Rapid construction of insect-plant interaction networks via multiplexed long-fragment DNA metabarcoding and NGS

Abstract:1. Molecular identification of insect food webs can accurately reveal complex trophic interactions and, serving as a foundation for understanding ecosystem functioning and advancing biodiversity conservation. This process typically involves DNA library construction, high-throughput sequencing, and subsequent steps including data assembly, annotation, denoising, and taxonomic classification. However, the use of multiple long genetic markers substantially increases both the cost of high-throughput sequencing and the complexity of downstream data analysis in insect diet studies.2. In this study, we developed a low-cost, time-efficient, and highly accurate method for analyzing the dietary habits of herbivorous insects, and introduced an automated tool, “NGSdiet,”designed for processing high-throughput sequencing data related to insect food webs to enable rapid identification of animal diets.3. We utilized four long-fragment DNA barcodes (insect COI and plant rbcL, ITS, and trnL) in a multiplex PCR approach, which significantly reduced the cost of DNA library preparation. The resulting high-throughput sequencing data can be automatically processed through a one-click command using the NGSdiet tool, greatly simplifying the otherwise complex workflow. NGSdiet integrates functions such as BWA alignment, Trinity de novo assembly, sequencing depth calculation, adjacent-base depth ratio (“base_ratio”) filter, sequence annotation, and barcode marker sorting. By setting thresholds for average barcode sequencing depth, length, base_ratio, and applying a sliding window based on base_ratio values, the tool effectively filters out errors caused by PCR chimeras, contamination, or misassemblies. The workflow begins by aligning reads against a local reference database using BWA, followed by filtering and output of mapped results. If the initial alignment fails, the pipeline automatically initiates de novo assembly with Trinity and filtering. It also enables automatic separation of different genetic markers across samples. To validate the robustness of the method, three batches of Lepidoptera larval samples were sequenced. The identification accuracy of the COI gene was 98.26%, 92.13%, and 100%, respectively, while the combined plant barcodes achieved identification accuracies of 92.17%, 68.53%, and 92.06%.4. Experimental results demonstrate that NGSdiet is a fast, cost-effective, and highly sensitive tool for high-throughput analysis. It also shows strong potential for scalability, making it applicable to animal diet identification across various ecosystems.