Chapter 43 : Sequence-Based Fungal Identification and Classification

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Identification and classification of fungi from clinical samples are important for antifungal susceptibility testing and epidemiological investigation. Sequence-based molecular techniques are increasingly used in the identification and taxonomy characterization of fungal infections. Phenotypic characteristics used for the recognition and classification of fungi are those that are either easily observed or measured, or a combination of both. An ultimate goal of fungal classification is to draw inferred phylogenetic relationships. In brief, it is sequencing by synthesis, and it is based on the synthesis of cDNA from PCR amplicons. DNA targets that have been used for fungal identification and classification include rDNA, cytochrome b, β-tubulin, calmodulin, enolase, chitin synthase, heat shock protein, and other housekeeping and functional genes. The increased use of gene sequences to recognize different clades within traditional medically important species emphasizes the importance of using sequence data in contrast to species-specific probes to identify particular fungal species. Clinical laboratories are under increasing pressure to provide rapid identification and classification of fungal infections due to the growing number of immunocompromised patients that are susceptible to fungal infection, and the availability of targeted antifungal agents. Sequencing and database comparison of PCR amplicons coupled with phylogenetic methods provide a robust strategy for species recognition, especially for uncommon and emerging pathogenic fungi.

Citation: Dong J, Loeffelholz M, McGinnis M. 2011. Sequence-Based Fungal Identification and Classification, p 669-676. In Persing D, Tenover F, Tang Y, Nolte F, Hayden R, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555816834.ch43
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Image of FIGURE 1

Organization of fungal rDNA. ETS, external transcribed spacer; SSU, small subunit; LSU, large subunit.

Citation: Dong J, Loeffelholz M, McGinnis M. 2011. Sequence-Based Fungal Identification and Classification, p 669-676. In Persing D, Tenover F, Tang Y, Nolte F, Hayden R, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555816834.ch43
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Image of FIGURE 2

26S rDNA ESs.

Citation: Dong J, Loeffelholz M, McGinnis M. 2011. Sequence-Based Fungal Identification and Classification, p 669-676. In Persing D, Tenover F, Tang Y, Nolte F, Hayden R, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555816834.ch43
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Image of FIGURE 3

Phylogenetic tree for isolates using large-subunit (26S) rDNA sequence data. These groupings correlate with six proposed phylogenetic clades. Numbers in the tree are UTMB culture collection numbers.

Citation: Dong J, Loeffelholz M, McGinnis M. 2011. Sequence-Based Fungal Identification and Classification, p 669-676. In Persing D, Tenover F, Tang Y, Nolte F, Hayden R, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555816834.ch43
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Generic image for table

Target genes used for sequence identification of fungi

Citation: Dong J, Loeffelholz M, McGinnis M. 2011. Sequence-Based Fungal Identification and Classification, p 669-676. In Persing D, Tenover F, Tang Y, Nolte F, Hayden R, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555816834.ch43
Generic image for table

Traditional and sequence-based species names

Citation: Dong J, Loeffelholz M, McGinnis M. 2011. Sequence-Based Fungal Identification and Classification, p 669-676. In Persing D, Tenover F, Tang Y, Nolte F, Hayden R, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555816834.ch43

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