Accurate identification of environmental isolates is required to support active environmental monitoring programs within pharmaceutical manufacturing environments and to implement accurate Corrective and Preventive Actions (CAPAs) for products and processes to reduce the risk of contamination. The comparative gene sequence analysis of ribosomal DNA (rDNA) has been shown to have the highest accuracy of microbial identification system technologies and has been considered the gold standard for microbial identification for over a decade. The MicroSEQ ID microbial identification system, based on comparative rDNA sequencing of the 16S region (for bacteria) or the LSU D2 region (for fungi), is a proven method for rapid and accurate microbial identification.
The target for bacterial identification is the 16S ribosomal RNA (rRNA) gene sequence. The 16S rRNA is ubiquitous and can therefore be used to study phylogenetic relationships among all bacteria. With the MicroSEQ ID method, users have the option of choosing to sequence the first 500 bp of the 16S rDNA gene or the full 1,500 bp. Kits and the supporting validated library databases are available for both options. The first 500 bp are sufficient for a routine dentification that encompasses 3 of the 9 hypervariable regions of the 16S gene (Figure 1). In some cases the 500 bp region is not sufficient to discriminate among very closely related bacteria and therefore requires a more informative full-gene read (Figure 2). Furthermore, sequencing of the entire 1,500 bp sequence is required when describing a new species.
Fungi and yeast
Comparative sequence analyses of both the expansion region D2 of the larger rRNA molecule in the large subunit of the eukaryotic ribosome (LSU-D2) and ITS regions have been successfully used for identification and classification of fungi down to the species level. The ITS region exhibits more variability, which can give rise to many more “sequence types” than the D2 region when used for sequence-based fungal identification. Fungal genomes may contain more than 100 copies of the rDNA cluster; it therefore is critical to understand and differentiate between simple sequence variability and sequence variability that reflects actual relatedness. The MicroSEQ ID fungal database is derived from sequences from LSU-D2 rDNA, which helps deliver highly reliable fungal identification and classification in routine identification tests.
Figure 1. Hypervariable regions within the 16S rRNA gene. There are 9 hypervariable regions within the bacterial 16S gene, indicated in green. The conserved regions are indicated in blue.
Figure 2. Sequence data taken from the first 500 bp (lower tree) are not sufficient to differentiate Mycobacterium abscessus (ATCC 19977) and Mycobacterium chelonae chelonae (ATCC 35752). Extending the sequence to the full 1,500 bp allows for differentiation of these two species (upper tree). Similarly, the differences between Mycobacterium farcinogenes (ATCC 35753) and Mycobacterium senegalense (ATCC 35796) as well as Mycobacterium africanum (ATCC 25420) and Mycobacterium microti (ATCC 19422) become more apparent when analyzed using the full 1,500 bp sequence.