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. 2022 Mar 2:13:818531.
doi: 10.3389/fphys.2022.818531. eCollection 2022.

A New Method to Address the Importance of Detoxified Enzyme in Insecticide Resistance - Meta-Analysis

Yifan Wang  1 , Alan E Wilson  2 , Nannan Liu  1
Affiliations

A New Method to Address the Importance of Detoxified Enzyme in Insecticide Resistance - Meta-Analysis

Yifan Wang et al. Front Physiol. .

Abstract

Insect-borne diseases, such as malaria, and plant pathogens, like the tobacco mosaic virus, are responsible for human deaths and poor crop yields in communities around the world. The use of insecticides has been one of the major tools in pest control. However, the development of insecticide resistance has been a major problem in the control of insect pest populations that threaten the health of both humans and plants. The overexpression of detoxification genes is thought to be one of the major mechanisms through which pests develop resistance to insecticides. Hundreds of research papers have explored how overexpressed detoxification genes increase the resistance status of insects to an insecticide in recent years. This study is, for the first time, a synthesis of these resistance and gene expression data aimed at (1) setting up an example for the application of meta-analysis in the investigation of the mechanisms of insecticide resistance and (2) seeking to determine if the overexpression detoxification genes are responsible for insecticide resistance in insect pests in general. A strong correlation of increased levels of insecticide resistance has been observed in tested insects with cytochrome P450 (CYP), glutathione-S-transferase (GST), and esterase gene superfamilies, confirming that the overexpression of detoxification genes is indeed involved in the insecticide resistance through the increased metabolism of insecticides of insects, including medically (e.g., mosquito and housefly) and agriculturally (e.g., planthopper and caterpillar) important insects.

Keywords: diverse insect species; insecticide resistance; meta-analysis; metabolic detoxification genes; overexpression of detoxification genes.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flow diagram based on PRISMA.
FIGURE 2
FIGURE 2
Meta-analytical, sub-group estimate of gene expression related to insecticide resistance, with 95% CI in the P450, GST, and esterase gene families. The overall effect represents the collective effect of all the studies in terms of their unweighted analyses.
FIGURE 3
FIGURE 3
Meta-analytical, sub-group estimate of gene expression related to insecticide resistance, with 95% CI in the P450 gene families classified by gene clade. The overall effect represents the collective effect of all P450 studies.
FIGURE 4
FIGURE 4
Meta-analytical, sub-group estimate of the gene expression related to insecticide resistance, with 95% CI across different insecticide classes. The overall effect represents the collective effect of all the relevant studies.
FIGURE 5
FIGURE 5
Meta-analytical, sub-group estimate of the gene expression related to insecticide resistance, with 95% CI in different insect genus and vector types. The overall effect represents the collective effect of all the relevant studies.
FIGURE 6
FIGURE 6
Meta-analytical, sub-group estimate of the gene expression related to insecticide resistance, with 95% CI in the medical vector with different gene families and the P450 gene clade. The overall effect represents the collective effect of all the studies investigating the medical vector.
FIGURE 7
FIGURE 7
Meta-analytical, sub-group estimate of the gene expression related to insecticide resistance, with 95% CI in the agricultural vector with different gene families and the P450 gene clade. The overall effect represents the collective effect of each insecticide class in all the relevant studies.
FIGURE 8
FIGURE 8
Funnel plot of the weighted meta-analysis of the studies, including both susceptible and resistant expression data. The dark blue area means represent the 95% confidence interval.
See this image and copyright information in PMC

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