Impact of preservation method and storage period on ribosomal metabarcoding of marine microbes: Implications for remote automated samplings
FRONTIERS IN MICROBIOLOGY(2022)
Abstract
Automated sampling technologies can enhance the temporal and spatial resolution of marine microbial observations, particularly in remote and inaccessible areas. A critical aspect of automated microbiome sampling is the preservation of nucleic acids over long-term autosampler deployments. Understanding the impact of preservation method on microbial metabarcoding is essential for implementing genomic observatories into existing infrastructure, and for establishing best practices for the regional and global synthesis of data. The present study evaluates the effect of two preservatives commonly used in autosampler deployments (mercuric chloride and formalin) and two extraction kits (PowerWater and NucleoSpin) on amplicon sequencing of 16S and 18S rRNA gene over 50 weeks of sample storage. Our results suggest the combination of mercuric chloride preservation and PowerWater extraction as most adequate for 16S and 18S rRNA gene amplicon-sequencing from the same seawater sample. This approach provides consistent information on species richness, diversity and community composition in comparison to control samples (nonfixed, filtered and frozen) when stored up to 50 weeks at in situ temperature. Preservation affects the recovery of certain taxa, with specific OTUs becoming overrepresented (SAR11 and diatoms) or underrepresented (Colwellia and pico-eukaryotes) after preservation. In case eukaryotic sequence information is the sole target, formalin preservation and NucleoSpin extraction performed best. Our study contributes to the design of long-term autonomous microbial observations in remote ocean areas, allowing cross-comparison of microbiome dynamics across sampling devices (e.g., water and particle samplers) and marine realms.
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Key words
seawater microbiome,sample preservation,DNA extraction,amplicon sequencing,16S rRNA,18S rRNA,autonomous sampling,time-series
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