| Literature DB >> 33194372 |
Susheel Bhanu Busi1, Paraskevi Pramateftaki2, Jade Brandani2, Stilianos Fodelianakis2, Hannes Peter2, Rashi Halder1, Paul Wilmes1, Tom J Battin2.
Abstract
Glacier-fed streams (GFS) are harsh ecosystems dominated by microbial life organized in benthic biofilms, yet the biodiversity and ecosystem functions provided by these communities remain under-appreciated. To better understand the microbial processes and communities contributing to GFS ecosystems, it is necessary to leverage high throughput sequencing. Low biomass and high inorganic particle load in GFS sediment samples may affect nucleic acid extraction efficiency using extraction methods tailored to other extreme environments such as deep-sea sediments. Here, we benchmarked the utility and efficacy of four extraction protocols, including an up-scaled phenol-chloroform protocol. We found that established protocols for comparable sample types consistently failed to yield sufficient high-quality DNA, delineating the extreme character of GFS. The methods differed in the success of downstream applications such as library preparation and sequencing. An adapted phenol-chloroform-based extraction method resulted in higher yields and better recovered the expected taxonomic profile and abundance of reconstructed genomes when compared to commercially-available methods. Affordable and straight-forward, this method consistently recapitulated the abundance and genomes of a mock community, including eukaryotes. Moreover, by increasing the amount of input sediment, the protocol is readily adjustable to the microbial load of the processed samples without compromising protocol efficiency. Our study provides a first systematic and extensive analysis of the different options for extraction of nucleic acids from glacier-fed streams for high-throughput sequencing applications, which may be applied to other extreme environments. ©2020 Busi et al.Entities:
Keywords: Alpine streams; Biofilms; Biomolecular extraction; Glacier fed streams; Glaciers; Metagenomics; Streams
Year: 2020 PMID: 33194372 PMCID: PMC7597623 DOI: 10.7717/peerj.9973
Source DB: PubMed Journal: PeerJ ISSN: 2167-8359 Impact factor: 2.984