ABSTRACT Peatlands are waterlogged ecosystems that store large amounts of carbon, harbor highly specialized endemic biodiversity, and play a crucial role in climate regulation. However, the microbial communities of tropical peatlands, particularly in the Atlantic Forest, remain largely unexplored. Molecular tools are essential for characterizing these communities, yet conventional DNA extraction kits are often ineffective for peat soils, likely due to their high organic matter content, low pH, and the presence of PCR inhibitors. This study aimed to optimize a conventional soil DNA extraction protocol to enhance DNA yield and purity from tropical peat soils, ensuring more reliable molecular analyses. The optimized protocol resulted in a four-times increase in DNA concentration compared to the conventional protocol and improved DNA purity. Agarose gel electrophoresis confirmed higher DNA integrity and also revealed the presence of RNA in samples subjected to the optimized protocol, indicating its potential for co-extraction of DNA and RNA. Quantitative PCR (qPCR) analysis showed significantly higher abundances of bacterial and archaeal 16S rRNA genes, as well as the mcrA gene, in optimized compared to conventional protocol, with statistically significant differences across soil profiles and depths. Furthermore, the optimized protocol yielded a lower coefficient of variation among replicates, enhancing reproducibility and consistency in microbial quantification. These findings demonstrate that the optimized protocol effectively extracts high-quality DNA from tropical peat soils, addressing common challenges associated with commercial kits. Its implementation can support improved future research on microbial-driven biogeochemical processes in tropical peatlands, particularly in the context of greenhouse gas emissions and climate change. IMPORTANCE Tropical peatlands play a central role in carbon storage and greenhouse gas dynamics, yet their microbial communities remain largely unexplored due to analytical challenges. High organic matter content, low pH, and PCR inhibitors commonly limit the efficiency of molecular tools in these environments. This study presents an optimized DNA extraction protocol that improves yield, purity, and reproducibility across peat profiles. The protocol enhanced the detection of microbial marker genes, such as bacterial and archaeal 16S rRNA and mcrA, and showed potential for co-extraction of RNA, expanding possibilities for future multi-omic approaches. This methodological advance enables more accurate and consistent microbial analyses in tropical peat soils, contributing to a better understanding of microbial roles in biogeochemical cycles and climate-related processes. Tropical peatlands play a central role in carbon storage and greenhouse gas dynamics, yet their microbial communities remain largely unexplored due to analytical challenges. High organic matter content, low pH, and PCR inhibitors commonly limit the efficiency of molecular tools in these environments. This study presents an optimized DNA extraction protocol that improves yield, purity, and reproducibility across peat profiles. The protocol enhanced the detection of microbial marker genes, such as bacterial and archaeal 16S rRNA and mcrA, and showed potential for co-extraction of RNA, expanding possibilities for future multi-omic approaches. This methodological advance enables more accurate and consistent microbial analyses in tropical peat soils, contributing to a better understanding of microbial roles in biogeochemical cycles and climate-related processes.
An effective DNA extraction protocol optimized for tropical swamp peat samples
J. B. Gontijo,Gabriel Valverde Firmino,J. A. Mandro,André Luiz Miranda Reis,W. Bieluczyk,Júlio César Feitosa Fernandes,Plínio Barbosa de Camargo,J. L. Mazza Rodrigues,Siu Mui Tsai,Pablo Vidal‐Torrado
Published 2025 in Microbiology spectrum
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- Publication year
2025
- Venue
Microbiology spectrum
- Publication date
2025-01-06
- Fields of study
Biology, Medicine, Environmental Science
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- Source metadata
Semantic Scholar, PubMed
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