Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNA primarily expressed in germ cells that can silence transposons at the post-transcriptional level. Accurate prediction of piRNAs remains a significant challenge. We developed a program for piRNA annotation (Piano) using piRNA-transposon interaction information. We downloaded 13,848 Drosophila piRNAs and 261,500 Drosophila transposons. The piRNAs were aligned to transposons with a maximum of three mismatches. Then, piRNA-transposon interactions were predicted by RNAplex. Triplet elements combining structure and sequence information were extracted from piRNA-transposon matching/pairing duplexes. A support vector machine (SVM) was used on these triplet elements to classify real and pseudo piRNAs, achieving 95.3 ± 0.33% accuracy and 96.0 ± 0.5% sensitivity. The SVM classifier can be used to correctly predict human, mouse and rat piRNAs, with overall accuracy of 90.6%. We used Piano to predict piRNAs for the rice stem borer, Chilo suppressalis, an important rice insect pest that causes huge yield loss. As a result, 82,639 piRNAs were predicted in C. suppressalis. Piano demonstrates excellent piRNA prediction performance by using both structure and sequence features of transposon-piRNAs interactions. Piano is freely available to the academic community at http://ento.njau.edu.cn/Piano.html .

Summary: Rice stem borer (RSB, Chilo suppressalis) is an insect pest that causes huge economic losses every year. Control efforts rely heavily on chemical insecticides, which leads to serious problems such as insecticide resistance, environment pollution, and food safety issues. Therefore, developing alternative pest control methods is an important task. Here, we identified an insect‐specific microRNA, miR‐14, in RSB, which was predicted to target Spook (Spo) and Ecdysone receptor (EcR) in the ecdysone signalling network. In‐vitro dual luciferase assays using HEK293T cells confirmed the interactions of Csu‐miR‐14 with CsSpo and with CsEcR. Csu‐miR‐14 exhibited high levels of expression at the end of each larval instar stage, and its expression was negatively correlated with the expression of its two target genes. Overexpression of Csu‐miR‐14 at the third day of the fifth instar stage led to high mortality and developmental defects in RSB individuals. We produced 35 rice transformants to express miR‐14 and found that three lines had a single copy with highly abundant miR‐14 mature transcripts. Feeding bioassays using both T₀ and T₁ generations of transgenic miR‐14 rice indicated that at least one line (C#24) showed high resistance to RSB. These results indicated that the approach of miRNAs as targets has potential for improving pest control methods. Moreover, using insect‐specific miRNAs rather than protein‐encoding genes for pest control may prove benign to non‐insect species, and thus is worthy of further exploration.

Long non-coding RNA (lncRNA) is a class of noncoding RNA >200 bp in length that has essential roles in regulating a variety of biological processes. Here, we constructed a computational pipeline to identify lncRNA genes in the diamondback moth (Plutella xylostella), a major insect pest of cruciferous vegetables. In total, 3,324 lncRNAs corresponding to 2,475 loci were identified from 13 RNA-Seq datasets, including samples from parasitized, insecticide-resistant strains and different developmental stages. The identified P. xylostella lncRNAs had shorter transcripts and fewer exons than protein-coding genes. Seven out of nine randomly selected lncRNAs were validated by strand-specific RT-PCR. In total, 54–172 lncRNAs were specifically expressed in the insecticide resistant strains, among which one lncRNA was located adjacent to the sodium channel gene. In addition, 63–135 lncRNAs were specifically expressed in different developmental stages, among which three lncRNAs overlapped or were located adjacent to the metamorphosis-associated genes. These lncRNAs were either strongly or weakly co-expressed with their overlapping or neighboring mRNA genes. In summary, we identified thousands of lncRNAs and presented evidence that lncRNAs might have key roles in conferring insecticide resistance and regulating the metamorphosis development in P. xylostella.

Insecticide resistance is a substantial problem in controlling agricultural and medical pests. Detecting target site mutations is crucial to manage insecticide resistance. Though PCR-based methods have been widely used in this field, they are time-consuming and inefficient, and typically have a high false positive rate. Acetylcholinesterases (Ace) is the neural target of the widely used organophosphate (OP) and carbamate insecticides. However, there is not any software available to detect insecticide resistance associated mutations in RNA-Seq data at present. A computational pipeline ACE was developed to detect resistance mutations of ace in insect RNA-Seq data. Known ace resistance mutations were collected and used as a reference. We constructed a Web server for ACE, and the standalone software in both Linux and Windows versions is available for download. ACE was used to analyse 971 RNA-Seq data from 136 studies in 7 insect pests. The mutation frequency of each RNA-Seq dataset was calculated. The results indicated that the resistance frequency was 30%–44% in an eastern Ugandan Anopheles population, thus suggesting this resistance-conferring mutation has reached high frequency in these mosquitoes in Uganda. Analyses of RNA-Seq data from the diamondback moth Plutella xylostella indicated that the G227A mutation was positively related with resistance levels to organophosphate or carbamate insecticides. The wasp Nasonia vitripennis had a low frequency of resistant reads (<5%), but the agricultural pests Chilo suppressalis and Bemisia tabaci had a high resistance frequency. All ace reads in the 30 B. tabaci RNA-Seq data were resistant reads, suggesting that insecticide resistance has spread to very high frequency in B. tabaci. To the best of our knowledge, the ACE pipeline is the first tool to detect resistance mutations from RNA-Seq data, and it facilitates the full utilization of large-scale genetic data obtained by using next-generation sequencing.