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Merging Fungal and Bacterial Community Profiles via an Internal Control

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Abstract

Integrated measurements of fungi and bacteria are critical to understand how interactions between these taxa drive key processes in ecosystems ranging from soils to animal guts. High-throughput amplicon sequencing is commonly used to census microbiomes, but the genetic markers targeted for fungi and bacteria (typically ribosomal regions) are domain-specific so profiling must be performed separately, obscuring relationships between these groups. To solve this problem, we developed a spike-in method with an internal control (IC) construct containing primer sites commonly used for bacterial and fungal taxonomic profiling. The internal control offers several advantages: estimation of absolute abundances, estimation of fungal to bacterial ratios (F:B), integration of bacterial and fungal profiles for holistic community analysis, and lower costs compared to other quantitation methods. To validate the IC as a scaling method, we compared IC-derived measures of F:B to measures from quantitative PCR (qPCR) using a commercial mock community (the ZymoBiomic Microbial Community DNA Standard II, containing two fungi and eight bacteria) and complex environmental samples. For both the mock community and the environmental samples, the IC produced F:B values that were statistically consistent with qPCR. Merging the environmental fungal and bacterial profiles based on the IC-derived F:B values revealed new relationships among samples in terms of community similarity. This IC method is the first spike-in method to employ a single construct for cross-domain amplicon sequencing, offering more reliable measurements.

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Data Availability

Unprocessed sequences are available through NCBI’s Sequence Read Archive (PRJNA478595).

References

  1. Marco DE, Abram F (2019) Using genomics, metagenomics and other “Omics” to assess valuable microbial ecosystem services and novel biotechnological applications. Front Microbiol 10:151

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bell TAS, Doig L, Peyton BM, Gerlach R, Fields MW (2019) Contributions of the microbial community to algal biomass and biofuel productivity in a wastewater treatment lagoon system. Algal Res 39:101461

    Article  Google Scholar 

  3. Wardle DA (2004) Ecological linkages between aboveground and belowground biota. Science 304:1629–1633. https://doi.org/10.1126/science.1094875

    Article  CAS  PubMed  Google Scholar 

  4. Zhai B, Ola M, Rolling T, Tosini NL, Joshowitz S, Littmann ER, Amoretti LA, Fontana E, Wright RJ, Miranda E, Veelken CA, Morjaria SM, Peled JU, Van Den Brink MRM, Babady NE, Butler G, Taur Y, Hohl TM (2020) High-resolution mycobiota analysis reveals dynamic intestinal translocation preceding invasive candidiasis. Nat Med 26:59–64. https://doi.org/10.1038/s41591-019-0709-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, Bengtsson-Palme J, Anslan S, Coelho LP, Harend H, Huerta-Cepas J, Medema MH, Maltz MR, Mundra S, Olsson PA, Pent M, Põlme S, Sunagawa S, Ryberg M, Tedersoo L, Bork P (2018) Structure and function of the global topsoil microbiome. Nature 560:233–237. https://doi.org/10.1038/s41586-018-0386-6

    Article  CAS  PubMed  Google Scholar 

  6. Xu J, Zhang Y, Zhang P, Trivedi P, Riera N, Wang Y, Liu X, Fan G, Tang J, Coletta-Filho HD, Cubero J, Deng X, Ancona V, Lu Z, Zhong B, Roper MC, Capote N, Catara V, Pietersen G, Vernière C, Al-Sadi AM, Li L, Yang F, Xu X, Wang J, Yang H, Jin T, Wang N (2018) The structure and function of the global citrus rhizosphere microbiome. Nat Commun 9:4894. https://doi.org/10.1038/s41467-018-07343-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C, Debelius J, Gonzalez A, Kosciolek T, McCall L-I, McDonald D, Melnik AV, Morton JT, Navas J, Quinn RA, Sanders JG, Swafford AD, Thompson LR, Tripathi A, Xu ZZ, Zaneveld JR, Zhu Q, Caporaso JG, Dorrestein PC (2018) Best practices for analysing microbiomes. Nat Rev Microbiol 16:410–422. https://doi.org/10.1038/s41579-018-0029-9

    Article  CAS  PubMed  Google Scholar 

  8. Tkacz A, Hortala M, Poole PS (2018) Absolute quantitation of microbiota abundance in environmental samples. Microbiome 6:110. https://doi.org/10.1186/s40168-018-0491-7

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bonfante P, Desirò A (2017) Who lives in a fungus? The diversity, origins and functions of fungal endobacteria living in Mucoromycota. ISME J 11:1727–1735 110. https://doi.org/10.1038/ismej.2017.21

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhang Y, Kastman EK, Guasto JS, Wolfe BE (2018) Fungal networks shape dynamics of bacterial dispersal and community assembly in cheese rind microbiomes. Nat Commun 9:336. https://doi.org/10.1038/s41467-017-02522-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Deveau A, Bonito G, Uehling J, Paoletti M, Becker M, Bindschedler S, Hacquard S, Hervé V, Labbé J, Lastovetsky OA, Mieszkin S, Millet LJ, Vajna B, Junier P, Bonfante P, Krom BP, Olsson S, Van Elsas JD, Wick LY (2018) Bacterial–fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol Rev 42:335–352. https://doi.org/10.1093/femsre/fuy008

    Article  CAS  PubMed  Google Scholar 

  12. Fierer N (2017) Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol 15:579–590. https://doi.org/10.1038/nrmicro.2017.87

    Article  CAS  PubMed  Google Scholar 

  13. Bardgett RD, Hobbs PJ, Frostegård Å (1996) Changes in soil fungal: bacterial biomass ratios following reductions in the intensity of management of an upland grassland. Biol Fertil Soils 22:261–264

    Article  Google Scholar 

  14. Fierer N, Jackson JA, Vilgalys R, Jackson RB (2005) Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl Environ Microbiol 71:4117–4120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Orwin K, Dickie I, Holdaway R, Wood J (2018) A comparison of the ability of PLFA and 16S rRNA gene metabarcoding to resolve soil community change and predict ecosystem functions. Soil Biol Biochem 117:27–35

    Article  CAS  Google Scholar 

  16. Vilgalys R, Hester M (1990) Rapid genetic identification and mapping of enzymatically amplified ribosomal DNA from several Cryptococcus species. J Bacteriol 172:4238–4246. https://doi.org/10.1128/jb.172.8.4238-4246.1990

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mueller RC, Gallegos-Graves LV, Kuske CR (2016) A new fungal large subunit ribosomal RNA primer for high-throughput sequencing surveys. FEMS Microbiol Ecol 92:fiv153

    Article  PubMed  Google Scholar 

  18. Bates ST, Berg-Lyons D, Caporaso JG, Walters WA, Knight R, Fierer N (2011) Examining the global distribution of dominant archaeal populations in soil. ISME J 5:908–917. https://doi.org/10.1038/ismej.2010.171

    Article  CAS  PubMed  Google Scholar 

  19. Van Cuyk S, Deshpande A, Hollander A, Duval N, Ticknor L, Layshock J, Gallegos-Graves L, Omberg KM (2011) Persistence of Bacillus thuringiensis subsp. kurstaki in urban environments following spraying 77:7954–7961. https://doi.org/10.1128/aem.05207-11

  20. White TJ, Bruns T, Lee S, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics

  21. Ihrmark K, Bödeker I, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, Strid Y, Stenlid J, Brandström-Durling M, Clemmensen KE (2012) New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol 82:666–677

    Article  CAS  PubMed  Google Scholar 

  22. Blue Heron Biotech GeneMaker® Technology. https://www.blueheronbio.com/genemaker/. Accessed Jun 1 2020

  23. Nilsson RH, Anslan S, Bahram M, Wurzbacher C, Baldrian P, Tedersoo L (2019) Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat Rev Microbiol 17:95–109

    Article  CAS  PubMed  Google Scholar 

  24. Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson H, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10:1–43. https://doi.org/10.3897/mycokeys.10.4852

    Article  Google Scholar 

  25. Johansen R, Albright M, Lopez D, Runde A, Yoshida T, Dunbar J (2019) Tracking replicate divergence in microbial community composition and function in experimental microcosms. Microb Ecol 78:1035–1039

    Article  PubMed  Google Scholar 

  26. Gloor GB, Hummelen R, Macklaim JM, Dickson RJ, Fernandes AD, MacPhee R, Reid G (2010) Microbiome profiling by illumina sequencing of combinatorial sequence-tagged PCR products. PLoS One 5:e15406

    Article  PubMed  PubMed Central  Google Scholar 

  27. Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, Chen W, Bolchacova E, Voigt K, Crous PW, Miller AN, Wingfield MJ, Aime MC, An KD, Bai FY, Barreto RW, Begerow D, Bergeron MJ, Blackwell M, Boekhout T, Bogale M, Boonyuen N, Burgaz AR, Buyck B, Cai L, Cai Q, Cardinali G, Chaverri P, Coppins BJ, Crespo A, Cubas P, Cummings C, Damm U, De Beer ZW, De Hoog GS, Del-Prado R, Dentinger B, Dieguez-Uribeondo J, Divakar PK, Douglas B, Duenas M, Duong TA, Eberhardt U, Edwards JE, Elshahed MS, Fliegerova K, Furtado M, Garcia MA, Ge ZW, Griffith GW, Griffiths K, Groenewald JZ, Groenewald M, Grube M, Gryzenhout M, Guo LD, Hagen F, Hambleton S, Hamelin RC, Hansen K, Harrold P, Heller G, Herrera C, Hirayama K, Hirooka Y, Ho HM, Hoffmann K, Hofstetter V, Hognabba F, Hollingsworth PM, Hong SB, Hosaka K, Houbraken J, Hughes K, Huhtinen S, Hyde KD, James T, Johnson EM, Johnson JE, Johnston PR, Jones EBG, Kelly LJ, Kirk PM, Knapp DG, Koljalg U, Kovacs GM, Kurtzman CP, Landvik S, Leavitt SD, Liggenstoffer AS, Liimatainen K, Lombard L, Luangsa-Ard JJ, Lumbsch HT, Maganti H, Maharachchikumbura SSN, Martin MP, May TW, McTaggart AR, Methven AS, Meyer W, Moncalvo JM, Mongkolsamrit S, Nagy LG, Nilsson RH, Niskanen T, Nyilasi I, Okada G, Okane I, Olariaga I, Otte J, Papp T, Park D, Petkovits T, Pino-Bodas R, Quaedvlieg W, Raja HA, Redecker D, Rintoul TL, Ruibal C, Sarmiento-Ramirez JM, Schmitt I, Schussler A, Shearer C, Sotome K, Stefani FOP, Stenroos S, Stielow B, Stockinger H, Suetrong S, Suh SO, Sung GH, Suzuki M, Tanaka K, Tedersoo L, Telleria MT, Tretter E, Untereiner WA, Urbina H, Vagvolgyi C, Vialle A, Vu TD, Walther G, Wang QM, Wang Y, Weir BS, Weiss M, White MM, Xu J, Yahr R, Yang ZL, Yurkov A, Zamora JC, Zhang N, Zhuang WY, Schindel D (2012) Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci 109:6241–6246. https://doi.org/10.1073/pnas.1117018109

    Article  PubMed  PubMed Central  Google Scholar 

  28. Borneman J, Hartin RJ (2000) PCR primers that amplify fungal rRNA genes from environmental samples. Appl Environ Microbiol 66:4356–4360. https://doi.org/10.1128/aem.66.10.4356-4360.2000

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461

    Article  CAS  PubMed  Google Scholar 

  30. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267. https://doi.org/10.1128/aem.00062-07

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM (2014) Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42:D633–D642

    Article  CAS  PubMed  Google Scholar 

  32. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Article  CAS  PubMed  Google Scholar 

  33. Team RC (2000) R language definition. Vienna: R foundation for statistical computing

  34. McMurdie PJ, Holmes S (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara R, Simpson GL, Solymos P, Stevens MHH, Wagner H (2013) Package ‘vegan’. Community ecology package, version 2: 1-295

  36. Wickham H (2016) ggplot2: elegant graphics for data analysis. springer

  37. Kassambara A (2018) ggpubr:“ggplot2” based publication ready plots. R package version 01 7

  38. Zymo Research Instruction Manual: ZymoBIOMICS™ Microbial Community DNA Standard II (Log Distribution). https://files.zymoresearch.com/protocols/_d6311_zymobiomics_microbial_community_dna_standard_ii_(log_distribution).pdf. Accessed Jun 1 2020

  39. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Thurber RLV, Knight R (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31:814–821

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Crowther TW, Van den Hoogen J, Wan J, Mayes MA, Keiser A, Mo L, Averill C, Maynard DS (2019) The global soil community and its influence on biogeochemistry. Science 365:eaav0550

    Article  CAS  PubMed  Google Scholar 

  41. Malik AA, Chowdhury S, Schlager V, Oliver A, Puissant J, Vazquez PG, Jehmlich N, von Bergen M, Griffiths RI, Gleixner G (2016) Soil fungal: bacterial ratios are linked to altered carbon cycling. Front Microbiol 7:1247

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wagg C, Bender SF, Widmer F, Van Der Heijden MGA (2014) Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc Natl Acad Sci 111:5266–5270. https://doi.org/10.1073/pnas.1320054111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Soliveres S, Van Der Plas F, Manning P, Prati D, Gossner MM, Renner SC, Alt F, Arndt H, Baumgartner V, Binkenstein J, Birkhofer K, Blaser S, Blüthgen N, Boch S, Böhm S, Börschig C, Buscot F, Diekötter T, Heinze J, Hölzel N, Jung K, Klaus VH, Kleinebecker T, Klemmer S, Krauss J, Lange M, Morris EK, Müller J, Oelmann Y, Overmann J, Pašalić E, Rillig MC, Schaefer HM, Schloter M, Schmitt B, Schöning I, Schrumpf M, Sikorski J, Socher SA, Solly EF, Sonnemann I, Sorkau E, Steckel J, Steffan-Dewenter I, Stempfhuber B, Tschapka M, Türke M, Venter PC, Weiner CN, Weisser WW, Werner M, Westphal C, Wilcke W, Wolters V, Wubet T, Wurst S, Fischer M, Allan E (2016) Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536:456–459. https://doi.org/10.1038/nature19092

    Article  CAS  PubMed  Google Scholar 

  44. Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, Berdugo M, Campbell CD, Singh BK (2016) Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun 7:1–8

    Article  Google Scholar 

  45. Kong HH, Segre JA (2020) Cultivating fungal research. Science 368:365–366

    Article  CAS  PubMed  Google Scholar 

  46. Tso GHW, Reales-Calderon JA, Tan ASM, Sem X, Le GTT, Tan TG, Lai GC, Srinivasan KG, Yurieva M, Liao W, Poidinger M, Zolezzi F, Rancati G, Pavelka N (2018) Experimental evolution of a fungal pathogen into a gut symbiont. Science 362:589–595. https://doi.org/10.1126/science.aat0537

    Article  CAS  PubMed  Google Scholar 

  47. Baldrian P, Větrovský T, Cajthaml T, Dobiášová P, Petránková M, Šnajdr J, Eichlerová I (2013) Estimation of fungal biomass in forest litter and soil. Fungal Ecol 6:1–11

    Article  Google Scholar 

  48. Zhang Z, Qu Y, Li S, Feng K, Wang S, Cai W, Liang Y, Li H, Xu M, Yin H, Deng Y (2017) Soil bacterial quantification approaches coupling with relative abundances reflecting the changes of taxa. Sci Rep 7:4837. https://doi.org/10.1038/s41598-017-05260-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Peay KG, Kennedy PG, Talbot JM (2016) Dimensions of biodiversity in the Earth mycobiome. Nat Rev Microbiol 14:434–447. https://doi.org/10.1038/nrmicro.2016.59

    Article  CAS  PubMed  Google Scholar 

  50. Throndset W, Kim S, Bower B, Lantz S, Kelemen B, Pepsin M, Chow N, Mitchinson C, Ward M (2010) Flow cytometric sorting of the filamentous fungus Trichoderma reesei for improved strains. Enzym Microb Technol 47:335–341. https://doi.org/10.1016/j.enzmictec.2010.09.003

    Article  CAS  Google Scholar 

  51. Roper M, Ellison C, John N (2011) Nuclear and genome dynamics in multinucleate ascomycete fungi. Curr Biol 21:R786–R793. https://doi.org/10.1016/j.cub.2011.06.042

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gamper HA, Young JPW, Jones DL, Hodge A (2008) Real-time PCR and microscopy: are the two methods measuring the same unit of arbuscular mycorrhizal fungal abundance? Fungal Genet Biol 45:581–596

    Article  CAS  PubMed  Google Scholar 

  53. Wallander H, Ekblad A, Godbold DL, Johnson D, Bahr A, Baldrian P, Björk RG, Kieliszewska-Rokicka B, Kjøller R, Kraigher H, Plassard C, Rudawska M (2013) Evaluation of methods to estimate production, biomass and turnover of ectomycorrhizal mycelium in forests soils – a review. Soil Biol Biochem 57:1034–1047. https://doi.org/10.1016/j.soilbio.2012.08.027

    Article  CAS  Google Scholar 

  54. Crosby LD, Criddle CS (2003) Understanding bias in microbial community analysis techniques due to rrn operon copy number heterogeneity. Biotechniques 34:790–802

    Article  CAS  PubMed  Google Scholar 

  55. Case RJ, Boucher Y, Dahllöf I, Holmström C, Doolittle WF, Kjelleberg S (2007) Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology studies. Appl Environ Microbiol 73:278–288

    Article  CAS  PubMed  Google Scholar 

  56. Verbeke TJ, Sparling R, Hill JE, Links MG, Levin D, Dumonceaux TJ (2011) Predicting relatedness of bacterial genomes using the chaperonin-60 universal target (cpn60 UT): application to Thermoanaerobacter species. Syst Appl Microbiol 34:171–179. https://doi.org/10.1016/j.syapm.2010.11.019

    Article  CAS  PubMed  Google Scholar 

  57. Barberán A, Bates ST, Casamayor EO, Fierer N (2012) Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 6:343–351. https://doi.org/10.1038/ismej.2011.119

    Article  CAS  PubMed  Google Scholar 

  58. de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, Hallin S, Kaisermann A, Keith AM, Kretzschmar M, Lemanceau P, Lumini E, Mason KE, Oliver A, Ostle N, Prosser JI, Thion C, Thomson B, Bardgett RD (2018) Soil bacterial networks are less stable under drought than fungal networks. Nat Commun 9:3033. https://doi.org/10.1038/s41467-018-05516-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Thompson J, Johansen R, Dunbar J, Munsky B (2019) Machine learning to predict microbial community functions: an analysis of dissolved organic carbon from litter decomposition. PLoS ONE 14:e0215502. https://doi.org/10.1371/journal.pone.0215502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lo C, Marculescu R (2019) MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks. BMC Bioinforma 20:314. https://doi.org/10.1186/s12859-019-2833-2

    Article  Google Scholar 

  61. Albright MBN, Thompson J, Kroeger ME, Ulrich DEM, Munsky B, Dunbar JM (In press.) Differences in substrate use linked to divergent carbon flow during litter decomposition. FEMS Microbiol Ecol

  62. Moran NA, Ochman H, Hammer TJ (2019) Evolutionary and ecological consequences of gut microbial communities. Annu Rev Ecol Evol Syst 50:451–475

    Article  PubMed  PubMed Central  Google Scholar 

  63. Props R, Kerckhof F-M, Rubbens P, De Vrieze J, Hernandez Sanabria E, Waegeman W, Monsieurs P, Hammes F, Boon N (2017) Absolute quantification of microbial taxon abundances. ISME J 11:584–587. https://doi.org/10.1038/ismej.2016.117

    Article  PubMed  Google Scholar 

  64. Risso D, Ngai J, Speed TP, Dudoit S (2014) Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol 32:896–902. https://doi.org/10.1038/nbt.2931

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tourlousse DM, Yoshiike S, Ohashi A, Matsukura S, Noda N, Sekiguchi Y (2017) Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res 45:e23–e23

    PubMed  Google Scholar 

  66. Geiger T, Wisniewski JR, Cox J, Zanivan S, Kruger M, Ishihama Y, Mann M (2011) Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat Protoc 6:147–157

    Article  CAS  PubMed  Google Scholar 

  67. Wu L, Mashego MR, Van Dam JC, Proell AM, Vinke JL, Ras C, Van Winden WA, Van Gulik WM, Heijnen JJ (2005) Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards. Anal Biochem 336:164–171. https://doi.org/10.1016/j.ab.2004.09.001

    Article  CAS  PubMed  Google Scholar 

  68. Smets W, Leff JW, Bradford MA, McCulley RL, Lebeer S, Fierer N (2016) A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol Biochem 96:145–151. https://doi.org/10.1016/j.soilbio.2016.02.003

    Article  CAS  Google Scholar 

  69. Stämmler F, Gläsner J, Hiergeist A, Holler E, Weber D, Oefner PJ, Gessner A, Spang R (2016) Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4:28. https://doi.org/10.1186/s40168-016-0175-0

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Division, under award number F255LANL2018.

We thank Joany Babilonia, Kelli Feeser, Sanna Sevanto, and Eric Moore for their edits and comments to drafts.

Funding

This work was supported by grant F255LANL2018 from the U.S. Department of Energy Office of Biological and Environmental Research, Genomic Science program.

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Authors and Affiliations

  1. Biosciences Division, Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM, 87545, USA

    Miriam I. Hutchinson, Tisza A. S. Bell, La Verne Gallegos-Graves, John Dunbar & Michaeline Albright

  2. Division of Biological Sciences, University of Montana, Missoula, MT, USA

    Tisza A. S. Bell

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  1. Miriam I. Hutchinson
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  2. Tisza A. S. Bell
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  3. La Verne Gallegos-Graves
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Contributions

J.D., L.V.G.G., and M.B.N.A. designed research. M.H. and L.V.G.G. performed the research. M.I.H. and M.B.N.A. analyzed the data. M. I. H., T.B., J.D, and M.B.N.A. wrote the manuscript.

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Correspondence to Michaeline Albright.

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Supplementary Information

Fig. A7

Environmental sample alpha diversity indices for fungi (a), bacteria (b), and the aggregated dataset (c), respectively. (PNG 1017 kb)

High resolution image (EPS 211 kb)

Fig. A8

Titration of Internal Control: The IC was titrated from 10-0.001 pg/μl in PCR reactions for both Bacteria (16S) and Fungi (LSU) with and without the addition of microcosm DNA template, sample O2, normalized to 1 ng/μl. PCR product for the IC alone is only visible from 10pg/μl-0.01 pg/μl, so 0.01 pg/μl, the minimum amplifiable concentration, was used for downstream reactions. (PNG 6168 kb)

High resolution image (EPS 282 kb)

ESM 1

Construct Sequences: MS Word File with FASTA sequences for the original PCR-derived construct as well as the modified version that includes ITS2 primers. Primer regions are coded by text color. (DOCX 12 kb)

ESM 2

SRA Run Metadata. (TXT 3 kb)

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Hutchinson, M.I., Bell, T.A.S., Gallegos-Graves, L.V. et al. Merging Fungal and Bacterial Community Profiles via an Internal Control. Microb Ecol 82, 484–497 (2021). https://doi.org/10.1007/s00248-020-01638-y

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