The subgingival microbial community of feline periodontitis and gingivostomatitis: characterization and comparison between diseased and healthy cats.

Periodontitis is a common and important health problem in domestic cats. The subgingival microbiota of cats diagnosed with chronic periodontitis (CP), aggressive periodontitis (AP), and feline chronic gingivostomatitis (FCGS) are not well characterized. Thus, the aim of the present study was to characterize and compare the periodontal microbiota of periodontally healthy cats versus cats diagnosed with CP, AP, and FCGS by using next-generation sequencing. In total, 44 domestic cats were enrolled, and 139 subgingival samples were subjected to 16S rRNA gene sequencing to investigate the microbiota composition of each periodontal group evaluated. Our results identified several key genera previously described in periodontal disease (e.g. Treponema and Filifactor) and in the oral microbiota (e.g. Moraxella and Capnocytophaga) of healthy cats. Phylogenetic beta diversity analysis showed that the microbiota of periodontally healthy cats were distinguishable from diseased cats. Even though most of the genera known to be associated with periodontal disease were also identified in healthy cats, they were present at significantly lower relative abundance. Remarkably, alpha diversity was found to be higher in the disease groups compared to healthy animals. These results suggest a pathological mechanism involving opportunistic behavior. Our findings corroborate those in the current literature regarding the complexity of the subgingival microbiota of the domestic cat and reveal both differences and similarities among periodontally healthy and diseased cats.

The Bos taurus maternal microbiome: Role in determining the progeny early-life upper respiratory tract microbiome and health.

Natural transference of maternal microbes to the neonate, especially at birth via the vaginal canal, has recently been recognized in humans and cows; however, its microbial influence on calf health has not yet been documented. We compared the bacterial communities in vaginal and fecal samples from 81 pregnant dairy cows versus those in nasopharyngeal and fecal samples collected at 3, 14 and 35 days of life from their respective progeny. The microbiota of the calf upper respiratory tract (URT), regardless of calf age, was found to be highly similar to the maternal vaginal microbiota. Calf fecal microbiota clustered closely to the maternal fecal microbiota, progressing toward an adult-like state over the first 35 days when relative abundances of taxa were considered. Sixty-four, 65 and 87% of the detected OTUs were shared between cow and calf fecal microbiota at days 3, 14 and 35 respectively, whereas 73, 76 and 87% were shared between maternal vaginal microbiome and calf URT microbiota at days 3, 14 and 35, respectively. Bacteroidetes, Ruminococcus, Clostridium, and Blautia were the top four genera identified in maternal and calf fecal samples. Mannheimia, Moraxella, Bacteroides, Streptococcus and Pseudomonas were the top five genera identified in maternal vaginal and calf URT samples. Mannheimia was relatively more abundant in the vaginal microbiota of cows whose progeny were diagnosed with respiratory and middle ear disease. Our results indicate that maternal vaginal microbiota potentially influences the initial bacterial colonization of the calf URT, and that might have an important impact on the health of the calf respiratory tract and middle ear.

Evaluation of milk sample fractions for characterization of milk microbiota from healthy and clinical mastitis cows.

Amplicon sequencing technique has been increasingly applied to the clinical setting as a sensitive diagnostic tool. Therefore, it is of great importance to develop a DNA extraction method that accurate isolates DNA from complex host-associated microbiota. Given the multifactorial etiology of clinical mastitis and the diversified lifestyle of bacterial species harboring in milk, here four distinct milk sample fractions: raw whole milk, milk fat, casein-pellet, and casein-pellet + fat from healthy cows and cows with clinical mastitis, were subjected to bead-beating DNA extraction, followed by high-throughput sequencing. We aimed to identify the best approach for characterization of the milk microbiota and detection of mastitis pathogens (Klebsiella spp., Streptococcus spp. and Escherichia coli). DNA from each milk fraction tested was extracted by two commercial kits, which include physical, mechanical and chemical lysis; in total 280 DNA samples from 35 cows were analyzed. Milk-health-status were categorized into four groups (healthy group; E. coli-mastitis group; Klebsiella spp.-mastitis group; and Streptococcus spp.-mastitis group). Bacterial phyla and families were described for each milk-health-status group across milk sample fractions and DNA extraction kits. For the mastitis groups the relative abundance of f__Enterobacteriaceae and f__Streptococcaceae were compared to determine the efficacy of procedures in detecting the mastitis pathogens. The four milk fractions used allowed efficiently and uniformly detection of the causative agent of mastitis. Only 27% of the families detected in healthy milk were shared among the samples extracted from all fractions of milk samples; followed by 3, 4, and 12% for the samples from E. coli-mastitis, Klebsiella spp.-mastitis and Streptococcus spp-mastitis, respectively. However, the shared families comprised a mean relative abundance greater than 85%, regardless of milk-health-status, milk fraction and DNA isolation method. Taxonomic data at the family level showed that sequences from mastitis milk samples cultured positive for E. coli and Klebsiella spp. were predominantly affiliated with f__Enterobacteriaceae, while for Streptococcus spp. were dominated by f__Streptococcacea, followed by f__Pseudomonadaceae and f__Enterococcaceae. Microbial community analysis revealed that most of the microbial community composition corresponded to milk bacterial species irrespective of the DNA isolation method and milk fraction evaluated.

Lameness Prevalence and Risk Factors in Large Dairy Farms in Upstate New York. Model Development for the Prediction of Claw Horn Disruption Lesions.

The main objectives of this prospective cohort study were a) to describe lameness prevalence at drying off in large high producing New York State herds based on visual locomotion score (VLS) and identify potential cow and herd level risk factors, and b) to develop a model that will predict the probability of a cow developing claw horn disruption lesions (CHDL) in the subsequent lactation using cow level variables collected at drying off and/or available from farm management software. Data were collected from 23 large commercial dairy farms located in upstate New York. A total of 7,687 dry cows, that were less than 265 days in gestation, were enrolled in the study. Farms were visited between May 2012 and March 2013, and cows were assessed for body condition score (BCS) and VLS. Data on the CHDL events recorded by the farm employees were extracted from the Dairy-Comp 305 database, as well as information regarding the studied cows' health events, milk production, and reproductive records throughout the previous and subsequent lactation period. Univariable analyses and mixed multivariable logistic regression models were used to analyse the data at the cow level. The overall average prevalence of lameness (VLS > 2) at drying off was 14%. Lactation group, previous CHDL, mature equivalent 305-d milk yield (ME305), season, BCS at drying off and sire PTA for strength were all significantly associated with lameness at the drying off (cow-level). Lameness at drying off was associated with CHDL incidence in the subsequent lactation, as well as lactation group, previous CHDL and ME305. These risk factors for CHDL in the subsequent lactation were included in our predictive model and adjusted predicted probabilities for CHDL were calculated for all studied cows. ROC analysis identified an optimum cut-off point for these probabilities and using this cut-off point we could predict CHDL incidence in the subsequent lactation with an overall specificity of 75% and sensitivity of 59%. Using this approach, we would have detected 33% of the studied population as being at risk, eventually identifying 59% of future CHDL cases. Our predictive model could help dairy producers focusing their efforts on CHDL reduction by implementing aggressive preventive measures for high risk cows.