Plant-herbivorous insect networks: who is eating what revealed by long barcodes using high-throughput sequencing and Trinity assembly

Interactions between plants and insects are among the most important life functions for all organism at a particular natural community. Usually a large number of samples are required to identify insect diets in food web studies. Previously, Sanger sequencing and next generation sequencing (NGS) with short DNA barcodes were used, resulting in low species-level identification; meanwhile the costs of Sanger sequencing are expensive for metabarcoding together with more samples. Here, we present a fast and effective sequencing strategy to identify larvae of Lepidoptera and their diets at the same time without increasing the cost on Illumina platform in a single HiSeq run, with long-multiplex-metabarcoding (COI for insects, rbcL, matK, ITS and trnL for plants) obtained by Trinity assembly (SHMMT). Meanwhile, Sanger sequencing (for single individuals) and NGS (for polyphagous) were used to verify the reliability of the SHMMT approach. Furthermore, we show that SHMMT approach is fast and reliable, with most high-quality sequences of five DNA barcodes of 63 larvae individuals (54 species) recovered (full length of 100% of the COI gene and 98.3% of plant DNA barcodes) using Trinity assembly (up-sized to 1015 bp). For larvae diets identification, 95% are reliable; the other 5% failed because their guts were empty. The diets identified by SHMMT approach are 100% consistent with the host plants that the larvae were feeding on during our collection. Our study demonstrates that SHMMT approach is reliable and cost-effective for insect-plants network studies. This will facilitate insect-host plant studies that generally contain a huge number of samples.

Automated summary

The study presents a fast and effective sequencing strategy for identifying larvae of Lepidoptera and their diets, with high-quality sequences obtained through long-multiplex-metabarcoding. The SHMMT approach is shown to be reliable and cost-effective, with accurate identification of larvae diets. This new method will facilitate research on insect-plant networks.