TY - JOUR
AU - Cassone, B, J
AB - Abstract The Colorado potato beetle (Leptinotarsa decemlineata (Say)) is an important pest of the cultivated potato (Solanum tuberosum (L.) [Solanales: Solanaceae]). With its broad resistance toward commonly used insecticides, it is clear that more sophisticated control strategies are needed. Due to their importance in insect development, microRNAs (miRNAs) represent a potential tool to employ in insect control strategies. However, most studies conducted in this area have focused on model species with well-annotated genomes. In this study, next-generation sequencing was used to catalogue the miRNAs produced by L. decemlineata across all eight stages of its development, from eggs to adults. For most stages, the length of miRNAs peaked between 21 and 22 nt, though it was considerably longer for the egg stage (26 nt). Global profiling of miRNAs revealed three distinct developmental clusters: 1) egg stage; 2) early stage (first, second, and third instar); and 3) late stage (fourth instar, prepupae, pupae, and adult). We identified 86 conserved miRNAs and 33 bonafide novel miRNAs, including stage-specific miRNAs and those not previously identified in L. decemlineata. Most of the conserved miRNAs were found in multiple developmental stages, whereas the novel miRNAs were often stage specific with the bulk identified in the egg stage. The identified miRNAs have a myriad of putative functions, including growth, reproduction, and insecticide resistance. We discuss the putative roles of some of the most notable miRNAs in the regulation of L. decemlineata development, as well as the potential applications of this research in Colorado potato beetle management. agricultural entomology, genetics, molecular biology MicroRNAs (miRNAs) are small noncoding RNAs of ~22 nucleotides derived from hairpin miRNA precursors (Singh and Nagaraju 2008). Their primary function is to regulate gene expression posttranscriptionally (Asgari 2013). Indeed, through interactions with mature mRNA, these small RNAs finely modulate complex pathways of gene expression to optimal levels (Bartel and Chen 2004). Over the past 15 years, thousands of miRNAs have been discovered in plants and animals and implicated in a variety of cellular processes (Aravin et al. 2003, Biryukova et al. 2009, Asgari 2013, Banerjee and Roy 2017). In insects, most of the identified miRNAs are from species whose entire genome sequence is available, which includes the red flour beetle (Tribolium castaneum (Herbst) [Coleoptera: Chrysomelidae]; Luo et al. 2008), Drosophila [Diptera: Drosophilidae] species (Stark et al. 2007), silkworm (Bombyx mori (Linnaeus) [Lepidoptera: Bombycidae]; Yu et al. 2008), western honey bee (Apis mellifera (Linnaeus) [Hymenoptera: Bombycidae]; Weaver et al. 2007), and several mosquito species (Aedes albopictus (Skuse) [Diptera: Culicidae], Culex quinquefasciatus (Say) [Diptera: Culicidae], Anopheles gambiae (Giles) [Diptera: Culicidae]; Winter et al. 2007, Skalsky et al. 2010). As the number of whole-genome sequencing projects continues to rapidly expand, our capacity to catalogue and functionally characterize miRNAs is emerging as a promising tool for pest management. As first noted in Caenorhabditis elegans (Brenner) (Rhabditida: Rhabditidae) (Lee et al. 1993), miRNAs play a role in developmental regulation that has become increasingly studied in insects. For instance, Drosophila miRNAs have been implicated in embryogenic mechanisms, including segmentation (miR-31), dorsal closure/head involution (miR-310 family), repression of pro-apoptotic factors (miR-2 Family) as well as oogenesis, nervous system, and internal organ development (Leaman et al. 2005, Iovino et al. 2009). Other studies have highlighted the role of miRNAs in tissue- or stage-specific RNA expression (Aravin et al. 2003, Yu et al. 2008). However, only a few insects have had their miRNAs catalogued across development, and only a fraction of these are fully characterized (Ylla et al. 2016). While these organisms serve as adequate models for species-specific miRNAs discovery, little is known about miRNAs in agriculturally important coleopteran species. Among agricultural pests, the Colorado potato beetle, Leptinotarsa decemlineata (Say), is considered one of the most significant insect defoliators of cultivated potato (Solanum tuberosum L). This beetle is particularly difficult to control and can cause serious economic losses through crop destruction (Alyokhin et al. 2013). If left unmanaged, defoliation can result in yield losses upwards of 60% (Hare 1980). While pest populations of L. decemlineata are thought to have originated in the U.S. Plains states (Izzo et al. 2018), host plant variation, and incredible adaptive abilities have contributed to their range expansion and success across North America, Europe, and Asia (Hsiao 1981, Casagrande 1987, Hare 1990). Leptinotarsa decemlineata feeds on most plants in the family Solanaceae and can achieve pest status in many, but its crop of choice is potato (S. tuberosum). Current management of L. decemlineata is primarily through the application of neonicotinoid insecticides, such as clothianidin and dinotefuran (Huseth et al. 2014). However, these beetles have shown an alarming ability to develop insecticide resistance (Alyokhin 2007). This is principally achieved through the increased activity of detoxifying enzymes and xenobiotic transporters, such as esterases and cytochrome P450s (Li and Schuler 2007, Dermauw and Van Leeuwen 2014). Leptinotarsa decemlineata’s ability to develop resistance may originate from a need to detoxify the dietary poisons – glycoalkaloids – produced by Solanaceae (Pelletier et al. 2013, Dermauw and Van Leeuwen 2014). Consequently, traditional chemical-based pest control approaches are often insufficient for long-term management of L. decemlineata. While some studies have examined the transcriptional regulation of detoxifying enzymes (Kaplanoglu et al. 2017), there has been little research geared towards identifying developmental molecular targets. Leptinotarsa decemlineata development consists of eight primary stages. Adult beetles consume ~10 cm2 of foliage per day (Ferro et al. 1985) and deposit clusters of yellow eggs on the underside of leaves (Coombs et al. 2002). Each female can lay 300–800 eggs in their lifetime (Harcourt 1971); depending on environmental conditions, there may be several generations in one growing season (Capinera 2001). From egg clusters, larvae progress from first- to fourth-instar stages, distinguished by the width of their head capsules (Capinera 2001). During this time, they can consume as much as 40 cm2 of foliage per day (Ferro et al. 1985). From there, the larvae enter into a prepupal stage where they eat very little and prepare to bury into the soil near the base of potato plants. Once underground, they metamorphose into pupae and will remain in this stage for 1–3 wk (Capinera 2001). They emerge as adults and begin laying eggs within a week (Ferro et al. 1985). The entire lifecycle takes between 2 and 8 wk, depending on environment conditions (Ferro et al. 1985, Logan et al. 1985). The genome of the Colorado potato beetle was recently sequenced (Schoville et al. 2018), making it more feasible to carry out miRNA studies on a genome-wide level. In our current study, small RNA sequencing was conducted to identify conserved and novel miRNAs across all eight stages of L. decemlineata development. Further, we discuss the putative roles of some of the most interesting miRNAs and the potential applications of this research in integrated pest management. Methods Colony Rearing and Beetle Collections Experiments were carried out using a laboratory colony of L. decemlineata established in 2016 from beetles provided by Dr. Brent Sinclair (University of Western Ontario) and subjected to multiple ongoing introductions of beetles collected in Manitoba. The colony was reared in environmental chambers under controlled conditions of 25 ± 1°C, 70 ± 5% relative humidity with a 12-h day/night cycle that included 1 h dusk and dawn transitions. All stages had continuous access to fresh potato seedlings (Solanum tuberosum L. cv. ‘Russet Burbank’). For the experimental collections, L. decemlineata developmental stages were sampled as follows: L1 (first instar, n = 50), L2 (second instar, n = 50), L3 (third instar, n = 31), L4 (fourth instar, n = 29), prepupae (n = 20), pupae (n = 21), and adults (≤7 d old, n = 15). The number of beetles collected per stage was based on relative body sizes. In addition, four egg batches (~100 eggs) were collected from the undersides of potato leaves. Samples were snap frozen in liquid nitrogen and stored at −80°C until nucleic acid isolation. Isolation of MicroRNAs and Next-Generation Sequencing Samples were homogenized in liquid nitrogen and RNA was isolated using the AllPrep DNA/RNA Mini Kit (Qiagen, Valencia, CA) in accordance with the manufacturer’s recommended protocol. Samples were diluted 10- to 40-fold with molecular grade water and quantified using the Qubit 4 Fluorometer (Thermo Fisher, Waltham, MA). RNA quality was assessed using the BioAnalyzer 2100 (Agilent Technologies, Santa Clara, CA). RNA (~1 mg per sample) was sent to Génome Québec Innovation Centre at McGill University for library preparation and sequencing. Adaptor-ligated double-stranded cDNA libraries were constructed using the NEBNext Small RNA Library Prep Set (New England Biolabs, Ipswich, MA). Libraries were multiplexed and sequenced in 50-bp single-read fashion on a single flowcell lane using the Illumina HiSeq2000 platform. The raw reads for this project were deposited at NCBI Sequence Read Archive (SRA) under the accession SRR5482811 to SRR5482818 (study SRP105295). MicroRNA Analysis Quality check of raw sequencing reads was done using FastQC software (Andrews 2010). Adapter sequences were trimmed using the Cutadapt tool (Martin 2011); reads between 18 and 26 nucleotides were retained with the following parameters: −e 0.15 –m 18 –M 26. The processed reads were mapped to the L. decemlineata genome assembly 1.0 (accessed at https://data.nal.usda.gov/dataset/leptinotarsa-decemlineata-genome-assembly-10) using Burrows Wheeler Aligner (Li and Durbin 2009) and the following optimized parameters: –n 1 –o 0 –e 0 –k 1 –t 4. Mapped reads were then imported into mirDeep2 for miRNA discovery (Friedländer et al. 2012). Conserved miRNAs were identified using known T. castaneum miRNAs as the reference and D. melanogaster (Meigen) (Diptera: Drosophilidae) as the closely related comparison species. The RNAfold module of miRDeep2 was used to predict novel miRNAs by scoring based on the presence of the star sequence, precursor sequence, low folding energy (negative), and absence/presence of mature sequence in the miRBAse (Rehmsmeier et al. 2004). Read counts for conserved and novel miRNAs were calculated and normalized using the quantifier module of miRDeep2. Other noncoding RNAs identified were filtered using the available RNA database on BLASTn and tRNAscan-SE (available at http://lowelab.ucsc.edu/tRNAscan-SE/). Only miRNAs with read counts ≥10 in at least one developmental stage were included in subsequent analyses. To ensure the identified novel miRNAs were indeed specific to L. decemlineata, all were searched against the 48,860 mature miRNAs in the miRBase (v22) database (Kozomara et al. 2019). The relevant output files for mirDeep2 are found in Supp File 1 (online only). The subset of novel miRNAs was further interrogated to identify ‘bonafide miRNAs’ based off of commonly accepted criteria for metazoan miRNAs (Fromm et al. 2015). This criteria included the following: 1) reads between 20 and 26 nt in length; 2) two nucleotide offset between the miRNA 5p-arm and 3p-arm in the folded precursor structures; 3) pairing of ≥16 nt between the 5p- and 3p-arm sequences; 4) loop sequence >8 nt; 5) presence of both mature and star sequences; and 6) score for maximum folding energy (mfe) ≥ 1. Criteria 1 through 5 were deduced manually, whereas maximum folding energy was calculated using MiPred (Jiang et al. 2007). It has been shown that, in certain cases, both arms of an miRNA can be co-expressed toward a certain function (Choo et al. 2014, Mitra et al. 2015, Pan et al. 2017). In our study, both arms were considered novel miRNAs if the expression of the mature sequence was less than twice that of the star sequence (Desvignes et al. 2015, Fromm et al. 2015). MicroRNA Target Prediction and Enrichment Analyses Target prediction for the conserved miRNAs was based on the 3′-UTR sequences derived from the L. decemlineata mRNA dataset (obtained from RefSeq; Pruitt et al. 2005). First, ExUTR (v.0.1.0) was used to predict open reading frames of the mRNA from which the 3′-UTR sequences were retrieved (Huang and Teeling 2017). Only 3’-UTR regions longer than 20 nt were retained. We then used miRANDA (Enright et al. 2003) and PITA (v6) (Kertesz et al. 2007) to predict miRNA targets using default settings. Results from miRANDA were filtered by retaining targets with an energy score cutoff of ≤20 and minimum pairing score threshold of 150 (Marín and Vaníek 2011, Oliveira et al. 2017). Similarly, only sites with dG ≤ −10 were retained for the PITA predicted targets (Marín and Vaníek 2011). Our final dataset consisted of only the predicted targets shared between miRANDA and PITA. Target prediction of the bonafide microRNA was initially performed using TargetScanFly (V.6.2 custom; Ruby et al. 2007). This web tool takes into account the seed region of the miRNAs (situated at positions 2–8 from the miRNA 5´-end) and predicts the target mRNAs from the fly genome; it does so using an exact match to the seed region followed by an adenine nucleotide (Ruby et al. 2007). Targets were then imported into the DAVID Enrichment Analysis tool (Huang et al. 2007) to derive putatively enriched biological processes (P < 0.05). Due to the low sensitivity rate of TargetScanFly, the results were supplemented by using an approach similar to the target prediction of conserved miRNAs. However, the 3′-UTR regions of the T. castaneum genome were used in lieu since this beetle species is the closest relative to L. decemlineata with annotated mRNA transcripts. Results and Discussion Overview of the Small RNA Dataset In total, eight miRNA libraries were constructed using a small RNA sequencing approach. Each library represented a specific developmental stage in the life cycle of L. decemlineata: egg (28,392,458 reads), first instar (25,146,417 reads), second instar (26,656,695 reads), third instar (26,669,828 reads), fourth instar (26,467,040 reads), prepupae (26,734,432 reads), pupae (27,748,787 reads), and adult (28,740,530 reads) (see Supp File 2 [online only]). Figure 1 shows the read length distribution of small RNAs between 18 and 26 nt identified at each developmental stage. For most stages, the highest peaks were obtained between 21 and 22 nt, which are typically representative of miRNAs. However, the egg stage peaked at 26 nt, characteristic of longer miRNAs or Piwi-interacting RNAs (piRNAs) (Chen et al. 2010, Burke et al. 2016, Wenda et al. 2017). High levels of piRNAs are typical in early development, as they are essential for transposon activity in the embryo (Praher et al. 2017). Fig. 1. Open in new tabDownload slide Read length distribution of small RNA across all eight developmental stages of Leptinotarsa decemlineata. The egg stage peaked at 26 nt, whereas the other stages peaked between 21 and 22 nt. Each individual bar graph for a given nucleotide length is displayed left to right in order of development. Fig. 1. Open in new tabDownload slide Read length distribution of small RNA across all eight developmental stages of Leptinotarsa decemlineata. The egg stage peaked at 26 nt, whereas the other stages peaked between 21 and 22 nt. Each individual bar graph for a given nucleotide length is displayed left to right in order of development. The proportion of reads mapping to the L. decemlineata genome was greatest in the earlier developmental stages: egg (68.5%), first instar (64.4%), second instar (61.6%), and third instar (64.6%). The later stages had mapping ratios ≤50%, with the prepupal stage having the lowest overall (43.2%). The relatively low mapping ratios of our dataset is expected; since the L. decemlineata genome was only very recently assembled (Schoville et al. 2018) and is not as complete as other extensively studied genomes. Further, as noted by Schoville et al. (2018), genetic variation within this beetle species is high. Therefore, comparisons of our population to the sequenced Long Island population may contribute to the reduced mapping efficiencies. Conserved MicroRNAs As of 2018, there were >48,000 miRNAs from 271 organisms in miRBase (v22) (Kozomara et al. 2019); however, there is some bias in determining how many conserved miRNAs truly exist given some insects orders are less studied than others. We identified 86 miRNAs that were expressed in at least one developmental stage and previously found in at least one other insect species. Of these miRNAs, 26 of them expressed both 3p- and 5p-arms. This is consistent with other studies, which have reported between 59 and 274 genes encoding conserved miRNAs (Li et al. 2015, Rebijith et al. 2016, Ylla et al. 2016, Wu et al. 2013). Further, Schoville et al. (2018) previously reported 85 putative miRNA loci representing 61 miRNA families in the L. decemlineata genome, of which 73 miRNA sequences and locations were in agreement with our predicted list. However, we identified several miRNAs (miR-137, miR-190, miR-210, miR-2765, miR-276, miR-279d, miR-279e, miR-3841, miR-6012, miR-932, miR-989, miR-9b, and miR-9c) not previously reported in the L. decemlineata genome (Supp File 3 (online only)). The egg stage had the lowest number of miRNAs, whereas first instar had the highest number (see Fig. 2). Only one miRNA was stage-specific: miR-282-5p identified in first instar larvae. In D. melanogaster, this miRNA is thought to play a role in nervous system activities at different stages of metamorphosis (Vilmos et al. 2013). Fig. 2. Open in new tabDownload slide Bar plot showing the number of conserved miRNAs and putative stage-specific novel miRNAs found across Leptinotarsa decemlineata development. The egg stage had the lowest number of conserved miRNAs, but the highest number of putative stage-specific miRNAs. Fig. 2. Open in new tabDownload slide Bar plot showing the number of conserved miRNAs and putative stage-specific novel miRNAs found across Leptinotarsa decemlineata development. The egg stage had the lowest number of conserved miRNAs, but the highest number of putative stage-specific miRNAs. Conserved MicroRNAs found across all developmental stages. In total, 69 microRNAs were identified across all eight L. decemlineata developmental stages. This includes 10 previously identified miRNA clusters obtained from miRBAse: miR-317/277/34, miR-2/13/71, miR-11/998, miR-279c/3843/3841/9c/2944a, miR-87a/87b, miR-92a/92b, miR-279e/9e/2944c, miR-283/3477/12, miR-193/2788, and miR-100/125/let-7. These clusters are defined by their close proximity on a chromosome (between 100 bp and 50 kbp), and are usually transcribed by the same promoter (Lai and Vera 2013). The five most abundant were miR-1-3p (15.5%), miR-276-3p (12.5%,), miR-13a-3p (9.2%), miR-279e-3p (7.3%), and miR-bantam-3p (6.9%). Most of these miRNAs are also found in relatively high abundance in other insects, such as B. mori and T. castaneum (Liu et al. 2010, Wu et al. 2013). Table 1 displays the putative functions of the top five most abundant conserved miRNAs. Of particular interest is the mir-2/13/71 family, since it is known to regulate cellular proliferation and differentiation, and is involved in insect metamorphosis (Lozano et al. 2015). The relative proportions of conserved miRNAs found at each developmental stage are shown in Supp File 4 (online only) with visual representation in Supp File 5 (online only). Table 1. The five most abundant conserved miRNAs found across all stages of Leptinotarsa decemlineata development miRNA . Sequence (5′ to 3′) . Read count . Putative function . miR-1-3p uggaauguaaagaaguauggag 3,447,326 Skeletal and cardiac muscle development in insects (Townley- Tilson et al. 2010, McCarthy 2011). Showed increased expression in cold exposed beetles (Morin et al. 2017a,b). miR-13a-3p uaucacagccacuuuaaugaacu 2,037,170 Member of the mir-2 family, which regulates metamorphosis in insects (Lozano et al. 2015). Plays a role in deltamethrin resistance in female mosquitoes (Guo et al. 2017), and in the suppression apoptosis early in development (Leaman et al. 2005). bantam-3p ugagaucauugugaaagcugauu 1,531,260 Involved in multiple signaling pathways during insect growth and development (Hipfner et al. 2002, Nolo et al. 2006, Becam et al. 2011, Boulan et al. 2013), as well as in regulating cell proliferation and survival (Parrish et al. 2009). miR-276-3p uaggaacuucauaccgugcucu 2,784,163 Undetermined. miR-279e-3p ugacuagaucgaacacucgcuugc 1,620,907 Undetermined; part of the miR-279e ~ miR-2944c cluster previously only identified in T. castaneum. miRNA . Sequence (5′ to 3′) . Read count . Putative function . miR-1-3p uggaauguaaagaaguauggag 3,447,326 Skeletal and cardiac muscle development in insects (Townley- Tilson et al. 2010, McCarthy 2011). Showed increased expression in cold exposed beetles (Morin et al. 2017a,b). miR-13a-3p uaucacagccacuuuaaugaacu 2,037,170 Member of the mir-2 family, which regulates metamorphosis in insects (Lozano et al. 2015). Plays a role in deltamethrin resistance in female mosquitoes (Guo et al. 2017), and in the suppression apoptosis early in development (Leaman et al. 2005). bantam-3p ugagaucauugugaaagcugauu 1,531,260 Involved in multiple signaling pathways during insect growth and development (Hipfner et al. 2002, Nolo et al. 2006, Becam et al. 2011, Boulan et al. 2013), as well as in regulating cell proliferation and survival (Parrish et al. 2009). miR-276-3p uaggaacuucauaccgugcucu 2,784,163 Undetermined. miR-279e-3p ugacuagaucgaacacucgcuugc 1,620,907 Undetermined; part of the miR-279e ~ miR-2944c cluster previously only identified in T. castaneum. Open in new tab Table 1. The five most abundant conserved miRNAs found across all stages of Leptinotarsa decemlineata development miRNA . Sequence (5′ to 3′) . Read count . Putative function . miR-1-3p uggaauguaaagaaguauggag 3,447,326 Skeletal and cardiac muscle development in insects (Townley- Tilson et al. 2010, McCarthy 2011). Showed increased expression in cold exposed beetles (Morin et al. 2017a,b). miR-13a-3p uaucacagccacuuuaaugaacu 2,037,170 Member of the mir-2 family, which regulates metamorphosis in insects (Lozano et al. 2015). Plays a role in deltamethrin resistance in female mosquitoes (Guo et al. 2017), and in the suppression apoptosis early in development (Leaman et al. 2005). bantam-3p ugagaucauugugaaagcugauu 1,531,260 Involved in multiple signaling pathways during insect growth and development (Hipfner et al. 2002, Nolo et al. 2006, Becam et al. 2011, Boulan et al. 2013), as well as in regulating cell proliferation and survival (Parrish et al. 2009). miR-276-3p uaggaacuucauaccgugcucu 2,784,163 Undetermined. miR-279e-3p ugacuagaucgaacacucgcuugc 1,620,907 Undetermined; part of the miR-279e ~ miR-2944c cluster previously only identified in T. castaneum. miRNA . Sequence (5′ to 3′) . Read count . Putative function . miR-1-3p uggaauguaaagaaguauggag 3,447,326 Skeletal and cardiac muscle development in insects (Townley- Tilson et al. 2010, McCarthy 2011). Showed increased expression in cold exposed beetles (Morin et al. 2017a,b). miR-13a-3p uaucacagccacuuuaaugaacu 2,037,170 Member of the mir-2 family, which regulates metamorphosis in insects (Lozano et al. 2015). Plays a role in deltamethrin resistance in female mosquitoes (Guo et al. 2017), and in the suppression apoptosis early in development (Leaman et al. 2005). bantam-3p ugagaucauugugaaagcugauu 1,531,260 Involved in multiple signaling pathways during insect growth and development (Hipfner et al. 2002, Nolo et al. 2006, Becam et al. 2011, Boulan et al. 2013), as well as in regulating cell proliferation and survival (Parrish et al. 2009). miR-276-3p uaggaacuucauaccgugcucu 2,784,163 Undetermined. miR-279e-3p ugacuagaucgaacacucgcuugc 1,620,907 Undetermined; part of the miR-279e ~ miR-2944c cluster previously only identified in T. castaneum. Open in new tab Target prediction of conserved miRNAs in the L. decemlineata genome. MicroRNA target prediction can be challenging due to high false-positive rates and low sensitivity of the available prediction tools (Seitz 2017, Fridrich et al. 2019). Ideally, miRNA target prediction will be conducted experimentally; however, current approaches are unable to provide throughput identification. Several research studies have compare target prediction tools and consequently identified optimal parameters for miRANDA and PITA to increase specificity and sensitivity (Marín and Vaníek 2011, Peterson et al. 2014). Using these programs, we carried out target prediction against the 3′-UTR of the L. decemlineata mRNA dataset. In total, 77 (91%) conserved miRNAs had identified targets that were consistent with both miRANDA and PITA (Supp File 6 (online only)). Of these, let-7, miR-210, miR-276, miR-34, miR-6012, miR-71, bantam, miR-1, and miR-13a had the largest number of concordant targets. Partitioning of conserved microRNAs across development. A multidimensional scaling (MDS) plot was constructed to visualize the relationships among the global profiles of conserved miRNAs and developmental stage (Fig. 3a). A pattern of three distinct clusters emerged: 1) egg stage; 2) early stage (first, second, and third instar); and 3) late stage (fourth instar, prepupae, pupae, and adult). Below we discuss the miRNAs that are specific to each of these clusters and their putative functional roles. Fig. 3. Open in new tabDownload slide Multidimensional scaling (MDS) plots showing clustering of Leptinotarsa decemlineata developmental stages based on the log fold change values of (A) conserved miRNAs and (B) novel miRNAs. Three distinct clusters emerge for both: egg stage, early stage and late stage. Fig. 3. Open in new tabDownload slide Multidimensional scaling (MDS) plots showing clustering of Leptinotarsa decemlineata developmental stages based on the log fold change values of (A) conserved miRNAs and (B) novel miRNAs. Three distinct clusters emerge for both: egg stage, early stage and late stage. Egg stage microRNAs. The majority of reads were derived from only two miRNAs: miR-279c-3p (30%) and miR-279e-3p (28%) (Supp File 4 [online only]). Members of the miR-279 family are responsible for the evolution of eusociality-related traits across Arthropoda (Søvik et al. 2015). The family is also one of the most prominent, maternally loaded miRNAs in insect oocytes (Ninova et al. 2016). Most of the other miRNAs identified at this stage were lower in abundance relative to other developmental stages. The absence or reduced expression of these may be due to maternal-zygotic transition (MZT), which occurs during embryogenesis in many arthropods (Ninova et al. 2014). During MZT, specific miRNAs target maternally deposited transcripts to degrade them. A small subset (e.g., miR-92a-3p, miR-993-5p, miR-993-3p, mir-998-3p, miR-2944b-5p, miR-2944a-3p, miR-315, miR-279c, and miR-9e-3p) did show higher abundance in the egg stage compared to other stages (Supp File 7 [online only]). In T. castaneum, some of these miRNAs are found in clusters and putatively function in MZT and maternal transcript degradation (Ninova et al. 2016). Indeed, our results suggest that closely related species share similar miRNAs involved in MZT (Bushati et al. 2008). Both T. castaneum and L. decemlineata are short-band development type arthropods (Ninova et al. 2016); thus, miRNAs involved in MZT (as well as embryogenesis) are likely more similar between these two beetles relative to arthropods that undergo long-band development (e.g., D. melanogaster). Consequently, we find miRNAs previously implicated in MZT and specific to T. castaneum (e.g., miR-2944b-5p, miR-279c, and miR-3841) are also present in L. decemlineata but absent in D. melanogaster. Early development microRNAs. After hatching from eggs, L. decemlineata undergoes four distinct larval instar stages (Capinera 2001). Developing beetles at their early larval stages cause the most damage to foliar plant tissues; thus, pest control strategies targeting these stages may be particularly effective (Gelman et al. 2001, Alyokhin et al. 2013). Of note is miR-219, which was only expressed in the first three instar stages. This miRNA is involved in neurodegeneration (Hudish et al. 2013, Santa-Maria et al. 2015), learning, and memory (Wang et al. 2012). Other miRNAs found in relatively higher proportions in early development were bantam-3p, miR-929-5p, miR-1000-5p, miR-927a-5p, and miR-124-3p (Supp Files 4 and 7). The latter miRNA has been implicated in tumor suppression and in the inhibition of the transition from epithelial to mesenchymal tissue (Liang et al. 2013, Michely et al. 2017, Zhang et al. 2018). This transition (Type I) is associated with embryogenesis and organ development at the egg stage of organisms (Kalluri and Weinberg 2009); thus, the relatively high abundance of miR-124 in the early stages of L. decemlineata could indicate a halting of these processes. In honeybees, miR-124 is thought to interact with miR-12 for the development of early, long-term, and short-term memory (Michely et al. 2017). It is also one of the few miRNAs showing highest relative abundance in third instar larvae. Also of note is miR-927a, which is negligibly expressed during the egg stage but shows elevated expression during these early larval stages (Supp File 7 [online only]). There is evidence from L. decemlineata that this miRNA is a key transcriptional regulator and possibly involved in insecticide resistance, imaginal disc derived wing morphogenesis, and axon guidance (Morin et al. 2017a,b). Late development microRNAs. As larval development nears completion, L. decemlineata ceases most feeding activity, buries under the soil and metamorphoses into the adult beetle (Capinera 2001, Alyokhin et al. 2013). Consistent with B. mori, miR-8 showed peak relative abundance during the fourth larval stage (Liang et al. 2013). Given miR-8 is involved in wingless signaling and regulation of growth hormones (Kennell et al. 2008), its elevated expression could be indicative of regulation of growth hormones in preparation for the prepupal stage. miR-317-3p is one of the few conserved miRNAs that is found in higher proportion in the prepupae than any other developmental stage. Enrichment analysis of terms associated with miR-317 targets indicated this miRNA may function in metamorphosis, pupation, and neuron remodeling; all of which align closely with this stage’s developmental hallmarks. In addition, miR-317-3p had mushroom body development as an enriched term. Mushroom bodies are structures of higher-level functions in arthropods (Kurusu et al. 2002). Although the neuroblasts responsible for the cells that compose the mushroom bodies start dividing into the embryonic stages and larval stages, the increase in the proportion of miR-317-3p in prepupae may be correlated with an end to the mushroom bodies dividing. During the pupal stage, internal tissue reorganization occurs and the beetle begins to develop its adult features (Díaz-García and Baonza 2013). Both mir-275 and miR-2765 showed comparatively higher expression at this stage (Supp File 7 [online only]). The former is involved in pupal metamorphoses in Anopheles funestus (Giles) (Diptera: Culicidae) and Eupolyphaga sinensis (Walker) (Blattodea: Corydiidae) (Wu et al. 2013, Allam et al. 2016). Adult beetles showed higher relative abundance of miR-277, which is a prominent target of transcripts encoding a guanine exchange factor (vimar). Vimar has been used as a marker for its direct involvement in the regulation of several aerobic metabolism pathways, including the first stage of the TCA cycle (Courteau et al. 2012). Indeed, downregulation of miR-277 in goldenrod gall fly larvae leads to a decrease in various mitochondrial enzymes associated with aerobic respiration, potentially allowing for a reduction in metabolism during diapause (Courteau et al. 2012). Given adult L. decemlineata had higher expression of this miRNA, it may be linked to their capacity to undergo diapause overwinter. Of further interest was the miR-100/125/let-7 cluster (Supp File 7 [online only]). Let-7 was discovered in C. elegans, functionally controlling larval development over time, and was later found to cluster with miR-100 and miR-125 (Reinhart et al. 2000, Hertel et al. 2012). These miRNAs display comparatively elevated levels throughout the late development of L. decemlineata (Supp File 4 [online only]). Similarly, both mir-100 and let-7 increase later in development in B. mori and Blattela germanica (Linnaeus) (Blattodea: Blattellidae) (Liang et al. 2013, Rubio and Belles 2013). In other species, this cluster plays an important role in developmental processes, including wing morphogenesis in B. germanica, and maturation of neuromuscular junctions of adult abdominal muscles in Drosophila (Caygill and Johnston 2008). This could explain the relative high abundance of this cluster in the adult stages of L. decemlineata. Given their ubiquity and the high degree of conservation of this cluster in diverse species ranging from C. elegans to H. sapiens, these conserved miRNAs are not good molecular targets for pest management. Novel MicroRNAs In total, 861 novel L. decemlineata miRNA candidates were identified by mIRDeep2 (Supp File 8 [online only]). After filtering based on redundancy and prediction score, 296 putative novel miRNAs were expressed in at least one developmental stage (Supp Files 9 and 10 [online only]). This is similar to Ylla et al. (2017), which identified 264 novel miRNA candidates using a similar approach. Of this subset, 33 miRNA genes passed all six stringent criteria (see Methods) and are considered ‘bonafide miRNAs’ (Supp File 9 [online only]). Five bonafide miRNAs were found across all developmental stages, though in relatively low abundance compared with the ubiquitous conserved miRNAs: LdeNovel-1,2,3,4,5. This is expected because species-specific miRNAs generally have less target mRNAs than conserved ones, which have evolved to target multiple transcripts (Ylla et al. 2016). Furthermore, LdeNovel4 and LdeNovel5 showed similar seed regions to miR-3843 and miR-9. Although the other miRNAs did not pass our robust criteria, they cannot be disregarded as potential miRNA candidates, as not all miRNAs exhibit the expected characteristics (Berezikov et al. 2007, Havens et al. 2012). For instance, noncanonical miRNAs follow distinctive preprocessing pathways, and thus, may not meet all criteria required of a canonical miRNA (Abdelfattah et al. 2014). The MDS plot shows the similarities in distribution of novel miRNA profiles across developmental stages (Fig. 3b). Similar to the conserved miRNAs, discrete clustering was observed between the egg, early stages (first to third instar) and later stages (fourth instar to adult). Target prediction of bonafide novel miRNAs in the L. decemlineata genome. All 33 bonafide microRNAs had predicted targets using each of the three prediction tools (i.e., TargetScanFly, miRANDA, and PITA). The enriched terms associated with these targets are shown in Supp Tables 11 and 12 (online only). Below, we discuss the putative functional roles of some of the more interesting and developmental-stage specific miRNAs. The majority of novel microRNAs are stage- and egg-specific. Over 70% (n = 211) of the novel miRNAs were stage-specific, with the highest proportions found in the egg stage (29%, n = 62) (Fig. 2). The other developmental stages contained between 7 and 13% stage-specific miRNAs. Our findings of a relatively large number of novel- and stage-specific miRNAs at the egg stage is not unexpected; fast-evolving miRNAs have been shown to be important for MZT and often target maternally deposited transcripts during embryogenesis (Ninova et al. 2014). Ylla et al. (2017) reported that both T. castaneum and D. virilis (Sturtevant) (Diptera: Drosophilidae) have high proportions of species-specific miRNAs in their embryonic stages. Interestingly, many of the egg stage-specific miRNAs had seed regions similar to miRNA families and clusters responsible for MZT and maternal transcript degradation in T. castaneum (Ninova et al. 2016). Indeed, some of the prominent seed sequences reported by Ninova et al. (2016; e.g., AGUACG, AGUACA [miR-3851 family], CACUGG [miR-309 family], and AUCACA [miR-2/13 family and miR-2944 family]) were identified in some novel miRNAs specific to the egg stage of L. decemlineata. Of the subset of egg-stage specific novel miRNAs, only three were bonafide: LdeNovel24, LdeNovel42-3p, and LdeNovel45-5p. LdeNovel24 was expressed for both the 3p and 5p arms in approximately equal levels. LdeNovel42-3p had a similar seed region to tca-miR-3851b-3p, which belongs to the miR-3851 family, and was previously determined to be specific to Tribolium (Ninova et al. 2014). Some significantly enriched terms associated with the targets of the seed region of this miRNA include cell fate specification and neuron projection morphogenesis (Supp Table 12 [online only]). Novel microRNAs found in early development. In total, 149 novel miRNAs were identified in at least one of the three early developmental stages, including 62 stage-specific and 3 exclusively found across early development: LdeNovel19-5p, Lde_Putative177, and Lde_Putative178. Of these, LdeNovel19-5p was the only one showing sensory organ development as an enriched term. Further, LdeNovel20-5p was a bonafide candidate found only in the first- and third-instar stages. Compellingly, three of its predicted targets (Adh transcription factor, Kinesin heavy chain, and foraging) are associated with larval locomotory behavior (Supp Table 12 [online only]). Further investigation is warranted to confirm the involvement of this miRNA, and others, in controlling complex biological processes in L. decemlineata. Novel microRNAs found in late development. In total, 136 putative novel miRNAs were identified in at least one of the four stages associated with late development, including 70 stage-specific miRNAs and 21 present across all four stages. Of these, only one was specific to late development. Of particular interest was bonafide miRNA LdeNovel15-5p, which increased in relative expression from the fourth-instar stage to the adult stage. Its putative targets include somatic muscle development and myoblast fusion. Myoblast fusion occurs in adults for muscle regeneration by satellite cells (Kim et al. 2015). In addition, somatic muscle development in D. melanogaster occurs in the early stages initially, after which these muscles are destroyed during the prepupal and pupal stages and regenerated during the later stages (Gunage et al. 2017). It is possible that the expression of LdeNovel15-5p is connected to muscle regeneration in adult L. decemlineata. LdeNovel17-3p was expressed in only the fourth instar, prepupal, and pupal stages, and its predicted targets are associated with the body morphogenesis. Because L. decemlineata undergoes total reconstruction of its body from the fourth instar stage to the pupal stage, LdeNovel17-3p could be an miRNA associated with the metamorphosis from the larval to adult stage. Novel microRNA clusters. In total, seven putative miRNA clusters consisting of only L. decemlineata-specific novel miRNAs were identified (Table 2). A notable cluster found on KI579029Scaffold547 had two bonafide miRNAs with the same seed region, which is characteristic of some miRNA families (Kamanu et al. 2013). In addition, two new clusters composed of only novel L. decemlineata-specific miRNAs with high read counts in the late stages were discovered on KI578802Scaffold320 and KI578784Scaffold302. The seed regions of the miRNAs in these clusters had no similarity to any other miRNA, suggesting they could represent new families of miRNAs specific to L. decemlineata. In both clusters, some miRNAs began their expression in the third instar stage. All four miRNAs in the KI578784 cluster showed the highest expression in the adult stage. Therefore, these putative clusters could be the focus of future research into highly specific late-stage pest control strategies aimed exclusively for L. decemlineata. Table 2. Putative novel miRNA clusters identified in Leptinotarsa decemlineata that are less than 10 Kb from one another Contig . Start (bp) . End (bp) . Strand . Predicted mature sequence . KI579029Scaffold547 39029 39088 + ucagguacggucacgaucuuuugu 37943 38003 + aucuggcacucauugcgacuaca 36820 39881 + uaguacguugaguaccgggcacu 38494 38555 + uaguacguggagugucagguaa 39254 39312 + uaguacaaucaguauggggugg KI578515scaffold33 18462 18520 + uaaggaacuauuggugugaugu 18331 18392 + uaaagcuauauuaaccuaagaaa 26924 26983 + ucacuggguguuaguaugucaca KI581027scaffold2545 20074 20132 - uuuugaaauuaguuguggaggauc 27950 28040 - uaguauaucgaggaugcagaugagu KI578802scaffold320 183160 183217 + ccuguggcuccggaauacauuu 182766 182823 + ccuguggcuccggaauacauuu 177720 177779 + ugaaucucucugccauaggaua KI578784scaffold302 1638454 1638512 + acuggugaacacugaaaccgcu 1638613 1638674 + uauugguacgcccugcuucaacu 1625797 1625852 + ugcgguaccaguuguaauuagc 1625926 1625985 + uagucauagcgcuuggcauagu KI579071scaffold589 339288 339347 + uuuuaucucaaucaccauuuucg 330724 330779 + uucugcucugauuucacaaga 338565 338524 + uuugugcuggagccauggaaau Contig . Start (bp) . End (bp) . Strand . Predicted mature sequence . KI579029Scaffold547 39029 39088 + ucagguacggucacgaucuuuugu 37943 38003 + aucuggcacucauugcgacuaca 36820 39881 + uaguacguugaguaccgggcacu 38494 38555 + uaguacguggagugucagguaa 39254 39312 + uaguacaaucaguauggggugg KI578515scaffold33 18462 18520 + uaaggaacuauuggugugaugu 18331 18392 + uaaagcuauauuaaccuaagaaa 26924 26983 + ucacuggguguuaguaugucaca KI581027scaffold2545 20074 20132 - uuuugaaauuaguuguggaggauc 27950 28040 - uaguauaucgaggaugcagaugagu KI578802scaffold320 183160 183217 + ccuguggcuccggaauacauuu 182766 182823 + ccuguggcuccggaauacauuu 177720 177779 + ugaaucucucugccauaggaua KI578784scaffold302 1638454 1638512 + acuggugaacacugaaaccgcu 1638613 1638674 + uauugguacgcccugcuucaacu 1625797 1625852 + ugcgguaccaguuguaauuagc 1625926 1625985 + uagucauagcgcuuggcauagu KI579071scaffold589 339288 339347 + uuuuaucucaaucaccauuuucg 330724 330779 + uucugcucugauuucacaaga 338565 338524 + uuugugcuggagccauggaaau Open in new tab Table 2. Putative novel miRNA clusters identified in Leptinotarsa decemlineata that are less than 10 Kb from one another Contig . Start (bp) . End (bp) . Strand . Predicted mature sequence . KI579029Scaffold547 39029 39088 + ucagguacggucacgaucuuuugu 37943 38003 + aucuggcacucauugcgacuaca 36820 39881 + uaguacguugaguaccgggcacu 38494 38555 + uaguacguggagugucagguaa 39254 39312 + uaguacaaucaguauggggugg KI578515scaffold33 18462 18520 + uaaggaacuauuggugugaugu 18331 18392 + uaaagcuauauuaaccuaagaaa 26924 26983 + ucacuggguguuaguaugucaca KI581027scaffold2545 20074 20132 - uuuugaaauuaguuguggaggauc 27950 28040 - uaguauaucgaggaugcagaugagu KI578802scaffold320 183160 183217 + ccuguggcuccggaauacauuu 182766 182823 + ccuguggcuccggaauacauuu 177720 177779 + ugaaucucucugccauaggaua KI578784scaffold302 1638454 1638512 + acuggugaacacugaaaccgcu 1638613 1638674 + uauugguacgcccugcuucaacu 1625797 1625852 + ugcgguaccaguuguaauuagc 1625926 1625985 + uagucauagcgcuuggcauagu KI579071scaffold589 339288 339347 + uuuuaucucaaucaccauuuucg 330724 330779 + uucugcucugauuucacaaga 338565 338524 + uuugugcuggagccauggaaau Contig . Start (bp) . End (bp) . Strand . Predicted mature sequence . KI579029Scaffold547 39029 39088 + ucagguacggucacgaucuuuugu 37943 38003 + aucuggcacucauugcgacuaca 36820 39881 + uaguacguugaguaccgggcacu 38494 38555 + uaguacguggagugucagguaa 39254 39312 + uaguacaaucaguauggggugg KI578515scaffold33 18462 18520 + uaaggaacuauuggugugaugu 18331 18392 + uaaagcuauauuaaccuaagaaa 26924 26983 + ucacuggguguuaguaugucaca KI581027scaffold2545 20074 20132 - uuuugaaauuaguuguggaggauc 27950 28040 - uaguauaucgaggaugcagaugagu KI578802scaffold320 183160 183217 + ccuguggcuccggaauacauuu 182766 182823 + ccuguggcuccggaauacauuu 177720 177779 + ugaaucucucugccauaggaua KI578784scaffold302 1638454 1638512 + acuggugaacacugaaaccgcu 1638613 1638674 + uauugguacgcccugcuucaacu 1625797 1625852 + ugcgguaccaguuguaauuagc 1625926 1625985 + uagucauagcgcuuggcauagu KI579071scaffold589 339288 339347 + uuuuaucucaaucaccauuuucg 330724 330779 + uucugcucugauuucacaaga 338565 338524 + uuugugcuggagccauggaaau Open in new tab Given miRNAs function as key regulators of various insect processes, including development, their possible use in agriculture for pest control is intriguing. Current management practices typically focus on pesticide applications, which are plagued by a variety of issues, including environmental pollution, insecticide resistance and harmful effects to nontarget organisms (Whyard and Singh 2009, Li et al. 2018). A recent study engineered potato plants to express long double-stranded RNAs (dsRNAs) in their chloroplasts, which targeted the L. decemlineata β-actin gene (Zhang et al. 2015). The coauthors showed that the RNA interference (RNAi) response resulted in mortality to the feeding beetle larvae. Similarly, using transgenic plants that express anti-miRNAs or miRNA mimics to target insect genes represents a promising avenue for pest management (Li et al. 2018). MicroRNAs are particularly enticing because they can conceivably target multiple genes/pathways that regulate insect development (Zotti and Smagghe 2015). By targeting miRNA genes/pathways that are species-specific, integral to development, and expressed at specific stages, more sustainable insect control could be achieved. However, there are some issues with the optimization of these miRNA-based approaches, which are largely attributed to inadequate delivery due to off-target effects and poor miRNA stability. There are several active areas of research aimed at improving the stability, such as RNA chemical modifications and novel deliver mechanisms (e.g., viral vectors, nanoparticles; Lukasik and Zielenkiewicz 2017). Ultimately, miRNAs represent a promising tool for insect control, and the data generated in this study provide a list of key developmental targets that could be candidates for future research related to L. decemlineata management. Conclusions While miRNAs have been implicated in various regulatory processes in diverse insect taxa, the complex regulatory pathways of these small noncoding RNAs are not completely understood. It is at least clear that they play a vital role in development (Hilgers et al. 2010, Courteau 2012, Guo et al. 2017). In our study, we used next-generation sequencing to identify conserved and novel miRNAs across all eight stages of L. decemlineata development. The miRNA profiles showed distinct clustering associated with development: 1) egg stage; 2) early stage (first, second, and third instar); and 3) late stage (fourth instar, prepupae, pupae, and adult). Within these clusters, we highlight the potential roles of some of the most notable miRNAs in the regulation of Colorado potato beetle development. Indeed, we have identified and profiled several intriguing miRNAs throughout the life stages of L. decemlineata, which have diverse roles (e.g., growth, reproduction, insecticide resistance) and represent possible candidates for insect control. This includes miRNAs that are abundantly expressed throughout development (e.g., miR-1-3p, miR-13a-3p, bantam-3p, miR-276-3p, miR-279e-3p), and others that are stage-specific (e.g., miR-282-5p) or novel. Ultimately, this study has contributed valuable information to the miRNA databases and generated the baseline data needed for more targeted functional studies toward improved Colorado potato beetle management. Acknowledgments We thank Charlotte Smith for assistance with the molecular work. We also thank Brent Sinclair and Jackie Lebenzon for providing the beetles. This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants Program awarded to B. J. Cassone (RGPIN-2016–04335). References Cited Abdelfattah , A. M. , C. Park, and M. Y. Choi. 2014 . Update on non-canonical microRNAs . Biomol. Concepts . 5 : 275 – 287 . Google Scholar Crossref Search ADS PubMed WorldCat Allam , M. , B. L. Spillings, H. Abdalla, D. Mapiye, L. L. Koekemoer, and A. Christoffels. 2016 . Identification and characterization of microRNAs expressed in the African malaria vector Anopheles funestus life stages using high throughput sequencing . Malar. J . 15 : 542 . Google Scholar Crossref Search ADS PubMed WorldCat Alyokhin , A . 2007 . Colorado potato beetle management on potatoes: Current challenges and future prospects . Fruit, Veg. Cereal Sci. Biotech . 3 : 10 – 19 . OpenURL Placeholder Text WorldCat Alyokhin , A. , M. Udalov, and G. Benkovskaya. 2013 . The Colorado potato beetle. Insect pests of potato: global perspectives on biology and management , pp. 11 – 29 . In P. Giordanengo, C. Vincent, and A. Alyokhin (eds.). Academic Press , Oxford, United Kingdom . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Andrews , S . 2010 . FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc Aravin , A. A. , M. Lagos-Quintana, A. Yalcin, M. Zavolan, D. Marks, B. Snyder, T. Gaasterland, J. Meyer, and T. Tuschl. 2003 . The small RNA profile during Drosophila melanogaster development . Dev. Cell . 5 : 337 – 350 . Google Scholar Crossref Search ADS PubMed WorldCat Asgari , S . 2013 . MicroRNA functions in insects . Insect Biochem. Mol. Biol . 43 : 388 – 397 . Google Scholar Crossref Search ADS PubMed WorldCat Banerjee , A. , and J. K. Roy. 2017 . Dicer-1 regulates proliferative potential of Drosophila larval neural stem cells through bantam miRNA based down-regulation of the G1/S inhibitor Dacapo . Dev. Biol . 423 : 57 – 65 . Google Scholar Crossref Search ADS PubMed WorldCat Bartel , D. P. , and C. Z. Chen. 2004 . Micromanagers of gene expression: the potentially widespread influence of metazoan microRNAs . Nat. Rev. Genet . 5 : 396 – 400 . Google Scholar Crossref Search ADS PubMed WorldCat Becam , I. , N. Rafel, X. Hong, S. M. Cohen, and M. Milán. 2011 . Notch-mediated repression of bantam miRNA contributes to boundary formation in the Drosophila wing . Development . 138 : 3781 – 3789 . Google Scholar Crossref Search ADS PubMed WorldCat Berezikov , E. , W. J. Chung, J. Willis, E. Cuppen, and E. C. Lai. 2007 . Mammalian mirtron genes . Mol. Cell . 28 : 328 – 336 . Google Scholar Crossref Search ADS PubMed WorldCat Biryukova , I. , J. Asmar, H. Abdesselem, and P. Heitzler. 2009 . Drosophila mir-9a regulates wing development via fine-tuning expression of the LIM only factor, dLMO . Dev. Biol . 327 : 487 – 496 . Google Scholar Crossref Search ADS PubMed WorldCat Boulan , L. , D. Martín, and M. Milán. 2013 . bantam miRNA promotes systemic growth by connecting insulin signaling and ecdysone production . Curr. Biol . 23 : 473 – 478 . Google Scholar Crossref Search ADS PubMed WorldCat Burke , J. M. , R. P. Kincaid, R. M. Nottingham, A. M. Lambowitz, and C. S. Sullivan. 2016 . DUSP11 activity on triphosphorylated transcripts promotes Argonaute association with noncanonical viral microRNAs and regulates steady-state levels of cellular noncoding RNAs . Genes Dev . 30 : 2076 – 2092 . Google Scholar Crossref Search ADS PubMed WorldCat Bushati , N. , A. Stark, J. Brennecke, and S. M. Cohen. 2008 . Temporal reciprocity of miRNAs and their targets during the maternal-to-zygotic transition in Drosophila . Curr. Biol . 18 : 501 – 506 . Google Scholar Crossref Search ADS PubMed WorldCat Capinera , J . 2001 . Handbook of vegetable pests . Academic Press , New York . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Casagrande , R. A . 1987 . The Colorado potato beetle: 125 years of mismanagement . B. Entomol. Soc. Am . 33 : 142 – 150 . OpenURL Placeholder Text WorldCat Caygill , E. E. , and L. A. Johnston. 2008 . Temporal regulation of metamorphic processes in Drosophila by the let-7 and miR-125 heterochronic microRNAs . Curr. Biol . 18 : 943 – 950 . Google Scholar Crossref Search ADS PubMed WorldCat Chen , H. M. , L. T. Chen, K. Patel, Y. H. Li, D. C. Baulcombe, and S. H. Wu. 2010 . 22-Nucleotide RNAs trigger secondary siRNA biogenesis in plants . Proc. Natl. Acad. Sci. U. S. A . 107 : 15269 – 15274 . Google Scholar Crossref Search ADS PubMed WorldCat Choo , K. B. , Y. L. Soon, P. N. Nguyen, M. S. Hiew, and C. J. Huang. 2014 . MicroRNA-5p and -3p co-expression and cross-targeting in colon cancer cells . J. Biomed. Sci . 21 : 95 . Google Scholar Crossref Search ADS PubMed WorldCat Coombs , J. J. , D. S. Douches, W. Li, E. J. Grafius, and W. L. Pett. 2002 . Combining engineered (Bt-cry3A) and natural resistance mechanisms in potato for control of Colorado potato beetle . J. Am. Soc. Hortic. Sci . 127 : 62 – 68 . Google Scholar Crossref Search ADS WorldCat Courteau , L. A. , K. B. Storey, and P. Morin , Jr. 2012 . Differential expression of microRNA species in a freeze tolerant insect, Eurosta solidaginis . Cryobiology . 65 : 210 – 214 . Google Scholar Crossref Search ADS PubMed WorldCat Dermauw , W. , and T. Van Leeuwen. 2014 . The ABC gene family in arthropods: comparative genomics and role in insecticide transport and resistance . Insect Biochem. Mol. Biol . 45 : 89 – 110 . Google Scholar Crossref Search ADS PubMed WorldCat Desvignes , T. , P. Batzel, E. Berezikov, K. Eilbeck, J. T. Eppig, M. S. McAndrews, A. Singer, and J. H. Postlethwait. 2015 . miRNA Nomenclature: a view incorporating genetic origins, biosynthetic pathways, and sequence variants . Trends Genet . 31 : 613 – 626 . Google Scholar Crossref Search ADS PubMed WorldCat Díaz-García , S. , and A. Baonza. 2013 . Pattern reorganization occurs independently of cell division during Drosophila wing disc regeneration in situ . Proc. Natl. Acad. Sci. U. S. A . 110 : 13032 – 13037 . Google Scholar Crossref Search ADS PubMed WorldCat Enright , A. J. , B. John, U. Gaul, T. Tuschl, C. Sander, and D. S. Marks. 2003 . MicroRNA targets in Drosophila . Genome Biol . 5 : R1 . Google Scholar Crossref Search ADS PubMed WorldCat Ferro , D. N. J. A. Logan , R. H. Voss, and J. S. Elkinton. 1985 . Colorado potato beetle (Coleoptera: Chrysomelidae) temperature-dependent growth and feeding rates . Env. Entomol . 14 : 343 – 348 . Google Scholar Crossref Search ADS WorldCat Fridrich , A. , Y. Hazan, and Y. Moran. 2019 . Too many false targets for MicroRNAs: challenges and pitfalls in prediction of miRNA targets and their gene ontology in model and non-model organisms . Bioessays . 41 : e1800169 . Google Scholar Crossref Search ADS PubMed WorldCat Friedländer , M. R. , S. D. Mackowiak, N. Li, W. Chen, and N. Rajewsky. 2012 . miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades . Nucleic Acids Res . 40 : 37 – 52 . Google Scholar Crossref Search ADS PubMed WorldCat Fromm , B. , T. Billipp, L. E. Peck, M. Johansen, J. E. Tarver, B. L. King, J. M. Newcomb, L. F. Sempere, K. Flatmark, E. Hovig, et al. 2015 . A uniform system for the annotation of vertebrate microRNA genes and the evolution of the human microRNAome . Annu. Rev. Genet . 49 : 213 – 242 . Google Scholar Crossref Search ADS PubMed WorldCat Gelman , D. B. , R. A. Bell, L. J. Liska, and J. S. Hu. 2001 . Artificial diets for rearing the Colorado potato beetle, Leptinotarsa decemlineata . J. Insect Sci . 1 : 7 . Google Scholar Crossref Search ADS PubMed WorldCat Gunage , R. D. , N. Dhanyasi, H. Reichert, and K. VijayRaghavan. 2017 . Drosophila adult muscle development and regeneration . Semin. Cell Dev. Biol . 72 : 56 – 66 . Google Scholar Crossref Search ADS PubMed WorldCat Guo , Q. , Y. Huang, F. Zou, B. Liu, M. Tian, W. Ye, J. Guo, X. Sun, D. Zhou, Y. Sun, et al. 2017 . The role of miR-2∼13∼71 cluster in resistance to deltamethrin in Culex pipiens pallens . Insect Biochem. Mol. Biol . 84 : 15 – 22 . Google Scholar Crossref Search ADS PubMed WorldCat Hare , D. J . 1980 . Impact of defoliation by the Colorado potato beetle on potato yields . J. Econ. Entomol . 73 : 369 – 373 . Google Scholar Crossref Search ADS WorldCat Hare , J. D . 1990 . Ecology and management of the Colorado potato beetle . Ann. Rev. Entomol . 35 : 81 – 100 . Google Scholar Crossref Search ADS WorldCat Harcourt , D. G . 1971 . Population dynamics of Leptinotarsa decemlineata (Say) in Eastern Ontario: III. Major population processes . Can. Entomol . 103 : 1049 – 1061 . Google Scholar Crossref Search ADS WorldCat Havens , M. A. , A. A. Reich, D. M. Duelli, and M. L. Hastings. 2012 . Biogenesis of mammalian microRNAs by a non-canonical processing pathway . Nucleic Acids Res . 40 : 4626 – 4640 . Google Scholar Crossref Search ADS PubMed WorldCat Hertel , J. , S. Bartschat, A. Wintsche, C. Otto, and P. F. Stadler; Students of the Bioinformatics Computer Lab. 2012 . Evolution of the let-7 microRNA family . RNA Biol . 9 : 231 – 241 . Google Scholar Crossref Search ADS PubMed WorldCat Hilgers , V. , N. Bushati, and S. M. Cohen. 2010 . Drosophila microRNAs 263a/b confer robustness during development by protecting nascent sense organs from apoptosis . PLoS Biol . 8 : e1000396 . Google Scholar Crossref Search ADS PubMed WorldCat Hipfner , D. R. , K. Weigmann, and S. M. Cohen. 2002 . The bantam gene regulates Drosophila growth . Genetics . 161 : 1527 – 1537 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Hsiao , T . 1981 . Ecophysiological adaptations among geographic populations of the Colorado potato beetle in North America, pp. 69 – 85 , In J. H. Lashomb and R. Casagrande (eds.). Advances in potato pest management . Hutchinson Ross , Stroudsburg, PA . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Huang , Z. , and E. C. Teeling. 2017 . ExUTR: a novel pipeline for large-scale prediction of 3’-UTR sequences from NGS data . BMC Genomics . 18 : 847 . Google Scholar Crossref Search ADS PubMed WorldCat Huang , D. W. , B. T. Sherman, Q. Tan, J. R. Collins, W. G. Alvord, J. Roayaei, R. Stephens, M. W. Baseler, H. C. Lane, and R. A. Lempicki. 2007 . The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists . Genome Biol . 8 : R183 . Google Scholar Crossref Search ADS PubMed WorldCat Hudish , L. I. , A. J. Blasky, and B. Appel. 2013 . miR-219 regulates neural precursor differentiation by direct inhibition of apical par polarity proteins . Dev. Cell . 27 : 387 – 398 . Google Scholar Crossref Search ADS PubMed WorldCat Huseth , A. S. , R. L. Groves, S. A. Chapman, A. Alyokhin, T. P. Kuhar, I. V. Macrae et al. 2014 . Managing Colorado potato beetle insecticide resistance: New tools and strategies for the next decade of pest control in potato . J. Integr. Pest Manag . 5 : 1 – 8 . Google Scholar Crossref Search ADS WorldCat Iovino , N. , A. Pane, and U. Gaul. 2009 . miR-184 has multiple roles in Drosophila female germline development . Dev. Cell . 17 : 123 – 133 . Google Scholar Crossref Search ADS PubMed WorldCat Izzo , V. M. , Y. H. Chen, S. D. Schoville, C. Wang, and D. J. Hawthorne. 2018 . Origin of pest lineages of the Colorado Potato Beetle (Coleoptera: Chrysomelidae) . J. Econ. Entomol . 111 : 868 – 878 . Google Scholar Crossref Search ADS PubMed WorldCat Jiang , P. , H. Wu, W. Wang, W. Ma, X. Sun, and Z. Lu. 2007 . MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features . Nucleic Acids Res . 35 : W339 – W344 . Google Scholar Crossref Search ADS PubMed WorldCat Kalluri , R. , and R. A. Weinberg. 2009 . The basics of epithelial-mesenchymal transition . J. Clin. Invest . 119 : 1420 – 1428 . Google Scholar Crossref Search ADS PubMed WorldCat Kamanu , T. K. , A. Radovanovic, J. A. Archer, and V. B. Bajic. 2013 . Exploration of miRNA families for hypotheses generation . Sci. Rep . 3 : 2940 . Google Scholar Crossref Search ADS PubMed WorldCat Kaplanoglu , E. , P. Chapman, I. M. Scott, and C. Donly. 2017 . Overexpression of a cytochrome P450 and a UDP-glycosyltransferase is associated with imidacloprid resistance in the Colorado potato beetle, Leptinotarsa decemlineata . Sci. Rep . 7 : 1762 . Google Scholar Crossref Search ADS PubMed WorldCat Kennell , J. A. , I. Gerin, O. A. MacDougald, and K. M. Cadigan. 2008 . The microRNA miR-8 is a conserved negative regulator of Wnt signaling . Proc. Natl. Acad. Sci. U. S. A . 105 : 15417 – 15422 . Google Scholar Crossref Search ADS PubMed WorldCat Kertesz , M. , N. Iovino, U. Unnerstall, U. Gaul, and E. Segal. 2007 . The role of site accessibility in microRNA target recognition . Nat. Genet . 39 : 1278 – 1284 . Google Scholar Crossref Search ADS PubMed WorldCat Kim , J. H. , P. Jin, R. Duan, and E. H. Chen. 2015 . Mechanisms of myoblast fusion during muscle development . Curr. Opin. Genet. Dev . 32 : 162 – 170 . Google Scholar Crossref Search ADS PubMed WorldCat Kozomara , A. , M. Birgaoanu, S. Griffiths-Jones. 2019 . miRBase: from microRNA sequences to function . Nucleic Acids Res . 47 : D155 – D162 . Google Scholar Crossref Search ADS PubMed WorldCat Kurusu , M. , T. Awasaki, L. M. Masuda-Nakagawa, H. Kawauchi, K. Ito, and K. Furukubo-Tokunaga. 2002 . Embryonic and larval development of the Drosophila mushroom bodies: concentric layer subdivisions and the role of fasciclin II . Development . 129 : 409 – 419 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Lai , X. , and J. Vera, MicroRNA clusters . 2013 . MicroRNA clusters, pp. 1310–1314 . In W. Dubitzky, O. Wolkenhauer, K. H. Cho, and H. Yokota (eds.), Encyclopedia of systems biology . Springer , New York . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Leaman , D. , P. Y. Chen, J. Fak, A. Yalcin, M. Pearce, U. Unnerstall, D. S. Marks, C. Sander, T. Tuschl, and U. Gaul. 2005 . Antisense-mediated depletion reveals essential and specific functions of microRNAs in Drosophila development . Cell . 121 : 1097 – 1108 . Google Scholar Crossref Search ADS PubMed WorldCat Lee , R. C. , R. L. Feinbaum, and V. Ambros. 1993 . The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 . Cell . 75 : 843 – 854 . Google Scholar Crossref Search ADS PubMed WorldCat Li , H. , and R. Durbin. 2009 . Fast and accurate short read alignment with Burrows-Wheeler transform . Bioinformatics . 25 : 1754 – 1760 . Google Scholar Crossref Search ADS PubMed WorldCat Li , X. , M. A. Schuler, and M. R. Berenbaum. 2007 . Molecular mechanisms of metabolic resistance to synthetic and natural xenobiotics . Annu. Rev. Entomol . 52 : 231 – 253 . Google Scholar Crossref Search ADS PubMed WorldCat Li , J. M. , Y. R. Zhou, Z. T. Sun, X. Wang, L. Xie, and J. P. Chen. 2015 . Identification and profiling of conserved and novel microRNAs in Laodelphax striatellus in response to rice black-streaked dwarf virus (RBSDV) infection . Genom. Data . 3 : 63 – 69 . Google Scholar Crossref Search ADS PubMed WorldCat Li , C. , A. Y. P. Wong, S. Wang, Q. Jia, and W.-P. Chuang. 2018 . miRNA-mediated interactions in and between plants and insects . Int. J. Mol. Sci . 19 : 3239 . Google Scholar Crossref Search ADS WorldCat Liang , P. , B. Feng, X. Zhou, and X. Gao. 2013 . Identification and developmental profiling of microRNAs in diamondback moth, Plutellaxylostella (L.) . PLoS One . 8 : e78787 . Google Scholar Crossref Search ADS PubMed WorldCat Liu , S. , S. Gao, D. Zhang, J. Yin, Z. Xiang, and Q. Xia. 2010 . MicroRNAs show diverse and dynamic expression patterns in multiple tissues of Bombyx mori . BMC Genomics . 11 : 85 . Google Scholar Crossref Search ADS PubMed WorldCat Logan , P. A. , R. A. Casagrande, H. H. Faubert, and F. Drummond. 1985 . Temperature-dependent development and feeding of immature Colorado potato beetles, Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae) . Env. Entomol . 14 : 275 – 283 . Google Scholar Crossref Search ADS WorldCat Lozano , J. , R. Montañez, and X. Belles. 2015 . MiR-2 family regulates insect metamorphosis by controlling the juvenile hormone signaling pathway . Proc. Natl. Acad. Sci. U. S. A . 112 : 3740 – 3745 . Google Scholar Crossref Search ADS PubMed WorldCat Lukasik , A. , and P. Zielenkiewicz. 2017 . Plant microRNAs—novel players in natural medicine? Int. J. Mol. Sci . 18 : 9 . Google Scholar Crossref Search ADS WorldCat Luo , Q. , Q. Zhou, X. Yu, H. Lin, S. Hu, and J. Yu. 2008 . Genome-wide mapping of conserved microRNAs and their host transcripts in Tribolium castaneum . J. Genet. Genomics . 35 : 349 – 355 . Google Scholar Crossref Search ADS PubMed WorldCat Martin , M . 2011 . Cutadapt removes adapter sequences from high-throughput sequencing reads . EMBnet. J . 17 : 10 – 12 . Google Scholar Crossref Search ADS WorldCat Marín , R. M. , and J. Vanícek. 2011 . Efficient use of accessibility in microRNA target prediction . Nucleic Acids Res . 39 : 19 – 29 . Google Scholar Crossref Search ADS PubMed WorldCat McCarthy , J. J . 2011 . The MyomiR network in skeletal muscle plasticity . Exerc. Sport Sci. Rev . 39 : 150 – 154 . Google Scholar Crossref Search ADS PubMed WorldCat Michely , J. , S. Kraft, and U. Müller. 2017 . miR-12 and miR-124 contribute to defined early phases of long-lasting and transient memory . Sci. Rep . 7 : 7910 . Google Scholar Crossref Search ADS PubMed WorldCat Mitra , R. , C. C. Lin, C. M. Eischen, S. Bandyopadhyay, and Z. Zhao. 2015 . Concordant dysregulation of miR-5p and miR-3p arms of the same precursor microRNA may be a mechanism in inducing cell proliferation and tumorigenesis: a lung cancer study . Rna . 21 : 1055 – 1065 . Google Scholar Crossref Search ADS PubMed WorldCat Morin , M. D. , J. J. Frigault, P. J. Lyons, N. Crapoulet, S. Boquel, K. B. Storey, and P. J. Morin. 2017a . Amplification and quantification of cold-associated microRNAs in the Colorado potato beetle (Leptinotarsa decemlineata) agricultural pest . Insect Mol. Biol . 26 : 574 – 583 . Google Scholar Crossref Search ADS WorldCat Morin , M. D. , P. J. Lyons, N. Crapoulet, S. Boquel, and P. J. Morin. 2017b . Identification of differentially expressed miRNAs in Colorado potato beetles (Leptinotarsa decemlineata (Say)) exposed to imidacloprid . Int. J. Mol. Sci . 18 : 2728 . Google Scholar Crossref Search ADS WorldCat Ninova , M. , M. Ronshaugen, and S. Griffiths-Jones. 2014 . Fast-evolving microRNAs are highly expressed in the early embryo of Drosophila virilis . RNA . 20 : 360 – 372 . Google Scholar Crossref Search ADS PubMed WorldCat Ninova , M. , M. Ronshaugen, and S. Griffiths-Jones. 2016 . MicroRNA evolution, expression, and function during short germband development in Tribolium castaneum . Genome Res . 26 : 85 – 96 . Google Scholar Crossref Search ADS PubMed WorldCat Nolo , R. , C. M. Morrison, C. Tao, X. Zhang, and G. Halder. 2006 . The bantam microRNA is a target of the hippo tumor-suppressor pathway . Curr. Biol . 16 : 1895 – 1904 . Google Scholar Crossref Search ADS PubMed WorldCat Oliveira , A. C. , L. A. Bovolenta, P. G. Nachtigall, M. E. Herkenhoff, N. Lemke, and D. Pinhal. 2017 . Combining results from distinct MicroRNA target prediction tools enhances the performance of analyses . Front. Genet . 8 : 59 . Google Scholar Crossref Search ADS PubMed WorldCat Pan , C. Y. , W. T. Kuo, C. Y. Chiu, and W. C. Lin. 2017 . Visual display of 5p-arm and 3p-arm miRNA expression with a mobile application . Biomed Res. Int . 2017 : 6037168 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Parrish , J. Z. , P. Xu, C. C. Kim, L. Y. Jan, and Y. N. Jan. 2009 . The microRNA bantam functions in epithelial cells to regulate scaling growth of dendrite arbors in drosophila sensory neurons . Neuron . 63 : 788 – 802 . Google Scholar Crossref Search ADS PubMed WorldCat Pelletier , Y. , F. G. Horgan, and J. Pompon. 2013 . Potato resistance against insect herbivores, insect pests of potato, pp. 439 – 462 . In A. Alyokhin, C. Vincent, and P. Giordanengo (eds.), Insect pests of potato . Academic Press , Cambridge, MA . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Peterson , S. M. , J. A. Thompson, M. L. Ufkin, P. Sathyanarayana, L. Liaw, C. B. Congdon. 2014 . Common features of microRNA target prediction tools . Front Genet . 5 : 23 . Google Scholar Crossref Search ADS PubMed WorldCat Praher , D. , B. Zimmermann, G. Genikhovich, Y. Columbus-Shenkar, V. Modepalli, R. Aharoni, Y. Moran, and U. Technau. 2017 . Characterization of the piRNA pathway during development of the sea anemone Nematostella vectensis . RNA Biol . 14 : 1727 – 1741 . Google Scholar Crossref Search ADS PubMed WorldCat Pruitt , K. D. , T. Tatusova, and D. R. Maglott. 2005 . NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins . Nucleic Acids Res . 33 : D501 – D504 . Google Scholar Crossref Search ADS PubMed WorldCat Rebijith , K. B. , R. Asokan, H. R. Hande, and N. K. Krishna Kumar. 2016 . The first report of miRNAs from a Thysanopteran insect, thrips palmi karny using high-throughput sequencing . PLoS One . 11 : e0163635 . Google Scholar Crossref Search ADS PubMed WorldCat Rehmsmeier , M. , P. Steffen, M. Hochsmann, and R. Giegerich. 2004 . Fast and effective prediction of microRNA/target duplexes . RNA . 10 : 1507 – 1517 . Google Scholar Crossref Search ADS PubMed WorldCat Reinhart , B. J. , F. J. Slack, M. Basson, A. E. Pasquinelli, J. C. Bettinger, A. E. Rougvie, H. R. Horvitz, and G. Ruvkun. 2000 . The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans . Nature . 403 : 901 – 906 . Google Scholar Crossref Search ADS PubMed WorldCat Rubio , M. , and X. Belles. 2013 . Subtle roles of microRNAs let-7, miR-100 and miR-125 on wing morphogenesis in hemimetabolan metamorphosis . J. Insect Physiol . 59 : 1089 – 1094 . Google Scholar Crossref Search ADS PubMed WorldCat Ruby , J. G. , A. Stark, W. K. Johnston, M. Kellis, D. P. Bartel, and E. C. Lai. 2007 . Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs . Genome Res . 17 : 1850 – 1864 . Google Scholar Crossref Search ADS PubMed WorldCat Santa-Maria , I. , M. E. Alaniz, N. Renwick, C. Cela, T. A. Fulga, D. Van Vactor, T. Tuschl, L. N. Clark, M. L. Shelanski, B. D. McCabe, et al. 2015 . Dysregulation of microRNA-219 promotes neurodegeneration through post-transcriptional regulation of tau . J. Clin. Invest . 125 : 681 – 686 . Google Scholar Crossref Search ADS PubMed WorldCat Schoville , S. D. , Y. H. Chen, M. N. Andersson, J. B. Benoit, A. Bhandari, J. H. Bowsher, K. Brevik, K. Cappelle, M. M. Chen, A. K. Childers, et al. 2018 . A model species for agricultural pest genomics: the genome of the Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera: Chrysomelidae) . Sci. Rep . 8 : 1931 . Google Scholar Crossref Search ADS PubMed WorldCat Seitz , H . 2017 . Issues in current microRNA target identification methods . RNA Biol . 14 : 831 – 834 . Google Scholar Crossref Search ADS PubMed WorldCat Singh , J. , and J. Nagaraju. 2008 . In silico prediction and characterization of microRNAs from red flour beetle (Tribolium castaneum) . Insect Mol. Biol . 17 : 427 – 436 . Google Scholar Crossref Search ADS PubMed WorldCat Skalsky , R. L. , D. L. Vanlandingham, F. Scholle, S. Higgs, and B. R. Cullen. 2010 . Identification of microRNAs expressed in two mosquito vectors, Aedes albopictus and Culex quinquefasciatus . BMC Genomics . 11 : 119 . Google Scholar Crossref Search ADS PubMed WorldCat Søvik , E. , G. Bloch, and Y. Ben-Shahar. 2015 . Function and evolution of microRNAs in eusocial Hymenoptera . Front. Genet . 6 : 193 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Stark , A. , P. Kheradpour, L. Parts, J. Brennecke, E. Hodges, G. J. Hannon, and M. Kellis. 2007 . Systematic discovery and characterization of fly microRNAs using 12 Drosophila genomes . Genome Res . 17 : 1865 – 1879 . Google Scholar Crossref Search ADS PubMed WorldCat Townley-Tilson , W. H. , T. E. Callis, and D. Wang. 2010 . MicroRNAs 1, 133, and 206: critical factors of skeletal and cardiac muscle development, function, and disease . Int. J. Biochem. Cell Biol . 42 : 1252 – 1255 . Google Scholar Crossref Search ADS PubMed WorldCat Vilmos , P. , A. Bujna, M. Szuperák, Z. Havelda, É. Várallyay, J. Szabad, L. Kucerova, K. Somogyi, I. Kristó, T. Lukácsovich, et al. 2013 . Viability, longevity, and egg production of Drosophila melanogaster are regulated by the miR-282 microRNA . Genetics . 195 : 469 – 480 . Google Scholar Crossref Search ADS PubMed WorldCat Wang , W. , E. J. Kwon, and L. H. Tsai. 2012 . MicroRNAs in learning, memory, and neurological diseases . Learn. Mem . 19 : 359 – 368 . Google Scholar Crossref Search ADS PubMed WorldCat Weaver , D. B. , J. M. Anzola, J. D. Evans, J. G. Reid, J. T. Reese, K. L. Childs, E. M. Zdobnov, M. P. Samanta, J. Miller, and C. G. Elsik. 2007 . Computational and transcriptional evidence for microRNAs in the honey bee genome . Genome Biol . 8 : R97 . Google Scholar Crossref Search ADS PubMed WorldCat Wenda , J. M. , D. Homolka, Z. Yang, P. Spinelli, R. Sachidanandam, R. R. Pandey, and R. S. Pillai. 2017 . Distinct roles of RNA helicases MVH and TDRD9 in PIWI slicing-triggered mammalian piRNA biogenesis and function . Dev. Cell . 41 : 623 – 637.e9 . Google Scholar Crossref Search ADS PubMed WorldCat Winter , F. , S. Edaye, A. Hüttenhofer, and C. Brunel. 2007 . Anopheles gambiae miRNAs as actors of defence reaction against Plasmodium invasion . Nucleic Acids Res . 35 : 6953 – 6962 . Google Scholar Crossref Search ADS PubMed WorldCat Wu , W. , Q. Ren, C. Li, Y. Wang, M. Sang, Y. Zhang, and B. Li. 2013 . Characterization and comparative profiling of MicroRNAs in a sexual dimorphism insect, Eupolyphaga sinensis Walker . PLoS One . 8 : e59016 . Google Scholar Crossref Search ADS PubMed WorldCat Whyard , S. , A. D. Singh, S. Wong. 2009 . Ingested double-stranded RNAs can act as species-specific insecticides . Insect Biochem. Mol. Biol . 39 : 824 – 832 . Google Scholar Crossref Search ADS PubMed WorldCat Ylla , G. , B. Fromm, M. D. Piulachs, and X. Belles. 2016 . The microRNA toolkit of insects . Sci. Rep . 6 : 37736 . Google Scholar Crossref Search ADS PubMed WorldCat Ylla , G. , M. D. Piulachs, and X. Belles. 2017 . Comparative analysis of miRNA expression during the development of insects of different metamorphosis modes and germ-band types . BMC Genomics . 18 : 774 . Google Scholar Crossref Search ADS PubMed WorldCat Yu , X. , Q. Zhou, S. C. Li, Q. Luo, Y. Cai, W. C. Lin, H. Chen, Y. Yang, S. Hu, and J. Yu. 2008 . The silkworm (Bombyx mori) microRNAs and their expressions in multiple developmental stages . PLoS One . 3 : e2997 . Google Scholar Crossref Search ADS PubMed WorldCat Zhang , J. , S. Afzal Khan, C. Hasse, S. Ruf, D. G. Heckel, R. Bock. 2015 . Full crop protection from an insect pest by expression of long double-stranded RNAs in plastids . Science 6225 : 991 – 994 . Google Scholar Crossref Search ADS WorldCat Zhang , L. , X. Chen, B. Liu, and J. Han. 2018 . MicroRNA-124-3p directly targets PDCD6 to inhibit metastasis in breast cancer . Oncol. Lett . 15 : 984 – 990 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Zotti , M. J. , and G. Smagghe. 2015 . RNAi technology for insect management and protection of beneficial insects from diseases: lessons, challenges and risk assessments . Neotrop. Entomol . 44 : 197 – 213 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes " These authors contributed equally to the manuscript. © The Author(s) 2020. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - A Day in the Life: Identification of Developmentally Regulated MicroRNAs in the Colorado Potato Beetle (Leptinotarsa decemlineata; Coleoptera: Chrysomelidae)
JO - Journal of Economic Entomology
DO - 10.1093/jee/toaa020
DA - 2020-06-06
UR - https://www.deepdyve.com/lp/oxford-university-press/a-day-in-the-life-identification-of-developmentally-regulated-bfI4F4lgcw
SP - 1445
VL - 113
IS - 3
DP - DeepDyve
ER -