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MicroSEQ ID Technology—Genotypic Approach
Genotypic microbial identification—a proven technology
Accurate identification of isolates is required to support active environmental monitoring programs within pharmaceutical manufacturing facilities and to implement accurate Corrective and Preventive Actions (CAPAs) for products and processes to help 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.
Why choose a genotypic method for microbial identification?
The Applied Biosystems MicroSEQ Rapid 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.
Designed to support regulatory guidelines
The FDA guidance is intended to help manufacturers meet the requirements in the Agency's current good manufacturing practice (cGMP) regulations (2l CFR parts 210 and 211) when manufacturing sterile drug and biological products using aseptic processing. In the guidance document Sterile Drug Products Produced by Aseptic Processing—Current Good Manufacturing Practice the FDA clearly identifies genotypic methods of microbial identification to be more accurate and precise than traditional biochemical and phenotypical techniques, so why settle for less?
“Genotypic methods have been shown to be more accurate and precise than traditional biochemical and phenotypic techniques. These methods are especially valuable for investigations into failures (e.g., sterility test; media fill contamination).”
U.S. Food and Drug Administration. Sterile Drug Products Produced by Aseptic Processing—Current Good Manufacturing Practice U.S. Food and Drug Administration. FDA, October 2004.
MicroSEQ ID is designed to support the recommended qualification guidelines from:
Identification of bacteria
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. The gene is 1,500 base pairs (bp) long and contains 9 variable regions. The conserved regions are identical in all micro-organisms and can be used for amplification via PCR, while the divergent regions are used for identification through sequencing.
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 identification that encompasses 3 of the 9 hypervariable regions of the 16S gene (Figure 1). In some cases, the 500 bp region is not enough to discriminate among very closely related bacteria and therefore requires a more informative full-gene read. Furthermore, sequencing of the entire 1,500 bp sequence is required when describing a new species.
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 grey.
Identification of 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 fungal identification database is derived from sequences from LSU-D2 rDNA, which helps deliver highly reliable fungal identification and classification in routine identification tests.
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