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Microorganism Identification

A modern tool for an old question

In veterinary as in human medicine, the accurate and rapid identification of microorganisms (bacteria, fungi, viruses) is critical to proper diagnosis and treatment of disease. Highly sensitive methods are necessary to prevent the misdiagnosis of truly infected patients and the potential spread of disease. The absence of reliable diagnostic methods can result in erroneous judgments that may have serious consequences, both to the individual patient and to public health in general.

The traditional method for identifying microorganisms consists of culture on growth media followed by phenotypic identification methods. Various methods exist to identify microorganisms based on phenotypic criteria, such as serial propagation in the presence of varied energy sources, analysis of metabolic by-products, detection of cell-surface proteins and lipids, and use of specific immunologic reagents. While these methods have proven very successful for many types of microorganisms, and are well-developed for most of the known human pathogens, they are limited by several factors. First, only a small fraction of the estimated number of microorganism species (or phylotypes) have been discovered, and so standard phenotypic identification methods are applicable to relatively few species. Second, many microorganisms are challenging to culture or will not grow at all, and therefore cannot be identified using traditional phenotypic techniques - as many as 99% of bacterial species are estimated to fall into this category. These limitations are particularly problematic in veterinary medicine, since most clinical microbiology resources have targeted human diseases.

Model of a DNA Strand

Over the past 20 years, sequence analysis of conserved genes has become the gold-standard as a reliable, accurate, inexpensive, and scalable method of microorganism identification in medical and environmental fields (among others). These advantages have resulted in the routine use of sequencing methods to compliment, and sometimes replace, traditional phenotypic methods formicroorganism identification. My research in this field is focused on the development and implementation of a sequence-based method for identifying bacterial and fungal species for veterinary clinical applications at Texas A&M University and beyond. The identification of disease-causing bacterial and fungal species will assist the diagnosis and treatment of veterinary species, and will reveal novel associations between microorganism species and disease.