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.
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.