微生物学

Careful dissection of insect gut tissues is essential for microbiome studies to ensure accurate characterization of internal microbial communities and preservation of DNA integrity. Because insect-associated microbiomes are highly sensitive to contamination, effective removal of external microbes prior to dissection is critical to minimize bias in downstream analyses. While ethanol- and bleach-based surface sterilization methods are commonly used, standardized workflows integrating field collection, sterilization, and dissection remain limited. Here, we present a step-by-step protocol for the field collection, surface sterilization, and dissection of gut tissues from the agricultural pest Diaprepes abbreviatus (Coleoptera: Curculionidae), optimized for genomic DNA extraction and microbiome analyses. Using wild-caught specimens, this workflow incorporates a rigorous surface sterilization and dissection strategy that minimizes external contamination while preserving biologically relevant microbial signatures and DNA integrity for downstream microbiome analyses. The protocol provides a standardized framework for insect gut microbiome studies and can be broadly adapted to other wild-caught insect species requiring careful collection, disinfection, and sterile dissection prior to molecular analysis. The protocol integrates field collection and laboratory processing steps into a streamlined workflow that minimizes contamination while preserving tissue integrity for downstream applications.

Accurate profiling of soil and root-associated bacterial communities is essential for understanding ecosystem functions and improving sustainable agricultural practices. Here, a comprehensive, modular workflow is presented for the analysis of full-length 16S rRNA gene amplicons generated with Oxford Nanopore long-read sequencing. The protocol integrates four standardized steps: (i) quality assessment and filtering of raw reads with NanoPlot and NanoFilt, (ii) removal of plant organelle contamination using a curated Viridiplantae Kraken2 database, (iii) species-level taxonomic assignment with Emu, and (iv) downstream ecological analyses, including rarefaction, diversity metrics, and functional inference. Leveraging high-performance computing resources, the workflow enables parallel processing of large datasets, rigorous contamination control, and reproducible execution across environments. The pipeline’s efficiency is demonstrated on full-length 16S rRNA gene datasets from yellow pea rhizosphere and root samples, with high post-filter read retention and high-resolution community profiles. Automated SLURM scripts and detailed documentation are provided in a public GitHub repository (https://github.com/henrimdias/emu-microbiome-HPC; release v1.0.2, emu-pipeline-revised) and archived on Zenodo (DOI: 10.5281/zenodo.17764933).