High throughput whole rumen metagenome profiling using untargeted massively parallel sequencing

Abstract

BackgroundVariation of microorganism communities in the rumen of cattle (Bos taurus) is of great interest because of possible links to economically or environmentally important traits, such as feed conversion efficiency or methane emission levels. The resolution of studies investigating this variation may be improved by utilizing untargeted massively parallel sequencing (MPS), that is, sequencing without targeted amplification of genes. The objective of this study was to develop a method which used MPS to generate “rumen metagenome profiles”, and to investigate if these profiles were repeatable among samples taken from the same cow. Given faecal samples are much easier to obtain than rumen fluid samples; we also investigated whether rumen metagenome profiles were predictive of faecal metagenome profiles.ResultsRather than focusing on individual organisms within the rumen, our method used MPS data to generate quantitative rumen micro-biome profiles, regardless of taxonomic classifications. The method requires a previously assembled reference metagenome. A number of such reference metagenomes were considered, including two rumen derived metagenomes, a human faecal microflora metagenome and a reference metagenome made up of publically available prokaryote sequences. Sequence reads from each test sample were aligned to these references. The “rumen metagenome profile” was generated from the number of the reads that aligned to each contig in the database. We used this method to test the hypothesis that rumen fluid microbial community profiles vary more between cows than within multiple samples from the same cow. Rumen fluid samples were taken from three cows, at three locations within the rumen. DNA from the samples was sequenced on the Illumina GAIIx. When the reads were aligned to a rumen metagenome reference, the rumen metagenome profiles were repeatable (P < 0.00001) by cow regardless of location of sampling rumen fluid. The repeatability was estimated at 9%, albeit with a high standard error, reflecting the small number of animals in the study. Finally, we compared rumen microbial profiles to faecal microbial profiles. Our hypothesis, that there would be a stronger correlation between faeces and rumen fluid from the same cow than between faeces and rumen fluid from different cows, was not supported by our data (with much greater significance of rumen versus faeces effect than animal effect in mixed linear model).ConclusionsWe have presented a simple and high throughput method of metagenome profiling to assess the similarity of whole metagenomes, and illustrated its use on two novel datasets. This method utilises widely used freeware. The method should be useful in the exploration and comparison of metagenomes.

Microbial biodiversity of the liquid fraction of rumen content from lactating cows

“…Mosoni et al, 2011). The advent of Next-Generation Sequencing (NGS) offers a breakthrough opportunity to go deeper into this knowledge (Woelders et al, 2011) and new rumen research in this field has recently been published (Brulc et al, 2009;Kong et al, 2010;Jami and Mizrahi, 2012;Ross et al, 2012). As a first attempt to depict the variation of the microbiome composition subsequent to yeast supplementation, NGS was applied to the rumen fluid fraction.…”

Metatranscriptomic Profiling Reveals Linkages between the Active Rumen Microbiome and Feed Efficiency in Beef Cattle

“…It is noticeable that only a small fraction of assembled mRNA contigs (22.4% in the data sets) could be mapped to the KEGG database, and the functional analysis only focused on these annotated mRNAs in the present study. Similarly, Ross et al (58) reported that only 6.0% of metagenomic reads could be mapped to existing databases. A low ratio of mapped reads highlights that the majority of rumen microbial genes have not been deposited in current databases and that bovine rumen microbial functions have yet to be sufficiently addressed.…”

Stochastic changes over time and not founder effects drive cage effects in microbial community assembly in a mouse model

“…Mixed linear models have many advantages including a solid theoretical base (Raudenbush and Bryk, 2002;Smyth, 2004), wide utilization in the literature (Brown et al, 2011;Listgarten et al;Ross et al, 2012;Vilhjalmsson and Nordborg, 2013) and robust implementations in statistical packages such as R and SAS. However, mixed linear models impose an additional set of parametric assumptions over canonical linear models.…”