Diagnostic accuracy of a standardized scheme for identification of Streptococcus uberis in quarter milk samples: A comparison between conventional bacteriological examination, modified Rambach agar medium culturing, and 16S rRNA gene sequencing

Abstract

Bacteriological examination of milk samples is a prerequisite for pathogen-specific therapy and aids in limiting antimicrobial resistance. The aims of this study were to establish a standardized scheme for reliable Streptococcus uberis identification in routine diagnosis and to evaluate the accuracy of conventional tests and growing patterns of Strep. uberis on a selective medium (modified Rambach agar medium, MRAM) using 16S rRNA gene sequencing analysis as a reference method. We obtained isolates of presumptive Strep. uberis (n = 336) from quarter milk samples of dairy cows with intramammary infections and classified the isolates into 2 clusters using biochemical characterization. In cluster 1 (n = 280), cocci grew as non-hemolytic colonies, hydrolyzing esculin, carrying no Lancefield antigen (A/B/C/D/G) or Christie Atkins Munch-Petersen factor, and their growth was inhibited on an Enterococcus agar. Production of β-d-galactosidase on MRAM was shown by 257 of the cluster 1 isolates (91.79%), and 16S rRNA gene sequencing verified 271 (96.79%) of the isolates to be Strep. uberis. In 264 isolates (94.29%), MRAM agreed with the sequencing results. In cluster 2 (n = 56), isolates showed different characteristics: 37 (66.07%) were β-d-galactosidase-positive, and based on 16S sequencing results, 36 (64.29%) were identified correctly as Strep. uberis using biochemical methods. Identification success in this group differed significantly between routine diagnosis and MRAM application: MRAM agreed with sequencing results in 47 isolates (83.93%). To identify Strep. uberis and differentiate it from other lactic acid bacteria in routine diagnosis, we suggest using catalase reaction, hemolysis, esculin hydrolysis, and growth on enterococci agar. Isolates that show a typical biochemical profile can be identified satisfactorily with these tests. For Strep. uberis isolates with divergent patterns, application of MRAM as a follow-up test increased the diagnostic accuracy to 94.64%.

Immune profiling of experimental murine mastitis reveals conserved response to mammary pathogenic Escherichia coli, Mycoplasma bovis, and Streptococcus uberis

“…Bacteriological examination was performed according to National Mastitis Council guidelines (Shwimmer et al, 2007). Strains identification was further validated using 16S rRNA PCR analysis and sequencing as previously described (Wald et al, 2017). Strains were cryopreserved in 20% glycerol at −80°C until further processing.…”

Comparison of the population structure of Streptococcus uberis mastitis isolates from Austrian small-scale dairy farms and a Slovakian large-scale farm

“…All field isolates were confirmed as Strep. uberis phenotypically and with molecular tools (Wald et al, 2017), and the isolates were further classified by genetic fingerprinting (PFGE) and sequence-based analysis of housekeeping genes (MLST). Antimicrobial susceptibilities were estimated for persistent and sporadic Strep.…”

“…The samples were either sent to the laboratory for bacteriological examinations or obtained during herd visits. Biochemical characterizations and confirmation with 16S rRNA gene sequence analysis are described elsewhere (Wald et al, 2017). The partial rRNA gene sequence data have been deposited in the GenBank database under accession number KX389930 to KX390233 (https: / / www .ncbi .nlm .nih .gov/ genbank/ ).…”

16S ribosomal RNA-depletion PCR and its application in cause analysis of yogurt package shrinkage

“…A major problem in microbial detection is the elimination of interference by the bacteria of yogurt starter culture and the identification of unintended bacterial contaminants. High-throughput sequencing based on the 16S rRNA gene has been widely applied in the study of the composition of microorganisms (Hou et al, 2015;Wald et al, 2017;Chen et al, 2019;Catozzi et al, 2020); nonetheless, most of the detected sequences are from fermentative bacteria. In addition, due to the low abundance of unintended bacterial contaminants, it is necessary to increase sequencing depth and quantity, which in turn increases detection costs.…”