571) previously described in Cry1F-resistant and field-collected *S. frugiperda* from Brazil [10]. However, detection of this GY deletion in Cry1F-suceptible individuals from Brazil [40] questions the relevance of this mutation to resistance.

The extracellular loops 1 and 4 in SfABCC2 have been proposed as relevant to Cry1F
toxicity [11], with loop 4 being most critical and also a hot spot for resistance mutations [10].
Only the Q788P mutation localized to that loop in our dataset. Interestingly, samples
containing this mutation were detected in pooled sequencing of Brazilian *S. frugiperda*
populations in one sample each from four locations (Balsas, Dourados, Santa Cruz das
Palmeiras, and Uberlandia) [10]. These particular collection sites in Brazil are separated by
>1200 km, suggesting that the Q788P mutation may be common in Brazilian *S. frugiperda*
populations. Among our samples, the mutation was rare and a single heterozygote indi-
vidual (PRS_14) was detected. Functional tests would be needed to test the effect of Q788P
on Cry1F toxicity.

Detection of common mutations may be suggestive of gene flow, including resistance alleles, between *S. frugiperda* populations from Brazil and the Caribbean. Although this flow is not expected based on predicted migratory movements [3], commercial exchange of contaminated plant materials could result in unintended dissemination of resistance alleles. This hypothesis should be further tested with increased sample sizes to better understand the potential paths for the spread of *S. frugiperda* resistance to *Bt* crops.
Overall, our analyses detected 374 synonymous and 24 (including *SfABCC2mut*) non-synonymous or frameshift *SfABCC2* mutations in 41 tested *S. frugiperda* individuals from Puerto Rico. These results indicate the *SfABCC2mut* and novel mutations predicted to result in truncated or aberrant SfABCC2 proteins and thus a Cry1F-resistant phenotype are not rare in *S. frugiperda* from Puerto Rico. Out of the 41 samples tested, the majority (38, 93%) carried at least one copy of a nonsense or frameshift SfABCC2 mutation. There were 13 (32% of the total) homozygotes for these mutations, including 6 for *SfABCC2mut* and 6 for the A15 allele. Based on similar mutations in knockout and resistant strains of *S. frugiperda* [8, 9, 11, 13], these homozygotes would display a Cry1F-resistant phenotype. The relatively high frequency of mutations predicted to result in resistance to Cry1F through SfABCC2 truncation is in line with the high levels of resistance to Cry1F maintained in Puerto Rico [18,43] and the lack of relevant fitness costs in disruptive SfABCC2 mutants [15,44]. Our targeted approach does not allow us to identify additional Cry1F-resistant individuals carrying alternative resistance genes, which could potentially increase the frequency of Cry1F-resistant individuals. In addition, while missense SfABCC2 mutations were also detected, a Cry1F-resistant phenotype cannot be predicted as their role in intoxication is unknown. For instance, the Q788P and A768G mutations could affect the predicted Cry1F-binding ability of the SfABCC2 loop 4 region [10]. Functional assays would be needed to test the effect of these mutations on susceptibility to Cry1F.

**5. Conclusions**

We provide proof of concept for the use of targeted sequencing in the monitoring of known mutations and the discovery of candidate resistance alleles to Cry1F in *S. frugiperda*. Furthermore, based on solid experimental evidence for the critical role of SfABCC2 in susceptibility to Cry1F, we predict the resistance phenotype of homozygous individuals for nonsense and frameshift mutations. This strategy provides higher sensitivity and should reduce labor and costs compared to traditional F1 and F2 screens to determine the frequency of diverse resistance alleles and, in specific mutations, susceptibility to Cry1F. Based on our observations, targeted sequencing overcomes the specificity limitations of Taqman genotyping, especially when targeting highly polymorphic genes. In addition, Taqman assays are limited to known alleles and cannot provide detection of novel candidate resistance alleles. Targeted sequencing provided preliminary evidence of a common mutation associated with Cry1F resistance in *S. frugiperda* from Brazil and Puerto Rico, which may suggest genetic flow between these populations as supported by whole genome-level comparisons [42]. Targeted sequencing such as Hi-Plex is adaptable to other pest-toxin resistance gene models, as long as known or suspected resistance loci are available, and the method can be expanded to sequence multiple genes within a single sample. While targeted sequencing provides an interesting list of candidate resistance alleles, further functional testing is needed to determine the role of missense mutations and their linkage with resistance to Cry1F.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10.3390/insects12070618/s1, Figure S1: Representative partial visualization of targeted sequencing reads aligned to the *SfABCC2* reference gene; Figure S2: Consensus SfABCC2 protein sequence used in this study; Table S1: List of sequenced *S. frugiperda* samples and their genotype for detected mutations; Table S2: List of TILING primers used in PCR amplification of *SfABCC2*.

**Author Contributions:** Conceptualization, K.S., K.L., S.J.E. and J.L.J.-F.; methodology, K.S., K.L., P.T., S.J.E., C.P.d.B., T.R. and J.L.J.-F.; software, K.S., K.L., P.T. and S.J.E.; formal analysis, K.S., K.L., S.J.E. and J.L.J.-F.; resources, D.M.V.D., A.M.L.-R. and J.L.J.-F.; data curation, K.S., K.L., S.J.E., C.P.d.B. and J.L.J.-F.; writing—original draft preparation, K.S., K.L., P.T., S.J.E. and J.L.J.-F.; writing—review and editing, all authors.; visualization, K.S., S.J.E. and J.L.J.-F.; funding acquisition, S.J.E. and J.L.J.-F. All authors have read and agreed to the published version of the manuscript.
**Funding:** This research was supported by Agriculture and Food Research Initiative Foundational Program competitive grant No. 2018-67013-27820 from the USDA National Institute of Food and Agriculture.

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** All targeted sequencing data used for this study are available through NCBI BioProject ID PRJNA729034.

**Conflicts of Interest:** The authors declare no conflict of interest.

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<figure><img src="image_82.png" /></figure>
Review

# Resistance in the Genus *Spodoptera*: Key Insect Detoxification Genes

Frédérique Hilliou<fnref n="1" />,*, Thomas Chertemps<fnref n="2" />, Martine Maïbèche<fnref n="2" /> and Gaëlle Le Goff<fnref n="1" />

<figure><img src="image_83.png" /></figure>

check for
updates

**Citation:** Hilliou, F.; Chertemps, T.; Maïbèche, M.; Le Goff, G. Resistance in the Genus *Spodoptera*: Key Insect Detoxification Genes. *Insects* 2021, **12**, 544. https://doi.org/10.3390/insects12060544

Academic Editor: Béla Darvas

Received: 18 May 2021

Accepted: 8 June 2021

Published: 11 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_84.png" /></figure>

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

**Simple Summary:** The moth larvae are among the most damaging pest species on crops worldwide. In this review, we focus on the genus *Spodoptera*, which can feed on many crops such as rice, cotton or corn. The massive use of insecticides to control these insects has led to the development of resistance. Here, we aim to compare the resistance mechanisms of four species (*Spodoptera exigua*, *Spodoptera frugiperda*, *Spodoptera littoralis* and *Spodoptera litura*) and highlight the role of enzymes and transporters in resistance to help us understand the molecular basis of their origin.

**Abstract:** The genus *Spodoptera* (Lepidoptera: Noctuidae) includes species that are among the most important crop pests in the world. These polyphagous species are able to feed on many plants, including corn, rice and cotton. In addition to their ability to adapt to toxic compounds produced by plants, they have developed resistance to the chemical insecticides used for their control. One of the main mechanisms developed by insects to become resistant involves detoxification enzymes. In this review, we illustrate some examples of the role of major families of detoxification enzymes such as cytochromes P450, carboxyl/cholinesterases, glutathione S-transferases (GST) and transporters such as ATP-binding cassette (ABC) transporters in insecticide resistance. We compare available data for four species, *Spodoptera exigua*, *S. frugiperda*, *S. littoralis* and *S. litura*. Molecular mechanisms underlying the involvement of these genes in resistance will be described, including the duplication of the CYP9A cluster, over-expression of GST epsilon or point mutations in acetylcholinesterase and ABCC2. This review is not intended to be exhaustive but to highlight the key roles of certain genes.

**Keywords:** resistance; *Spodoptera*; cytochromes P450; carboxyl/cholinesterases; glutathione S-transferases; ATP-binding cassette transporters

## 1. Introduction

The genus *Spodoptera* (Lepidoptera: Noctuidae) contains some of the most important insect crop pests, many of which are highly polyphagous species, able to feed on more than 100 host plants including maize, rice, cotton (e.g., *Spodoptera litura* (Fabricius) [1]). They are present on all continents and their potential invasiveness has been highlighted in recent years, notably with the species *S. frugiperda* (J.E. Smith). Native to the American continent, it was detected in Africa in 2016 [2] and has since invaded Asia and Australia (CABI, Wallingford, UK, 2021). With its flight capabilities [3,4] and under favorable climatic conditions, its invasion of Europe in the near future seems inevitable.

The control of these pests requires the massive use of insecticides. They have developed resistance to all chemical families and three of the four *Spodoptera* species present in the Arthropod Pesticide Resistance Database are in the top 15 most resistant arthropods on the planet: *S. litura*, *S. frugiperda* and *S. exigua* (Hübner) [5].

Table 1 shows the available data on the molecules for which these three species, as well as *S. littoralis* (Boisduval) have developed resistance. These insects have developed resis-

<footnote n="1">¹ Université Côte D'Azur, INRAE, CNRS, ISA, F-06903 Sophia Antipolis, France; frederique.hilliou@inrae.fr</footnote>

<footnote n="2">² Institut D'Ecologie et des Sciences de L'Environnement de Paris, Sorbonne Université, CNRS, INRAE, IRD, iEES-Paris, F-75005 Paris, France; thomas.chertemps@sorbonne-universite.fr (T.C.); martine.maibèche@sorbonne-universite.fr (M.M.)</footnote>

<footnote>* Correspondence: gaelle.le-goff@inrae.fr; Tel.: +33-492-386-578</footnote>
tance to all the chemical families: organophosphates, carbamates, pyrethroids but also for a more recent family such as diamides. There are usually two main mechanisms in insecticide resistance, either target modification or mechanisms that reduce the amount of insecticide reaching the target (reduced penetration, sequestration or intervention of detoxification enzymes). Detoxification involves enzymes that catalyze successive reactions to make the insecticidal molecule less toxic and more easily excreted from the body. Cytochromes P450 (P450) and carboxyl/cholinesterases (CCE) are phase I enzymes that catalyze oxidation, hydrolysis and reduction. Glutathione S-transferases (GST) are phase II enzymes and catalyze the addition of a group such as glutathione. This step is called conjugation and is followed by phase III excretion involving ATP-binding cassette (ABC) transporters. These proteins use the energy of ATP hydrolysis to transport substrates across lipid membranes. The resistance caused by the enzymes involved in these three phases implies either a modification of their level of expression or their catalytic activity. The molecular process behind this can be of several kinds. A point mutation in the sequence of a gene can modify the catalytic activity of the enzyme. For example, in the mosquito *Anopheles funestus*, the increased activity of GSTe2 is due to a point mutation (L119F) that leads to an increase in the accessibility of the active site, allowing high resistance to DDT [6]. Overexpression is one of the other commonly demonstrated mechanisms, involving duplications, amplifications or cis-or trans-regulations [7]. A well-known example of gene duplication is the case of *CYP6G1* in *Drosophila melanogaster*. The overexpression of this gene in multi-insecticide resistant populations is due to duplication as well as insertion of transposable elements in its promoter [8]. In the aphid *Myzus persicae*, it is a gene amplification of one or two F4 and FE4 esterases that is at the origin of the resistance to organophosphates, carbamates and pyrethroids [9]. Up to 80 copies of the same gene are found in some resistant aphids. The esterase in this case represented up to 3% of the total proteins [10]. A recent example illustrates cis and trans regulation in a chlorpyrifos-resistant strain of *S. exigua* [11]. The authors showed that the resistance was mainly due to the overexpression of a P450, CYP321A8, a P450 capable of metabolizing chlorpyrifos, cypermethrin and deltamethrin. This overexpression is due to both constitutive overexpression of the transcription factors Cap'n'collar isoform C (CncC) and Maf (Muscle aponeurosis fibromatosis, trans-regulation), major regulators of detoxification in insects [12], and to a mutation in the promoter of this P450. The mutation creates a cis-regulatory element that promotes binding of a Knirps nuclear receptor.

This review highlights some examples that demonstrate the involvement of each of the four families of detoxification genes (P450, CCE, GST and ABC) in insecticide resistance and the molecular mechanisms identified for pests of the genus *Spodoptera*. We have chosen to focus on specific genes that we consider emblematic rather than an exhaustive review of their roles.

<table><caption>**Table 1.** Assessment of insecticide resistance worldwide for the four species of *Spodoptera*, data extracted from the Arthropod Pesticide Resistance Database (http://www.pesticideresistance.org, accessed on 18 May 2021).</caption><thead><tr><th>Insecticide<br>Chemical Class</th><th>S. exigua</th><th>S. frugiperda</th><th>S. littoralis</th><th>S. litura</th></tr></thead><tbody><tr><td>Avermectins</td><td>Abamectin (#31), emamectin<br>benzoate (#48)</td><td></td><td></td><td>Abamectin (#43),<br>emamectin benzoate (#27)</td></tr><tr><td>Benzoylurea</td><td>Chlorfluazuron (#9),<br>diflubenzuron, lufenuron (#10),<br>teflubenzuron</td><td>Lufenuron (#2),<br>triflumuron</td><td>Diflubenzuron,<br>teflubenzuron (#3)</td><td>Lufenuron (#8)</td></tr><tr><td>Bacillus thuringensis</td><td>Bt var. unspecified (#10),<br>var aizawai, var. kurstaki</td><td></td><td>Bt var unspecified (#3),<br>var aizawai</td><td></td></tr><tr><td>Bt toxins</td><td>Cry1Ca</td><td>Cry1Aa, Cry1A.105,<br>Cry1Ab (#2), Cry1Ac (#3),<br>Cry1F (#54), Cry2Ab2 (#2),<br>Vip3A (#3)</td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 1. Cont.**</caption><thead><tr><th>Insecticide<br>Chemical Class</th><th>S. exigua</th><th>S. frugiperda</th><th>S. littoralis</th><th>S. litura</th></tr></thead><tbody><tr><td>Carbamates</td><td>Methomyl (#18),<br>thiodicarb</td><td>Carbaryl (#7),<br>methomyl (#6),<br>thiodicarb (#2)</td><td>Carbaryl (#2),<br>methomyl (#2),<br>thiodicarb (#33)</td><td>Carbaryl (#2),<br>methomyl (#38),<br>thiodicarb (#33)</td></tr><tr><td>Cyclodienes</td><td>BHC/cyclodiene—<br>unspecified in literature<br>(#3), endosulfan (#19)</td><td>Aldrin, dieldrin,<br>lindane (#2)</td><td>Endrin, toxaphene (#2)</td><td>Endosulfan (#31), lindane</td></tr><tr><td>Diamides</td><td>Chlorantraniliprole (#26),<br>cyantraniliprole,<br>flubendiamide (#5)</td><td>Chlorantraniliprole (#2),<br>flubendiamide (#2)</td><td></td><td>Chlorantraniliprole (#11)</td></tr><tr><td>Diacylhydrazines</td><td>Methoxyfenozide (#25),<br>tebufenozide (#19)</td><td></td><td>Tebufenozide</td><td>Methoxyfenozide (#36),<br>tebufenozide</td></tr><tr><td>Neonicotinoids</td><td></td><td></td><td></td><td>Acetamiprid</td></tr><tr><td>Organochlorine</td><td>DDT (#4)</td><td>DDT (#3)</td><td>DDT</td><td>DDT (#2)</td></tr><tr><td>Organophosphates</td><td>Chlorpyrifos (#48),<br>parathion-methyl (#3),<br>profenofos (#22),<br>quinalphos (#9)</td><td>Acephate,<br>chlorpurifos (#7),<br>diazinon (#2), dichlorvos,<br>malathion (#2),<br>parathion-methyl (#4),<br>sulprofos, trichlorfon,</td><td>Acephate,<br>azinphos-methyl,<br>chlorpyrifos (#4),<br>fenitrothion, leptophos,<br>methamidophos,<br>methidathion,<br>monocrotophos (#4),<br>parathion (#2),<br>parathion-methyl (#3),<br>profenofos, sulprofos,<br>triazophos, trichlorfon</td><td>Chlorfenvinphos,<br>chlorpyrifos (#55),<br>diazinon, dichlorvos,<br>malathion,<br>monocrotophos (#5),<br>phoxim (#4),<br>profenofos (#56),<br>quinalphos (#9),<br>triazophos (#2), trichlorfon</td></tr><tr><td>Oxadiazines</td><td>Indoxacarb (#44),<br>metaflumizone (#4)</td><td></td><td>Indoxacarb</td><td>Indoxacarb (#50),<br>metaflumizone</td></tr><tr><td>Phenylpyrazoles</td><td></td><td></td><td></td><td>Fipronil (#15)</td></tr><tr><td>Pyrethroids</td><td>Bifenthrine (#13),<br>cyfluthrin (#2),<br>cyhalothrin-lambda (#2),<br>cypermethrin (#56),<br>cypermethrin-beta,<br>deltamethrin (#43),<br>fenpropathrin (#14),<br>fenvalerate (#5),<br>permethrin (#2),<br>pyrethroids-unspecified in<br>literature (#3)</td><td>Bifenthrine, cyfluthrin,<br>cyhalothrin,<br>cyhalothrin-lambda (#11),<br>cypermethrin (#2),<br>cypermethrin-zeta,<br>deltamethrin (#2),<br>fenvalerate, fluvalinate,<br>permethrin (#5),<br>tau-fluvalinate,<br>tralomethrin</td><td>Cypermethrin (#4),<br>deltamethrin (#2),<br>fenvalerate, flucythrinate,<br>permethrin</td><td>Bifenthrine (#33),<br>cyfluthrin (#12),<br>cyfluthrin-beta (#9),<br>cyhalothrin (#3),<br>cyhalothrin-lambda (#11),<br>cypermethrin (#49),<br>cypermethrin-beta (#15),<br>deltamethrin (#50),<br>esfenvalerate (#9),<br>fenpropathrin (#10),<br>fenvalerate (#7),<br>pyrethrins</td></tr><tr><td>Pyrroles</td><td>Chlorfenapyr (#11)</td><td></td><td></td><td>Chlorfenapyr (#5)</td></tr><tr><td>Spinosyns</td><td>Spinetoram,<br>spinosad (#56)</td><td></td><td>Spinetoram (#2),<br>spinosad (#2)</td><td>Spinosad (#39)</td></tr></tbody></table>

# number of cases reported in Arthropod Pesticide Resistance database.

## 2. Phase I: Functionalization

Once a xenobiotic enters the cell, it is processed by enzymes that make it functional. The phase I enzymes carry out oxidation reactions, hydrolysis, etc., making the metabolite more polar and more easily excreted.

### 2.1. Cytochrome P450s

Cytochrome P450 genes (CYP) constitute one of the largest gene families with representatives in nearly all living organisms [13]. CYPs catalyze a large number of reactions including hydroxylation, epoxidation, oxidation [14]. In insects, CYPs are involved in endogenous metabolism such as the biosynthesis of ecdysteroid [15] or the production of hydrocarbon [16], as well as in detoxification mechanisms with the metabolization of xenobiotics (e.g., plant secondary metabolites and insecticides). Their number varies from 36 in the louse *Pediculus humanus* [17] to 196 in the mosquito, *Culex quinquefasciatus* [18]. Nomenclature has been proposed based on sequence homology [19,20]. P450s belong to the same family designated by an Arabic numeral when they share 40% identity and to the same subfamily designated by capital letters when this percentage is higher than
55%. The nomenclature was revised and the notion of clan was proposed as a higher level of classification to take into account the increasing number of available sequences [21]. Initially, the arthropod P450s were divided into four clans, but a recent study brings this number to 6 with the historical clans, clan 2, clan 3, clan 4, mitochondrial clan to which clans 16 and 20 were added [22]. Clans 3 and 4 are the two main clans known to have P450s involved in resistance mechanisms, but not exclusively, for example, *CYP12D1* from *D. melanogaster* which belongs to the mitochondrial clan is over-expressed in resistant strains [23] and has been shown to confer resistance to DDT and dicyclanil [24]. We will highlight the role of some of P450s in insecticide resistance and evolution of the *CYP9A* subfamily within the four *Spodoptera* species.

### 2.1.1. Phylogeny of CYP9A

CYP9A belongs to clan 3. In Lepidoptera, CYP9As were identified as a cluster in a comparative study of BAC sequences from three species *Bombyx mori*, *Helicoverpa armigera* and *S. frugiperda* [25] with the presence of four, five and nine genes respectively, showing the rapid evolution of the cluster. In the two more closely related species (*H. armigera* and *S. frugiperda* which diverged about ±20 MYA), only three orthologous pairs were identified [26]. The availability of the genome of the most closely related species in the genus *Spodoptera* enabled the evolution of this group to be assessed. In *S. frugiperda*, obtaining the chromosomally resolved genome of the maize strain helped identify 14 CYP9A genes (genome Version 6 [27]), 12 of which were in a cluster (Figure 1), i.e., three more than the nine genes previously identified in the BAC study, whereas 15 genes were identified in the rice strain (genome version 3 [28]). These numbers are very similar to those found in other species, 15 genes in *S. litura* (Figure 1) [1], 12 genes in *S. exigua* and 11 genes in *S. littoralis* (genome in progress, transcriptome available). In the latter, the number is potentially underestimated and improved genome resolution could lead to increases.

<figure><img src="image_85.png" /><figcaption>**Figure 1.** CYP9A cluster in the genome of *S. litura* (A), *S. frugiperda* corn strain (B) and *S. frugiperda* rice strain (C). Numbers in blue correspond to CYP9A genes. Arrows correspond to gene orientation, ADH: alcohol dehydrogenase, FT: fucosyl transferase, EST TF: E26 transformation specific transcription factor, * pseudogene.</figcaption></figure>

Among these genes, eight were true orthologs, CYP9A25, CYP9A26, CYP9A27, CYP9A28, CYP9A30, CYP9A31, CYP9A58 and CYP9A59 in *S. frugiperda*, *S. litura* and *S. littoralis*, prob-
ably already clustered in their common ancestor (Figure 2). CYP9A24 is only found in *S. frugiperda*. Some genes of the cluster are *S. frugiperda* corn strain specific (CYP9A32, CYP9A60, and CYP9A76), *S. frugiperda* rice strain specific (CYP9A91) or *S. litura* specific (CYP9A39-157-158-161-162). Orthologs of CYP9A26, CYP9A59 and CYP9A27 were also found in the genome of *S. exigua*.

<figure><img src="image_86.png" /></figure>

**Figure 2.** CYP9A subtree: ML phylogeny with bootstrap value. SfC: *S. frugiperda* corn strain, SfR: *S. frugiperda* rice strain, Sl: *S. litura*, Slit: *S. littoralis*, Bm: *B. mori*. Slit9A58, Slit9A81, and Slit9A40-like are missing from the tree. Black square: ancient speciation representing *B. mori* split from *Spodoptera* species. Black dot: duplication events.
The synteny of the CYP9 cluster is conserved between *S. frugiperda* (corn and rice variants) and *S. litura* (Figure 1) where the same genes, alcohol dehydrogenase (ADH), fucosyl transferase (FT) and E26 transformation specific (ETS, a transcription factor) were found on each side of the cluster. In *S. litura*, two gustatory receptors from the GR29 family are part of the CYP9 cluster. They form a head to tail tandem between CYP9A58 and CYP9A25 and they replace CYP9A24. The GR29 family is specific to *Spodoptera* and based on the evolutionary history of *S. litura* and *S. littoralis* [29], we expect GR genes to be present in the CYP9 cluster of *S. littoralis*. The mechanisms resulting in blooming in the CYP9A family in the *Spodoptera* complex still remain to be deciphered. Several origins are possible, one of which corresponds to transposable elements as they have been shown to be prevalent in the surrounding of CYP genes in Lepidoptera [25,30,31], *Drosophila* [32] or mosquito [33]. Other mechanisms include duplications (tandem, chromosome or genome duplications) and retropositions [26]. This CYP9A bloom likely provides *Spodoptera* species with a selective advantage and potentially diverse catalytic capabilities for each of these enzymes. Indeed, induction experiments show that within the CYP9A cluster, genes have their own induction profile depending on the xenobiotics used. For example, CYP9A31 is the most induced gene in response to xanthotoxin treatment in both *S. frugiperda* and *S. litura* whereas CYP9A28 is not induced by this molecule but by indole [34,35]. This suggests that within the cluster, each gene has its own catalytic competence unrelated to its position in the cluster or phylogeny as it has been suggested for the CYP6AE cluster in *H. armigera* [36]. Dermauw et al., (2020) suggested that there would be a selective advantage to keeping the cluster as a heritable unit, which would enable adaptation to new environments [22].

2.1.2. Resistance through Over-Expression of CYP9A

We next examine CYP9As in the light of insecticide resistance: the first CYP9A was
found in *Heliothis virescens* and named CYP9A1 [37]. It was in a thiodicarb-resistant popu-
lation of *H. virescens*. An elevated level of CYP9A1 mRNA was detected in the resistant
strain compared to the susceptible strain. Several subsequent studies have associated
over-expression of certain CYP9As with insecticide resistance, but very few studies have
gone so far as to demonstrate their involvement in insecticide metabolism. An example
came from *H. armigera*, where pyrethroid resistance was associated with constitutive over-
expression of P450s in the laboratory-selected YGF strain [38]. In this strain, CYP9A12
and CYP9A14 were over-expressed 433-and 59-fold, respectively, in the fat body. The
functional expression of these two P450s in *Saccharomyces cerevisiae* demonstrated their ca-
pacity to metabolize a pyrethroid insecticide, esfenvalerate [39]. Elevated levels of CYP9As
associated with resistance have also been shown in *Spodoptera* spp. In a recent study,
a Brazilian population of *S. frugiperda* resistant to *Bacillus thuringiensis* (Bt) Cry1 toxins
was tested against 15 insecticide molecules with different modes of action. In addition
to resistance to Bt toxins, the Sf-Des strain had showed 14- and eight-fold resistance to
deltamethrin and chlorpyrifos, respectively [40]. P450 activity was increased in the Sf-Des
strain compared to the susceptible Bt strain. RNAseq experiments confirmed the over-
expression of several P450s, including CYP9As and RT-qPCR analysis validated that a
CYP9A-like was expressed more than 200-fold in the resistant strain. In another study on
a laboratory-selected population of *S. frugiperda* with lufenuron, a CYP9A-like gene was
over-expressed 45-fold [41]. In both of these studies, the cause of the over-expression of the
CYP9As was not identified, whereas Gimenez et al., (2020) reported the existence of two
copies of the full CYP9A cluster of in a Puerto Rican resistant strain (PR) of *S. frugiperda*.
PR was resistant to deltamethrin and the use of a P450 synergist, piperonil butoxide (PBO)
abolished the resistance, confirming the primary role of P450. The CYP9A cluster locus
is under positive selection in this strain [27]. Several field-collected strains of *S. exigua*
resistant to organophosphates, pyrethroids and diamides also exhibited an over-expression
of CYP9A. For a chlorpyrifos-resistant strain, the resistance was mainly associated with
CYP321A8, which is over-expressed and capable of metabolising insecticides, however
monitoring the expression of 68 P450s by RT-qPCR showed significant over-expression of
several *CYP9As* including *CYP9A11*, *CYP9A27*, *CYP9A97* and *CYP9A98* [11]. Are these
genes also involved in resistance? This remains to be demonstrated. However, there is
some evidence of their involvement in resistance, such as the RNAi knockout (KO) of
*CYP9A98*, which makes *S. exigua* larvae more sensitive to deltamethrin [42], the knock-
out of *CYP9A10*, which makes them more sensitive to alpha-cypermethrin [43], and the
knockout of *CYP9A105*, which increases mortality to deltamethrin, alpha-cypermethrin,
and fenvalerate [44]. Similar results were also obtained in the species *S. litura*. Indeed,
strains resistant to fenvalerate, beta-cypermethrin and cyhalothrin had their resistance
reduced by the use of PBO [45]. RNAseq experiments for these field-collected Chinese
strains (LF and NJ) showed over-expression of several P450s including *CYP9As*. *CYP9A40*
was over-expressed 663.4- and 76.13-fold in LF and NJ, respectively, compared with the sus-
ceptible strain. Previous experiments showed that dsRNA silencing of *CYP9A40* increased
the sensitivity of *S. litura* to deltamethrin [46]. To our knowledge, no data suggesting a link
between insecticide resistance and *CYP9A* of *S. littoralis* are available at this time. There is
sufficient evidence across closely related species to suggest a major role for *CYP9As* at least
in pyrethroid resistance; however, the ability of these enzymes to metabolise insecticides
remains to be demonstrated. The cause of the over-expression of these genes also remains
to be identified. In one case, it is due to the duplication of the *CYP9A* cluster [27] while in
the study of Hu et al., (2021) it can be assumed that the over-expression of the transcription
factor CncC, already shown in several insects to be the major regulator of detoxification
genes, leads to the over-expression of *CYP9A*.

## 2.2. *Carboxyl/Cholinesterases* (CCEs)

Insect carboxylesterases or carboxyl/cholinesterases (CCEs) have a wide range of physiologial functions across living organisms, from metabolism of endogenous compounds (hormones, pheromones, neurotransmitters) to detoxification of various xenobiotics. They play a critical role in defense against various allelochemicals associated with plants and insecticides [47]. In particular, CCEs have been implicated in resistance to pyrethroids (PYRs), organophosphates and carbamates (CBs) in numerous pest species [48,49]. The mechanisms of esterase-mediated resistance involve either metabolic resistance or target site mutation [49]. Metabolic resistance can be based either on insecticide sequestration or insecticide hydrolysis: overproduction of CCEs through gene amplification or transcriptional up-regulation can lead to insecticide molecule sequestration without any (or very slow) hydrolysis of the insecticide, whereas point mutations can alter the catalytic properties of some CCEs, leading to increased hydrolysis towards insecticides. Target site resistance is due to mutation of acetylcholinesterases (AChEs) that renders them less sensitive to inhibition by insecticides.

Insect CCEs fall into three main functional groups, representing dietary/detoxification, hormone/semiochemical processing, and neuro/developmental functions [50]. A comprehensive phylogenetic analysis of CCE sequences isolated from *H. armigera*, *H. zea*, *B. mori* and *Manduca sexta* genomes has recently refined the CCE nomenclature in Lepidoptera [51]. The neuro/developmental class comprises seven clades (027 to 033) with generally catalytically incompetent proteins, excepted for AChEs (clade 027). The hormone/semiochemical processing class (clades 020 to 026) includes among others, juvenile hormone esterases, several pheromone-processing esterases, but also some CCEs (clade 026) previously associated with organophosphate (OP) resistance in Hemiptera (namely E4, FE4, [10]). Finally, the dietary/detoxification group (clades 001 to 019 and 034) is the most diversified: some enzymes have been implicated in semiochemical processing but most of them have been associated with dietary and detoxification functions, including insecticide resistance for some dipteran and hymenopteran CCEs (the α-esterases, reviewed in [47]). CCEs from this third group are the most abundant in Lepidoptera and in particular the three most numerous clades (001, 006 and 016) with species-specific radiations. Many clades in this third group are also Lepidoptera specific [51]. The total number of CCE genes identified
so far in *S. litura* and *S. frugiperda* genomes were 110 and 96, respectively [1,28]. For *S. exigua*, we were able to retrieve 73 sequences from public database (GenBank:GCA 011316535.1). For *S. littoralis*, we have previously identified 30 CCE transcripts expressed in antennae [52–54]. This repertoire was supplemented with 26 new sequences by Walker et al., (2019) [55]. Searching in our RNAseq databases allowed us to identify an additional 19 CCE transcripts (Table S1), leading to a total of 65 SlitCCE sequences, which is likely underestimated compared to its sister species *S. litura*.

Although comprehensive complete repertoires of CCEs are now available in many lepidopteran species, including spodopterans, still very few CCE genes have been directly linked with insecticide resistance. Here, we will focus on some spodopteran CCEs for which a direct role in resistance is supported either by in vitro and/or in vivo approaches, i.e., enzymes both identified at the molecular level and directly involved in insecticide hydrolysis or insecticide susceptibility.

### 2.2.1. Resistance through Over-Expression of CCE

As reviewed previously in Farnsworth et al., (2010), there is a strong correlation between insecticide resistance and higher esterase activities in the four *Spodoptera* species studied here [56]. Esterase activities toward artificial substrates in vitro (generally α-or β-naphthyl acetate) were compared between homogenates of susceptible and resistant strains, which allowed for the detection of both higher overall CCE activities or higher staining intensities of certain isozymes after analysis by native PAGE electrophoresis. For example, higher staining intensities of several esterases isozymes have been associated with OP and PYR resistance in *S. littoralis* [57], *S. litura* [58], *S. exigua* [59] and *S. frugiperda* [60]. The underlying biochemical mechanisms are not yet known, but it is hypothesized that increased sequestration of insecticides by CCEs and catabolism apply [56].

In lepidopterans, over-expression of CCE associated with insecticide resistance has been intensively studied in *H. armigera* [61–63], especially through proteomic and real-time polymerase chain reaction (RT-PCR) approaches. Most of the over-expressed CCEs identified at the molecular level belonged to the 001 clade, which is very diverse in this species, with 21 CCE001 genes. This clade also shows a large expansion in spodopterans, with 23 and 19 CCE001 genes annotated in *S. litura* [1] and *S. frugiperda* [28], respectively. We counted 14 CCE001 sequences in *S. littoralis* ([52–55] Table S1), a number which is probably again underestimated in the absence of genome annotation. In *S. litura*, some of these CCE001 genes were inducible by imidacloprid [1]. Most interestingly, knock-down of two SlituCEE001s (SlituCOE57 and SlituCOE58) by siRNA injection increased the sensitivity of *S. litura* larvae to imidacloprid when fed with an insecticide-containing diet [1]. This is the first demonstration of a direct link between CCE induction and insecticide sensitivity at the molecular level in a lepidopteran species. After BLAST (Basic Local Alignment Search Tool) searches, we found orthologous sequences of SlituCOE57 and SlituCOE58 in *S. littoralis* (SlitCXE50 and SlitCXE48, respectively, Table S1) and *S. frugiperda* (SfruCCE001n and SfruCCE001f, respectively, [55]), suggesting that they might play a similar role in insecticide resistance. In *S. litura*, all of these CCE001s were grouped into a large cluster on chromosome 2 [1], as previously observed for the CCE001 of *H. armigera* [51].

### 2.2.2. Metabolic Resistance through Point Mutations of CCEs

Structural mutations in the active site of a carboxylesterase could result in a reduction in the ability of this enzyme to hydrolyze common carboxylesterase substrates (such as naphthyl acetate esters) but convert it to an OP hydrolase. This corresponds to the mutant ali-esterase mechanism [64]. Metabolic resistance by point mutation in CCE sequences has been observed for quite some time in several non-lepidopteran species, mostly in Diptera [49,65,66]. Two point mutations, G137D and W251L (or G151D and W271L as reported in Cui et al., (2011) following the sequence of *D. melanogaster* AChE [67]), were indeed found in field-resistant populations of several species, including *Lucilia cuprina* and *Musca domestica*, and involved in altering the substrate specificity of CCE, leading
to increased activity towards OPs [65–67]. In addition to dipterans, another amino acid
change at position 251 (W251G) was also found in an OP-resistant strain of the parasitoid
wasp *Anisopteromalus calandrae* [68].

To the best of our knowledge, no point mutation-based resistance in CCE has been
resolved at the molecular level in resistant Lepidopteran strains. However, an in vitro
study conducted on CCEs from four insect orders including Lepidoptera, tested whether
the change of substrate specificity associated with these two mutations could be a more
general feature in OP-resistant insects [67]. The catalytic properties of seven CCEs mostly
belonging to the dietary/detoxification group were analyzed, including one CCE from S.
litura (GenBankEU783914). Recombinant mutant proteins were tested in vitro towards two
OPs (paraoxon and chlorfenvinphos) and β-naphthyl acetate. For the seven enzymes tested,
the G151D and W271L mutations conferred OP activity in 62.5% and 87.5% cases, respec-
tively [67]. The same in vitro approach was developed more recently on eight *H. armigera*
CCEs but with more contrasting results [62], with increased insecticide hydrolysis being
observed only for some enzymes. However, for one esterase (HarmCCE001c), PYRs (fen-
valerate and cypermethrine), hydrolysis was enhanced 14-fold after the leucine mutation.
The *S. litura* CCE tested by [67] matched the SlituCOE082 genomic sequence identified
later [1]. We found sequences orthologous to SlituCOE082 in *S. frugiperda* and *S. littoralis*,
but not in *S. exigua*. SlituCOE82 presented 95.7% of amino acid identity with SlitCXE64
(Table S1) and 91.4% with SfruCCE16j [55], with residues conserved at the putative muta-
tion positions (Figure 3).

<figure><img src="image_87.png" /></figure>

**Figure 3.** Alignment of two 50 amino acid sequence portions of SlituCOE082, SlitCXE64, SfruCCE16j and MdaE7 from *Musca domestica* (Rutgers diazinon-R resistant strain). The putative mutation positions G151D and W271L are shaded in grey. According to [67], the amino acid corresponding to G137D and W251L were G107 and W219 in *S. litura* sequence; for *S. litura* carboxyl/cholinesterase (CCE), the mutation tested in vitro was G151D. Genbank accession number, *S. frugiperda* (XP_035450257.1), *S. litura* (XP_022828113.1), *M. domestica* (AAD29685.1).

SlituCOE082 belongs to the clade 016 which is also expanded in Lepidoptera [51]. It includes 16 genes in *S. litura*, 14 in *S. frugiperda*, eight in *S. exigua* and 16 in *S. littoralis* (including nine additional sequences from RNAseq data). Phylogenetic analysis of these 54 sequences (Figure 4) illustrates their diversification within the four Spodoptera species, and in particular the 1:1 orthologous relationship is clear for seven sequences from each species. In *S. litura*, the CCEs from in clade 016 showed a major expansion on chromosome 25, with a cluster of 12 adjacent sequences [1]. Six genes in this cluster showed induction by neonicotinoid insecticide, i.e., imidacloprid in *S. litura* larvae. In particular, SlituCOE82 is moderately induced by imidacloprid in Malpighian tubules [1].
<figure><img src="image_88.png" /><figcaption>**Figure 4.** Maximum-likelihood phylogeny of spodopteran CCEs from Clade (016) and acetylcholinesterases (AChEs). The tree was built from amino-acid sequences of CCE repertoires of *S. littoralis* (branches colored in red), *S. frugiperda* Corn strains (green), *S. litura* (blue), *S. exigua* (purple). Sequences from [1,28] for *S. litura* and *S. frugiperda*, respectively. For *S. littoralis*, from [52,55] and new sequence from our RNAseq database (Table S1). For *S. exigua*, GenBank numbers were inserted the sequence name, SexiAChE1 (AZB49078.1) and SexiAChE2 (AZB49079.1). Likelihood-ratio test values are only indicated in the internal nodes defining orthogroups, when aLRT > 0.9. The scale bar represents 0.2 expected amino-acid substitutions per site.</figcaption></figure>

To date, the presence of the G151D and/or W271L mutations in field-resistant spodopteran strains has not been found. However, although metabolic resistance by overproduction of CCE could induce broader resistance than qualitative mutation, the two studies discussed below suggest that point mutations could be a more common mechanism for insecticide resistance than expected. A systematic comparison of the corresponding CCE gene sequences in susceptible and resistant lepidopteran pest strains would be necessary to assess their precise role in resistance.

### 2.2.3. Target Site Resistance through Point Mutations; the Case of Acetylcholinesterases

Acetylcholinesterase (AChE or ace) terminates nerve impulses by catalyzing the hydrolysis of acetylcholine in cholinergic synapses. Irreversible inhibition of AChE by CBs and OPs thus causes acetylcholine to accumulate in synapses and acetylcholine receptors to open permanently, resulting in insect death. Most insects have two *AChE* genes but only the gene expressed in the central nervous system (namely ace-1 or *AChE1*) is essential for synapse functioning [69]. All four *Spodoptera* species possess two *AChE* genes as expected
(Figure 4) with a clear orthologous relationship and highly conserved sequences (more than 98% of amino acid identity). Target site resistance mediated by *AChE1* insensitivity to insecticides has been identified by biochemical approaches in several insect species, and subsequently elucidated at the molecular level for some species [49,69]. Sequencing of *AChE* genes from field-resistant strains and comparison with susceptible strains described several point mutations conserved across species, most of which modify the active site of the enzyme. Three substitutions (A201S, G227A and F290V, numbering corresponding to the mature enzyme of *Torpedo californica*) were first reported in CB- and/or OP-resistant strains of aphids (*A. gossypii* [70]), dipterans (*Bactrocera dorsalis* [71]) and lepidopterans (*Chilo suppressalis* [72] and in *P. xylostella* [73]).

The involvement of acetylcholinesterases in OP/CB resistance has been demonstrated in biochemical approaches on *S. litura* resistant strains from Korea [74] or India [75,76]. Similarly, in *S. exigua*, the AChE enzyme of a carbamate resistant strain from California was found to be approximately 30-fold more insensitive to methomyl compared to the enzyme from the susceptible laboratory strain [77]. However, within Spodoptera species, molecular data on the AChE point mutation are mostly available for *S. frugiperda*. In this species, Yu et al., (2003) showed that acetylcholinesterase of a field strain collected from corn fields in Florida was clearly far less sensitive (up to 85-fold) than that of a susceptible strain to inhibition by CBs and OPs [60]. Kinetic data also showed that the apparent Km value of acetylcholinesterase from the field strain was 56% of that from the susceptible strain. By comparing the predicted amino acid sequences of a *S. frugiperda* ace-1 fragment from a chlorpyrifos susceptible strain with the corresponding sequence isolated from an 18.1-fold resistant strain from Brazil, Carvalho et al., (2013) were then able to identify the three previously described point mutations (Figure 5) [78].

<figure><img src="image_89.png" /><figcaption>**Figure 5.** Alignment of a 120 amino acid sequence portion of AChE1 *S. frugiperda* susceptible (Genbank accession number KC435023.1) and resistant strain (KC435024.1) with orthologous sequences from *S. litura* (XP_022819835.1 = SlituCOE002), *S. littoralis* (Table S1) and *S. exigua* (AZB49078.1). The mutation positions are shaded in grey.</figcaption></figure>

Genotyping revealed that of the three, the A201S allele was present at relatively low frequency (17.5%) while G227A and F290V were present at higher frequency (67.5% and 32.5%, respectively) [78]. More recently, Boaventura et al., (2020) searched for the presence of these mutations in 34 different resistant populations of *S. frugiperda* collected from four different continents [79] and showed that F290V was the most frequent substitution in all populations tested. Similar results were obtained by Guan et al., (2020) on *S. frugiperda* individuals collected from China and Africa, except that only two positions (A201S, F290V) were found, with the F290V allele at higher frequencies [80].

### 3. Phase II: Conjugation

Following oxido-reduction and hydrolysis reactions performed by phase I enzymes, xenobiotic metabolites (including insecticides) are then conjugated to small hydrophilic molecules by phase II enzymes, a group of transferases that metabolize hydrophobic compounds containing nucleophilic or electrophilic groups [81].
### 3.1. *Glutathione-S-Transferases* (GSTs)

Glutathione-S-transferases (GSTs, EC 2.5.1.18) are one of the most important classes of this group whose biotransformation reaction leads to the generation of hydrophilic metabolites, which are readily excreted by efflux transporters such as ABC transporters. In arthropods, GSTs are highly diverse and are involved in a variety of cellular functions, from the detoxification of a wide range of both endogenous and xenobiotic compounds, to intracellular transport, hormone biosynthesis and reduction of oxidative stress [82]. GSTs primarily catalyze the conjugation of electrophilic lipophilic compounds with the thiol group of reduced glutathione (GSH) but are also capable of catalyzing a dehydrochlorination reaction using reduced glutathione as a cofactor. The enzymatic structure of cytosolic GSTs classically consists of hetero- or homo-dimeric proteins, with each monomer consisting of a highly conserved amino-terminal domain providing the GSH-binding site (G-site) while the carboxyl terminal domain interacts with the hydrophobic substrate (H-site) [82].

In insects, cytosolic GSTs belong to a diverse gene family divided into six classes (Delta, Epsilon, Omega, Sigma, Theta, and Zeta) based on their substrate specificities and phylogenetic relationships [83]. The growing number of available genomes reveals a large disparity in the total number of GSTs among insect species, with diversity ranging from low in hymenopterans (eight GSTs in *Apis mellifera*) to high in dipterans (39 in *Culex quinquefasciatus*) [84]. This variability is mainly due to genetic expansions observed in the Insecta-specific Delta and Epsilon classes due to multiple duplication events [85].

Insect GSTs have been intensively studied for their role in insecticide resistance: reports correlating high levels of GST activity with high resistance to various pesticides, including organophosphates, organochlorines, cyclodienes, and pyrethroids, exist for many species and among them *S. littoralis* and *S. frugiperda* [86]. This phenomenon relies on the precise regulation or induction of GST expression by xenobiotic compounds, and in particular the Epsilon class (GSTE) [87,88]. Here, we will focus on spodopteran GSTes that have been shown to contribute to insecticide response and resistance.

#### 3.1.1. Phylogeny of GST Epsilon

GST-mediated resistance could be triggered by different mechanisms, including gene amplification by multiple duplication events could lead to enhanced detoxification of insecticides underlying the resistance process. Based on the recent availability of chromosome-level assembly genomes from spodopterans, we re-annotated GST epsilon class. For convenience, we reconciled the previous nomenclature with our new annotation to generate a unified dataset (see Supplementary Materials Table S2) [1,28]. Overall, GSTe accounts for nearly half of the total number of GSTs identified in the Spodopteran genomes (20 over 40 on average). Our phylogenetic analysis (Figure 6) revealed clear orthologous relationships among the four species studied with 1:1 orthologs in almost all cases, and with epsilon GSTs of *H. armigera*, suggesting that this great diversification is occurring in the Noctuoidea clade, and especially in highly polyphagous species.

Such diversity could be explained by tandem and segmental gene duplications as demonstrated by the phylogenetic branching that corresponds to the genomic clustering of GSTe (Figure 6). Furthermore, synteny studies support this diversification, with half of the GSTe derived from only two duplication events, arguing that these clades share a common GST ancestor (in an ancient Lepidoptera ancestor) even though their genetic expansions occurred independently (Figure 7, [1]).
<figure><img src="image_90.png" /><figcaption>**Figure 6.** Maximum-likelihood phylogeny of lepidopteran epsilon glutathione-S-transferases (GSTes). The tree was built from amino-acid sequences of GST repertoires of *S. littoralis* (branches colored in red), *S. frugiperda* Corn and Rice strains (green), *S. litura* (blue), *S. exigua* (purple), *H. armigera*, *P. xylostella* and *B. mori* (black). Likelihood-ratio test values are only indicated in the internal nodes defining orthogroups, when aLRT > 0.9. Black stars indicate that GSTes are overexpressed in insecticide-resistant strains or when exposed to pesticides. Red dots indicate GSTes with demonstrated in vitro/in vivo insecticide resistance. The scale bar represents 0.2 expected amino-acid substitutions per site.</figcaption></figure>
<figure><img src="image_91.png" /><figcaption>**Figure 7.** GSTe clusters derived from *B. mori* GSTe2 and GSTe3 identified in the genomes of *S. litura*, *S. frugiperda* corn and rice strains and *S. exigua* (adapted from [1]).</figcaption></figure>

This extensive genetic amplification does not directly support a demonstration of insecticide resistance per se; however, this highly diverse class is expected to have ancient roles unique to polyphagous lepidopterans, such as the removal of harmful chemical compounds from natural sources. According to the pre-adaptation hypothesis, phytophagous insect species with more diverse diets are likely to acquire resistance to more diverse insecticides [89], and thus genetic diversity may underlie the origin and evolution of insecticide resistance.

### 3.1.2. GST Activity against Insecticides

The biochemical mechanisms of GST-based insecticide resistance are classically associated with the conjugation of GSH to the pre-inactivated phase I compound. Such conjugation resistance has been demonstrated for different classes of organophosphate or pyrethroid insecticides [48,82]. Moreover, dehydrochlorination reactions using GSH as a cofactor have been implicated in resistance to the organochlorine DDT [6] making GSTs potential phase I enzymes for hydrochloric compounds. In addition to these direct modes of action, GST has also been shown to reduce lipid hydroperoxides produced as a result of insecticide-induced damages [90]. This peroxidase activity of GSTs protects tissues from an excess of reactive oxygen species (ROS), which can be more harmful than the pesticide itself [91]. Finally, GSTs are also capable of simple binding and sequestration activity towards various compounds, such as pyrethroids [86].

In spodopterans, most in vitro insecticide studies rely on the use of competitive 1-chloro-2,4-dinitrobenzene (CDNB) assays, a method that compares the amount of CDNB, the universal substrate of GST, in the presence or absence of a competitor, i.e., an insecticide. While this technique clearly indicates an interaction between a GST and a pesticide, it does not decipher the mechanism by which this interaction occurs. *S. litura* SlGSTe2 and SlGSTe3 were the first spodotperan GSTes to be characterized in this manner, showing differential activity toward DDT and deltamethrin between the two enzymes, with SlGSTe2 having the greater activity toward the insecticide [92]. The SeGSTe2 ortholog was further characterized showing activity toward metaflumizone, indoxacarb, monosultap,
chlorpyrifos, malathion, cyhalothrin, and imidacloprid, suggesting a conserved insecticide
metabolizing function [93]. Similarly, GSTe1 was carefully analyzed in *S. litura* and *S. exigua*
species [94,95]. CDNB competition assays showed activity towards chlopyrifos, malathion,
phoxim, deltamethrin and cypermethrin and the specific activities of GSTE1 were further
analyzed using high-performance liquid chromatography (HPLC), confirming their active
binding to chlorpyrifos and cypermethrin. Interestingly, GSTe1 is also active towards
secondary plant metabolites [96] and has strong peroxidase activity. Therefore, this enzyme
has multi-detoxification properties with a wide range of substrates, potentially conferring
insecticide resistance.

In vivo studies have also confirmed the direct involvement of GSTs in insecticide tolerance. In Cheng et al., (2019), injection of siRNAi against SIGSTe20 and SIGSTe3 increased the sensitivity of *S. litura* larvae to imidacloprid, and the recombinant proteins subsequently showed activity towards diazinon, permethrin, chlorfenapyr, and bendiocarb [97].

3.1.3. Resistance through Over-Expression of GST Epsilon

GST overexpression has often been a prerequisite for the identification of enzymes
potentially involved in insecticide resistance [48]. Thus, many studies have identified
candidate GSTes whose expression could be altered upon exposure to insecticides. In
*S. litura*, SIGSTe1, e2, e3, e4, e10, e11, e12, e13, e15 and e20 showed overexpression fol-
lowing exposure to various pesticides such as tebufenozide, carbaryl, DDT, malathion,
deltamethrin, chlorpyrifos and imidacloprid [1,92,98,99]. It is noteworthy that among
these 10 over-expressed genes, no specific phylogeny-related pattern could be assigned
(Figure 6) indicating that, rather than defining an insecticide-specific clade, the induced
GSTe genes are diverse with potentially redundant and/or complementary activities. Sim-
ilar patterns are observed in *S. littoralis* and *S. exigua* when exposed to deltamethrin or
lambda-cyhalothrin, chlorpyrifos and chlorantraniliprole, respectively [94,100]. Neverthe-
less, induction after exposure may be only part of the animal's response to a given toxicant
and does not necessarily imply involvement in resistance [90]. Therefore, in an effort to find
genes directly associated with insecticide catabolism, studies focusing on the expression
profiles of genes highly expressed in resistant strains compared to the susceptible popu-
lation have indicated potential candidates in GSTe as directly responsible for insecticide
resistance. In *S. frugiperda*, SfGSTe5 was associated with an OP-resistant strain using a
transcriptomic approach [78]. SfGSTe5 is the ortholog of *Plutella xylostella* PxyIGSTe3, an ep-
silon GST induced and capable of metabolizing the organophosphate insecticides parathion
and methylparathion [101,102]. In an RNAseq experiment comparing indoxacarb-resistant
and susceptible *S. litura* populations [103], SIGSTe4 and SIGSTe20 were the only GSTs
overexpressed. Interestingly, both of these GSTes are active against imidacloprid, diazi-
non, permethrin, chlorfenapyr, or bendiocarb [97,101], indicating potential cross-activity
of SIGSTe for various insecticides. This complex pattern of gene expression regulation
must be orchestrated by specific regulatory operators that can effectively govern down-or
overexpression upon exposure but could also be affected by mutations in resistant strains
to account for specific activations. Promoter sequence analysis revealed that some *S. litura*
GSTs harbor the same cap 'n' collar 'C/muscle aponeurosis fibromatosis (CncC/Maf) bind-
ing site and the aryl hydrocarbon receptor/nuclear aryl hydrocarbon receptor translocator
(AhR/ARNT) binding site [84,94]. These transcription factors coordinately regulate GST
expression through intracellular production of ROS induced by insecticide exposure. In a
CncC RNAi experiment, Shi et al., (2020) demonstrate that this transcription factor not only
enhances indoxacarb sensitivity in susceptible and resistant strains of *S. litura*, but is also
involved in the down-regulation of detoxification genes related to indoxacarb resistance,
confirming its central role in insecticide-resistance mechanisms [104].

3.1.4. Resistance through Point Mutations in GST Epsilon

Insecticide resistance by GSTe has been clearly established in *Anopheles* where a key
residue, Phe120, has been implicated in the increased binding of DDT to GSTe2 [6]. Such a
substitution is also observed in 5 GSTes from spodopteran species (GSTe2, e12, e15, e17, e18, Figure 8) indicating potential conservation of resistance processes between these species. Thus, further investigations are needed to fully understand the detailed mechanisms of pesticide-GSTes interaction and their precise contribution to insecticide resistance.

<figure><img src="image_92.png" /><figcaption>**Figure 8.** Alignment of a subset of GSTes from *S. litura* (Sl) *S. frugiperda* corn and rice strains (SfC and SfR), *S. exigua* (Se), *S. littoralis* (Slit) and *AfunGSTe2* from *Anopheles funestus* (Afun, genbank accession number KC800363.1) harboring a L119F substitution. The L119F mutation (in red) is associated with DDT resistance in *Anophelines* [6].</figcaption></figure>

**4. Phase III: Elimination/Export**

The last phase concerns the elimination or excretion of metabolites outside the cell. Metabolites have been rendered less toxic by the reactions they underwent in phase I and/or phase II but they can also be directly excreted without prior chemical modification. This phase mainly involves ATP-binding cassette transporters.

**4.1. ATP-Binding Cassette Transporters (ABCs)**

ATP-binding cassette (ABC) transporters are proteins that use the energy of ATP hydrolysis to transport substrates such as amino acids, lipids, peptides, sugars and drugs across cell membranes. ABC transporters are classified into eight subfamilies from letter A to H based on similarities in their ATP binding domain. Full transporters (FT) consist of two cytosolic nucleotide-binding domains (NBDs) and two transmembrane domains (TMDs) whereas half transporters (HT) have only one of each domain and must dimerize to form a functional transporter. The NBD is involved in ATP binding and hydrolysis. The TMD consists of 5-6 transmembrane segments and is responsible for substrate specificity. In insects, their number varies from 32 in *Nilaparvata lugens* [105] to 82 in *Plutella xylostella* [106] while in the available genomes of Noctuidae about 50 ABCs have been found so far. Some subfamilies are conserved well from humans to arthropods. For example, ABCD, ABCE, ABCF have clear orthology and similar suspected physiological roles such as the transport of acyl-CoA molecules into the peroxisome, ribosome biogenesis and translation. Moreover, some ABC transporters subfamilies are involved in chemotherapy resistance in humans and pesticides resistance in insects. Their names reflect these roles, the B subfamily is known as multidrug-resistance protein (MDR) or P-glycoprotein (P-gp) while the C subfamily is known as multidrug-resistance associated protein (MRP). For example, the human MDR1 (ABCB1) excludes chemotherapeutic agents and confers resistance in several types of cancer when over-expressed [107]. MDR1 orthologs in *Drosophila Mdr50* and *Mdr65* are over-expressed in DDT-resistant strain 91-R. Knockdown of each gene in this strain by RNAi increases the susceptibility of flies to DDT [108]. These examples, in humans and *Drosophila*, illustrate one of the mechanisms of resistance involving ABC transporters: over-
expression of the transporter which allows exclusion of a greater amount of xenobiotics. An additional mechanism in the case of insecticide resistance can be mentioned and corresponds to the mutation of the target. Indeed, ABCs are one of the receptor proteins of *Bacillus thuringiensis* (Bt) toxins as are cadherin-like, aminopeptidase-N or alkaline phosphatase, and a modification in the sequence of these receptors can confer resistance (for a recent review see [109]).

### 4.1.1. Resistance through Point Mutations in ABCs, the Case of ABCC2

ABCC2 was first identified as a receptor for one of the Bt crystal toxins (Cry), Cry1Ac in the lepidopteran *Heliothis virescens* [110]. In this study, a strain of *H. virescens* was selected in the laboratory for several years with Cry1Ab and Cry1Ac toxins. Very high levels of resistance were obtained as well as a loss of membrane binding for the toxins. A deletion in exon 2 of *ABCC2* resulting in a 99 amino acid protein truncation was identified as the cause of resistance to Cry1Ac [110]. This study paved the way to investigating the role of ABC transporters in resistance to Bt-toxin. Special attention is given to the four species of interest in this review. Annotations of ABC transporters in the genome of *S. litura*, *S. frugiperda*, *S. exigua* and in the transcriptomic data of *S. littoralis* were mainly performed automatically and only a few subfamilies were annotated manually by experts (G. Le Goff, personal communication). In a recent publication, Liu et al., (2018) showed that the sensitivity towards Cry1Ac toxin differed by a factor of 65 between the more tolerant *S. litura*, and *S. frugiperda* [111]. SIABCC2 and SfABCC2 share 97% identity. Fragment substitutions and point mutations in the transporter expressed in insect cells allowed the authors to identify the amino acid at position 125 as the key to this difference in sensitivity. A glutamine is present at this position in *S. frugiperda* while it is a glutamic acid in *S. litura*. By aligning the sequences of ABCC2 between the four *Spodoptera* species (*S. exigua*, *S. frugiperda*, *S. littoralis* and *S. litura*), it is possible to predict their susceptibility to Cry1Ac toxin. *S. exigua* should be susceptible like *S. frugiperda* while *S. littoralis* should be more tolerant like *S. litura* (Figure 9).

<figure><img src="image_93.png" /><figcaption>**Figure 9.** Alignment of a 20 amino acid sequence of ABCC2 from four *Spodoptera* species, presence of a glutamine or glutamic acid at position 119. Genbank accession number, *S. exigua* AIB06822, *S. frugiperda* AUO38740, *S. littoralis*, *S. litura* XM_022967434.</figcaption></figure>

Three-dimensional structure predictions suggest that the G or E at position 125 is localized in the extracellular loop 1 (ECL1). Liu et al., hypothesize that it may have a role in toxin binding, which would explain these differences in sensitivity [111]. The importance of ECL1 in Bt toxin selectivity was previously demonstrated in *B. mori*, where replacement of certain amino acids in the ECL1 loop between BmABCC2 and BmABCC3 resulted in increased binding affinity of Cry1A toxins [112]. These variations in toxin receptor sequence may also explain the spectrum of specificity of Bt toxins. Indeed, some toxins primarily target lepidopterans, for example Cry1 toxins, while others are specific to Coleoptera, such as Cry3 [113].

Larger changes in the sequence of ABCC2 (Figure 10A) have been associated with resistance in some of these *Spodoptera* spp., especially in field-resistant *S. frugiperda* populations. Indeed, resistance to transgenic maize expressing the Cry1Fa toxin has been reported since 2007 in Puerto Rico for *S. frugiperda*. Banerjee and colleagues have demonstrated that the resistance was due to mutations in the SfABCC2 toxin receptor [114]. These mutations result in a truncated protein that loses the second transmembrane domain and at the same time toxin binding. A nine-base deletion (position 39–47) and a two-base insertion (GC at position 2218) lead to a frameshift and the occurrence of a premature stop codon. The
truncated protein is 746 amino acids (Figure 10B) whereas the ABCC2 of the susceptible strain encodes a protein of 1349 amino acids (Figure 10A). The frequency of mutated SfABCC2 in Puerto Rico increased between 2007 and 2009 from 1% to 42% and stabilized at 55% in 2017. This truncated receptor was not found by the authors in the populations from Florida or the Dominican Republic [114].

<figure><img src="image_94.png" /><figcaption>**Figure 10.** Protein structure of ABCC2 of *Spodoptera spp* from susceptible or resistant populations. (A) Protein structure of SfABCC2 and SeABCC2 wild-type. (B,C) Protein structures of SfABCC2 in resistant populations from Puerto Rico, * corresponds to an insertion of amino acids [114,115]. (D,E) Protein structure of SfABCC2 in resistant populations from Brazil [80,116]. (F) Protein structure of SeABCC2 from a laboratory selected strain [117].</figcaption></figure>
Another group confirmed these results. Flagel et al., isolated a population of *S. frugiperda* from a site in Puerto Rico in 2010 and this population was selected in the laboratory with Cry1Fa for 50 generations [115]. The resulting strain had a resistance factor of 500 for Cry1Fa and 87-fold cross-resistance to Cry1A.105 compared to the susceptible reference strain. They found the same GC insertion in the ABCC2 sequence that creates a stop codon at position 747 and results in a truncated protein. By conducting a sampling campaign in various localities of Puerto Rico and Brazil, the mutation was found to be in Puerto Rico only. In addition to this allele, the authors mention the existence of a second resistance allele and the insertion of a sequence in the fourth exon of the gene. However, sequencing difficulties due to repeated sequences did not allow them to identify precisely the effect of this insertion on the ABCC2 sequence (Figure 10C) although an aberrant splicing of this allele 2 was suspected.

Furthermore, a recent study reported resistance to Cry1F toxin in isolated Brazilian populations of *S. frugiperda* [116]. The authors identified two mutations in the extracellular loop 4 (ECL4) of ABCC2: a deletion of two amino acids (GY) at positions 788 and 789 and the change of a proline to either lysine or arginine at position 799 (Figure 10D). Unlike the resistance reported in Puerto Rico, these mutations do not cause premature termination of the sequence. Expression of mutated ABCC2 in insect cells confirmed the role of these mutations in toxin binding. Analysis of populations collected in different regions of Brazil showed a high frequency of the GY deletion as well as many rare alleles that result in sequence changes between amino acids 783–799. According to the authors, these results reinforce the primary role of the ECL4 extracellular loop in Cry1F toxicity. Further analysis is needed to demonstrate the role that these mutations may have in resistance to other toxins. Indeed, in addition to resistance to Cry1F other studies on Puerto Rican populations with truncated SfABCC2 have shown cross-resistance for Cry1A.105, Cry1Ac, and Cry1Ab toxins [114,115]. Do mutations in the ECL4 loop also confer these cross-resistances? Another mutation in ABCC2 has been identified in Brazilian populations corresponding to the insertion of 12 bases at the intracellular loop between transmembrane domains 8 and 9 resulting in a premature stop codon [80]. The truncated protein lost the last four transmembrane segments and the second intracellular ATP-binding domain (Figure 10E). The study was performed on insects collected three years after those of Boaventura et al., (2020) and changes in the use of transgenic crops may have occurred. The authors did not investigate the effect of this mutation on resistance and of Bt toxin binding. Instead, they tested whether invasive populations of *S. frugiperda* (also sampled in Africa and Asia) carried mutations known to cause insecticide resistance.

None of the mutations identified so far in ABCC2 were found in these invasive popu-
lations. This truncated protein retains an intact extracellular loop 4 and thus potentially
to bind to the Cry1F toxin. Further experiments are needed to determine if it can confer
resistance to some of the Bt toxins.

Looking at other *Spodoptera* species, resistance to some Bt toxins was reported as
early as 1994–1995 for *S. littoralis* and *S. exigua* respectively [118,119]. However, in both
cases, these were laboratory selected strains. No resistance has been reported to date for
*S. litura* in the Arthropod Pesticide Resistance Database although one study mentioned the
development of resistance to Cry1C and Vip3A toxins in a laboratory selected strain [120].
But these studies did not involve ABCC2. In *S. exigua*, a strain was selected with a
commercial *B. thuringiensis* product, Xentari. The main toxins contained in this product
are Cry1a and Cry1Ca. The selected strain (Xen-R) showed a resistance factor of more
than 1000 [121]. Bulk segregant analysis based on high-throughput sequencing identified
the region of the genome carrying the major resistance loci, which contained three genes
encoding *ABCC1*, *ABCC2* and *ABCC3* [117]. ABCC2 and ABCC3 played a major role in
resistance. ABCC2 contained a mutation in the resistant strain, which resulted in the loss of
the ATP binding domain II caused by the truncation of 82 terminal amino acids (Figure 10F).
However, the binding of Cry1Ca to brush border membrane vesicles was not significantly
different between resistant and susceptible strains. RNAi experiments confirmed the role
of ABCC2 and ABCC3 in the resistance to Cry1A and Cry1Ca in *S. exigua* [117]. In addition, Pinos and co-workers showed that the loss of the second nucleotide binding domain did not affect the binding of Cry1A toxins [122]. Indeed, expression of the truncated transporter in Sf21 insect cells conferred sensitivity to Cry1A, as specific binding of the toxin was still effective. These results suggest that this domain is not required for a functional toxin receptor [122]. Nevertheless, the use of techniques such as CRISPR/Cas9 validated the main role of ABCC2 as a receptor for Cry1Ac and Cry1Fa toxin in *S. exigua* [123]. The knock-out (KO) strain for SeABCC2 was more resistant than the parental strain by a factor of 470 and 240 for Cry1Ac and Cry1Fa, respectively. Using the same technique in *S. frugiperda* to produce a KO for ABCC2 conferred resistance to Cry1F toxin [124].

### 4.1.2. Resistance through Over- or Reduced Expression of ABCs

Data on the involvement of ABC transporters in resistance to chemical insecticides in *Spodoptera* spp. are scarce, as it is in Lepidoptera in a broader sense. A few examples exist, such as the involvement of *H. armigera* Pgp1 insecticide transport. HaPgp1 expression was induced in the larval gut after exposure to abamectin [125]. When the expression of this transporter is suppressed by RNAi, larvae became more sensitive to the insecticide and mortality increased from 26% to 84% for a fixed abamectin dose of 0.4 µg/g diet [125]. Another study confirmed the role of HaPgp in abamectin transport, as the use of a specific Pgp inhibitor, verapamil, increased the sensitivity of larvae towards abamectin [126].

The development of the CRISPR/Cas9 technique has paved the way to study the role of ABCs in the transport of chemical insecticides and potentially in the development of resistance. Here, we report the few examples in which an increase in LD₅₀ was observed for insecticides after KO of certain ABC transporters in *Spodoptera* spp. In *S. exigua*, the ability of ABCB1 to transport 12 insecticidal molecules from 10 different chemical families was investigated by knocking out this protein using CRISPR/Cas9 [127]. The ABCB1 KO strain showed a 2.73-fold and 3.01-fold increase in sensitivity to abamectin and emamectin benzoate, respectively, while no significant difference was observed for the other insecticides tested. In *S. frugiperda*, the KO of ABCC2 rendered the insects 7.8 and 3.1 times more sensitive to abamectin and spinosad, respectively. Similarly, a reduction in tolerance to these two insecticides was observed for the ABCC3 KO by factors of 4.5 and 2 [128]. Another study reported that SfABCC2 KO does not induce resistance to insecticide molecules such as bifenthrin, chlorantraniliprole, spinetoram and acephate [124]. To our knowledge, there are no CRISPR/Cas9-mediated ABC KO data for *S. litura* and *S. littoralis* to date.

### 4.1.3. Resistance and Regulation of ABCs Expression

While the mechanisms leading to the acquisition of chemotherapy resistance through changes in ABC transporters expression of are well known in humans, those leading to pesticide resistance are still largely unexplored in insects. For example, over-expression of MDR1 (ABCB1) in human tumors has been associated with increased gene copy number through chromosomal region amplification, epigenetic modifications and single nucleotide polymorphisms (SNPs) [129]. In insects, the acquisition of resistance may involve either under- or over-expression of a given ABC transporter. Indeed, in the case of Bt toxins, decreased expression of the ABC receptor can confer resistance, while conversely, an over-expression of ABCs that can transport insecticides would allow the development of resistance. MicroRNAs (miRNA), which are small non-coding RNA sequences (between 19 and 24 nucleotides in length) are known to regulate gene expression. The miRNA miR-998-3p has target sites in the ABCC2 coding sequence (CDS) in several Lepidoptera species [130]. The involvement of these sites in the regulation of ABCC2 has been demonstrated in *P. xylostella*. MiR-998-3p was over-expressed by a factor of two in a Cry1Ac-resistant strain (GX-R) compared to a susceptible strain and ABCC2 expression in the larval midgut was reduced by approximately 50%, demonstrating the involvement of this miRNA in Bt resistance. A conserved target site for miR998-3p had been found in the four *Spodoptera* spp. as well as one and two non-conserved sites for *S. exigua* and the
other three species respectively ([130] and the present study). MiR-998-3p could play a role
in the acquisition of resistance in these species but to the best of our knowledge there has
been no study has so far reported such cases and more generally the role of miRNAs in
insecticide resistance remains largely unexplored.

Another regulator of *ABCC2* expression is the transcription factor Forkhead box protein A (FoxA). Its transfection into Sf9 cells (*S. frugiperda* cell model) induces the expression of ABCC2 and ABCC3 and results in increased susceptibility of the cells to Cry1Ac toxin [131]. Potential resistance could arise through under-expression of this transcription factor but has not been described at this time.

Cap 'n' collar isoform C (CncC) is a major transcription factor for controlling the expression of detoxification genes in insects [12]. CncC is constitutively overexpressed in a Chinese field population of *S. exigua* resistant to chlorpyrifos and cypermethrin [94]. Several studies on this resistant strain showed that CncC is involved in controlling the expression of detoxification genes including P450s (CYP321A8, CYP321A16, and CYP332A1) [11,132] and GSTs (GSTo2, GSTe6, GSTd3) [94]. However, none of these studies mention the role of CncC in the expression of ABC transporters. Nevertheless, this role is not excluded and an RNAseq analysis would allow a global approach to identify CncC-regulated genes in this resistant population. In *Drosophila*, a DDT-resistant strain (91R) constitutively overexpressing CncC exhibits multifactorial resistance involving some P450 and GST in addition to ABC transporters [108]. None of these studies investigated the underlying cause of CncC overexpression. In other *Spodoptera* species, CncC regulates the expression of several detoxification genes involved in indoxacarb resistance, including the ABC transporter SlituABCH-1 in *S. litura* [104]. Although the CncC gene from *S. frugiperda* has been cloned [133], it has not been shown to control of the expression of resistance-related ABC transporters and, to date, this information is also lacking in *S. littoralis*.

A number of questions remain unanswered. What is the mechanism behind the over-expression of CncC in some resistant populations? What other regulatory mechanisms could control the expression of ABC transporters involved in resistance? Do epigenetic regulations play a major role in the expression of certain ABC transporters and the acquisition of resistance, as has been observed in humans?

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10.3390/insects12060544/s1, Table S1: Protein sequences of *S. littoralis* carboxyl/cholinesterases, Table S2: Spodopteran GSTes reconciled nomenclature, with new annotations combined with previous studies.

**Author Contributions:** All authors contribute equally to the writing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank Abby Cuttriss (Université Côte d'Azur, Office of International Visibility). Le Goff would like to thank Dries Amezian for his very helpful comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

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