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      Microbiota of Cow’s Milk; Distinguishing Healthy, Sub-Clinically and Clinically Diseased Quarters

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          Abstract

          The objective of this study was to use pyrosequencing of the 16S rRNA genes to describe the microbial diversity of bovine milk samples derived from clinically unaffected quarters across a range of somatic cell counts (SCC) values or from clinical mastitis, culture negative quarters. The obtained microbiota profiles were used to distinguish healthy, subclinically and clinically affected quarters. Two dairy farms were used for the collection of milk samples. A total of 177 samples were used. Fifty samples derived from healthy, culture negative quarters with a SCC of less than 20,000 cells/ml (group 1); 34 samples derived from healthy, culture negative quarters, with a SCC ranging from 21,000 to 50,000 cells/ml (group 2); 26 samples derived from healthy, culture negative quarters with a SCC greater than 50,000 cells/ml (group 3); 34 samples derived from healthy, culture positive quarters, with a SCC greater than 400,000 (group 4, subclinical); and 33 samples derived from clinical mastitis, culture negative quarters (group 5, clinical). Bacterial DNA was isolated from these samples and the 16S rRNA genes were individually amplified and pyrosequenced. All samples analyzed revealed great microbial diversity. Four bacterial genera were present in every sample obtained from healthy quarters ( Faecalibacterium spp., unclassified Lachnospiraceae, Propionibacterium spp. and Aeribacillus spp.). Discriminant analysis models showed that samples derived from healthy quarters were easily discriminated based on their microbiota profiles from samples derived from clinical mastitis, culture negative quarters; that was also the case for samples obtained from different farms. Staphylococcus spp. and Streptococcus spp. were among the most prevalent genera in all groups while a general multivariable linear model revealed that Sphingobacterium and Streptococcus prevalences were associated with increased 10 log SCC. Conversely, Nocardiodes and Paenibacillus were negatively correlated, and a higher percentage of the genera was associated with a lower 10 log SCC.

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          DECIPHER, a search-based approach to chimera identification for 16S rRNA sequences.

          DECIPHER is a new method for finding 16S rRNA chimeric sequences by the use of a search-based approach. The method is based upon detecting short fragments that are uncommon in the phylogenetic group where a query sequence is classified but frequently found in another phylogenetic group. The algorithm was calibrated for full sequences (fs_DECIPHER) and short sequences (ss_DECIPHER) and benchmarked against WigeoN (Pintail), ChimeraSlayer, and Uchime using artificially generated chimeras. Overall, ss_DECIPHER and Uchime provided the highest chimera detection for sequences 100 to 600 nucleotides long (79% and 81%, respectively), but Uchime's performance deteriorated for longer sequences, while ss_DECIPHER maintained a high detection rate (89%). Both methods had low false-positive rates (1.3% and 1.6%). The more conservative fs_DECIPHER, benchmarked only for sequences longer than 600 nucleotides, had an overall detection rate lower than that of ss_DECIPHER (75%) but higher than those of the other programs. In addition, fs_DECIPHER had the lowest false-positive rate among all the benchmarked programs (<0.20%). DECIPHER was outperformed only by ChimeraSlayer and Uchime when chimeras were formed from closely related parents (less than 10% divergence). Given the differences in the programs, it was possible to detect over 89% of all chimeras with just the combination of ss_DECIPHER and Uchime. Using fs_DECIPHER, we detected between 1% and 2% additional chimeras in the RDP, SILVA, and Greengenes databases from which chimeras had already been removed with Pintail or Bellerophon. DECIPHER was implemented in the R programming language and is directly accessible through a webpage or by downloading the program as an R package (http://DECIPHER.cee.wisc.edu).
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            Next Generation Sequencing to Define Prokaryotic and Fungal Diversity in the Bovine Rumen

            A combination of Sanger and 454 sequences of small subunit rRNA loci were used to interrogate microbial diversity in the bovine rumen of 12 cows consuming a forage diet. Observed bacterial species richness, based on the V1–V3 region of the 16S rRNA gene, was between 1,903 to 2,432 species-level operational taxonomic units (OTUs) when 5,520 reads were sampled per animal. Eighty percent of species-level OTUs were dominated by members of the order Clostridiales, Bacteroidales, Erysipelotrichales and unclassified TM7. Abundance of Prevotella species varied widely among the 12 animals. Archaeal species richness, also based on 16S rRNA, was between 8 and 13 OTUs, representing 5 genera. The majority of archaeal OTUs (84%) found in this study were previously observed in public databases with only two new OTUs discovered. Observed rumen fungal species richness, based on the 18S rRNA gene, was between 21 and 40 OTUs with 98.4–99.9% of OTUs represented by more than one read, using Good’s coverage. Examination of the fungal community identified numerous novel groups. Prevotella and Tannerella were overrepresented in the liquid fraction of the rumen while Butyrivibrio and Blautia were significantly overrepresented in the solid fraction of the rumen. No statistical difference was observed between the liquid and solid fractions in biodiversity of archaea and fungi. The survey of microbial communities and analysis of cross-domain correlations suggested there is a far greater extent of microbial diversity in the bovine rumen than previously appreciated, and that next generation sequencing technologies promise to reveal novel species, interactions and pathways that can be studied further in order to better understand how rumen microbial community structure and function affects ruminant feed efficiency, biofuel production, and environmental impact.
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              Survey of the incidence and aetiology of mastitis on dairy farms in England and Wales.

              A survey of clinical and subclinical mastitis was carried out on 97 dairy farms in England and Wales, selected at random from members of a national milk recording scheme. The farmers were asked to collect aseptic milk samples from five consecutive cases of clinical mastitis and from five quarters with high somatic cell counts using a defined protocol, and they completed a questionnaire that included information on the cows sampled, the herd and the history of mastitis in the herd. The samples were collected throughout the year. The mean incidence of clinical mastitis was 47 cases per 100 cows per year (estimated from historic farm records) and 71 cases per 100 cows per year (estimated from the samples collected). Streptococcus uberis and Escherichia coli were isolated in pure culture from 23.5 per cent and 19.8 per cent, respectively, of the clinical samples; 26.5 per cent of the clinical samples produced no growth. The most common isolates from the samples with high cell counts were coagulase-negative staphylococci (15 per cent), S uberis (14 per cent) and Corynebacterium species (10 per cent). Staphylococcus aureus and coagulase-positive staphylococci together accounted for 10 per cent of the samples with high somatic cell counts; 39 per cent produced no bacterial growth.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                20 January 2014
                : 9
                : 1
                : e85904
                Affiliations
                [1]Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America
                University of Alberta, Canada
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GO YHS RCB. Performed the experiments: GO MLB EM RER CF VSM AGT CS. Analyzed the data: GO YHS RCB. Contributed reagents/materials/analysis tools: YHS RCB. Wrote the paper: GO YHS RCB.

                Article
                PONE-D-13-29048
                10.1371/journal.pone.0085904
                3896433
                24465777
                aaf6c205-18f2-4459-a81a-58b16d15b7d0
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 July 2013
                : 8 December 2013
                Page count
                Pages: 11
                Funding
                No current external funding sources for this study.
                Categories
                Research Article
                Agriculture
                Animal Management
                Animal Production
                Biology
                Ecology
                Microbial Ecology
                Microbiology
                Microbial Ecology
                Veterinary Science
                Animal Management
                Animal Production
                Animal Types
                Large Animals
                Veterinary Microbiology

                Uncategorized
                Uncategorized

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