Diversity and biogeography of the bacterial microbiome in glacier-fed streams

. 2025 Jan ; 637 (8046) : 622-630. [epub] 20250101

Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid39743584
Odkazy

PubMed 39743584
PubMed Central PMC11735386
DOI 10.1038/s41586-024-08313-z
PII: 10.1038/s41586-024-08313-z
Knihovny.cz E-zdroje

The rapid melting of mountain glaciers and the vanishing of their streams is emblematic of climate change1,2. Glacier-fed streams (GFSs) are cold, oligotrophic and unstable ecosystems in which life is dominated by microbial biofilms2,3. However, current knowledge on the GFS microbiome is scarce4,5, precluding an understanding of its response to glacier shrinkage. Here, by leveraging metabarcoding and metagenomics, we provide a comprehensive survey of bacteria in the benthic microbiome across 152 GFSs draining the Earth's major mountain ranges. We find that the GFS bacterial microbiome is taxonomically and functionally distinct from other cryospheric microbiomes. GFS bacteria are diverse, with more than half being specific to a given mountain range, some unique to single GFSs and a few cosmopolitan and abundant. We show how geographic isolation and environmental selection shape their biogeography, which is characterized by distinct compositional patterns between mountain ranges and hemispheres. Phylogenetic analyses furthermore uncovered microdiverse clades resulting from environmental selection, probably promoting functional resilience and contributing to GFS bacterial biodiversity and biogeography. Climate-induced glacier shrinkage puts this unique microbiome at risk. Our study provides a global reference for future climate-change microbiology studies on the vanishing GFS ecosystem.

Zobrazit více v PubMed

Hugonnet, R. et al. Accelerated global glacier mass loss in the early twenty-first century. Nature592, 726–731 (2021). PubMed

Milner, A. M. et al. Glacier shrinkage driving global changes in downstream systems. Proc. Natl Acad. Sci. USA114, 9770–9778 (2017). PubMed PMC

Battin, T. J., Besemer, K., Bengtsson, M. M., Romani, A. M. & Packmann, A. I. The ecology and biogeochemistry of stream biofilms. Nat. Rev. Microbiol.14, 251–263 (2016). PubMed

Cauvy-Fraunié, S. & Dangles, O. A global synthesis of biodiversity responses to glacier retreat. Nat. Ecol. Evol.3, 1675–1685 (2019). PubMed

Hotaling, S., Hood, E. & Hamilton, T. L. Microbial ecology of mountain glacier ecosystems: biodiversity, ecological connections and implications of a warming climate. Environ. Microbiol.19, 2935–2948 (2017). PubMed

Pörtner, H.-O. et al. (eds) IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Cambridge Univ. Press, 2019).

Ménot, G. et al. Early reactivation of European rivers ruring the last deglaciation. Science313, 1623–1625 (2006). PubMed

Immerzeel, W. W. et al. Importance and vulnerability of the world’s water towers. Nature577, 364–369 (2020). PubMed

Clason, C. et al. Contribution of glaciers to water, energy and food security in mountain regions: current perspectives and future priorities. Ann. Glaciol.63, 73–78 (2022).

Jacobsen, D., Milner, A. M., Brown, L. E. & Dangles, O. Biodiversity under threat in glacier-fed river systems. Nat. Clim. Chang.2, 361–364 (2012).

Hotaling, S., Finn, D. S., Joseph Giersch, J., Weisrock, D. W. & Jacobsen, D. Climate change and alpine stream biology: progress, challenges, and opportunities for the future. Biol. Rev.92, 2024–2045 (2017). PubMed

Vega, E., Bastidas Navarro, M., Martyniuk, N., Balseiro, E. & Modenutti, B. Glacial recession in Andean North-Patagonia (Argentina): microbial communities in benthic biofilms of glacier-fed streams. Hydrobiologia850, 3965–3979 (2023).

Ren, Z., Gao, H., Elser, J. J. & Zhao, Q. Microbial functional genes elucidate environmental drivers of biofilm metabolism in glacier-fed streams. Sci. Rep.7, 12668 (2017). PubMed PMC

Wilhelm, L., Singer, G. A., Fasching, C., Battin, T. J. & Besemer, K. Microbial biodiversity in glacier-fed streams. ISME J.7, 1651–1660 (2013). PubMed PMC

Fodelianakis, S. et al. Microdiversity characterizes prevalent phylogenetic clades in the glacier-fed stream microbiome. ISME J. 16, 666–675 (2022). PubMed PMC

Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat. Rev. Microbiol.10, 497–506 (2012). PubMed

Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol.85, 183–206 (2010). PubMed

Martiny, J. B. H. et al. Microbial biogeography: putting microorganisms on the map. Nat. Rev. Microbiol.4, 102–112 (2006). PubMed

Milner, A. M. & Petts, G. E. Glacial rivers: physical habitat and ecology. Freshw. Biol.32, 295–307 (1994).

Kohler, T. J. et al. Global emergent responses of stream microbial metabolism to glacier shrinkage. Nat. Geosci.17, 309–315 (2024).

Bourquin, M. et al. The microbiome of cryospheric ecosystems. Nat. Commun.13, 3087 (2022). PubMed PMC

Bastida, F. et al. Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes. ISME J.15, 2081–2091 (2021). PubMed PMC

Shoemaker, W. R., Locey, K. J. & Lennon, J. T. A macroecological theory of microbial biodiversity. Nat. Ecol. Evol.1, 107 (2017). PubMed

Orsi, W. D. Ecology and evolution of seafloor and subseafloor microbial communities. Nat. Rev. Microbiol.16, 671–683 (2018). PubMed

Danovaro, R., Corinaldesi, C., Rastelli, E. & Anno, A. D. Towards a better quantitative assessment of the relevance of deep-sea viruses, Bacteria and Archaea in the functioning of the ocean seafloor. Aquat. Microb. Ecol.75, 81–90 (2015).

Hotaling, S. et al. Microbial assemblages reflect environmental heterogeneity in alpine streams. Glob. Chang. Biol.25, 2576–2590 (2019). PubMed

Ezzat, L. et al. Benthic biofilms in glacier-fed streams from Scandinavia to the Himalayas host distinct bacterial communities compared with the streamwater. Appl. Environ. Microbiol.88, e00421–e00422 (2022). PubMed PMC

Wang, J. et al. Embracing mountain microbiome and ecosystem functions under global change. New Phytol.234, 1987–2002 (2022). PubMed

Bertuzzo, E. et al. Geomorphic controls on elevational gradients of species richness. Proc. Natl Acad. Sci. USA113, 1737–1742 (2016). PubMed PMC

Picazo, F. et al. Climate mediates continental scale patterns of stream microbial functional diversity. Microbiome8, 92 (2020). PubMed PMC

Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol.2, 936–943 (2018). PubMed

Allison, S. D. & Martiny, J. B. H. Resistance, resilience, and redundancy in microbial communities. Proc. Natl Acad. Sci. USA105, 11512–11519 (2008). PubMed PMC

Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods13, 581–583 (2016). PubMed PMC

Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems2, e00191-16 (2017). PubMed PMC

Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at bioRxiv10.1101/081257 (2016).

Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature403, 853–858 (2000). PubMed

Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science365, 1108–1113 (2019). PubMed

Souza, V., Eguiarte, L. E., Siefert, J. & Elser, J. J. Microbial endemism: does phosphorus limitation enhance speciation? Nat. Rev. Microbiol.6, 559–564 (2008). PubMed

Rinaldo, A., Gatto, M. & Rodríguez-Iturbe, I. River Networks as Ecological Corridors: Species, Populations, Pathogens (Cambridge Univ. Press, 2020).

Nemergut, D. R. et al. Global patterns in the biogeography of bacterial taxa. Environ. Microbiol.13, 135–144 (2011). PubMed PMC

Wu, L. et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat. Microbiol.4, 1183–1195 (2019). PubMed

Clark, D. R., Underwood, G. J. C., McGenity, T. J. & Dumbrell, A. J. What drives study-dependent differences in distance–decay relationships of microbial communities? Glob. Ecol. Biogeogr.30, 811–825 (2021).

Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science365, 1114–1119 (2019). PubMed

Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci.11, 718–725 (2018).

Shapiro, B. J. et al. Population genomics of early events in the ecological differentiation of bacteria. Science336, 48–51 (2012). PubMed PMC

Stegen, J. C. et al. Quantifying community assembly processes and identifying features that impose them. ISME J.7, 2069–2079 (2013). PubMed PMC

Ning, D. et al. A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nat. Commun.11, 4717 (2020). PubMed PMC

Fodelianakis, S., Valenzuela-Cuevas, A., Barozzi, A. & Daffonchio, D. Direct quantification of ecological drift at the population level in synthetic bacterial communities. ISME J.15, 55–66 (2021). PubMed PMC

Larkin, A. A. & Martiny, A. C. Microdiversity shapes the traits, niche space, and biogeography of microbial taxa. Environ. Microbiol. Rep.9, 55–70 (2017). PubMed

Averill, C. et al. Defending Earth’s terrestrial microbiome. Nat. Microbiol.7, 1717–1725 (2022). PubMed

Kohler, T. J. et al. Patterns and drivers of extracellular enzyme activity in New Zealand glacier-fed streams. Front. Microbiol. 11, 591465 (2020). PubMed PMC

Busi, S. B. et al. Optimised biomolecular extraction for metagenomic analysis of microbial biofilms from high-mountain streams. PeerJ8, e9973 (2020). PubMed PMC

Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res.41, e1 (2013). PubMed PMC

Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J.17, 10–12 (2011).

Bolyen, E. et al. QIIME 2: reproducible, interactive, scalable, and extensible microbiome data science. Nat. Biotechnol. 37, 852–857 (2019). PubMed PMC

Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res.41, D590–D596 (2012). PubMed PMC

Piñeiro, C., Abuín, J. M. & Pichel, J. C. Very Fast Tree: speeding up the estimation of phylogenies for large alignments through parallelization and vectorization strategies. Bioinformatics36, 4658–4659 (2020). PubMed

Busi, S. B. et al. Genomic and metabolic adaptations of biofilms to ecological windows of opportunities in glacier-fed streams. Nat. Commun. 13, 2168 (2022). PubMed PMC

Narayanasamy, S. et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses. Genome Biol.17, 260 (2016). PubMed PMC

Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics31, 1674–1676 (2015). PubMed

Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010). PubMed PMC

Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol.35, 1026–1028 (2017). PubMed

Queirós, P., Delogu, F., Hickl, O., May, P. & Wilmes, P. Mantis: flexible and consensus-driven genome annotation. Gigascience10, giab042 (2021). PubMed PMC

Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics36, 2251–2252 (2020). PubMed PMC

Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol.7, 1451–1456 (2016).

Jost, L. Entropy and diversity. Oikos113, 363–375 (2006).

Li, D. hillR: taxonomic, functional, and phylogenetic diversity and similarity through Hill Numbers. J. Open Source Softw.3, 1041 (2018).

Wood, S. mgcv: mixed GAM computation vehicle with automatic smoothness estimation. R package v.1.8-42 (CRAN, 2023).

Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).

Oksanen, J., Kindt, R. & O’Hara, B. Vegan: R functions for vegetation ecologists. Date of access15, 2014 (2005).

Martinez Arbizu, P. pairwiseAdonis: pairwise multilevel comparison using adonis. R package v.0.4 (GitHub, 2017).

Warton, D. I., Wright, S. T. & Wang, Y. Distance‐based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol.3, 89–101 (2012).

Wang, Y. I., Naumann, U., Wright, S. T. & Warton, D. I. mvabund—An R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol.3, 471–474 (2012).

Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett.16, 951–963 (2013). PubMed

Dray, S. et al. adespatial: multivariate multiscale spatial analysis. R package v.0.3-21 (CRAN, 2023).

Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol.57, 289–300 (1995).

Ricotta, C., Pavoine, S., Cerabolini, B. E. L. & Pillar, V. D. A new method for indicator species analysis in the framework of multivariate analysis of variance. J. Veg. Sci.32, e13013 (2021).

Pavoine, S. adiv: An R package to analyse biodiversity in ecology. Methods Ecol. Evol.11, 1106–1112 (2020).

Wilke, C. O. ggridges: ridgeline plots in ‘ggplot2’. R package v.0.5.4 (CRAN, 2021).

McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE8, e61217 (2013). PubMed PMC

Hijmans, R. J., Williams, E., Vennes, C. & Hijmans, M. R. J. Package ‘geosphere’. Spherical trigonometry1, 1–45 (2017).

Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology73, 1045–1055 (1992).

Legendre, P., Borcard, D. & Peres-Neto, P. R. Analyzing beta diversity: partitioning the spatial variation of community composition data. Ecol. Monogr.75, 435–450 (2005).

Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology87, 2614–2625 (2006). PubMed

Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model.196, 483–493 (2006).

Blanchet, F. G., Legendre, P. & Borcard, D. Forward selection of explanatory variables. Ecology89, 2623–2632 (2008). PubMed

Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia129, 271–280 (2001). PubMed

Dray, S., Legendre, P. & Blanchet, F. G. packfor: forward selection with permutation (Canoco p. 46). R package v.0.0-8 (R-Forge, 2007).

Legendre, P. et al. Partitioning beta diversity in a subtropical broad‐leaved forest of China. Ecology90, 663–674 (2009). PubMed

Xu, S. et al. ggtreeExtra: compact visualization of richly annotated phylogenetic data. Mol. Biol. Evol.38, 4039–4042 (2021). PubMed PMC

Keck, F., Rimet, F., Bouchez, A. & Franc, A. phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol. Evol.6, 2774–2780 (2016). PubMed PMC

Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol.17, e1009442 (2021). PubMed PMC

Tenenbaum, D. et al. KEGGREST: client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG). R package v.1.32.0 (Bioconductor, 2021).

Wickham, H. et al. ggplot2: create elegant data visualisations using the grammar of graphics. R package v.3.5.0 (CRAN, 2024).

Kolde, R. pheatmap: pretty heatmaps. R package v.1.0.12 (CRAN, 2019).

Yan, Q. et al. Distinct strategies of the habitat generalists and specialists in sediment of Tibetan lakes. Environ. Microbiol.24, 4153–4166 (2022). PubMed

Salazar, G. EcolUtils: utilities for community ecology analysis. R package v.3 (2020).

R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

RStudio Team. RStudio: integrated development environment for R (RStudio, 2021).

Massicotte, P. & South, A. rnaturalearth: world map data from Natural Earth. R package v.1.0.1 (CRAN, 2017).

Ezzat, L. et al. Diversity and biogeography of the glacier-fed stream bacterial microbiome. Zenodo10.5281/zenodo.13897903 (2024).

Nejnovějších 20 citací...

Zobrazit více v
Medvik | PubMed

Deciphering the biosynthetic landscape of biofilms in glacier-fed streams

. 2025 Feb 18 ; 10 (2) : e0113724. [epub] 20241231

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...