Background, Leucoptera coffeella (Guerin-Meneville, 1842) is a moth species (Lyonetiidae, Lepidoptera) pest that causes severe losses to coffee crops. Further information about its genomic data is required to allow molecular strategies for the development of sustainable pesticides and to gain in-depth knowledge on phylogenetics. However, the closest complete genome available is within the superfamily level (Yponomeutoidea). Here we report the generation of the first long-read genome, transcriptome and proteome results of L. coffeella and the in silico analysis performed in these molecular levels to investigate genes involved in the siRNA processing. Results, PACBio and paired-end Illumina combined DNA sequencing from pupae samples resulted in more than 436 Gb subreads and 31Mb reads with N50 read length of 15,512 nt, mean read length 13.8 Kb and max read length 420.7 Kb. Additionally, 20Gb data of short DNA sequencing was combined to produce 1,984 contigs comprising 397 Mb in total. The longest and shortest scaffold sizes are 10,809,567 nt and 15,247 nt, respectively (mean size 200,178 nt). The N50 scaffold was 275,598 nt and the GC content was 36.10%. Predicted coding DNA sequences counted 39.930 gene models. Searching of 5286 BUSCO groups revealed 91.7 percent of completeness (single and duplicated genes combined) compared to lepidoptera genomes (lepidoptera_odb10). Flow cytometry showed the 1C DNA content is approximately 295 Mb. RNA-Seq from seven development stages resulted in 28294 identified transcripts. Additionally, proteomics from immature stages resulted in 2045 proteins matching the gene models. Conclusions, This first nuclear genome of the Lyonetiidae family brings valuable molecular resources to study Lepidoptera genomes. Genome, transcriptome and proteome sequencing to raise genome annotation precision may resolve uncovered taxonomic issues. In addition, these combined approaches provide insights into plant-insect interaction players, as horizontally transferred genes (HGT) and endosymbionts. Put together, the generated data enables the development of molecular tools towards sustainable biotechnology solutions for lepidopteran pest control.

Invasive insects cost the global economy around USD 70 billion per year. Moreover, increasing agricultural insect pests raise concerns about global food security constraining and infestation rising after climate changes. Current agricultural pest management largely relies on plant breeding—with or without transgenes—and chemical pesticides. Both approaches face serious technological obsolescence in the field due to plant resistance breakdown or development of insecticide resistance. The need for new modes of action (MoA) for managing crop health is growing each year, driven by market demands to reduce economic losses and by consumer demand for phytosanitary measures. The disabling of pest genes through sequence-specific expression silencing is a promising tool in the development of environmentally-friendly and safe biopesticides. The specificity conferred by long dsRNA-base solutions helps minimize effects on off-target genes in the insect pest genome and the target gene in non-target organisms (NTOs). In this review, we summarize the status of gene silencing by RNA interference (RNAi) for agricultural control. More specifically, we focus on the engineering, development and application of gene silencing to control Lepidoptera through non-transforming dsRNA technologies. Despite some delivery and stability drawbacks of topical applications, we reviewed works showing convincing proof-of-concept results that point to innovative solutions. Considerations about the regulation of the ongoing research on dsRNA-based pesticides to produce commercialized products for exogenous application are discussed. Academic and industry initiatives have revealed a worthy effort to control Lepidoptera pests with this new mode of action, which provides more sustainable and reliable technologies for field management. New data on the genomics of this taxon may contribute to a future customized target gene portfolio. As a case study, we illustrate how dsRNA and associated methodologies could be applied to control an important lepidopteran coffee pest.

There is a strong demand for sustainable, durable, safe, and specific solutions against agricultural pests due to breeding limitations and chemical control constraints. Biotechnological approaches to insect pests require phylogenetic information at the molecular level for RNAi application and other tailored strategies, such as insecticide resistance molecular markers. Sequencing technologies have advanced in resolution, speed, and cost-effectiveness to offer striking impact on genomics and transcriptomics. To provide deep and reliable data to high-throughput studies, state-of-the art technologies require high purification and integrity of the submitted samples. However, the limited number of existing protocols to obtain high mass nucleic acids from insects render difficult to obtain elevated purity and integrity standards. Species characteristics, as scales covered bodies of Lepidoptera and mining eating habits, hinder the quality of nucleic acid samples. Additionally, small size individuals are limited to yield sufficient amounts to robust base sequence coverage. The coffee leaf miner (CLM) Leucoptera coffeella is microlepdoptera insect responsible for severe leaf damage and huge yield losses to the coffee crop. Here we present simple and reproducible procedures we used to successfully obtain the whole-genome long-read PACBIO and Illumina (DNA) and transcriptome (RNA) of L. coffeella for de novo assembly. Therefore, we developed customized protocols applicable to insects to the extraction of genomic High Molecular Weight DNA and total RNA suitable for downstream high standard sequencing applications and other sensitive molecular analyses.