Introduction
Insect pests have a great impact on many aspects of human life.
Among all of these aspects, the harm to human health and the yield loss
in agricultural production are the most concerning. To make matters
worse, some insects serve as medium of pathogens, spreading diseases and
causing damage simultaneously. For example, Anopheles gambiaespread malaria and caused millions of deaths annually in Africa
(Consortium, 2017). As for agricultural production, the estimated yield
loss of crops due to insect pests is over 18% globally (Oerke,
2005).
Although there are many insect pest control methods available,
application of insecticides is still one of the most frequently used
method. Chemical insecticides were first introduced to controlinsect pests in the 1940s. Since then, thousands of insecticides
have been developed to protect human health and crops. Unfortunately,
long-term mismanagement of insecticide application led to the
development of insecticide resistance within insect pestpopulations. So far, more than 553 insect species have been reported to
have developed resistance to approximately 331 insecticides (Gould,
Brown, & Kuzma, 2018). The development of insecticide resistance
necessitates the application of higher dosages of said insecticide for
controlling insect pests , which in turn causes more serious
threats to human and environmental health (Kim, Kabir, & Jahan, 2017;
Tang et al., 2018). Insecticide resistance has become one of the most
formidable obstacles in insect pest control (Gould, Brown, &
Kuzma, 2018).
Insecticide target-site insensitive mutations and overexpression of
detoxification gene(s) are two major mechanisms conferring insecticide
resistance (Ffrench-Constant, 2013). Due to long-term selection by
insecticides, the individuals containing resistance associated genotypes
rapidly accumulate within populations. Generally, insecticide resistance
of insect pest populations can be predicted according to the
prevalence of target insensitive mutations and overexpression of
detoxification genes (Sonoda, 2010). To date, most resistance cases
occurred within five classes of insecticides: organophosphates,
pyrethroids, carbamates, neonicotinoids and diamides (Thomas & Ralf,
2015). According to the modes of action listed by the Insecticide
Resistance Action Committee (IRAC), organophosphates and carbamates
target acetylcholinesterases (AChE), pyrethroids target voltage gated
sodium channels (VGSC), diamides target ryanodine receptors (RyR), and
neonicotinoids target nicotinic acetylcholine receptor (nAChR). In
addition, metabolic resistances of these five classes of insecticides
are mainly associated with three important detoxification gene families:
cytochrome P450 (P450), glutathione S-transferase (GST) and
carboxyl/cholinesterases (CCE) (L. Yan et al., 2012).
Detecting the target insensitive mutations and overexpressed
detoxification genes within an insect pest population has long
been a useful method in monitoring resistance. Many methods have been
developed to detect target mutations such as PCR amplification of
specific alleles (PASA) (H. H. Yan et al., 2014) and random amplified
polymorphic DNA (RAPD) (Ferguson & Pineda, 2010). DNA microarray has
been used detecting overexpressed detoxification genes (Mavridis et al.,
2019). However, these methods are inefficient and time-consuming.
RNA-Seq data contains information allowing detection of single
nucleotide polymorphisms (SNPs) and gene expression levels (Costa,
Angelini, De Feis, & Ciccodicola, 2010). Thus, RNA-Seq data can be used
to detect target-site insensitive mutations and overexpressed
detoxification genes (Bonizzoni et al., 2015; De Wit, Pespeni, &
Palumbi, 2015). Hereļ¼to monitor the resistance of the aforementioned
five classes of insecticides, we collected reported target insensitive
mutations, target gene allelic sequences, three groups of detoxification
gene sequences from 82 insect species, and then developed a program,
FastD, to detect target insensitive mutations and overexpressed
detoxification genes from RNA-Seq data. To validate the reliability, we
applied FastD to detect target-site mutations and overexpressed
detoxification genes in five populations of two notorious insect
pest species, P. xylostella and A. gossypii .