Dataset Information

Vaginal Microbiome Characterization of Nellore Cattle Using Metagenomic Analysis.

ABSTRACT: Understanding of microbial communities inhabiting cattle vaginal tract may lead to a better comprehension of bovine physiology and reproductive health being of great economic interest. Up to date, studies involving cattle microbiota are focused on the gastrointestinal tract, and little is known about the vaginal microbiota. This study aimed to investigate the vaginal microbiome in Nellore cattle, heifers and cows, pregnant and non-pregnant, using a culture independent approach. The main bacterial phyla found were Firmicutes (~40-50%), Bacteroidetes (~15-25%) and Proteobacteria (~5-25%), in addition to ~10-20% of non-classified bacteria. 45-55% of the samples were represented by only ten OTUs: Aeribacillus, Bacteroides, Clostridium, Ruminococcus, Rikenella, Alistipes, Bacillus, Eubacterium, Prevotella and non-classified bacteria. Interestingly, microbiota from all 20 animals could be grouped according to the respiratory metabolism of the main OTUs found, creating three groups of vaginal microbiota in cattle. Archaeal samples were dominated by the Methanobrevibacter genus (Euryarchaeota, ~55-70%). Ascomycota was the main fungal phylum (~80-95%) and Mycosphaerella the most abundant genus (~70-85%). Hormonal influence was not clear, but a tendency for the reduction of bacterial and increase of archaeal populations in pregnant animals was observed. Eukaryotes did not vary significantly between pregnant and non-pregnant animals, but tended to be more abundant on cows than on heifers. The present work describes a great microbial variability in the vaginal community among the evaluated animals and groups (heifers and cows, pregnant and non-pregnant), which is significantly different from the findings previously reported using culture dependent methods, pointing out the need for further studies on this issue. The microbiome found also indicates that the vaginal colonization appears to be influenced by the gastrointestinal community.

SUBMITTER: Laguardia-Nascimento M

PROVIDER: S-EPMC4657983 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Vaginal Microbiome Characterization of Nellore Cattle Using Metagenomic Analysis.

Laguardia-Nascimento Mateus M Branco Kelly Moreira Grillo Ribeiro KM Gasparini Marcela Ribeiro MR Giannattasio-Ferraz Silvia S Leite Laura Rabelo LR Araujo Flávio Marcos Gomes FM Salim Anna Christina de Matos AC Nicoli Jacques Robert JR de Oliveira Guilherme Corrêa GC Barbosa-Stancioli Edel Figueiredo EF

PloS one 20151124 11

Understanding of microbial communities inhabiting cattle vaginal tract may lead to a better comprehension of bovine physiology and reproductive health being of great economic interest. Up to date, studies involving cattle microbiota are focused on the gastrointestinal tract, and little is known about the vaginal microbiota. This study aimed to investigate the vaginal microbiome in Nellore cattle, heifers and cows, pregnant and non-pregnant, using a culture independent approach. The main bacteria ...[more]

PMID: 26599789

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Project description:While current major national research efforts (i.e., the NIH Human Microbiome Project) will enable comprehensive metagenomic characterization of the adult human microbiota, how and when these diverse microbial communities take up residence in the host and during reproductive life are unexplored at a population level. Because microbial abundance and diversity might differ in pregnancy, we sought to generate comparative metagenomic signatures across gestational age strata. DNA was isolated from the vagina (introitus, posterior fornix, midvagina) and the V5V3 region of bacterial 16S rRNA genes were sequenced (454FLX Titanium platform). Sixty-eight samples from 24 healthy gravidae (18 to 40 confirmed weeks) were compared with 301 non-pregnant controls (60 subjects). Generated sequence data were quality filtered, taxonomically binned, normalized, and organized by phylogeny and into operational taxonomic units (OTU); principal coordinates analysis (PCoA) of the resultant beta diversity measures were used for visualization and analysis in association with sample clinical metadata. Altogether, 1.4 gigabytes of data containing >2.5 million reads (averaging 6,837 sequences/sample of 493 nt in length) were generated for computational analyses. Although gravidae were not excluded by virtue of a posterior fornix pH >4.5 at the time of screening, unique vaginal microbiome signature encompassing several specific OTUs and higher-level clades was nevertheless observed and confirmed using a combination of phylogenetic, non-phylogenetic, supervised, and unsupervised approaches. Both overall diversity and richness were reduced in pregnancy, with dominance of Lactobacillus species (L. iners crispatus, jensenii and johnsonii, and the orders Lactobacillales (and Lactobacillaceae family), Clostridiales, Bacteroidales, and Actinomycetales. This intergroup comparison using rigorous standardized sampling protocols and analytical methodologies provides robust initial evidence that the vaginal microbial 16S rRNA gene catalogue uniquely differs in pregnancy, with variance of taxa across vaginal subsite and gestational age.

Project description:BACKGROUND: Nellore cattle play an important role in beef production in tropical systems and there is great interest in determining if genomic selection can contribute to accelerate genetic improvement of production and fertility in this breed. We present the first results of the implementation of genomic prediction in a Bos indicus (Nellore) population. METHODS: Influential bulls were genotyped with the Illumina Bovine HD chip in order to assess genomic predictive ability for weight and carcass traits, gestation length, scrotal circumference and two selection indices. 685 samples and 320 238 single nucleotide polymorphisms (SNPs) were used in the analyses. A forward-prediction scheme was adopted to predict the genomic breeding values (DGV). In the training step, the estimated breeding values (EBV) of bulls were deregressed (dEBV) and used as pseudo-phenotypes to estimate marker effects using four methods: genomic BLUP with or without a residual polygenic effect (GBLUP20 and GBLUP0, respectively), a mixture model (Bayes C) and Bayesian LASSO (BLASSO). Empirical accuracies of the resulting genomic predictions were assessed based on the correlation between DGV and dEBV for the testing group. RESULTS: Accuracies of genomic predictions ranged from 0.17 (navel at weaning) to 0.74 (finishing precocity). Across traits, Bayesian regression models (Bayes C and BLASSO) were more accurate than GBLUP. The average empirical accuracies were 0.39 (GBLUP0), 0.40 (GBLUP20) and 0.44 (Bayes C and BLASSO). Bayes C and BLASSO tended to produce deflated predictions (i.e. slope of the regression of dEBV on DGV greater than 1). Further analyses suggested that higher-than-expected accuracies were observed for traits for which EBV means differed significantly between two breeding subgroups that were identified in a principal component analysis based on genomic relationships. CONCLUSIONS: Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions. Recurrent updates of the training population would be required to enable accurate prediction of the genetic merit of young animals. The technical feasibility of applying genomic prediction in a Bos indicus (Nellore) population was demonstrated. Further research is needed to permit cost-effective selection decisions using genomic information.

Project description:BackgroundBiogas production is an economically attractive technology that has gained momentum worldwide over the past years. Biogas is produced by a biologically mediated process, widely known as "anaerobic digestion." This process is performed by a specialized and complex microbial community, in which different members have distinct roles in the establishment of a collective organization. Deciphering the complex microbial community engaged in this process is interesting both for unraveling the network of bacterial interactions and for applicability potential to the derived knowledge.ResultsIn this study, we dissect the bioma involved in anaerobic digestion by means of high throughput Illumina sequencing (~51 gigabases of sequence data), disclosing nearly one million genes and extracting 106 microbial genomes by a novel strategy combining two binning processes. Microbial phylogeny and putative taxonomy performed using >400 proteins revealed that the biogas community is a trove of new species. A new approach based on functional properties as per network representation was developed to assign roles to the microbial species. The organization of the anaerobic digestion microbiome is resembled by a funnel concept, in which the microbial consortium presents a progressive functional specialization while reaching the final step of the process (i.e., methanogenesis). Key microbial genomes encoding enzymes involved in specific metabolic pathways, such as carbohydrates utilization, fatty acids degradation, amino acids fermentation, and syntrophic acetate oxidation, were identified. Additionally, the analysis identified a new uncultured archaeon that was putatively related to Methanomassiliicoccales but surprisingly having a methylotrophic methanogenic pathway.ConclusionThis study is a pioneer research on the phylogenetic and functional characterization of the microbial community populating biogas reactors. By applying for the first time high-throughput sequencing and a novel binning strategy, the identified genes were anchored to single genomes providing a clear understanding of their metabolic pathways and highlighting their involvement in anaerobic digestion. The overall research established a reference catalog of biogas microbial genomes that will greatly simplify future genomic studies.

Dataset Information

Vaginal Microbiome Characterization of Nellore Cattle Using Metagenomic Analysis.

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Vaginal Microbiome Characterization of Nellore Cattle Using Metagenomic Analysis.

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