1887

Microbial Genomics logo

Volume 8, Issue 7

Genome-resolved metagenomics reveals novel archaeal and bacterial genomes from Amazonian forest and pasture soils Open Access

Abstract

Amazonian soil microbial communities are known to be affected by the forest-to-pasture conversion, but the identity and metabolic potential of most of their organisms remain poorly characterized. To contribute to the understanding of these communities, here we describe metagenome-assembled genomes (MAGs) recovered from 12 forest and pasture soil metagenomes of the Brazilian Eastern Amazon. We obtained 11 forest and 30 pasture MAGs (≥50% of completeness and ≤10 % of contamination), distributed among two archaeal and 11 bacterial phyla. The taxonomic classification results suggest that most MAGs may represent potential novel microbial taxa. MAGs selected for further evaluation included members of , , , , , , , , and , thus revealing their roles in carbohydrate degradation and mercury detoxification as well as in the sulphur, nitrogen, and methane cycles. A methane-producing of the genus was almost exclusively recovered from pasture soils, which can be linked to a sink-to-source shift after the forest-to-pasture conversion. The novel MAGs constitute an important resource to help us unravel the yet-unknown microbial diversity in Amazonian soils and its functional potential and, consequently, the responses of these microorganisms to land-use change.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): land-use change , forest-to-pasture conversion , metagenome-assembled genomes , methane cycle , nitrogen cycle and soil microbial communities
© 2022 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000853
2022-07-27
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/mgen/8/7/mgen000853.html?itemId=/content/journal/mgen/10.1099/mgen.0.000853&mimeType=html&fmt=ahah

References

  1. Verstraete W, Mertens B. Chapter 5 The key role of soil microbes. In Doelman P, Eijsackers HJP. eds Developments in Soil Science Elsevier; 2004 pp 127–157 [View Article]
     [Google Scholar]
  2. Fierer N, Wood SA, Bueno de Mesquita CP. How microbes can, and cannot, be used to assess soil health. Soil Biol Biochem 2021; 153:108111 [View Article]
     [Google Scholar]
  3. Jansson JK, Hofmockel KS. Soil microbiomes and climate change. Nat Rev Microbiol 2020; 18:35–46 [View Article]
     [Google Scholar]
  4. Stein LY. The long-term relationship between microbial metabolism and greenhouse gases. Trends Microbiol 2020; 28:500–511 [View Article]
     [Google Scholar]
  5. Lehmann J, Bossio DA, Kögel-Knabner I, Rillig MC. The concept and future prospects of soil health. Nat Rev Earth Environ 2020; 1:544–553 [View Article]
     [Google Scholar]
  6. Cameron EK, Martins IS, Lavelle P, Mathieu J, Tedersoo L et al. Global gaps in soil biodiversity data. Nat Ecol Evol 2018; 2:1042–1043 [View Article]
     [Google Scholar]
  7. Guerra CA, Heintz-Buschart A, Sikorski J, Chatzinotas A, Guerrero-Ramírez N et al. Blind spots in global soil biodiversity and ecosystem function research. Nat Commun 2020; 11:3870 [View Article]
     [Google Scholar]
  8. Heckenberger MJ, Russell JC, Toney JR, Schmidt MJ. The legacy of cultural landscapes in the Brazilian Amazon: implications for biodiversity. Philos Trans R Soc Lond B Biol Sci 2007; 362:197–208 [View Article]
     [Google Scholar]
  9. Instituto Nacional de Pesquisas Espaciais - INPE. TerraBrasilis n.d http://terrabrasilis.dpi.inpe.br/app/dashboard/deforestation/biomes/legal_amazon/rates accessed 7 August 2021
     [Google Scholar]
  10. Rodrigues JLM, Pellizari VH, Mueller R, Baek K, Jesus EC et al. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc Natl Acad Sci U S A 2013; 110:988–993 [View Article]
     [Google Scholar]
  11. Paula FS, Rodrigues JLM, Zhou J, Wu L, Mueller RC et al. Land use change alters functional gene diversity, composition and abundance in Amazon forest soil microbial communities. Mol Ecol 2014; 23:2988–2999 [View Article] [PubMed]
     [Google Scholar]
  12. Mendes LW, de Lima Brossi MJ, Kuramae EE, Tsai SM. Land-use system shapes soil bacterial communities in Southeastern Amazon region. Appl Soil Ecol 2015; 95:151–160 [View Article]
     [Google Scholar]
  13. Meyer KM, Morris AH, Webster K, Klein AM, Kroeger ME et al. Belowground changes to community structure alter methane-cycling dynamics in Amazonia. Environ Int 2020; 145:106131 [View Article] [PubMed]
     [Google Scholar]
  14. Kroeger ME, Meredith LK, Meyer KM, Webster KD, de Camargo PB et al. Rainforest-to-pasture conversion stimulates soil methanogenesis across the Brazilian Amazon. ISME J 2021; 15:658–672 [View Article] [PubMed]
     [Google Scholar]
  15. Venturini AM, Dias NMS, Gontijo JB, Yoshiura CA, Paula FS et al. Increased soil moisture intensifies the impacts of forest-to-pasture conversion on methane emissions and methane-cycling communities in the Eastern Amazon. Environ Res 2022; 212:113139 [View Article] [PubMed]
     [Google Scholar]
  16. Saheb Kashaf S, Almeida A, Segre JA, Finn RD. Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data. Nat Protoc 2021; 16:2520–2541 [View Article] [PubMed]
     [Google Scholar]
  17. Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2017; 2:1533–1542 [View Article] [PubMed]
     [Google Scholar]
  18. Pasolli E, Asnicar F, Manara S, Zolfo M, Karcher N et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 2019; 176:649–662 [View Article] [PubMed]
     [Google Scholar]
  19. Nayfach S, Roux S, Seshadri R, Udwary D, Varghese N et al. A genomic catalog of Earth’s microbiomes. Nat Biotechnol 2021; 39:499–509 [View Article] [PubMed]
     [Google Scholar]
  20. Kroeger ME, Delmont TO, Eren AM, Meyer KM, Guo J et al. New biological insights into how deforestation in Amazonia affects soil microbial communities using metagenomics and metagenome-assembled genomes. Front Microbiol 2018; 9:1635 [View Article] [PubMed]
     [Google Scholar]
  21. Lemos LN, Manoharan L, Mendes LW, Venturini AM, Pylro VS et al. Metagenome assembled-genomes reveal similar functional profiles of CPR/patescibacteria phyla in soils. Environ Microbiol Rep 2020; 12:651–655 [View Article]
     [Google Scholar]
  22. Venturini AM, Nakamura FM, Gontijo JB, da França AG, Yoshiura CA et al. Robust DNA protocols for tropical soils. Heliyon 2020; 6:e03830 [View Article]
     [Google Scholar]
  23. Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat Biotechnol 2018; 36:566–569 [View Article]
     [Google Scholar]
  24. Andrews S. FastQC: A quality control tool for high throughput sequence data; 2010 http://www.bioinformatics.babraham.ac.uk/projects/fastqc
  25. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016; 32:3047–3048 [View Article]
     [Google Scholar]
  26. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
     [Google Scholar]
  27. 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. Bioinformatics 2015; 31:1674–1676 [View Article] [PubMed]
     [Google Scholar]
  28. Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015; 3:e1165 [View Article] [PubMed]
     [Google Scholar]
  29. Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016; 32:605–607 [View Article] [PubMed]
     [Google Scholar]
  30. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J et al. Binning metagenomic contigs by coverage and composition. Nat Methods 2014; 11:1144–1146 [View Article] [PubMed]
     [Google Scholar]
  31. Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 2018; 3:836–843 [View Article] [PubMed]
     [Google Scholar]
  32. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 2015; 25:1043–1055 [View Article] [PubMed]
     [Google Scholar]
  33. Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019; 36:1925–1927 [View Article] [PubMed]
     [Google Scholar]
  34. Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 2018; 9:5114 [View Article] [PubMed]
     [Google Scholar]
  35. Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol 2017; 35:725–731 [View Article] [PubMed]
     [Google Scholar]
  36. Wagner D. Methanosarcina. In Trujillo ME, Dedysh S, DeVos P, Hedlund B, Kämpfer P. eds Bergey’s Manual of Systematics of Archaea and Bacteria 2020 pp 1–23 [View Article]
     [Google Scholar]
  37. Murphy CL, Sheremet A, Dunfield PF, Spear JR, Stepanauskas R et al. Genomic analysis of the yet-uncultured Binatota reveals broad methylotrophic, alkane-degradation, and pigment production capacities. mBio 2021; 12:e00985-21 [View Article] [PubMed]
     [Google Scholar]
  38. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [View Article] [PubMed]
     [Google Scholar]
  39. Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 2020; 48:8883–8900 [View Article] [PubMed]
     [Google Scholar]
  40. Wickham H. Ggplot2: Elegant Graphics for Data Analysis Cham: Springer-Verlag; 2016 [View Article]
     [Google Scholar]
  41. Brunson JC, Read QD. ggalluvial: Alluvial plots in “ggplot2”. R package version 0.12.3; 2020 http://corybrunson.github.io/ggalluvial/ accessed 18 May 2022
  42. R Core Team R: A language and environment for statistical computing. R Foundation for statistical computing. Vienna, Austria: 2020 https://www.R-project.org/ accessed 2 November 2021
  43. Conway T. The Entner-Doudoroff pathway: history, physiology and molecular biology. FEMS Microbiol Rev 1992; 9:1–27 [View Article] [PubMed]
     [Google Scholar]
  44. Kornberg HL, Krebs HA. Synthesis of cell constituents from C2-units by a modified tricarboxylic acid cycle. Nature 1957; 179:988–991 [View Article] [PubMed]
     [Google Scholar]
  45. Steudler PA, Melillo JM, Feigl BJ, Neill C, Piccolo MC et al. Consequence of forest-to-pasture conversion on CH4 fluxes in the Brazilian Amazon Basin. J Geophys Res 1996; 101:18547–18554 [View Article]
     [Google Scholar]
  46. Verchot LV, Davidson EA, Cattânio JH, Ackerman IL. Land-use change and biogeochemical controls of methane fluxes in soils of Eastern Amazonia. Ecosystems 2000; 3:41–56 [View Article]
     [Google Scholar]
  47. Fernandes SAP, Bernoux M, Cerri CC, Feigl BJ, Piccolo MC. Seasonal variation of soil chemical properties and CO2 and CH4 fluxes in unfertilized and P-fertilized pastures in an Ultisol of the Brazilian Amazon. Geoderma 2002; 107:227–241 [View Article]
     [Google Scholar]
  48. Lammel DR, Feigl BJ, Cerri CC, Nüsslein K. Specific microbial gene abundances and soil parameters contribute to C, N, and greenhouse gas process rates after land use change in Southern Amazonian Soils. Front Microbiol 2015; 6:1057 [View Article] [PubMed]
     [Google Scholar]
  49. Andrade AC, Fróes A, Lopes FÁC, Thompson FL, Krüger RH et al. Diversity of microbial carbohydrate-active enZYmes (CAZYmes) associated with freshwater and soil samples from Caatinga biome. Microb Ecol 2017; 74:89–105 [View Article] [PubMed]
     [Google Scholar]
  50. Silva-Olaya AM, Mora-Motta DA, Cherubin MR, Grados D, Somenahally A et al. Soil enzyme responses to land use change in the tropical rainforest of the Colombian Amazon region. PLoS ONE 2021; 16:e0255669 [View Article] [PubMed]
     [Google Scholar]
  51. Kumar U, Panneerselvam P, Gupta V, Manjunath M, Priyadarshinee P et al. Diversity of sulfur-oxidizing and sulfur-reducing microbes in diverse ecosystems. In Adhya TK, Lal B, Mohapatra B, Paul D, Das S. eds Advances in Soil Microbiology: Recent Trends and Future Prospects. Microorganisms for Sustainability Singapore: Springer; 2018 pp 65–89 [View Article]
     [Google Scholar]
  52. Ravot G, Casalot L, Ollivier B, Loison G, Magot M. rdlA, a new gene encoding a rhodanese-like protein in Halanaerobium congolense and other thiosulfate-reducing anaerobes. Res Microbiol 2005; 156:1031–1038 [View Article] [PubMed]
     [Google Scholar]
  53. Friedrich CG, Bardischewsky F, Rother D, Quentmeier A, Fischer J. Prokaryotic sulfur oxidation. Curr Opin Microbiol 2005; 8:253–259 [View Article] [PubMed]
     [Google Scholar]
  54. Rother D, Orawski G, Bardischewsky F, Friedrich CG. SoxRS-mediated regulation of chemotrophic sulfur oxidation in Paracoccus pantotrophus. Microbiology (Reading) 2005; 151:1707–1716 [View Article] [PubMed]
     [Google Scholar]
  55. Kobayashi S, Hira D, Yoshida K, Toyofuku M, Shida Y et al. Nitric oxide production from nitrite reduction and hydroxylamine oxidation by copper-containing dissimilatory nitrite reductase (nirk) from the aerobic ammonia-oxidizing archaeon, Nitrososphaera viennensis. Microbes Environ 2018; 33:428–434 [View Article] [PubMed]
     [Google Scholar]
  56. Wu L, Chen X, Wei W, Liu Y, Wang D et al. A critical review on nitrous oxide production by ammonia-oxidizing archaea. Environ Sci Technol 2020; 54:9175–9190 [View Article] [PubMed]
     [Google Scholar]
  57. Kuypers MMM, Marchant HK, Kartal B. The microbial nitrogen-cycling network. Nat Rev Microbiol 2018; 16:263–276 [View Article] [PubMed]
     [Google Scholar]
  58. Lammel DR, Nüsslein K, Tsai SM, Cerri CC. Land use, soil and litter chemistry drive bacterial community structures in samples of the rainforest and Cerrado (Brazilian Savannah) biomes in Southern Amazonia. Eur J Soil Biol 2015; 66:32–39 [View Article]
     [Google Scholar]
  59. Kim J-G, Jung M-Y, Park S-J, Rijpstra WIC, Sinninghe Damsté JS et al. Cultivation of a highly enriched ammonia-oxidizing archaeon of thaumarchaeotal group I.1b from an agricultural soil. Environ Microbiol 2012; 14:1528–1543 [View Article] [PubMed]
     [Google Scholar]
  60. Stieglmeier M, Klingl A, Alves RJE, Rittmann SK-MR, Melcher M et al. Nitrososphaera viennensis gen. nov., sp. nov., an aerobic and mesophilic, ammonia-oxidizing archaeon from soil and a member of the archaeal phylum Thaumarchaeota. Int J Syst Evol Microbiol 2014; 64:2738–2752 [View Article] [PubMed]
     [Google Scholar]
  61. Zhalnina KV, Dias R, Leonard MT, Dorr de Quadros P, Camargo FAO et al. Genome sequence of Candidatus Nitrososphaera evergladensis from group I.1b enriched from Everglades soil reveals novel genomic features of the ammonia-oxidizing archaea. PLoS ONE 2014; 9:e101648 [View Article] [PubMed]
     [Google Scholar]
  62. Kerou M, Schleper C. Nitrososphaeraceae. In Trujillo ME, Dedysh S, DeVos P, Hedlund B, Kämpfer P. eds Bergey’s Manual of Systematics of Archaea and Bacteria 2016 pp 1–2 [View Article]
     [Google Scholar]
  63. Lehtovirta-Morley LE, Ross J, Hink L, Weber EB, Gubry-Rangin C et al. Isolation of “Candidatus Nitrosocosmicus franklandus”, a novel ureolytic soil archaeal ammonia oxidiser with tolerance to high ammonia concentration. FEMS Microbiol Ecol 2016; 92:fiw057 [View Article] [PubMed]
     [Google Scholar]
  64. Luton PE, Wayne JM, Sharp RJ, Riley PW. The mcrA gene as an alternative to 16S rRNA in the phylogenetic analysis of methanogen populations in landfill. Microbiology (Reading) 2002; 148:3521–3530 [View Article] [PubMed]
     [Google Scholar]
  65. Dziewit L, Pyzik A, Romaniuk K, Sobczak A, Szczesny P et al. Novel molecular markers for the detection of methanogens and phylogenetic analyses of methanogenic communities. Front Microbiol 2015; 6:694 [View Article] [PubMed]
     [Google Scholar]
  66. Siqueira GW, Aprile F, Irion G, Braga ES. Mercury in the Amazon basin: human influence or natural geological pattern?. J South Am Earth Sci 2018; 86:193–199 [View Article]
     [Google Scholar]
  67. Barkay T, Miller SM, Summers AO. Bacterial mercury resistance from atoms to ecosystems. FEMS Microbiol Rev 2003; 27:355–384 [View Article]
     [Google Scholar]
  68. Zhuang G-C, Peña-Montenegro TD, Montgomery A, Montoya JP, Joye SB. Significance of acetate as a microbial carbon and energy source in the water column of Gulf of Mexico: implications for marine carbon cycling. Global Biogeochem Cycles 2019; 33:223–235 [View Article]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000853
Loading
Genome-resolved metagenomics reveals novel archaeal and bacterial genomes from Amazonian forest and pasture soils
M Gen 8, 000853 (2022); https://doi.org/10.1099/mgen.0.000853
/content/journal/mgen/10.1099/mgen.0.000853
/content/journal/mgen/10.1099/mgen.0.000853
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error