Bacterial Community Profiling of Milk Samples as a Means to Understand Culture-Negative Bovine Clinical Mastitis

Inflammation and infection of bovine mammary glands, commonly known as mastitis, imposes significant losses each year in the dairy industry worldwide. While several different bacterial species have been identified as causative agents of mastitis, many clinical mastitis cases remain culture negative, even after enrichment for bacterial growth. To understand the basis for this increasingly common phenomenon, the composition of bacterial communities from milk samples was analyzed using culture independent pyrosequencing of amplicons of 16S ribosomal RNA genes (16S rDNA). Comparisons were made of the microbial community composition of culture negative milk samples from mastitic quarters with that of non-mastitic quarters from the same animals. Genomic DNA from culture-negative clinical and healthy quarter sample pairs was isolated, and amplicon libraries were prepared using indexed primers specific to the V1–V2 region of bacterial 16S rRNA genes and sequenced using the Roche 454 GS FLX with titanium chemistry. Evaluation of the taxonomic composition of these samples revealed significant differences in the microbiota in milk from mastitic and healthy quarters. Statistical analysis identified seven bacterial genera that may be mainly responsible for the observed microbial community differences between mastitic and healthy quarters. Collectively, these results provide evidence that cases of culture negative mastitis can be associated with bacterial species that may be present below culture detection thresholds used here. The application of culture-independent bacterial community profiling represents a powerful approach to understand long-standing questions in animal health and disease

Acinetobacter rudis sp. nov., isolated from raw milk and raw wastewater

Two bacterial strains, G30T and A1PC16, isolated respectively from raw milk and raw wastewater, were characterized using a polyphasic approach. Chemotaxonomic characterization supported the inclusion of these strains in the genus Acinetobacter, with Q-8 and Q-9 as the major respiratory quinones, genomic DNA G+C contents within the range observed for this genus(38–47 mol%) and C16 : 0, C18 : 1v9c and C16 : 1v7c/iso-C15 : 0 2-OH as the predominant fatty acids. The observation of 16S rRNA gene sequence similarity lower than 97% with other Acinetobacter species with validly published names led to the hypothesis that these isolates could represent a novel species. This hypothesis was supported by comparative analysis of partial sequences of the genes rpoB and gyrB, which showed that strains G30T and A1PC16 did not cluster with any species with validly published names, forming a distinct lineage. DNA–DNA hybridizations confirmed that the two strains were members of the same species, which could be distinguished from their congeners by several phenotypic characteristics. On the basis of these arguments, it is proposed that strains G30T and A1PC16 represent a novel species, for which the name Acinetobacter rudis sp. nov. is proposed. The type strain is strain G30T (5LMG 26107T 5CCUG 57889T 5DSM 24031T 5CECT 7818T).info:eu-repo/semantics/publishedVersio

Evaluation of Pseudomonas spp. through O2 and CO2 headspace analysis

This article proposes an approach to determine the level of Pseudomonas spp. in milk, based on the evaluation of the content of oxygen and carbon dioxide produced in the headspace of sealed vials; the research was divided into two phases: model building and preliminary validation. Three different strains of Pseudomonas spp., Ps. putida (wild strain) and Ps. fluorescens (wild and collection isolates), were used as targets. Data of CO2 and O2 were modelled through a modified positive (CO2) or a negative Gompertz equation (O2) to estimate the Minimum Detection Time (MDT), defined as the time to attain 3% of CO2 (MDT1) or a decrease in the content of O2 by 3% (MDT2). Then, MDT1 and MDT2 were submitted to a linear regression procedure, using cell concentration as independent variable; the correlations 'MDT1/cell concentration' and 'MDT2/cell concentration' showed high determination coefficients (>0.983). Moreover, the regression procedure pointed out that both MDT1 and MDT2 decreased by ca. 3 h for an increase in cell count of 1 log cfu mL-1. Preliminary validation in milk pointed out that the error associated with the regression line 'MDT2/cell concentration' was below 5%. © 2013 Institute of Food Science and Technology.[...