Longfei Chen

and 8 more

Target-site insensitive mutations and overexpression of detoxification genes are two major mechanisms conferring insecticide resistance. Many molecular assays were applied to detect these two kinds of resistance genetic markers in insect populations. Unfortunately, these assays are time-consuming and have high false-positive rates. RNA-Seq data, which contains information on the variation within expressed regions of the genome and expression information of detoxification genes, provides us a valuable resource to detect resistance-associated markers. At present, there is no corresponding method at present. Here, we collected 66 reported resistance mutations of four main insecticide targets (AChE, VGSC, RyR, and nAChR) of 82 insect species. Next, we obtained 403 sequences of the four target genes and 12,665 sequences of three kinds of detoxification genes including P450, GST, and CCE. Here, we developed a Perl program, FastD, to detect insecticide target-site insensitive mutations and overexpressed detoxification genes from RNA-Seq data, and constructed a web server for FastD (http://www.insect-genome.com/fastd). FastD program was then applied to detect two kinds of resistant markers in five populations of two insects, Plutella xylostella and Aphis gossypii. Results showed that RyR mutation G4946E was detected in all P. xylostella populations, with higher frequencies in two resistant populations, ZZ (66.1%) and CHR (94.55%), than a susceptible population CHS (2.32%). CYP6a2 was overexpressed 10.82-fold in ZZ population. As to A. gossypii, nAChR mutation R81T was detected in resistant population KR with 49.85% frequency, but not in susceptible population NS. CYP6CY22 and CYP6CY13 were overexpressed 39.61- and 22.04-fold respectively in KR population. FastD is a program using RNA-Seq data to detect two types of resistance markers to estimate resistance level of insect populations. Generally, resistance level estimated by FastD were consistent with previous reports, confirming the reliability of this program in predicting population resistance at omics-level.