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Optimization and Comparative Evaluation of DNA Extraction Protocols for Arbuscular Mycorrhizal Fungi in Coffee Roots
Lorena Jacqueline Gómez-Godínez1, Alejandra Ortiz-Gasca2, José Luis Aguirre-Noyola1
1Centro Nacional de Recursos Genéticos, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Boulevard de la Biodiversidad 2498, Rancho las Cruces, Tepatitlán de Morelos 47714, Jalisco, Mexico.
Abstract:
Molecular studies of arbuscular mycorrhizal fungi (AMF) in coffee root require DNA extracts that are sufficiently pure for PCR amplification. Coffea arabica roots remains challenging because of the abundance of phenolic compounds, polysaccharides, and other inhibitors. This study compared four DNA extraction protocols, including two CTAB-based phenol-chloroform methods, a commercial extraction kit, and a modified commercial kit incorporating mechanical disruption, proteinase K predigestion, and β-mercaptoethanol treatment. Quality of total DNA extracts was assessed using NanoDrop spectrophotometry (A260/A280 and A260/A230 ratios), agarose gel electrophoresis, and PCR amplification of the AMF SSU (18S rDNA) region using AMV4.5NF/AMDGR primers. Conventional phenol-chloroform methods yielded high DNA concentrations but poor purity, indicating substantial co-extraction of inhibitory compounds. The commercial kit without predigestion yielded lower spectrophotometric purity but still produced successful AMF-targeted PCR amplification. In contrast, the modified protocol yielded the highest spectrophotometric purity ratios while retaining successful amplification across all samples. These results indicate that incorporation of the predigestion step was associated with higher post-extraction spectrophotometric ratios without compromising successful AMF-targeted PCR amplification. The optimized protocol provides a reproducible method for obtaining PCR-quality DNA from coffee roots and represents a methodological contribution to molecular studies of the diversity of AMF and other root-associated microorganisms in coffee agroecosystems.

