New mitochondrial primers for metabarcoding of insects, designed and evaluated using in silico methods

Area of Science:

• Genomics
• Molecular Ecology
• Bioinformatics

Background:

• Insect metabarcoding commonly uses cytochrome oxidase I (COI) gene fragments.
• Existing COI primers face challenges with taxonomic coverage and amplification bias.
• These limitations reduce the reliability of insect species detection.

Purpose of the Study:

• To identify and evaluate novel PCR primers for improved insect metabarcoding.
• To assess primer performance using computational approaches and comprehensive genomic databases.
• To enhance the accuracy and reliability of insect species identification through DNA analysis.

Main Methods:

• Utilized computational tools (ecoprimers, degeprime) on extensive Hexapoda mitochondrial genome databases.
• Developed a new metric, exclusive taxonomic resolution, for evaluating primer performance.
• Performed in silico evaluation of new primers against established ones, focusing on taxonomic coverage and resolution.

Main Results:

• Ribosomal RNA genes (12S and 16S) yielded the most effective markers, resolving ~90% of species.
• New primers demonstrate superior taxonomic coverage and resolution compared to COI primers.
• Combining optimal markers can increase effective taxonomic resolution by up to 10%.

Conclusions:

• Alternative primer sets, particularly targeting 12S and 16S rRNA genes, offer significant advantages over current COI-based insect metabarcoding.
• The findings suggest a shift towards different genetic markers for more accurate and comprehensive insect biodiversity assessments.
• Optimized primer selection is crucial for advancing amplicon-based metabarcoding techniques in entomology.