Peer-reviewed veterinary case report
Rapid MALDI biotyper-based identification and cluster analysis of Streptococcus iniae.
- Journal:
- Journal of microbiology (Seoul, Korea)
- Year:
- 2017
- Authors:
- Kim, Si Won et al.
- Affiliation:
- Institute of Animal Medicine · South Korea
Plain-English summary
Researchers studied a type of bacteria called Streptococcus iniae, which can cause serious illness and death in fish, particularly the olive flounder, an important species in Korea and Japan. They looked at 89 samples of this bacteria taken from sick flounders over several years in South Korea. Using a special identification method called MALDI-TOF MS, they found that their new database was much better at correctly identifying the bacteria compared to an existing one, with almost all samples being accurately identified. They also grouped the bacteria into three clusters based on their characteristics. The study suggests that this new identification method is quicker, cheaper, and more effective for studying this harmful fish bacteria.
Abstract
Streptococcus iniae causes severe mortalities among cultured marine species, especially in the olive flounder (Paralichthys olivaceus), which is economically important in Korea and Japan. Recently, there has been growing concern regarding the emergence of S. iniae as a zoonotic pathogen. Here, 89 S. iniae isolates obtained from diseased olive flounders collected from 2003 to 2008 in Jeju Island, South Korea, were characterized using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). The results were aligned both with the available Bruker Daltonics data-base and with a new set of S. iniae data entries developed in our laboratory, and the results were compared. When we used the Bruker Daltonics database, the 89 isolates yielded either "no reliable identification" or were incorrectly identified as Streptococcus pyogenes at the genus level. When we used the new data entries from our laboratory, in contrast, all of the isolates were correctly identified as S. iniae at the genus (100%) and species (96.6%) levels. We performed proteomic analysis, divided the 89 isolates into cluster I (51.7%), cluster II (20.2%), and cluster III (28.1%), and then used the MALDI Biotyper software to identify specific mass peaks that enabled discrimination between clusters and between Streptococcus species. Our results suggest that the use of MALDI TOF MS could outperform the conventional methods, proving easier, faster, cheaper and more efficient in properly identifying S. iniae. This strategy could facilitate the epidemiological and taxonomical study of this important fish pathogen.
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Search related cases →Original publication: https://pubmed.ncbi.nlm.nih.gov/28124778/