How Do You Decide Which Microbial Identification System is Best?

Scott Sutton, Ph.D.
http://www.linkedin.com/in/scottvwsutton
This article first appeared in the PMF Newsletter of January, 2006 and is protected by copyright to PMF. It appears here with permission.

Introduction

Microbial identification plays a central role in the cleanroom control program (24). The method of identification, however, must be wedded to the need. For example, any organism isolated from the critical aseptic processing area must be identified to great detail, while those from class D/ISO 8/100,000 areas might only be characterized to the genus level. The key concern is providing sufficient detail to assist in the tracking of the state of control of the facility.

Most identification schemes still rely on the Gram stain, a differential staining technique developed in the late 1800’s by Christian Gram (15). This differential counterstaining technique is very good at distinguishing a real difference in cellular morphology. Unfortunately, this method is prone to a significant level of operator error, which has encouraged the development of alternate methods for showing the difference in cell structure (17, 23). Traditional methods of identification also consider a variety of phenotypic characteristics.

Phenotypic Methods

Phenotypic methods typically incorporate reactions to different chemicals or different biochemical markers. The API strip is basically a prepackaging of the standard method that required racks of test tubes into a convenient bubble-wrap. This method was further refined in the Vitek automated system which miniaturized the process (2, 20). This system has recently been enhanced to provide greater resolution of microorganisms (9, 10). A second phenotypic system is offered by Biolog, Inc. The fundamental unit in this system is a 96- well plate that has different carbohydrate sources in each well, with a tetrazolium redox dye. If the microorganism is capable of utilizing the carbohydrate the well turns dark indicating reduction of the dye (14, 19). The end-result is a pattern of wells (a “metabolic fingerprint”) that allows the user to identify the unknown microorganism. This method has recently been extended to include the identification of molds and filamentous fungi with a proprietary software package. The use of cellular fatty acid (FA) composition to identify the genus and species has been popular for several years (1, 5). The fatty acids are extracted from the cell cultures and then the patterns of fatty acid esters are determined by gas chromatography (22).

There are some new methods under development for the pharmaceutical QC lab. These include Fourier- Transform Infrared (FTIR) microscopy (16) and Matrix- Assisted Laser Desorption Ionization–Time of Flight (MALDI-TOF) mass spectroscopy (8, 18). However, these have not seen widespread use in the QC lab as of yet.

Genotypic Methods

The FDA has recently elevated the use of genotypic identification methods with the release of the revised aseptic processing guidance document late in 2004 (6).

The Riboprinter is fundamentally an automated Southern Blot apparatus using labeled ssDNA probe from the 16sRNA codon. The resulting pattern is then used to identify the unknown microorganism (4, 12). If the initial banding pattern is inconclusive, then the restriction endonuclease can be changed to provide an extraordinary level of strain discrimination (3). Another genotypic identification system on the market is the MicroSeq 500 16S rDNA Bacterial Sequencing Kit which is offered by Applied Biosystems. As the name implies, it provides the materials needed to sequence the first 500 base pairs of the unknown microorganism’s 16s ribosomal RNA codon (7). The technology involves amplification of the 16S codon by PCR, followed by automated sequencing.

A final genotypic method that is being marketed into the QC pharmaceutical laboratory is the Bacterial Barcodes system (11). This system is also based on PCR technology, using as a primer a sequence homologous to a repetitive sequence in the bacterial genome. The amplified sequence is then separated by gel electrophoresis and visualized to give the “barcode” specific to that strain.

Qualicon markets the BAX system to the food industry that contains primers for Salmonella, Listeria or E. coli O157:H7 (13). This system has promise for determination of the absence of specified organisms in the product. Other genetic methods have been published in the literature, although few are available to the pharmaceutical market (21).

How to Choose?

There are a variety of identification technologies available. When choosing one for the lab you must bear in mind the strengths, and weaknesses, of the various methodologies. For example, the recently released aseptic processing guidance document (FDA 2004) strongly recommends the use of genotypically based methods. However, if you choose PCR based methods or DNA sequencing, there is potentially an associated cost in facilities, labor (highly skilled technicians) and maintenance that is not present with the more traditional methods.

The most direct approach to deciding the appropriate technology is to research the choices fully based on an understanding of what your requirements may be. I recommend the development of a User Requirements Specification (URS) document to drive this process. This is a formal Quality document, similar in concept to a Design Qualification document. Different companies will have different formats for these documents, but the essential features of the document will be that it has the essential requirements and that it has upper management sign-off (for a variety of reasons it is a good idea to document upper management commitment).

A partial list of topics to be covered in any URS designed for an identification system should include:

  • Assay Throughput – How many samples a day?
  • Assay Time-to-Completion – How quickly?
  • Cost of Consumables – How much? Frequently the cost of consumables can soon dwarf the capital expense.
  • Labor Requirements – Including the technological sophistication of the operators—can your technicians actually operate the equipment reliably?
  • Size and Composition of Microorganism Identification Database – A major consideration. If you purchase two systems to cover identifications of unknowns, it is imperative to ensure that the databases are large and complementary; that is they both don’t have the same organisms in them, but that they include many different ones as well.
  • Facility Requirements – Obvious stuff like electrical and plumbing, but also less obvious concerns about RNA/DNA contamination and cleanroom issues.
  • Compatibility with Existing Systems (LIMS, workflow, etc.)
  • Need for Physiological Information – Do you need to know if the organisms are capable of degrading your product components? You may want to use a system that will help determine this.
  • Purpose – Do you plan to use this for routine identifications or for investigations? The use of the system may be different for different systems. A good system for routine work may not be the best for investigations, and vice versa.

In short, there are a wide variety of choices available to help with the identification of unknown organisms. It is important to define your specific requirements and to purchase the appropriate system to meet those needs.

References

  1. Abel, KH et al. 1963. Classification of Microorganisms by Analysis of Chemical Composition. I. Feasibility of Utilizing Gas Chromatography. J Bacteriol. 85:1039-1044.
  2. Aldridge, C et al. 1977. Automated Microbiological Detection/ Identification System. J Clin Microbiol. 6(4):406-413.
  3. Brisse, S et al. 2000. Distinguishing Species of the Burkholderia cepacia Complex and Burkholderia gladioli by Automated Ribotyping. J Clin Microbiol. 38(5):1876—1884.
  4. Bruce, JL. 1996. Automated System Rapidly Identifies and Characterizes Microorganisms in Food. Food Technol. Jan. pp77-81.
  5. Eerola, E and O-P Lehtonen. 1988. Optimal Data Processing Procedure for Automatic Bacterial Identification by Gas-Liquid Chromatography of Cellular Fatty Acids. J Clin Microbiol. 9:1745-1753.
  6. FDA. 2004. Guidance for Industry: Sterile Drug Products Produced by Aseptic Processing—Current Good Manufacturing Practice. Sept 29, 2004.
  7. Fontana, C et al. 2005. Use of the MicroSeq 500 16S rRNA Gene-Based Sequencing for Identification of Bacterial Isolates that Commercial Automated Systems Failed to Identify Correctly. J Clin Microbiol. 43(2):615-619.
  8. Fox, A. 2006. Mass Spectrometry for Species or Strain Identification after Culture or without Culture: Past, Present, and Future. J Clin Microbiol. 44(8):2677-2680.
  9. Funke, G and P Funke-Kissling. 2004. Evaluation of the New VITEK 2 Card for Identification of Clinically Relevant Gram-Negative Rods. J Clin Microbiol. 42(9):4067-4071.
  10. Funke, G and P Funke-Kissling. 2005. Performance of the New VITEK 2 GP Card for Identification of Medically Relevant Gram-Positive Cocci in a Routine Clinical Laboratory. J Clin Microbiol. 43(1):84-88.
  11. Healy, M et al. 2005. Microbial DNA Typing by Automated Repetitive-Sequence-Based PCR. J Clin Microbiol. 43(1):199-207.
  12. Jeffrey, M et al. 2004. Validation of an Enhanced Method of Bacterial Ribotyping for Improved Efficiency and Identification of Stressed Isolates. Pharm Technol. 28(3):156-166.
  13. Jimenez, L. 2001. PCR Detection of Salmonella typhimurium In Pharmaceutical Raw Materials and Products Contamination with a Mixed Bacterial Culture Using the BAX System. PDA J Pharm Sci Tech 55(5):286-289.
  14. Klingler, JM et al. 1992. Evaluation of the Biolog Automated Microbial Identification System. Appl Environ Microbiol. 58(6):2089-2092.
  15. McClelland, R. 2001. Gram’s Stain; the Key to Microbiology. Med Lab Observer. April. pp. 20-31.
  16. Maquelin, K. et al. 2002. Identification Of Medically Relevant Microorganisms By Vibrational Spectroscopy. J Microbiol Meth. 51:255-271.
  17. Mason, DJ et al. 1998. A Fluorescent Gram Stain for Flow Cytometry and Epifluorescence Microscopy. Appl Environ Microbiol. 64(7):2681-2685.
  18. Mazzeo, MF. et al. 2006. Matrix-Assisted Laser Desorption Ionization—Time of Flight Mass Spectrometry for the Discrimination of Food-Borne Microorganisms. Appl Environ Microbiol. 72(2):1180- 1189
  19. Miller, JM and DL Rhoden. 1991. Preliminary Evaluation of Biolog, a Carbon Source Utilization Method for Bacterial Identification. J Clin Microbiol. 29(6):1143-1147.
  20. Odlaug, TE et al. 1982. Evaluation of an Automated System for Rapid Identification of Bacillus Biological Indicators and Other Bacillus Species. PDA J Parenteral Sci Tech. 36(2):47-54.
  21. Olive, DM and P Bean. 1999. Principles and Applications for DNA-Based Typing of Microbial Organisms. J Clin Microbiol. 37(6):1661-1669.
  22. Olson, W.P. et al. 1990. Identification of Bacterial Contamination in a Pharmaceutical Manufacturing Facility by GC FA Analysis. Pharm Technol. February. pp. 32-36.
  23. Powers, EM. 1995. Efficacy of the Ryu Nonstaining KOH Technique for Rapidly Determining Gram Reactions of Food-Borne and Waterborne Bacteria and Yeasts. Appl Environ Microbiol. 61(10):3756-3758.
  24. Sutton, SVW and AM Cundell. 2004. Microbial Identification in the Pharmaceutical Industry. Pharm Forum. 30(5):1884-1894.

 

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