Influence of storage time on the stability of diatom assemblages using DNA from riverine biofilm samples
Authors/Creators
- 1. Environment Agency, Bristol, United Kingdom
- 2. Nottingham University, Nottingham, United Kingdom|Bowburn Consultancy, Durham, United Kingdom
- 3. UK Centre for Ecology & Hydrology, Wallingford, United Kingdom
Description
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.
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References
- Astudillo-García C, Hermans SM, Stevenson B, Buckley HL, Lear G (2019) Microbial assemblages and bioindicators as proxies for ecosystem health status: Potential and limitations. Applied Microbiology and Biotechnology 103(16): 6407–6421. https://doi.org/10.1007/s00253-019-09963-0
- Baricevic A, Chardon C, Kahlert M, Karjalainen SM, Pfannkuchen DM, Pfannkuchen M, Rimet F, Tankovic MS, Trobajo R, Vasselon V, Zimmermann J, Bouchez A (2022) Recommendations for the preservation of environmental samples in diatom metabarcoding studies, 349–365. https://doi.org/10.3897/mbmg.6.85844
- Bisanz JE (2018) qiime2R: Importing QIIME2 artifacts and associated data into R sessions. https://github.com/jbisanz/qiime2R
- Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, Huttley GA, Gregory Caporaso J (2018) Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome 6(1): 1–17. https://doi.org/10.1186/s40168-018-0470-z
- Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang K, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu Y-X, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS II, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37(8): 852–857. https://doi.org/10.1038/s41587-019-0209-9
- Bundgaard-Nielsen C, Hagstrøm S, Sørensen S (2018) Interpersonal Variations in Gut Microbiota Profiles Supersedes the Effects of Differing Fecal Storage Conditions. Scientific Reports 8(1): 1–9. https://doi.org/10.1038/s41598-018-35843-0
- Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nature methods 13: 581–583. https://doi.org/10.1038/nmeth.3869
- CEN (2018) CEN/TR 17245:2018 Water quality - Technical report for the routine sampling of benthic diatoms from rivers and lakes adapted for metabarcoding analyses. https://standards.iteh.ai/catalog/standards/cen/31e578ee-1135-49d8-9446-94b56d9c267e/cen-tr-17245-2018
- Codello A, Hose GC, Chariton A (2022) Microbial co-occurrence networks as a biomonitoring tool for aquatic environments: A review. Marine and Freshwater Research: 409–422. https://doi.org/10.1071/MF22045
- Cordier T, Lanzén A, Apothéloz-Perret-Gentil L, Stoeck T, Pawlowski J (2019) Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring. Trends in Microbiology 27(5): 387–397. https://doi.org/10.1016/j.tim.2018.10.012
- Delavaux CS, Bever JD, Karppinen EM, Bainard LD (2020) Keeping it cool: Soil sample cold pack storage and DNA shipment up to 1 month does not impact metabarcoding results. Ecology and Evolution 10(11): 4652–4664. https://doi.org/10.1002/ece3.6219
- Deutschmann IM, Lima-Mendez G, Krabberød AK, Raes J, Vallina SM, Faust K, Logares R (2021) Disentangling environmental effects in microbial association networks. Microbiome 9: 1–18. https://doi.org/10.1186/s40168-021-01141-7
- Dully V, Rech G, Wilding TA, Lanz A, Mackichan K, Berrill I, Stoeck T (2021) Comparing sediment preservation methods for genomic biomonitoring of coastal marine ecosystems. Marine Pollution Bulletin 173: 113129. https://doi.org/10.1016/j.marpolbul.2021.113129
- Eastwood N, Zhou J, Derelle R, Abdallah MA-E, Stubbings WA, Jia Y, Crawford SE, Davidson TA, Colbourne JK, Creer S, Bik H, Hollert H, Orsini L (2023) 100 years of anthropogenic impact causes changes in freshwater functional biodiversity. bioRxiv: 2023.02.26.530075. https://doi.org/10.1101/2023.02.26.530075
- Environment Agency (2023) Using DNA to understand river diatom communities. Environment Agency. gov.uk.
- European Parliment (2000) DIRECTIVE 2000/60/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL. Official Journal of the European Union 43: 1–44.
- European Parliment (2006) Directive 2006/7/EC of the European Parliament and of the Council of 15 February 2006 concerning the management of bathing water quality and repealing Directive 76/160/EEC. Official Journal of the European Union 53: 1–12.
- Falkowski PG, Fenchel T, Delong EF (2008) The microbial engines that drive earth's biogeochemical cycles. Science 320(5879): 1034–1039. https://doi.org/10.1126/science.1153213
- Fontaine L, Pin L, Savio D, Friberg N, Kirschner AKT, Farnleitner AH, Eiler A (2023) Bacterial bioindicators enable biological status classification along the continental Danube river. Communications Biology 6(1): 1–11. https://doi.org/10.1038/s42003-023-05237-8
- Gold Z, Shelton AO, Casendino HR, Duprey J, Gallego R, Van Cise A, Fisher M, Jensen AJ, D'Agnese E, Allan EA, Ramón-Laca A, Garber-Yonts M, Labare M, Parsons KM, Kelly RP (2023) Signal and noise in metabarcoding data. PLoS ONE 18(5): 1–21. https://doi.org/10.1371/journal.pone.0285674
- Guseva K, Darcy S, Simon E, Alteio LV, Montesinos-Navarro A, Kaiser C (2022) From diversity to complexity: Microbial networks in soils. Soil Biology & Biochemistry 169: 108604. https://doi.org/10.1016/j.soilbio.2022.108604
- Jackson MC, Weyl OLF, Altermatt F, Durance I, Friberg N, Dumbrell AJ, Piggott JJ, Tiegs SD, Tockner K, Krug CB, Leadley PW, Woodward G (2016) 55 Advances in Ecological Research Recommendations for the Next Generation of Global Freshwater Biological Monitoring Tools. 1st edn. Elsevier, 615–636. https://doi.org/10.1016/bs.aecr.2016.08.008
- Kelly M, Juggins S, Guthrie R, Pritchard S, Jamieson J, Rippey B, Hirst H, Yallop M (2008) Assessment of ecological status in U.K. rivers using diatoms. Freshwater Biology 53(2): 403–422. https://doi.org/10.1111/j.1365-2427.2007.01903.x
- Kelly M, Boonham N, Juggins S, Kille P, Mann DG, Pass D, Sapp M, Sato S, Glover R (2018) Environment Agency A DNA based diatom metabarcoding approach for Water Framework Directive classification of rivers, 157 pp.
- Kelly M, Juggins S, Mann DG, Sato S, Glover R, Boonham N, Sapp M, Lewis E, Hany U, Kille P, Jones T, Walsh K (2020) Development of a novel metric for evaluating diatom assemblages in rivers using DNA metabarcoding. Ecological Indicators 118: 106725. https://doi.org/10.1016/j.ecolind.2020.106725
- Kelly MG, Mann DG, Taylor JD, Juggins S, Walsh K, Pitt J-A, Read D (2024) Maximising environmental pressure-response relationship signals from diatom-based metabarcoding in rivers. The Science of the Total Environment 914: 169445. https://doi.org/10.1016/j.scitotenv.2023.169445
- Kim JH, Jeon JY, Im YJ, Ha N, Kim JK, Moon SJ, Kim MG (2023) Long-term taxonomic and functional stability of the gut microbiome from human fecal samples: 1–8. https://doi.org/10.1038/s41598-022-27033-w
- Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15(12): 1–21. https://doi.org/10.1186/s13059-014-0550-8
- Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. Journal 17(1): 10–12. https://doi.org/10.14806/ej.17.1.200
- Mathieu C, Hermans SM, Lear G, Buckley TR, Lee KC, Buckley HL (2020) A systematic review of sources of variability and uncertainty in eDNA data for environmental monitoring. Frontiers in Ecology and Evolution 8: 1–14. https://doi.org/10.3389/fevo.2020.00135
- McElhinney JMWR, Catacutan MK, Mawart A, Hasan A, Dias J (2022) Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges. Frontiers in Microbiology 13: 851450. https://doi.org/10.3389/fmicb.2022.851450
- McGuire KL, Treseder KK (2010) Microbial communities and their relevance for ecosystem models: Decomposition as a case study. Soil Biology & Biochemistry 42(4): 529–535. https://doi.org/10.1016/j.soilbio.2009.11.016
- McMurdie PJ, Holmes S (2013) phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4): e61217. https://doi.org/10.1371/journal.pone.0061217
- McMurdie PJ, Holmes S (2014) Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Computational Biology 10(4): e1003531. https://doi.org/10.1371/journal.pcbi.1003531
- Mishra S, Lin Z, Pang S, Zhang W, Bhatt P, Chen S (2021) Recent Advanced Technologies for the Characterization of Xenobiotic-Degrading Microorganisms and Microbial Communities. Frontiers in Bioengineering and Biotechnology 9: 632059. https://doi.org/10.3389/fbioe.2021.632059
- Neu TR, Lawrence JR (1997) Development and structure of microbial biofilms in river water studied by confocal laser scanning microscopy. FEMS Microbiology Ecology 24(1): 11–25. https://doi.org/10.1111/j.1574-6941.1997.tb00419.x
- Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR, O'Hara RB, Solymos P, Stevens MHH, Szoecs E, Wagner H, Barbour M, Bedward M, Bolker B, Borcard D, Carvalho G, Chirico M, De Caceres M, Durand S, Evangelista HBA, FitzJohn R, Friendly M, Furneaux B, Hannigan G, Hill MO, Lahti L, McGlinn D, Ouellette M-H, Ribeiro Cunha E, Smith T, Stier A, Ter Braak CJF, Weedon J (2022) vegan: Community Ecology Package. https://cran.r-project.org/package=vegan
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12: 2825–2830.
- R Core Team (2022) R: A Language and Environment for Statistical Computing. https://www.r-project.org/
- Rimet F, Gusev E, Kahlert M, Kelly MG, Kulikovskiy M, Maltsev Y, Mann DG, Pfannkuchen M, Trobajo R, Vasselon V, Zimmermann J, Bouchez A (2019) Diat.barcode, an open-access curated barcode library for diatoms. Scientific Reports 9(1): 1–12. https://doi.org/10.1038/s41598-019-51500-6
- Rivera SF, Vasselon V, Bouchez A, Rimet F (2023) eDNA metabarcoding from aquatic biofilms allows studying spatial and temporal fluctuations of fish communities from Lake Geneva. Environmental DNA 5(3): 1–12. https://doi.org/10.1002/edn3.413
- Sagova-Mareckova M, Boenigk J, Bouchez A, Cermakova K, Chonova T, Cordier T, Eisendle U, Elersek T, Fazi S, Fleituch T, Frühe L, Gajdosova M, Graupner N, Haegerbaeumer A, Kelly AM, Kopecky J, Leese F, Nõges P, Orlic S, Panksep K, Pawlowski J, Petrusek A, Piggott JJ, Rusch JC, Salis R, Schenk J, Simek K, Stovicek A, Strand DA, Vasquez MI, Vrålstad T, Zlatkovic S, Zupancic M, Stoeck T (2021) Expanding ecological assessment by integrating microorganisms into routine freshwater biomonitoring. Water Research 191: 116767. https://doi.org/10.1016/j.watres.2020.116767
- Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, Elberg A, Crowley J (2021) GGally: Extension to "ggplot2." https://cran.r-project.org/package=GGally
- Shirazi S, Meyer RS, Shapiro B (2021) Revisiting the effect of PCR replication and sequencing depth on biodiversity metrics in environmental DNA metabarcoding. Ecology and Evolution 11(22): 15766–15779. https://doi.org/10.1002/ece3.8239
- Song SJ, Amir A, Metcalf JL, Amato KR (2016) Microbiome Stability, Affecting. Msystems.Asm. Org 1: 1–12. https://doi.org/10.1128/mSystems.00021-16
- Tap J, Cools-portier S, Pavan S, Druesne A, Öhman L, Törnblom H, Simren M, Derrien M (2019) Effects of the long-term storage of human fecal microbiota samples collected in RNAlater.: 1–9. https://doi.org/10.1038/s41598-018-36953-5
- Warnasuriya SD, Udayanga D, Manamgoda DS, Biles C (2023) Fungi as environmental bioindicators. The Science of the Total Environment 892: 164583. https://doi.org/10.1016/j.scitotenv.2023.164583
- Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer, New York. https://doi.org/10.1007/978-3-319-24277-4_9
- Yan L (2023) ggvenn: Draw Venn Diagram by "ggplot2." https://cran.r-project.org/package=ggvenn
- Yutani H (2022) gghighlight: Highlight Lines and Points in "ggplot2." https://cran.r-project.org/package=gghighlight