Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

Bingdi Liu, Lin Zhang, Jason H. Knouft, Fangqiong Ling

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Front. Environ. Sci. Eng. ›› 2022, Vol. 16 ›› Issue (5) : 64. DOI: 10.1007/s11783-022-1543-6
RESEARCH ARTICLE
RESEARCH ARTICLE

Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

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Highlights

● Riverine microbiomes exhibited hyperlocal variation within a single transect.

● Certain family-level taxa directionally associated with river center and bank.

● Taxon accumulation curves within a transect urges more nuanced sampling design.

Abstract

Microbial communities inhabiting river ecosystems play crucial roles in global biogeochemical cycling and pollution attenuation. Spatial variations in local microbial assemblages are important for detailed understanding of community assembly and developing robust biodiversity sampling strategies. Here, we intensely analyzed twenty water samples collected from a one-meter spaced transect from the near-shore to the near-center in the Meramec River in eastern Missouri, USA and examined the microbial community composition with 16S rRNA gene amplicon sequencing. Riverine microbiomes across the transect exhibited extremely high similarity, with Pearson’s correlation coefficients above 0.9 for all pairwise community composition comparisons. However, despite the high similarity, PERMANOVA revealed significant spatial differences between near-shore and near-center communities (p = 0.001). Sloan’s neutral model simulations revealed that within-transect community composition variation was largely explained by demographic stochasticity (R2 = 0.89). Despite being primarily explained by neutral processes, LefSe analyses also revealed taxa from ten families of which relative abundances differed directionally from the bank to the river center, indicating an additional role of environmental filtering. Notably, the local variations within a river transect can have profound impacts on the documentation of alpha diversity. Taxon-accumulation curves indicated that even twenty samples did not fully saturate the sampling effort at the genus level, yet four, six and seven samples were able to capture 80% of the phylum-level, family-level, and genus-level diversity, respectively. This study for the first time reveals hyperlocal variations in riverine microbiomes and their assembly mechanisms, demanding attention to more robust sampling strategies for documenting microbial diversity in riverine systems.

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Keywords

Microbiome / Freshwater / Taxon accumulation curve / Community assembly

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Bingdi Liu, Lin Zhang, Jason H. Knouft, Fangqiong Ling. Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies. Front. Environ. Sci. Eng., 2022, 16(5): 64 https://doi.org/10.1007/s11783-022-1543-6

References

[1]
BattinT J, LuyssaertS, KaplanL A, AufdenkampeA K, RichterA, TranvikL J. (2009). The boundless carbon cycle. Nature Geoscience, 2( 9): 598– 600
CrossRef Google scholar
[2]
BolyenE, RideoutJ R, DillonM R, BokulichN A, AbnetC C, Al-GhalithG A, AlexanderH, AlmE J, ArumugamM, AsnicarF. . (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37( 8): 852– 857
CrossRef Google scholar
[3]
CallahanB J, McMurdieP J, RosenM J, HanA W, JohnsonA J A, HolmesS P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13( 7): 581– 583
CrossRef Google scholar
[4]
CharonN W, CockburnA, LiC, LiuJ, MillerK A, MillerM R, MotalebM A, WolgemuthC W. (2012). The unique paradigm of spirochete motility and chemotaxis. Annual Review of Microbiology, 66( 1): 349– 370
CrossRef Google scholar
[5]
ColeJ J PrairieY T CaracoN F McDowellW H TranvikL J StrieglR G DuarteC M P KortelainenJ A DowningJ A MiddelburgJ J MelackJ (2007). Plumbing the global carbon cycle: Integrating inland waters into the terrestrial carbon budget. Ecosystems (New York, N.Y.), 10( 1): 172− 185
[6]
CruaudP, VigneronA, FradetteM S, DoreaC C, CulleyA I, RodriguezM J, CharetteS J. (2020). Annual bacterial community cycle in a seasonally ice-covered river reflects environmental and climatic conditions. Limnology and Oceanography, 65( S1): S21– S37
CrossRef Google scholar
[7]
CrumpB C, Amaral-ZettlerL A, KlingG W. (2012). Microbial diversity in arctic freshwaters is structured by inoculation of microbes from soils. ISME journal, 6( 9): 1629– 1639
CrossRef Google scholar
[8]
EnsignS H DoyleM W (2006). Nutrient spiraling in streams and river networks. Journal of Geophysical Research. Biogeosciences, 111(G4)
[9]
FalkowskiP G, FenchelT, DelongE F. (2008). The microbial engines that drive Earth’s biogeochemical cycles. Science, 320( 5879): 1034– 1039
CrossRef Google scholar
[10]
FaschingC, AkotoyeC, BižićM, FonvielleJ, IonescuD, MathavarajahS, ZoccaratoL, WalshD A, GrossartH, XenopoulosM A. (2020). Linking stream microbial community functional genes to dissolved organic matter and inorganic nutrients. Limnology and Oceanography, 65( S1): S71– S87
CrossRef Google scholar
[11]
FukamiT ( 2015). Historical contingency in community assembly: Integrating niches, species pools, and priority effects. Annual Review of Ecology, Evolution, and Systematics, 46( 1): 1− 23
[12]
GilvearD J GreenwoodM T ThomsM C WoodP J ( 2016). River Science: Research and Management for the 21st Century. Hoboken: John Wiley & Sons
[13]
GweonH S, BowesM J, MoorhouseH L, OliverA E, BaileyM J, AcremanM C, ReadD S. (2021). Contrasting community assembly processes structure lotic bacteria metacommunities along the river continuum. Environmental Microbiology, 23( 1): 484– 498
CrossRef Google scholar
[14]
KhleborodovaA ( 2020). Lefser: R implementation of the LEfSE method for microbiome biomarker discovery (Version R package version 140)
[15]
LarsbrinkJ, McKeeL S. (2020). Bacteroidetes bacteria in the soil: Glycan acquisition, enzyme secretion, and gliding motility. Advances in Applied Microbiology, 110 : 63– 98
CrossRef Google scholar
[16]
LeventhalG E, BoixC, KuechlerU, EnkeT N, SliwerskaE, HolligerC, CorderoO X. (2018). Strain-level diversity drives alternative community types in millimetre-scale granular biofilms. Nature Microbiology, 3( 11): 1295– 1303
CrossRef Google scholar
[17]
LingF, WhitakerR, LeChevallierM W, LiuW T. (2018). Drinking water microbiome assembly induced by water stagnation. The ISME journal, 12( 6): 1520– 1531
CrossRef Google scholar
[18]
LoucaS, PolzM F, MazelF, AlbrightM B N, HuberJ A, O’ConnorM I, AckermannM, HahnA S, SrivastavaD S, CroweS A, DoebeliM, ParfreyL W. (2018). Function and functional redundancy in microbial systems. Nature Ecology & Evolution, 2( 6): 936– 943
CrossRef Google scholar
[19]
McLellanS L, FisherJ C, NewtonR J. (2015). The microbiome of urban waters. International microbiology: The official journal of the Spanish Society for Microbiology, 18( 3): 141– 149
[20]
McMurdieP J, HolmesS. (2013). Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One, 8( 4): e61217
CrossRef Google scholar
[21]
NewtonR J, JonesS E, EilerA, McMahonK D, BertilssonS. (2011). A guide to the natural history of freshwater lake bacteria. Microbiology and molecular biology reviews, 75( 1): 14– 49
CrossRef Google scholar
[22]
OksanenJ BlanchetF G KindtR LegendreP MinchinP R O’HaraR B SimpsonG L SolymosP StevensM H H WagnerH ( 2020). Vegan: Community Ecology Package (Version R package version 25−7)
[23]
ParadisE ClaudeJ StrimmerK ( 2004). APE: Analyses of phylogenetics and evolution in R language. Bioinformatics (Oxford, England), 20( 2): 289− 290
Pubmed
[24]
PreheimS P, PerrottaA R, FriedmanJ, SmilieC, BritoI, SmithM B, AlmE. (2013). Computational methods for high-throughput comparative analyses of natural microbial communities. Methods in Enzymology, 531 : 353– 370
CrossRef Google scholar
[25]
QuastC PruesseE YilmazP GerkenJ SchweerT YarzaP PepliesJ GlöcknerF O ( 2013). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue): D590–D596
Pubmed
[26]
ReadD S, GweonH S, BowesM J, NewboldL K, FieldD, BaileyM J, GriffithsR I. (2015). Catchment-scale biogeography of riverine bacterioplankton. ISME journal, 9( 2): 516– 526
CrossRef Google scholar
[27]
Ruiz-GonzálezC, Niño-GarcíaJ P, DelGiorgio P A. (2015). Terrestrial origin of bacterial communities in complex boreal freshwater networks. Ecology Letters, 18( 11): 1198– 1206
CrossRef Google scholar
[28]
SavioD, SinclairL, IjazU Z, ParajkaJ, ReischerG H, StadlerP, BlaschkeA P, BlöschlG, MachR L, KirschnerA K T, FarnleitnerA H, EilerA. (2015). Bacterial diversity along a 2600 km river continuum. Environmental Microbiology, 17( 12): 4994– 5007
CrossRef Google scholar
[29]
SegataN, IzardJ, WaldronL, GeversD, MiropolskyL, GarrettW S, HuttenhowerC. (2011). Metagenomic biomarker discovery and explanation. Genome Biology, 12( 6): R60
CrossRef Google scholar
[30]
SvobodaP, LindströmE S, AhmedOsman O, LangenhederS. (2018). Dispersal timing determines the importance of priority effects in bacterial communities. ISME journal, 12( 2): 644– 646
CrossRef Google scholar
[31]
TheMissouri Department of Natural Resources (2015). The state of our Missouri waters-Meramec river watershed. Retrieved from www.ewgatewayorg/wp-content/uploads/ 2017.08/MRP-MeramecRiverWatershed.pdf
[32]
ThompsonJ R, PacochaS, PharinoC, Klepac-CerajV, HuntD E, BenoitJ, Sarma-RupavtarmR, DistelD L, PolzM F. (2005). Genotypic diversity within a natural coastal bacterioplankton population. Science, 307( 5713): 1311– 1313
CrossRef Google scholar
[33]
VannoteR L, MinshallG W, CumminsK W, SedellJ R, CushingC E. (1980). The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences, 37( 1): 130– 137
CrossRef Google scholar
[34]
WickhamH, AverickM, BryanJ, ChangW, McGowanL, FrançoisR, GrolemundG, HayesA, HenryL, HesterJ. . (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4( 43): 1686
CrossRef Google scholar
[35]
WithersP J A, JarvieH P. (2008). Delivery and cycling of phosphorus in rivers: A review. Science of the total environment, 400( 1−3): 379– 395
CrossRef Google scholar

Acknowledgements

This work is partially supported by a Living Earth Collaborative Seed Grant to FL and JHK and a Ralph Powe Junior Faculty Enhancement Award to FL.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-022-1543-6 and is accessible for authorized users.

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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2022 The Author(s) 2022. This article is published with open access at link.springer.com and journal.hep.com.cn
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