High throughput whole rumen metagenome profiling using untargeted massively parallel sequencing - PubMed (original) (raw)
High throughput whole rumen metagenome profiling using untargeted massively parallel sequencing
Elizabeth M Ross et al. BMC Genet. 2012.
Abstract
Background: Variation 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.
Results: Rather 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).
Conclusions: We 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.
Figures
Figure 1
Flowchart of method. A representation of the steps involved in the analysis method. Bootstrap support is used to assess the required sequence depth.
Figure 2
Hierarchical clustering: between animal variation. Hierarchical clustering based on alignments of sequence reads to a) GreenGenes database, b) NCBI Prokaryotes database, c) Soil database, d) Human Stool database, e) DPI_rumen database, f) JGI_rumen database. The distance matrix method used was Canberra. Bootstrap (bp) and approximately unbias (au) values were generated using Pvclust [20] with 1000 iterations.
Figure 3
Hierarchical clustering: sequencing depth effect. Hierarchical clustering based on alignments of sequence reads to JGI_rumen database, at differing sequencing depths. The distance matrix method used was Canberra. Bootstrap (bp) and approximately unbias (au) values were generated using Pvclust [20] with 1000 iterations.
Figure 4
Differentially represented organisms in rumen fluid and faeces. The number of sequence read alignments to each contig in three databases were compared between faeces and rumen fluid using a _t_-test. BLASTn was then used to assign contigs that were significantly differently represented (p < 0.001) between rumen fluid and faeces to a taxon. The BLAST output was displayed using MEGAN[22].
Figure 5
Hierarchical clustering: comparing faeces to rumen samples. Hierarchical clustering of rumen and faecal samples from the same animals. Reads were aligned to a) DPI_rumen database, b) JGI_rumen database, c) Human Stool database. The distance matrix method used was Canberra. Bootstrap (bp) and approximately unbias (au) values were generated using Pvclust [20] with 1000 iterations.
Figure 6
Heatmap of the correlation between rumen fluid - faeces pairs. Samples were correlated with each other to asses the degree of similarity between samples. a) Correlation in the number of reads assigned to the second level KEGG pathway using BLASTx and MEGAN; b-d) correlation in the number of reads assigned to each contig in the database. The results are displayed as a heatmap with the correlation values shown. The correlation for rumen fluid - faeces pairs from the same animal is highlighted.
References
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- Guan LL, Nkrumah JD, Basarab JA, Moore SS. Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle's feed efficiency. FEMS Microbiol Lett. 2008;288:85–91. -PubMed
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