The folks at Purdue University continue to develop next generation technologies for the detection of pathogenic bacteria. The latest involves the use of light scattering.
A Purdue University-developed laser tool already effective in quickly detecting harmful bacteria has been shown to detect mutant varieties of listeria - and in the same amount of time.
The BARDOT (pronounced bar-DOH') laser scans bacteria colonies looking for unique patterns that each bacterium makes. When the light penetrates a bacteria colony, it produces a scatter pattern that can be matched against a library of known bacteria patterns to identify a match. The system can identify bacteria such as salmonella, listeria, bacillus, vibrio and E. coli within 24 hours.
Now, Arun Bhunia, professor of food microbiology, and Atul Singh, research scientist, have shown that BARDOT (acronym for "bacterial rapid detection using optical scatter technology") can pinpoint small genetic mutations in listeria just as quickly, significantly reducing the time it would take scientists to identify those mutations in bacterial strains used for research. Their study was published in the journal Applied and Environmental Microbiology.
"This is a versatile microbiology tool, and we wanted to see if we can use it for mutant strains," Bhunia said. "This is a really powerful tool to help researchers find those mutant strains much easier on a petri plate. You can avoid the laborious techniques required to screen or detect these mutant strains."
Scientists use mutant bacteria to understand biology of pathogens and how they can combat outbreaks in food that can cause illnesses or death. Current methods of identifying mutants can take several days, whereas BARDOT can do the same work in less than a day.
Singh said he visualized the changes in bacteria using the laser system.
"It's like if you compare a wild type that is a normal bacteria, you get a scatter pattern. And then if you delete a certain gene, you get a new picture," he said.
The reverse was also true. By restoring the deleted gene, the BARDOT system recognized the bacteria as a regular wild type of strain.
Bhunia said his lab will continue to study BARDOT's ability to identify mutants of other bacteria and build libraries so the tool can be used for that work. He will also test the system's ability to identify mutant bacteria from natural settings such as from contaminated food.
The U.S. Department of Agriculture funded this study through the Center for Food Safety Engineering.
ABSTRACT
Virulence Gene-Associated Mutant Bacterial Colonies Generate Differentiating Two-Dimensional Laser Scatter Fingerprints
Atul K. Singh, Lena Leprun a, Rishi Drolia, Xingjian Bai,
Huisung Kim , Amornrat Aroonual, Euiwon Bae, Krishna K. Mishra, Arun K. Bhunia
In this study, we investigated whether a laser scatterometer designated BARDOT (bacterial rapid detection using optical scattering technology) could be used to directly screen colonies of Listeria monocytogenes, a model pathogen, with mutations in several known virulence genes, including the genes encoding Listeria adhesion protein (LAP; lap mutant), internalin A (ΔinlA strain), and an accessory secretory protein (ΔsecA2 strain). Here we show that the scatter patterns of lap mutant, ΔinlA, and ΔsecA2 colonies were markedly different from that of the wild type (WT), with >95% positive predictive values (PPVs), whereas for the complemented mutant strains, scatter patterns were restored to that of the WT. The scatter image library successfully distinguished the lap mutant and ΔinlA mutant strains from the WT in mixed-culture experiments, including a coinfection study using the Caco-2 cell line. Among the biophysical parameters examined, the colony height and optical density did not reveal any discernible differences between the mutant and WT strains. We also found that differential LAP expression in L. monocytogenes serotype 4b strains also affected the scatter patterns of the colonies. The results from this study suggest that BARDOT can be used to screen and enumerate mutant strains separately from the WT based on differential colony scatter patterns.
SOURCE: Purdue University