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Chapter 49 : Application of Phylogenetic Techniques in Studies of Soil Microbial Communities
Authors:
Ryan T. Jones,
Elizabeth K. Costello,
Andrew P. Martin
Content Type: Reference
Publication Year:
2007
Category: Environmental Microbiology
Book DOI:
10.1128/9781555815882
Chapter DOI:
10.1128/9781555815882.ch49
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Abstract:
The author’s goal in writing this chapter is twofold: (i) to persuade the researcher that investigating soil microbial communities by using DNA sequence data is the most appropriate method for making community comparisons or for inferring ecological processes based on community membership, and (ii) to present the best available analytic methods, the steps needed to use those methods, and how to interpret the results. The advent of molecular techniques and cloning allowed microbiology to escape the petri dish and radically changed our understanding of microbial diversity. A section of the chapter presents a brief summary of how to construct phylogenies followed by descriptions of techniques to compare microbial communities and to infer ecological processes. The results suggested that successional changes associated with marked shifts from the wet, cool conditions of winter and spring to the drier and warmer conditions of summer involve the turnover of highly divergent groups rather than shifts in the relative abundance of closely related species. Lineage-per-time plots were introduced by Nee and coworkers, and others as a means of inferring rates of speciation and extinction from phylogenies. Webb developed methods for testing whether sampled phylogenetic lineages comprising ecological communities were more closely related or more distantly related to each other than expected by chance based on the available species pool. Both methods have been applied to the analysis of microbial communities with intriguing results. The best methods require understanding models of DNA sequence evolution, phylogenetic inference, and character evolution, challenging subjects and areas of active research.
Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49
Key Concept Ranking
- Denaturing Gradient Gel Electrophoresis
- 0.5143449
- Restriction Fragment Length Polymorphism
- 0.4937711
- Environmental Microbiology
- 0.4918478
- Microbial Ecology
- 0.48322093
0.5143449
Figures
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities
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Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49
/content/10.1128/9781555815882.ch49.ch49fig01
FIGURE 1
On the left is a typical phylogenetic tree. Branching events closer to the tips of the tree represent more-recent speciation events than branching events deeper in the tree. Closely related species share more life history traits than less related species. On the right is a polytomy—a type of phylogenetic tree in which all species are equally related to one another. If the evolutionary history of a collection of species can accurately be depicted as a polytomy, then species can be used as the unit of comparison.
10.1128/9781555815882/f0609-01_thmb.gif
10.1128/9781555815882/f0609-01.gif
FIGURE 1
On the left is a typical phylogenetic tree. Branching events closer to the tips of the tree represent more-recent speciation events than branching events deeper in the tree. Closely related species share more life history traits than less related species. On the right is a polytomy—a type of phylogenetic tree in which all species are equally related to one another. If the evolutionary history of a collection of species can accurately be depicted as a polytomy, then species can be used as the unit of comparison.
Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49
/content/10.1128/9781555815882.ch49.ch49fig02
FIGURE 2
Flow chart beginning with a collection of DNA sequences and ending with making biological inferences. After DNA sequences are aligned, the alignment can be sent to descriptive statistics or transformed into a distance matrix, or phylogenetic relationships can be inferred. After the second step, outputs can be analyzed in a variety of programs to make biological inferences.
10.1128/9781555815882/f0610-01_thmb.gif
10.1128/9781555815882/f0610-01.gif
FIGURE 2
Flow chart beginning with a collection of DNA sequences and ending with making biological inferences. After DNA sequences are aligned, the alignment can be sent to descriptive statistics or transformed into a distance matrix, or phylogenetic relationships can be inferred. After the second step, outputs can be analyzed in a variety of programs to make biological inferences.
Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49
/content/10.1128/9781555815882.ch49.ch49fig03
FIGURE 3
The effect of incorporating phylogenetic and character state mapping uncertainty into the Phylo-test when comparing nirK DNA sequences (from reference
28
). This demonstrates the perils of using a single evolutionary relationship when performing the Phylo-test. (A) When a single NJ tree is used (NJ-Parsimony) the two communities are significantly different. However, incorporating phylogenetic uncertainty through a bootstrap analysis (NJ Bootstrap-Parsimony) or using Bayesian trees (Bayesian-Parsimony) reverses the inference. (B) Incorporating character state mapping uncertainty causes a nearly complete overlap of the observed (Bayesian-Stochastic) and null (Null-Stochastic) distributions. Reprinted from Microbial Ecology (
16
) with permission from the publisher.
10.1128/9781555815882/f0612-01_thmb.gif
10.1128/9781555815882/f0612-01.gif
FIGURE 3
The effect of incorporating phylogenetic and character state mapping uncertainty into the Phylo-test when comparing nirK DNA sequences (from reference 28 ). This demonstrates the perils of using a single evolutionary relationship when performing the Phylo-test. (A) When a single NJ tree is used (NJ-Parsimony) the two communities are significantly different. However, incorporating phylogenetic uncertainty through a bootstrap analysis (NJ Bootstrap-Parsimony) or using Bayesian trees (Bayesian-Parsimony) reverses the inference. (B) Incorporating character state mapping uncertainty causes a nearly complete overlap of the observed (Bayesian-Stochastic) and null (Null-Stochastic) distributions. Reprinted from Microbial Ecology ( 16 ) with permission from the publisher.
Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49
/content/10.1128/9781555815882.ch49.ch49fig04
FIGURE 4
Lineage-per-time plots demonstrating an excess of highly divergent lineages (top) or an excess of closely related lineages (bottom right). Constant birth and extinction rates would yield an exponential lineage-per-time plot (indicated by the straight line). Note the logarithmic scale of the y axis. Reprinted from Applied and Environmental Microbiology (
22
) with permission from the publisher.
10.1128/9781555815882/f0613-01_thmb.gif
10.1128/9781555815882/f0613-01.gif
FIGURE 4
Lineage-per-time plots demonstrating an excess of highly divergent lineages (top) or an excess of closely related lineages (bottom right). Constant birth and extinction rates would yield an exponential lineage-per-time plot (indicated by the straight line). Note the logarithmic scale of the y axis. Reprinted from Applied and Environmental Microbiology ( 22 ) with permission from the publisher.
Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49
/content/10.1128/9781555815882.ch49.ch49fig05
FIGURE 5
Lineage-per-time plots of Costa Rican AOB communities (16S) and alpine tundra fungal communities (18S). In the AOB comparison, the forest community has significantly more lineages at each time point. The convex lineage-per-time plots of the pasture and plantation communities demonstrate an excess of closely related community members. In the fungal community comparison, the spring community has significantly more lineages at time 0.25 and the summer community has significantly fewer lineages at time 0.9.
10.1128/9781555815882/f0615-01_thmb.gif
10.1128/9781555815882/f0615-01.gif
FIGURE 5
Lineage-per-time plots of Costa Rican AOB communities (16S) and alpine tundra fungal communities (18S). In the AOB comparison, the forest community has significantly more lineages at each time point. The convex lineage-per-time plots of the pasture and plantation communities demonstrate an excess of closely related community members. In the fungal community comparison, the spring community has significantly more lineages at time 0.25 and the summer community has significantly fewer lineages at time 0.9.
Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49
References
/content/book/10.1128/9781555815882.ch49
1.
Acinas, S. G.,, V. Klepac-Ceraj,, D. E. Hunt,, C. Pharino,, I. Ceraj,, D. L. Distel, and, M. F. Polz. 2004. Fine-scale phylogenetic architecture of a complex bacterial community. Nature 430: 551– 554.
2.
Bohannan, B. J. M.,, and J. Hughes. 2003. New approaches to analyzing microbial biodiversity data. Curr. Opin. Microbiol. 6: 282– 287.
3.
Bollback, J. P. 2005. SIMMAP: stochastic character mapping of discrete traits on phylogenies, 1.0 Beta 2.0 ed.
4.
Carney, K. M.,, P. A. Matson, and, B. J. M. Bohannan. 2004. Diversity and composition of tropical soil nitrifiers across a plant diversity gradient and among land-use types. Ecol. Lett. 7: 684– 694.
5.
Chao, A. 1984. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11: 265– 270.
6.
Curtis, T. P.,, W. T. Sloan, and, J. W. Scannell. 2002. Estimating prokaryotic diversity and its limits. Proc. Natl. Acad. Sci. USA 99: 10494– 10499.
7.
DeSantis, T. Z.,, I. Dubosarskiy,, S. R. Murray, and, G. L. Andersen. 2003. Comprehensive aligned sequence construction for automated design of effective probes (CASCADE-P) using 16S rDNA. Bioinformatics 19: 1461– 1468.
8.
Dunbar, J.,, S. M. Barns,, L. O. Ticknor, and, C. R. Kuske. 2002. Empirical and theoretical bacterial diversity in four Arizona soils. Appl. Environ. Microbiol. 68: 3035– 3045.
9.
Felsenstein, J. 1985. Phylogenies and the comparative method. Am. Nat. 125: 1– 15.
10.
Harvey, P. H.,, and M. D. Pagel. 1991. The Comparative Method in Evolutionary Biology. Oxford University Press, Oxford, United Kingdom.
11.
Horner-Devine, M. C.,, and B. J. M. Bohannan. Phylogenetic clustering and overdispersion in bacterial communities. Ecology, in press.
12.
Huelsenbeck, J. P.,, R. Nielsen, and, J. P. Bollback. 2003. Stochastic mapping of morphological characters. Syst. Biol. 52: 131– 158.
13.
Huelsenbeck, J. P.,, B. Rannala, and, J. P. Masly. 2000. Accommodating phylogenetic uncertainty in evolutionary studies. Science 288: 2349– 2350.
14.
Hughes, J. B.,, J. J. Hellmann,, T. H. Ricketts, and, B. J. M. Bohannan. 2001. Counting the uncountable: statistical approaches to estimating microbial diversity. Appl. Environ. Microbiol. 67: 4399– 4406.
15.
Jeanmougin, F.,, J. D. Thompson,, M. Gouy,, D. G. Higgins, and, T. J. Gibson. 1998. Multiple sequence alignment with Clustal X. Trends Biochem. Sci. 23: 403– 405.
16.
Jones, R. T.,, and A. P. Martin. Testing for differentiation of microbial communities using phylogenetic methods: accounting for uncertainty of phylogenetic inference and character state mapping. Microb. Ecol., in press.
17.
Jukes, T. H.,, and C. R. Cantor. 1969. Evolution of protein molecules, p. 21–132. In H. N. Munro (ed.), Mammalian Protein Metabolism. Academic Press, New York, N.Y.
18.
Klepac-Ceraj, V.,, M. Bahr,, B. C. Crump,, A. P. Teske,, J. E. Hobbie, and, M. F. Polz. 2004. High overall diversity and dominance of microdiverse relationships in salt marsh sulphate-reducing bacteria. Environ. Microbiol. 6: 686– 698.
19.
Lozupone, C.,, and R. Knight. 2005. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71: 8228– 8235.
20.
Ludwig, W.,, O. Strunk,, R. Westram,, L. Richter,, H. Meier,, Yadhukumar,, A. Buchner,, T. Lai,, S. Steppi,, G. Jobb,, W. Forster,, I. Brettske,, S. Gerber,, A. W. Ginhart,, O. Gross,, S. Grumann,, S. Hermann,, R. Jost,, A. Konig,, T. Liss,, R. Lussmann,, M. May,, B. Nonhoff,, B. Reichel,, R. Strehlow,, A. Stamatakis,, N. Stuckmann,, A. Vilbig,, M. Lenke,, T. Ludwig,, A. Bode, and, K. H. Schleifer. 2004. ARB: a software environment for sequence data. Nucleic Acids Res. 32: 1363– 1371.
21.
Maddison, W. P.,, and M. Slatkin. 1991. Null models for the number of evolutionary steps in a character on a phylogenetic tree. Evolution 45: 1184– 1197.
22.
Martin, A. P. 2002. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl. Environ. Microbiol. 68: 3673– 3682.
23.
Martin, A. P.,, E. K. Costello,, A. F. Meyer,, D. R. Nemergut, and, S. K. Schmidt. 2004. The rate and pattern of cladogenesis in microbes. Evolution 58: 946– 955.
24.
Nee, S.,, A. O. Mooers, and, P. H. Harvey. 1992. Tempo and mode of evolution revealed from molecular phylogenies. Proc. Natl. Acad. Sci. USA 89: 8322– 8326.
25.
Nemergut, D. R.,, S. P. Anderson,, C. C. Cleveland,, A. P. Martin,, A. E. Miller,, A. Seimon, and, S. K. Schmidt. 2006. Microbial community succession in an unvegetated, recently deglaciated soil. Microb. Ecol., in press.
26.
Pace, N. R. 1997. A molecular view of microbial diversity and the biosphere. Science 276: 734– 740.
27.
Posada, D.,, and K. A. Crandall. 1998. MODELTEST: testing the model of DNA substitution. Bioinformatics 14: 817– 818.
28.
Prieme, A.,, G. Braker, and, J. M. Tiedje. 2002. Diversity of nitrite reductase ( nirK and nirS) gene fragments in forested upland and wetland soils. Appl. Environ. Microbiol. 68: 1893– 1900.
29.
Pybus, O. G.,, and P. H. Harvey. 2000. Testing macro-evolutionary models using incomplete molecular phylogenies. Proc. R. Soc. Lond. B Biol. Sci. 267: 2267– 2272.
30.
Ranjard, L.,, F. Poly,, J. C. Lata,, C. Mougel,, J. Thioulouse, and, S. Nazaret. 2001. Characterization of bacterial and fungal soil communities by automated ribosomal intergenic spacer analysis fingerprints: biological and methodological variability. Appl. Environ. Microbiol. 67: 4479– 4487.
31.
Rappe, M. S.,, and S. J. Giovannoni. 2003. The uncultured microbial majority. Annu. Rev. Microbiol. 57: 369– 394.
32.
Rodriguez, F.,, J. L. Oliver,, A. Marin, and, J. R. Medina. 1990. The general stochastic-model of nucleotide substitution. J. Theor. Biol. 142: 485– 501.
33.
Ronquist, F.,, and J. P. Huelsenbeck. 2003. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19: 1572– 1574.
34.
Schadt, C. W.,, A. P. Martin,, D. A. Lipson, and, S. K. Schmidt. 2003. Seasonal dynamics of previously unknown fungal lineages in tundra soils. Science 301: 1359– 1361.
35.
Schloss, P. D.,, B. R. Larget, and, J. Handelsman. 2004. Integration of microbial ecology and statistics: a test to compare gene libraries. Appl. Environ. Microbiol. 70: 5485– 5492.
36.
Singleton, D. R.,, M. A. Furlong,, S. L. Rathbun, and, W. B. Whitman. 2001. Quantitative comparisons of 16S rRNA gene sequence libraries from environmental samples. Appl. Environ. Microbiol. 67: 4374– 4376.
37.
Staley, J. T.,, and A. Konopka. 1985. Measurement of in situ activities of nonphotosynthetic microorganisms in aquatic and terrestrial habitats. Annu. Rev. Microbiol. 39: 321– 346.
38.
Thompson, J. D.,, D. G. Higgins, and, T. J. Gibson. 1994. Clustal-W—improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22: 4673– 4680.
39.
Webb, C. O. 2000. Exploring the phylogenetic structure of ecological communities: an example for rain forest trees. Am. Nat. 156: 145– 155.
40.
Webb, C. O.,, D. D. Ackerly, and, S. W. Kembel. 2005. Phylocom: software for the analysis of community phylogenetic structure and character evolution. Versions 3.22 and 3.34b.
41.
Webb, C. O.,, D. D. Ackerly,, M. A. McPeek, and, M. J. Donoghue. 2002. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33: 475– 505.
42.
Woese, C. R. 1987. Bacterial evolution. Microbiol. Rev. 51: 221– 271.
43.
Woese, C. R.,, and G. E. Fox. 1977. Phylogenetic structure of prokaryotic domain: the primary kingdoms. Proc. Natl. Acad. Sci. USA 74: 5088– 5090.
44.
Zhang, J. Z.,, and M. Nei. 1997. Accuracies of ancestral amino acid sequences inferred by the parsimony, likelihood, and distance methods. J. Mol. Evol. 44: S139– S146.
Tables
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities
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Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49
/content/10.1128/9781555815882.ch49.ch49tab01
TABLE 1
∫-LIBSHUFF comparisons of Costa Rican soil bacterial 16S rRNA gene libraries
a
TABLE 1
∫-LIBSHUFF comparisons of Costa Rican soil bacterial 16S rRNA gene libraries a
Citation:
Jones R, Costello E, Martin A.
2007.
Application of Phylogenetic Techniques in Studies of Soil Microbial Communities,
p 608-617.
In
Hurst C, Crawford R, Garland J, Lipson D, Mills A, Stetzenbach L (ed),
Manual of Environmental Microbiology, Third Edition.
ASM Press, Washington, DC.
doi: 10.1128/9781555815882.ch49