Biochemical basis of insecticide resistance in agricultural pests: A review

Introduction

The intensive and repeated use of synthetic insecticides across agricultural systems has exerted strong selection pressure on pest populations, driving the evolution of resistance mechanisms that reduce the efficacy of chemical control. Insecticide resistance is now documented in several hundred arthropod pest species and spans essentially every major insecticide class, from organochlorines and organophosphates to pyrethroids, neonicotinoids, and diamides. Understanding the biochemical basis of resistance is central to designing rational insecticiderotation programs, developing resistance-breaking chemistries, and interpreting field-monitoring data. Resistance mechanisms are conventionally grouped into four broad categories: (i) metabolic resistance, in which enhanced activity of detoxification enzymes accelerates insecticide breakdown or sequestration; (ii) target-site resistance, in which mutations at the insecticide's molecular target reduce binding affinity; (iii) reduced penetration, in which cuticular or gutbarrier modifications slow insecticide uptake; and (iv) behavioral resistance, in which altered feeding or movement patterns reduce exposure. In practice, field-resistant populations frequently combine two or more of these mechanisms, producing resistance phenotypes that are both quantitatively strong and difficult to reverse.

Relationship to Prior Review Literature

This review builds on a well-established body of prior review-level literature on the biochemical and molecular basis of insecticide resistance including Hemingway & Ranson (2000) [1] , Li et al. (2007) [2] , Bass & Field (2011) [3] , ffrench-Constant (2013) [4] , Bass et al. (2015) [5] , Sparks & Nauen (2015) [6] , and Van Leeuwen & Dermauw (2016) [7] , full citations for which appear in the References section at the end of this paper. Where those earlier reviews established the general mechanistic framework and, in several cases, focused on disease-vector or single-taxon (e.g., chelicerate mite) systems, the present review compiles primary quantitative data specifically for agricultural pest species across multiple insecticide classes, including resistance-ratio statistics (Table 2), enzyme-activity data (Table 3), and recent target-site mutation surveys (Table 5).

Major Biochemical Mechanisms of Resistance 2.1 Metabolic Resistance

Metabolic resistance is the most widespread biochemical mechanism reported in agricultural pests. Three enzyme families dominate: carboxylesterases, cytochrome P450 monooxygenases, and glutathione S-transferases. Esterasebased resistance typically arises from gene amplification, producing many-fold increases in enzyme titre that allow the insect to sequester or hydrolyse organophosphate and pyrethroid esters before they reach the target site. Cytochrome P450 monooxygenases catalyse oxidative detoxification of a broad range of insecticide chemistries and are frequently upregulated through cis-regulatory mutations or transcriptionfactor-mediated over-expression rather than gene amplification alone. Glutathione S-transferases contribute to resistance primarily through conjugation of reduced glutathione to electrophilic insecticide metabolites, and elevated GST activity is commonly associated with organochlorine and organophosphate resistance.

Target-Site Insensitivity

Target-site resistance results from point mutations in the genes encoding an insecticide's molecular target, reducing binding affinity while typically preserving normal physiological function of the target protein. , the knockdownresistance (kdr) and super-kdr mutations in the voltage-gated sodium channel (conferring pyrethroid and DDT resistance), the Rdl mutation in the GABA-gated chloride channel (conferring cyclodiene and phenylpyrazole resistance), and mutations in the ryanodine receptor associated with diamide resistance in lepidopteran pests such as Plutella xylostella.

Reduced Cuticular Penetration

Cuticular resistance involves quantitative and compositional changes to the insect cuticle, including increased thickness and altered cuticular protein and lipid profiles, which slow the rate of insecticide penetration into the haemolymph. Although its effect size is generally modest relative to metabolic or targetsite resistance, cuticular resistance is important because it acts synergistically, extending the time available for detoxification enzymes to act and effectively amplifying the resistance conferred by other mechanisms.

Behavioral Resistance

Behavioral resistance encompasses changes in feeding, resting, or oviposition behavior that reduce contact with insecticide residues, such as avoidance of treated leaf surfaces or shifts in feeding time. While less biochemically defined than the mechanisms above, behavioral changes are increasingly recognized as a contributing factor in fieldresistant populations and can complicate the interpretation of laboratory bioassay data.

Summary of Biochemical Resistance Mechanisms

Table 1 summarizes the principal biochemical mechanisms discussed above, their molecular basis, the insecticide classes most affected, and representative pest species in which each mechanism has been well characterized.

Quantitative Resistance Data across Pest Species

Table 2 compiles peer-reviewed resistance-ratio (RR) data for ten pest-insecticide combinations drawn directly from the primary literature, each cited with its DOI so that the original bioassay conditions, reference susceptible strain, and statistical methods can be verified. Resistance ratios are calculated as the LC50 or LD50 of a field or selected population divided by the LC50/LD50 of a susceptible reference strain; values above approximately 10 are commonly treated as indicating practically significant resistance in the field. [4] , Insects 10.3390/insects12110996

Plutella xylostella Chlorantraniliprole 684.54× (lab-selected) Qin et al. 2018 [10] , J. Econ. Entomol. 10.1093/jee/toy043 Myzus persicae Imidacloprid 106.9× (field, China) Hu et al. 2023 [11] , Agronomy 10.3390/agronomy13092276 Myzus persicae beta-Cypermethrin 324× (field, China) Hu et al. 2023 [11] , Agronomy [13] , PLOS ONE 10.1371/journal.pone.0147844

Musca domestica Diazinon 62.5-309.8× (field, Riyadh) Abobakr et al. 2022 [14] , Insects 10.3390/insects13020192

Long-term monitoring programs illustrate that resistance ratios can fluctuate considerably between seasons rather than rising monotonically, reflecting changes in local selection pressure, immigration of susceptible individuals, and fitness costs associated with resistance alleles. (2010) [9] , Pest Management Science 66:186-195, DOI: 10.1002/ps.1853.

Detoxification Enzyme Activity: Resistant versus Susceptible Strains

Beyond whole-organism resistance ratios, biochemical assays quantify the underlying enzyme activity differences between resistant and susceptible strains directly. Table 3 compiles specific-activity statistics (mean ± SD or SE, as reported by each source) for esterase and acetylcholinesterase assays in agricultural pests, again cited with DOI. These data illustrate that metabolic resistance is frequently associated with severalfold to an order-of-magnitude increase in detoxification enzyme activity, while target-site (AChE) assays can show comparatively little separation between strains when the resistance mechanism is instead due to a qualitative change in enzyme sensitivity rather than quantity. Shi et al. 2016 [15] , Sci. Rep. 10.1038/srep18646

Lygus lineolaris Esterase (α-naphthyl acetate) Baseline (1×) ~6× higher than susceptible

Zhu & Snodgrass 2004 [16] , Insect Biochem. Mol. Biol. 10.1016/j.ibmb.2004.07.008

Lygus lineolaris

Esterase (β-naphthyl acetate) Baseline (1×) ~3× higher than susceptible

Zhu & Snodgrass 2004 [16] , Insect Biochem. Mol. Biol. 10.1016/j.ibmb.2004.07.008

Lygus lineolaris

Esterase (p-nitrophenyl acetate) Baseline (1×) ~10× higher than susceptible

Zhu & Snodgrass 2004 [16] , Insect Biochem. Mol. Biol. Field populations within same range (no target-site insensitivity detected) Tiwari et al. 2012 [17] , J. Econ. Entomol.

10.1603/EC11228

The esterase data for Tetranychus spider mites illustrate a roughly two-fold increase in specific activity associated with the over-expressed TCE2 carboxylesterase relative to susceptible crude enzyme extracts, while the Lygus lineolaris data show that the fold-difference between resistant and susceptible strains varies with the substrate used in the assay (3-to 10-fold across three chromogenic substrates), underscoring that reported enzyme-activity ratios are assayspecific and not a single fixed property of the resistant phenotype. By contrast, the Diaphorina citri data show overlapping AChE activity ranges between the susceptible laboratory strain and field populations, consistent with the original authors' conclusion that organophosphate and carbamate resistance in that species was not attributable to AChE target-site insensitivity.

Recent kdr Mutation Frequency Data in Field Populations

Target-site knockdown resistance (kdr) mutations in the voltage-gated sodium channel (VGSC) continue to be documented in newly surveyed agricultural pest species. [20] , Sci. Rep. 10.1038/s41598-022-16366-1

Megalurothrips usitatus (bean flower thrips) T929I + K1774N (double mutation) Double mutation functionally confirmed to reduce pyrethroid-induced sodium channel currents in fieldresistant strains from Hainan Province Gao et al. 2024 [21] , J. Agric. Food Chem. 10.1021/acs.jafc.4c00355

The Ceutorhynchus data are particularly notable for directly linking kdr genotype frequency to field control outcomes: populations in which the resistant homozygote exceeded roughly half the sampled individuals corresponded to documented pyrethroid control failures at the recommended field rate, while populations with low homozygote frequency remained controllable. This dose-frequency relationship illustrates why kdr genotyping is increasingly used alongside bioassays in resistance-management decision-making, rather than as a replacement for them.

Biochemical and Molecular Detection of Resistance

Reliable detection of resistance mechanisms underpins effective resistance management. Approaches range from whole-organism bioassays that quantify the dose-mortality relationship, to biochemical microplate assays that measure detoxification enzyme activity directly, to molecular diagnostics that detect specific target-site mutations. Synergist bioassays, in which a candidate insecticide is co-applied with an enzyme inhibitor such as piperonyl butoxide (a P450 inhibitor), S,S,S-tributyl phosphorotrithioate (an esterase inhibitor, DEF), or diethyl maleate (a GST inhibitor, DEM), remain a widely used first step in implicating a particular enzyme class before committing to more resource-intensive molecular confirmation.

Discussion

Taken together, the mechanism summary in Table 1, the resistance-ratio data in Table 2, the enzymeactivity statistics in Table 3, and the kdr mutation survey in Table 5 point to several recurring patterns in how biochemical resistance actually manifests in field populations of agricultural pests, beyond the individual mechanism-by-mechanism descriptions given in Section 2.

Multi-Mechanism Resistance Is the Field Norm, Not the Exception

Few of the resistant populations summarized in this review rely on a single mechanism. Helicoverpa armigera field populations combine kdr-type sodium channel mutations with elevated cytochrome P450 activity to achieve pyrethroid resistance ratios in the thousands (Table 2), and Tetranychus species frequently pair over-expressed carboxylesterases (Table 3) with target-site mutations in the glutamategated chloride channel to resist avermectins. This layering matters for resistance management because a synergist or mode-of-action rotation designed around a single mechanism will typically fail once a second, independent mechanism has become established in the same population; the practical resistance ratio observed in the field is better modeled as the combined outcome of cooccurring mechanisms than as the signature of any one pathway acting alone.

Resistance Ratios Are Assay-and Substrate-Dependent

The Lygus lineolaris esterase data in Table 3 illustrate a point that is easy to overlook when comparing resistance ratios across studies: the same resistant strain returned a 3fold, 6-fold, or 10-fold difference from the susceptible strain depending solely on which chromogenic substrate (αnaphthyl acetate, β-naphthyl acetate, or p-nitrophenyl acetate) was used in the enzyme assay. Similarly, the Diaphorina citri acetylcholinesterase data show that a biochemical assay can fail to distinguish resistant from susceptible populations even when whole-organism bioassays and field control failures indicate that resistance is present, because in that case resistance was not conferred by AChE at all. Comparisons of resistance ratios between studies, therefore, are only meaningful when the assay conditions, reference susceptible strain, and life stage are comparable; a resistance ratio is a property of the measurement system as well as the pest population.

Resistance Levels Fluctuate Under Variable Selection Pressure

The Florida Bemisia tabaci monitoring data reproduced in Figure 2 (Schuster et al. 2010) [9] show that RR50 values for imidacloprid did not rise monotonically between 2000 and 2007 but instead fell in some seasons (from 12.0 in 2003 to 2.5 in 2005) before rising again to 26.3 in 2006. This nonmonotonic trajectory is consistent with a fitness cost associated with resistance alleles: when selection pressure from a given insecticide temporarily relaxes (for example, due to a shift to alternative chemistries or a fallow period), susceptible genotypes can partially recover in the population, only for resistance to re-emerge once the same insecticide is reintroduced. The same pattern is documented experimentally in the Schuster et al. dataset itself, where resistant Bemisia sub-populations reared for six generations without imidacloprid exposure reverted from RR50 values of 50-60 to ≤4. This has a direct management implication: insecticide rotation intervals that are too short to allow such reversion will not achieve the intended resistance-delaying effect.

Genotype-to-Phenotype Correlations Support Molecular Monitoring, With Caveats

The Ceutorhynchus kdr data (Table 5) demonstrate a reasonably tight relationship between homozygous mutant frequency and observed field control failure, which is one of the clearer examples in the current literature of a single genotyping assay being usable as a practical resistancemanagement indicator. However, this relationship does not hold universally: the Diaphorina citri and Tetranychus urticae cases in this review show that resistance can arise predominantly through metabolic up-regulation with no informative target-site marker, or through multiple independent target-site mutations whose individual contributions vary by population (as in the Tetranychus urticae backcrossing literature cited in Section 9.1). Molecular diagnostics are therefore best deployed as a complement to, rather than a replacement for, phenotypic bioassays, particularly when a pest species' dominant resistance mechanism has not yet been established for the population being monitored.

Conclusion

The biochemical basis of insecticide resistance in agricultural pests is multifactorial, involving metabolic detoxification, target-site insensitivity, reduced penetration, and, in some cases, behavioral avoidance, often acting in combination within a single field population. The quantitative data compiled in this review resistance ratios spanning less than 10-fold to more than 20,000-fold across pest-insecticide combinations (Table 2), enzyme-activity differences of 2-to 10-fold associated with metabolic resistance (Table 3), and kdr mutation frequencies ranging from near-zero to fixation within individual field populations (Table 5) illustrate both the scale of the problem and its variability across species, insecticide classes, and geographic regions. No single biochemical assay or molecular marker is sufficient to characterize resistance in a novel population; the data reviewed here consistently show that resistance phenotypes reflect the combined, and sometimes population-specific, contribution of several mechanisms. For resistance management, three conclusions follow directly from the evidence reviewed. First, because resistance ratios and mutation frequencies can decline as well as rise under fluctuating selection pressure, insecticide rotation and mode-of-action diversification (formalized in schemes such as the IRAC MoA classification) remain a rational strategy even where resistance is already established, rather than only as a preventive measure. Second, because metabolic and target-site mechanisms frequently co-occur, resistance-management programs that rely on a single diagnostic whether a synergist bioassay or a single-locus genotyping assay risk under-detecting resistance conferred by mechanisms outside that assay's scope. Third, continued biochemical and molecular monitoring, interpreted alongside field efficacy data rather than in isolation, remains essential to sustaining the effectiveness of chemical control within integrated pest management programs. As new insecticide chemistries such as diamides and butenolides come under increasing selection pressure, and as genomic and transcriptomic tools make population-scale resistance surveillance more accessible, the biochemical framework outlined in this review is likely to remain the basis for interpreting and managing resistance in agricultural pests for the foreseeable future.

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