in

Community RNA-Seq: multi-kingdom responses to living versus decaying roots in soil

  • 1.

    Swift MJ, Anderson JM, Heal OW. Decomposition in terrestrial ecosystems. Oxford: Blackwell Publishing; 1979.

  • 2.

    Scholes MC, Powlson D, Tian G. Input control of organic matter dynamics. Geoderma. 1997;79:25–47.

    CAS 

    Google Scholar 

  • 3.

    Sokol NW, Kuebbing SE, Ayala EK, Bradford MA. Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon. New Phytologist. 2019;221:233–46.

    CAS 

    Google Scholar 

  • 4.

    Jackson RB, Lajtha K, Crow SE, Hugelius G, Kramer MG, Piñeiro G. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Ann Rev Ecol Evol Syst. 2017;48:419–45.

    Google Scholar 

  • 5.

    Greyston SJ, Vaughan D, Jones D. Rhizosphere carbon flow in trees, in comparison with annual plants: the importance of root exudation and its impact on microbial activity and nutrient availability. Appl Soil Ecol. 1996;5:29–56.

    Google Scholar 

  • 6.

    Schimel DS. Terrestrial biogeochemical cycles: global estimates with remote sensing. Remote Sens Environ. 1995;51:49–56.

    Google Scholar 

  • 7.

    Angst G, Mueller KE, Nierop KGJ, Simpson MJ. Plant- or microbial-derived? A review on the molecular composition of stabilized soil organic matter. Soil Biol Biochem. 2021;156:108189.

    CAS 

    Google Scholar 

  • 8.

    Bardgett RD. The biology of soil: a community ecosystem approach. Oxford: Oxford University Press; 2005.

  • 9.

    Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3:1–11.

    Google Scholar 

  • 10.

    Geisen S, Mitchell EAD, Wilkinson DM, Adl S, Bonkowski M, Brown MW, et al. Soil protistology rebooted: 30 fundamental questions to start with. Soil Biol Biochem. 2017;111:94–103.

    CAS 

    Google Scholar 

  • 11.

    Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Kapturska D, et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol Ecol. 2016;25:4059–74.

    CAS 
    PubMed 

    Google Scholar 

  • 12.

    Osono T. Ecology of ligninolytic fungi associated with leaf litter decomposition. Ecol Res. 2007;22:955–74.

    Google Scholar 

  • 13.

    Hattenschwiler S, Tiunov AV, Scheu S. Biodiversity and litter decomposition in terrestrial ecosystems. Ann Rev Ecol Evol Syst. 2005;36:191–218.

    Google Scholar 

  • 14.

    Pugh G. Terrestrial fungi. In: Dickenson C, Pugh G, editors. Biology of plant litter decomposition. 2. London: Academic Press Inc.; 1974. p. 303–36.

  • 15.

    Sinsabaugh RL, Moorhead DL. Resource allocation to extracellular enzyme production: a model for nitrogen and phosphorus control of litter decomposition. Soil Biol Biochem. 1994;26:1305–11.

    Google Scholar 

  • 16.

    Geisen S, Koller R, Hünninghaus M, Dumack K, Urich T, Bonkowski M. The soil food web revisited: Diverse and widespread mycophagous soil protists. Soil Biol Biochem. 2016;94:10–8.

    CAS 

    Google Scholar 

  • 17.

    Chakraborty S, Old K. Ultrastructure and description of a fungus-feeding amoeba, Trichamoeba mycophaga n. sp. (Amoebidae, Amoebea), from Australia. J Eukaryot Microbiol. 1986;33:564–9.

    Google Scholar 

  • 18.

    Bjørnlund L, Rønn R. ‘David and Goliath’of the soil food web–Flagellates that kill nematodes. Soil Biol Biochem. 2008;40:2032–9.

    Google Scholar 

  • 19.

    Xiong W, Jousset A, Guo S, Karlsson I, Zhao Q, Wu H, et al. Soil protist communities form a dynamic hub in the soil microbiome. ISME J. 2018;12:634–8.

    PubMed 

    Google Scholar 

  • 20.

    Neher DA, Weicht TR, Barbercheck ME. Linking invertebrate communities to decomposition rate and nitrogen availability in pine forest soils. Appl Soil Ecol. 2012;54:14–23.

    Google Scholar 

  • 21.

    Bokhorst S, Wardle DA. Microclimate within litter bags of different mesh size: Implications for the ‘arthropod effect’ on litter decomposition. Soil Biol Biochem. 2013;58:147–52.

    CAS 

    Google Scholar 

  • 22.

    Carrillo Y, Ball BA, Bradford MA, Jordan CF, Molina M. Soil fauna alter the effects of litter composition on nitrogen cycling in a mineral soil. Soil Biol Biochem. 2011;43:1440–9.

    CAS 

    Google Scholar 

  • 23.

    Riutta T, Slade EM, Bebber DP, Taylor ME, Malhi Y, Riordan P, et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol Biochem. 2012;49:124–31.

    CAS 

    Google Scholar 

  • 24.

    Meyer WM, Ostertag R, Cowie RH. Macro-invertebrates accelerate litter decomposition and nutrient release in a Hawaiian rainforest. Soil Biol Biochem. 2011;43:206–11.

    CAS 

    Google Scholar 

  • 25.

    Stout JD. The Relationship between protozoan populations and biological activity in soils. Integr Comp Biol. 1973;13:193–201.

    Google Scholar 

  • 26.

    Bonkowski M, Griffiths B, Scrimgeour C. Substrate heterogeneity and microfauna in soil organic ‘hotspots’ as determinants of nitrogen capture and growth of ryegrass. Appl Soil Ecol. 2000;14:37–53.

    Google Scholar 

  • 27.

    Hünninghaus M, Dibbern D, Kramer S, Koller R, Pausch J, Schloter-Hai B, et al. Disentangling carbon flow across microbial kingdoms in the rhizosphere of maize. Soil Biol Biochem. 2019;134:122–30.

    Google Scholar 

  • 28.

    Tedersoo L, Anslan S. Towards PacBio‐based pan‐eukaryote metabarcoding using full‐length ITS sequences. Environ Microbiol Rep. 2019;11:659–68.

    CAS 
    PubMed 

    Google Scholar 

  • 29.

    Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, et al. Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. Mycokeys. 2015;10:1–43.

    Google Scholar 

  • 30.

    Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–604.

    CAS 
    PubMed 

    Google Scholar 

  • 31.

    Baldrian P, Kolařík M, Stursová M, Kopecký J, Valášková V, Větrovský T, et al. Active and total microbial communities in forest soil are largely different and highly stratified during decomposition. ISME J. 2012;6:248–58.

    CAS 
    PubMed 

    Google Scholar 

  • 32.

    Poisot T, Péquin B, Gravel D. High‐throughput sequencing: a roadmap toward community ecology. Ecol Evol. 2013;3:1125–39.

    PubMed 
    PubMed Central 

    Google Scholar 

  • 33.

    Nguyen NH, Smith D, Peay K, Kennedy P. Parsing ecological signal from noise in next generation amplicon sequencing. New Phytol. 2015;205:1389–93.

    CAS 
    PubMed 

    Google Scholar 

  • 34.

    Engelbrektson A, Kunin V, Wrighton KC, Zvenigorodsky N, Chen F, Ochman H, et al. Experimental factors affecting PCR-based estimates of microbial species richness and evenness. ISME J. 2010;4:642–7.

    CAS 
    PubMed 

    Google Scholar 

  • 35.

    Suzuki MT, Giovannoni SJ. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl Environ Microbiol. 1996;62:625–30.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 36.

    Soergel D, Dey N, Knight R, Brenner S. Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. ISME J. 2012;6:1440–4.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 37.

    Nomura M, Gourse R, Baughman G. Regulation of the synthesis of ribosomes and ribosomal components. Annu Rev Biochem. 1984;53:75–117.

    CAS 
    PubMed 

    Google Scholar 

  • 38.

    Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE. 2008;3:e2527.

    PubMed 
    PubMed Central 

    Google Scholar 

  • 39.

    Kembel SW, Wu M, Eisen JA, Green JL. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Comp Biol. 2012;8:e1002743.

    CAS 

    Google Scholar 

  • 40.

    Gong W, Marchetti A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front Mar Sci. 2019;6:219.

    Google Scholar 

  • 41.

    Miller CS, Baker BJ, Thomas BC, Singer SW, Banfield JF. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol. 2011;12:R44.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 42.

    Xue Y, Lanzén A, Jonassen I. Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data. Bioinformatics. 2020;36:3365–71.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 43.

    Bang-Andreasen T, Anwar MZ, Lanzén A, Kjøller R, Rønn R, Ekelund F, et al. Total RNA-sequencing reveals multi-level microbial community changes and functional responses to wood ash application in agricultural and forest soil. FEMS Microbiol Ecol. 2020;96:fiaa016.

    PubMed 
    PubMed Central 

    Google Scholar 

  • 44.

    Geisen S, Tveit AT, Clark IM, Richter A, Svenning MM, Bonkowski M, et al. Metatranscriptomic census of active protists in soils. ISME J. 2015;9:2178–90.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 45.

    Adl SM, Habura A, Eglit Y. Amplification primers of SSU rDNA for soil protists. Soil Biol Biochem. 2014;69:328–42.

    CAS 

    Google Scholar 

  • 46.

    Wagner M, Nielsen PH, Loy A, Nielsen JL, Daims H. Linking microbial community structure with function: fluorescence in situ hybridization-microautoradiography and isotope arrays. Curr Opin Biotechnol. 2006;17:83–91.

    CAS 
    PubMed 

    Google Scholar 

  • 47.

    Neufeld J, Wagner M, Murrell J. Who eats what, where and when? Isotope-labelling experiments are coming of age. ISME J. 2007;1:103–10.

    CAS 
    PubMed 

    Google Scholar 

  • 48.

    Radajewski S, Ineson P, Parekh NR, Murrell J. Stable-isotope probing as a tool in microbial ecology. Nature. 2000;403:646–9.

    CAS 
    PubMed 

    Google Scholar 

  • 49.

    Radajewski S, Murrell JC. Stable isotope probing for detection of methanotrophs after enrichment with 13CH4. In: de Muro MA, Rapley R, editors. Gene probes: principles and protocols. Totowa, NJ: Humana Press; 2002. p. 149–57.

  • 50.

    Manefield M, Whiteley AS, Griffiths R, Bailey MJ. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl Environ Microbiol. 2002;68:5367–73.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 51.

    Mayali X, Weber PK, Nuccio E, Lietard J, Somoza M, Blazewicz SJ, et al. Stable isotope probing, methods and protocols. Methods Mol Biol. 2019;2046:71–87.

    PubMed 

    Google Scholar 

  • 52.

    Mayali X, Weber PK, Brodie EL, Mabery S, Hoeprich PD, Pett-Ridge J. High-throughput isotopic analysis of RNA microarrays to quantify microbial resource use. ISME J. 2012;6:1210–21.

    CAS 
    PubMed 

    Google Scholar 

  • 53.

    Waldrop MP, Firestone MK. Seasonal dynamics of microbial community composition and function in oak canopy and open grassland soils. Microb Ecol. 2006;52:470–9.

    CAS 
    PubMed 

    Google Scholar 

  • 54.

    Shi S, Nuccio E, Herman DJ, Rijkers R, Estera K, Li J, et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio. 2015;6:e00746.

    PubMed 
    PubMed Central 

    Google Scholar 

  • 55.

    DeAngelis KM, Brodie EL, DeSantis TZ, Andersen GL, Lindow SE, Firestone MK. Selective progressive response of soil microbial community to wild oat. ISME J. 2009;3:168–78.

    CAS 
    PubMed 

    Google Scholar 

  • 56.

    Jaeger CH, Lindow SE, Miller W, Clark E, Firestone MK. Mapping of sugar and amino acid availability in soil around roots with bacterial sensors of sucrose and tryptophan. Appl Environ Microbiol. 1999;65:2685–90.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 57.

    Nuccio EE, Starr E, Karaoz U, Brodie EL, Zhou J, Tringe SG, et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 2020;269:1–16.

    Google Scholar 

  • 58.

    Griffiths RI, Whiteley AS, O’Donnell AG, Bailey M. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl Environ Microbiol. 2000;66:5488–91.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 59.

    Andrews S. FastQC: a quality control tool for high throughput sequence data (Version 0.10.1) 2012; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  • 60.

    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 61.

    McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 62.

    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 63.

    Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.

    PubMed 
    PubMed Central 

    Google Scholar 

  • 64.

    Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 65.

    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 66.

    Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 67.

    Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 68.

    Miller CS, Handley KM, Wrighton KC, Frischkorn KR, Thomas BC, Banfield JF. Short-read assembly of full-length 16S amplicons reveals bacterial diversity in subsurface sediments. PLoS ONE. 2013;8:e56018.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 69.

    Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 70.

    Choi J, Kim S-H. A genome tree of life for the Fungi kingdom. Proc Natl Acad Sci USA. 2017;114:9391–6.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 71.

    Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2018;47:D259–64.

    PubMed Central 

    Google Scholar 

  • 72.

    Adl SM, Simpson AGB, Farmer MA, Andersen RA, Anderson OR, Barta JR, et al. The new higher level classification of eukaryotes with emphasis on the taxonomy of protists. J Eukaryot Microbiol. 2005;52:399–451.

    Google Scholar 

  • 73.

    Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 74.

    Mayali X, Weber PK, Pett-Ridge J. Taxon-specific C/N relative use efficiency for amino acids in an estuarine community. FEMS Microbiol Ecol. 2013;83:402–12.

    CAS 
    PubMed 

    Google Scholar 

  • 75.

    Pausch J, Kramer S, Scharroba A, Scheunemann N, Butenschoen O, Kandeler E, et al. Small but active—pool size does not matter for carbon incorporation in below‐ground food webs. Funct Ecol. 2016;30:479–89.

    Google Scholar 

  • 76.

    el Zahar Haichar F, Achouak W, Christen R. Identification of cellulolytic bacteria in soil by stable isotope probing. Environ Microbiol. 2007;9:625–34.

    CAS 

    Google Scholar 

  • 77.

    Ha YE, Kang CI, Joo EJ, Park SY, Kang SJ, Wi YM, et al. Bacterial populations assimilating carbon from 13C-labeled plant residue in soil: analysis by a DNA-SIP approach. Soil Biol Biochem. 2011;43:814–22.

    Google Scholar 

  • 78.

    Eichorst SA, Kuske CR. Identification of cellulose-responsive bacterial and fungal communities in geographically and edaphically different soils by using stable isotope probing. Appl Environ Microbiol. 2012;78:2316–27.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 79.

    Pepe-Ranney C, Campbell AN, Koechli CN, Berthrong S, Buckley DH. Unearthing the ecology of soil microorganisms using a high resolution DNA-SIP approach to explore cellulose and xylose metabolism in soil. Front Microbiol. 2016;7:626.

    Google Scholar 

  • 80.

    Wilhelm RC, Pepe-Ranney C, Weisenhorn P, Lipton M, Buckley DH. Competitive exclusion and metabolic dependency among microorganisms structure the cellulose economy of an agricultural soil. mBio. 2021;12:e03099-20.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 81.

    Lehtovirta-Morley LE, Ross J, Hink L, Weber EB, Gubry-Rangin C, Thion 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.

    PubMed 
    PubMed Central 

    Google Scholar 

  • 82.

    Nuccio EE, Anderson-Furgeson J, Estera KY, Pett-Ridge J, De Valpine P, Brodie EL, et al. Climate and edaphic controllers influence rhizosphere community assembly for a wild annual grass. Ecology. 2016;97:1307–18.

    PubMed 

    Google Scholar 

  • 83.

    Ceja-Navarro JA, Wang Y, Arellano A, Ramanculova L, Yuan M, Byer A, et al. Protist diversity and network complexity in the rhizosphere are dynamic and changing as the plant develops. Microbiome. 2021;9. https://doi.org/10.1186/s40168-021-01042-9.

    Google Scholar 

  • 84.

    Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.

    CAS 
    PubMed 

    Google Scholar 

  • 85.

    Zhang L, Lueders T. Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey. FEMS Microbiol Ecol. 2017;93:fix103.

    Google Scholar 

  • 86.

    Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–76.

    CAS 
    PubMed 

    Google Scholar 

  • 87.

    Rosenberg K, Bertaux J, Krome K, Hartmann A, Scheu S, Bonkowski M. Soil amoebae rapidly change bacterial community composition in the rhizosphere of Arabidopsis thaliana. ISME J. 2009;3:675–84.

    CAS 
    PubMed 

    Google Scholar 

  • 88.

    Zaragoza SR, Mayzlish E, Steinberger Y. Seasonal changes in free-living Amoeba species in the root canopy of Zygophyllum dumosum in the Negev Desert, Israel. Microb Ecol. 2005;49:134–41.

    Google Scholar 

  • 89.

    Baldock BM, Baker JH, Sleigh MA. Laboratory growth rates of six species of freshwater Gymnamoebia. Oecologia. 1980;47:156–9.

    CAS 
    PubMed 

    Google Scholar 

  • 90.

    Bates ST, Clemente JC, Flores GE, Walters WA, Parfrey LW, Knight R, et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 2013;7:652–9.

    CAS 
    PubMed 

    Google Scholar 

  • 91.

    Cotrufo MF, Wallenstein MD, Boot CM, Denef K, Paul E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Global Change Biol. 2013;19:988–95.

    Google Scholar 

  • 92.

    Schmidt MW, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, et al. Persistence of soil organic matter as an ecosystem property. Nature. 2011;478:49–56.

    CAS 
    PubMed 

    Google Scholar 

  • 93.

    Allison SD, Martiny JB. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci USA. 2008;105:11512–9.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 94.

    Wickings K, Grandy AS, Reed SC, Cleveland CC. The origin of litter chemical complexity during decomposition. Ecol Lett. 2012;15:1180–8.

    PubMed 

    Google Scholar 

  • 95.

    Hungate BA, Marks JC, Power ME, Schwartz E, van Groenigen KJ, Blazewicz SJ, et al. The functional significance of bacterial predators. mBio. 2021;12:e00466–21.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 96.

    de Ruiter PC, Neutel AM, Moore JC. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science. 1995;269:1257–60.

    PubMed 

    Google Scholar 

  • 97.

    Glücksman E, Bell T, Griffiths RI, Bass D. Closely related protist strains have different grazing impacts on natural bacterial communities. Environ Microbiol. 2010;12:3105–13.

    PubMed 

    Google Scholar 

  • 98.

    Yeates GW, Bongers T, De Goede R, Freckman DW, Georgieva SS. Feeding habits in soil nematode families and genera—an outline for soil ecologists. J Nematol. 1993;25:315–31.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 99.

    Okada H, Harada H, Kadota I. Fungal-feeding habits of six nematode isolates in the genus Filenchus. Soil Biol Biochem. 2005;37:1113–20.

    CAS 

    Google Scholar 

  • 100.

    Rotem O, Pasternak Z, Jurkevitch E. Bdellovibrio and Like Organisms. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes, deltaproteobacteria and epsilonproteobacteria. Berlin: Springer-Verlag; 2014. p. 3–17.

  • 101.

    Griffiths BS. Microbial-feeding nematodes and protozoa in soil: their effectson microbial activity and nitrogen mineralization in decomposition hotspots and the rhizosphere. Plant Soil. 1994;164:25–33.

    CAS 

    Google Scholar 

  • 102.

    Bonkowski M, Clarholm M. Stimulation of plant growth through interactions of bacteria and protozoa: testing the auxiliary microbial loop hypothesis. Acta Protozool. 2012;51:237–47.

    Google Scholar 

  • 103.

    Clarholm M. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol Biochem. 1985;17:181–7.

    CAS 

    Google Scholar 

  • 104.

    Halter D, Goulhen-Chollet F, Gallien S, Casiot C, Hamelin J, Gilard F, et al. In situ proteo-metabolomics reveals metabolite secretion by the acid mine drainage bio-indicator, Euglena mutabilis. ISME J. 2012;6:1391–402.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 105.

    Yuan C, Lei J, Cole J, Sun Y. Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics. 2015;31:i35–43.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 106.

    Zeng F, Wang Z, Wang Y, Zhou J, Chen T. Large-scale 16S gene assembly using metagenomics shotgun sequences. Bioinformatics. 2017;33:1447–56.

    CAS 
    PubMed 

    Google Scholar 

  • 107.

    Pericard P, Dufresne Y, Couderc L, Blanquart S, Touzet H. MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes. Bioinformatics. 2017;34:585–91.

    Google Scholar 

  • 108.

    Callahan BJ, Wong J, Heiner C, Oh S, Theriot CM, Gulati AS, et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 2019;47:e103-e.

    Google Scholar 


  • Source: Ecology - nature.com

    Eco-evolutionary responses of the microbial loop to surface ocean warming and consequences for primary production

    Population genetics and independently replicated evolution of predator-associated burst speed ecophenotypy in mosquitofish