Metabarcoding and Metagenomics (Aug 2024)

Influence of storage time on the stability of diatom assemblages using DNA from riverine biofilm samples

  • Jonathan Warren,
  • Sean Butler,
  • Nick Evens,
  • Laura Hunt,
  • Martyn Kelly,
  • Lindsay Newbold,
  • Daniel S. Read,
  • Joe D. Taylor,
  • Kerry Walsh

DOI
https://doi.org/10.3897/mbmg.8.129227
Journal volume & issue
Vol. 8
pp. 169 – 186

Abstract

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DNA sequencing of diatom assemblages from biofilms has already been used to assess the ecological status of freshwater in the UK. However, recent work using DNA data from these biofilms suggests that alternate metrics that capture the broader taxonomic and functional information to demonstrate importance of microbial biofilms could be useful. Exploring this potential requires large numbers of samples over time and space to be analysed. Sample archives could be used to meet this need, but the compositional stability of microbial communities in stored biofilm samples for more than one year is uncertain. This study compared changes in diatom assemblage structure using metabarcoding analysis of river biofilm samples before and after storage at -20 °C in an RNAlater-based nucleic acid preservative. We found minimal changes in the diatom assemblages in the samples when stored for up to three years. Slight differences in certain groups observed resulted in four samples changing ecological status. However, the overall differences were not significant across replicates, suggesting any genuine differences in assemblages are likely masked by sub-sampling, PCR, or primer biases. These findings are similar to those observed in other studies looking at variations between analysts and sequencing instruments. This indicates that the diatom assemblages in the archived biofilm samples are stable. This will give greater confidence that archived samples can be used for further research, including exploring broader microbial taxa and their responses to environmental change, potentially leading to the development of reliable microbial metrics for integration into biomonitoring programs.