Igs Its Lsu Ssu 58s

Families

Populations

Orders

Species

Genera

FIGURE 6.7 Levels of taxonomic resolution provided by nucleotide sequence data of nuclear ribosomal DNA (rDNA) genes. IGS, intergenic spacer region; ITS, internal transcribed spacer regions (ITS1 and ITS2); LSU, 28S large ribosomal subunit gene; 5.8S ribosomal subunit gene; and SSU, 18S small ribosomal subunit gene. Figure kindly provided by R. Vilgalys, Department of Biology, Duke University.

FIGURE 6.7 Levels of taxonomic resolution provided by nucleotide sequence data of nuclear ribosomal DNA (rDNA) genes. IGS, intergenic spacer region; ITS, internal transcribed spacer regions (ITS1 and ITS2); LSU, 28S large ribosomal subunit gene; 5.8S ribosomal subunit gene; and SSU, 18S small ribosomal subunit gene. Figure kindly provided by R. Vilgalys, Department of Biology, Duke University.

small subunit (SSU) rDNA regions usually provide resolution at or above the genus level, while the nontranscribed intergenic spacer (IGS) and internal transcribed spacer regions (ITS1 and 2) are more rapidly evolving and usually provide resolution at or below the species level. Initially the IGS and/or ITS regions are sequenced, and the Basic Local Alignment Search Tool (BLAST) search algorithm http://www.ncbi.nlm.nih.gov/ is utilized to determine whether there is a significant match (>70% identity) among sequences currently deposited in a database [GenBank (USA), European Molecular Biology Laboratory (EMBL) (Europe), and DNA Database of Japan (DDBJ) (Japan)]. If a match of greater than 70% is identified, the sequence is aligned with other similar sequences in the database and subjected to phylogenetic and distance analyses to further optimize sequence identification. Support for the link between the unknown sequence and its closest known sequence is evaluated using the additional statistical methods that provide an indication of their relatedness. In cases where a significant match is not identified, additional regions of the genome that provide resolution above the species level (e.g., SSU, LSU, 5.8S) can be sequenced, aligned, and subjected to phylogenetic analysis for sequence identification. By using this approach with known reference sequences and in conjunction with other mitochondrial ribosomal RNA (rDNA) and protein-encoding genes such as actin, ATPase, a- and b-tubulin, chitin synthase, hydrophobins, laccase, and translation elongation factor 1-a, it is possible to identify most fungi at various taxonomic levels.23,52,54,55

DNA Sequencing

Bioinformatics Tools

Phylogenetic Analysis

Taxonomic Identification

Species of a known reference isolate

Species of an unknown reference isolate

BLAST searches of sequence databases with variable regions of genome

BLAST match of > 70% identity

BLAST match of < 70% identity

Alignment of ITS or

IGS regions with corresponding taxon specific databases

Alignment of conserved regions (5.8S, LSU or SSU) in databases

FIGURE 6.8 Molecular-based identification of fungi with ribosomal RNA (rDNA) gene sequences. Figure kindly provided by R. Vilgalys, Department of Biology, Duke University).

Although databases have been demonstrated to be useful in identifying a wide range of fungi, in certain instances, these available databases may not be sufficient to place a DNA sequence within a previously sampled genus or species. The nontranscribed spacer regions of the rDNA subunit can be employed to identify genetically distinct individuals in a species at the population level. However, additional regions of the fungal genome are usually needed for this purpose. The detection of DNA sequence variation from multiple regions of the fungal genome coupled with appropriate statistical analysis of the data provides a very powerful tool for identification of fungi at or below the species level. This approach is referred to as multilocus sequence typing (MLST) and offers great promise for assigning unknown fungal individuals to a population of origin.56

In the past 15 years, mycologists and plant pathologists have employed DNA fingerprinting and PCR-based techniques to develop multilocus genetic markers for the identification of genetically distinct individuals in fungal pathogen populations. These population genetics studies have contributed significantly to our understanding of the genetic diversity and structure of populations of plant pathogens, and have provided a conceptual framework for defining and differentiating genetically distinct individuals (clones). Information on the genetic diversity and structure of fungal populations has resulted in the development of appropriate sampling protocols required to differentiate introduced and indigenous genetic individuals. As newer techniques (real-time PCR and microfluidic methods) and more mitochondrial and nuclear DNA sequence data become available, rapid identification of genetically distinct individuals in field populations will be possible, facilitating fungal forensics.

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