Molecular ecology

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For the scientific journal, see Molecular Ecology.

Molecular ecology is a field of evolutionary biology that is concerned with applying molecular population genetics, molecular phylogenetics, and more recently genomics to traditional ecological questions (e.g., species diagnosis, conservation and assessment of biodiversity, species-area relationships, and many questions in behavioral ecology). It is virtually synonymous with the field of "Ecological Genetics" as pioneered by Theodosius Dobzhansky, E. B. Ford, Godfrey M. Hewitt and others.[citation needed] These fields are united in their attempt to study genetic-based questions "out in the field" as opposed to the laboratory. Molecular ecology is related to the field of Conservation genetics.

Methods frequently include using microsatellites to determine gene flow and hybridization between populations. The development of molecular ecology is also closely related to the use of DNA microarrays, which allows for the simultaneous analysis of the expression of thousands of different genes. Quantitative PCR may also be used to analyze gene expression as a result of changes in environmental conditions or different response by differently adapted individuals.

Bacterial diversity

Molecular ecological techniques have recently been used to study in situ questions of bacterial diversity. This stems from the fact that many microorganisms are not easily obtainable as cultured strains in the laboratory, which would allow for identification and characterisation. It also stems from the development of PCR technique, which allows for rapid amplification of genetic material.

The amplification of DNA from environmental samples using general of group-specific primers leads to a mix of genetic material that has to be sorted out before sequencing and identification. The classic technique to achieve this is through cloning, which involves incorporating the amplified DNA fragments into bacterial plasmids. Techniques such as temperature gradient gel electrophoresis, allow for a faster result. More recently, the advent of relatively low-cost, next-generation DNA sequencing technologies, such as 454 and Illumina platforms, has allowed exploration of bacterial ecology in relation to continental-scale environmental gradients such as pH[1] that was not feasible with traditional technology.

Fungal diversity

Exploration of fungal diversity in situ has also benefited from next-generation DNA sequencing technologies. The use of high-throughput sequencing techniques has been widely adopted by the fungal ecology community since the first publication of their use in the field in 2009.[2] Similar to exploration of bacterial diversity, these techniques have allowed high-resolution studies of fundamental questions in fungal ecology such as phylogeography,[3] fungal diversity in forest soils,[4] stratification of fungal communities in soil horizons,[5] and fungal succession on decomposing plant litter.[6]

The majority of fungal ecology research leveraging next-generation sequencing approaches involves sequencing of PCR amplicons of conserved regions of DNA (i.e. marker genes) to identify and describe the distribution of taxonomic groups in the fungal community in question, though more recent research has focused on sequencing functional gene amplicons[2] (e.g. Baldrian et al. 2012[5]). The locus of choice for description of the taxonomic structure of fungal communities has traditionally been the internal transcribed spacer (ITS) region of ribosomal RNA genes [7] due to its utility in identifying fungi to genus or species taxonomic levels,[8] and its high representation in public sequence databases.[7] A second widely used locus (e.g. Amend et al. 2010,[3] Weber et al. 2013[9]), the D1-D3 region of 28S ribosomal RNA genes, may not allow the low taxonomic level classification of the ITS,[10][11] but demonstrates superior performance in sequence alignment and phylogenetics.[3][12] In addition, the D1-D3 region may be a better candidate for sequencing with Illumina sequencing technologies.[13] Porras-Alfaro et al.[11] showed that the accuracy of classification of either ITS or D1-D3 region sequences was largely based on the sequence composition and quality of databases used for comparison, and poor-quality sequences and sequence misidentification in public databases is a major concern.[14][15] The construction of sequence databases that have broad representation across fungi, and that are curated by taxonomic experts is a critical next step.[12][16]

Next-generation sequencing technologies generate large amounts of data, and analysis of fungal marker-gene data is an active area of research.[2][17] Two primary areas of concern are methods for clustering sequences into operational taxonomic units by sequence similarity, and quality control of sequence data.[2][17] Currently there is no consensus on preferred methods for clustering,[17] and clustering and sequence processing methods can have a significant impact on results, especially for the variable-length ITS region.[2][17] In addition, fungal species vary in intra-specific sequence similarity of the ITS region.[18] Recent research has been devoted to development of flexible clustering protocols that allow sequence similarity thresholds to vary by taxonomic groups, which are supported by well-annotated sequences in public sequence databases.[16]

Notes and references

  1. ↑ Lauber CL, Hamady M, Knight R, Fierer N. (2009). Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Apllied and Environmental Microbiology 75: 5111-5120
  2. ↑ 2.0 2.1 2.2 2.3 2.4 Větrovský T, Baldrian P (2013). Analysis of soil fungal communities by amplicon pyrosequencing: current approaches to data analysis and the introduction of the pipeline SEED. Biol Fertil Soils 49: 1027-1037
  3. ↑ 3.0 3.1 3.2 Amend AS, Seifert KA, Samson R, Bruns TD. (2010). Indoor fungal composition is geographically patterned and more diverse in temperate zones than in the tropics. Proc Natl Acad Sci U S A 107: 13748–13753.
  4. ↑ Buéé M, Reich M, Murat C, Morin E, Nilsson RH, Uroz S, Martin F (2009) 454 Pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity. New Phytol 184:449–456
  5. ↑ 5.0 5.1 Baldrian P, Kolarik M, Stursova M, Kopecky J, Valaskova V, Vetrovsky T, et al. (2012). Active and total microbial communities in forest soil are largely different and highly stratified during decomposition. ISME J 6: 248-258
  6. ↑ Voříšková J, Baldrian P (2013). Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J 7:477– 486
  7. ↑ 7.0 7.1 Seifert KA. (2009). Progress towards DNA barcoding of fungi. Mol Ecol Resour 9: 83–89
  8. ↑ Horton TR and TD Bruns. (2001). The molecular revolution in ectomycorrhizal ecology: peeking into the black-box. Molecular Ecology 10: 1855-1871
  9. ↑ Weber, C. F., R. Vilgalys and C. R. Kuske. (2013). Changes in fungal community composition in response to elevated atmospheric CO2 and nitrogen fertilization varies with soil horizon. Frontiers in Terrestrial Microbiology 4: 78 doi: 10.3389/fmicb.2013.00078
  10. ↑ Schoch, C. L. and K. A. Seifert. (2012). Reply to Kiss: internal transcribed spacer (ITS) remains best candidate as a universal DNA barcode marker for Fungi despite imperfections. PNAS 109(27): E1812.
  11. ↑ 11.0 11.1 Porras-Alfaro A, Liu KL, Kuske CR, Xie G. (2013). From Genus to Phylum: LSU and ITS rRNA operon regions showed similar classification accuracy influenced by database composition. Appl Environ Microbiol doi:10.1128/AEM.02894-13
  12. ↑ 12.0 12.1 Kõljalg U, Larsson K-H, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, et al. (2005). UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol 166: 1063–1068
  13. ↑ Liu KL, Porras-Alfaro A, Kuske CR, Eichorst SA, Xie G. (2012). Accurate, rapid taxonomic classification of fungal large-subunit rRNA genes. Applied and Environmental Microbiology 78: 1523–1533
  14. ↑ Vilgalys R. (2003). Taxonomic misidentification in public DNA databases. New Phytol 160: 4-5
  15. ↑ Nilsson RH, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, et al. (2012). Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences MycoKeys 4: 37-63
  16. ↑ 16.0 16.1 Kõljalg U, Nilsson H, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, et al. (2013). Towards a unified paradigm for sequence-based identification of fungi. Molecular Ecology 22: 5271-5277
  17. ↑ 17.0 17.1 17.2 17.3 Lindahl, B. D., R. H. Nilsson, L. Tedersoo, K. Abarenkov, T. Carlsen, R. Kjoller, et al. (2013). Fungul community analysis by high-throughput sequencing of amplified markers – a user’s guide. New Phytologist doi: 10.1111/nph.12243
  18. ↑ Nilsson RH, Kristiansson E, Ryberg M, Hallenberg N. (2008). Intraspecific ITS variability in the kingdom Fungi as expressed in the international sequence databases and its implications for molecular species identification. Evol Bioinf Online 4: 193–201

See also

External links

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