Developing a Method for Rapid and Accurate Identification of Bacillus Species in Clinical Isolates Using Polymerase Chain Reaction

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Bacillus species have caused a variety of diseases in humans throughout history. These include foodborne illness, wound infections, and anthrax poisoning. With over 266 identified species within the Bacillus genus, isolates previously described as independent species (e.g. B. anthracis, B. cereus, B. thuringiensis) have been discovered to be so genetically and phenotypically similar that they are often very difficult to discriminate between. Therefore, there have been instances of misidentification reported in scientific literature. Therefore, the majority of clinical tests done may not be precise enough to distinguish between some species of Bacillus, as they are so genetically similar. A new software, called MALDI-TOF, has been very successful in species discrimination. However, this technology is relatively new and not widely available for all clinical settings. The misidentification of clinical Bacillus isolates from infected patients could have medical significance and negatively affect treatment. By finding improved approaches for correctly identifying and distinguishing between Bacillus species, the scientific community can increase its understanding of the pathogenic potential of certain Bacillus species and positively impact patient diagnosis and treatment. This study was intended to develop a technique for better distinguishing between Bacillus species. Recent research has suggested that the pycA gene may be the key to separating Bacillus species. A series of specially designed primers will be used to target the pycA gene in Bacillus thuringiensis and Bacillus cereus strains grown from isolates obtained from the NRRL repository. With the use of these primers, PCR testing will be performed on the various Bacillus strains. This will allow for identification of the isolate strains based on the PCR products and comparison to the initial repository identification. If successful, this approach could prove to be a more rapid and precise way to distinguish between Bacillus species in clinical settings, possibly improving the speed of diagnosis and treatment. Data collection and analysis are currently being executed.