Dataset Information

Identification of diversity-generating retroelements in human microbiomes.

ABSTRACT: Diversity-generating retroelements (DGRs) are a unique family of retroelements that confer selective advantages to their hosts by accelerating the evolution of target genes through a specialized, error-prone, reverse transcription process. First identified in a Bordetella phage (BPP-1), which mediates the phage tropism specificity by generating variability in an involved gene, DGRs were predicted to be present in a larger collection of viral and bacterial species. A minimal DGR system is comprised of a reverse transcriptase (RTase) gene, a template sequence (TR) and a variable region (VR) within a target gene. We developed a computational tool, DGRscan, to allow either de novo identification (based on the prediction of potential template-variable region pairs) or similarity-based searches of DGR systems using known template sequences as the reference. The application of DGRscan to the human microbiome project (HMP) datasets resulted in the identification of 271 non-redundant DGR systems, doubling the size of the collection of known DGR systems. We further identified a large number of putative target genes (651, which share no more than 90% sequence identity at the amino acid level) that are potentially under diversification by the DGR systems. Our study provides the first survey of the DGR systems in the human microbiome, showing that the DGR systems are frequently found in human-associated bacterial communities, although they are of low incidence in individual genomes. Our study also provides functional clues for a large number of genes (reverse transcriptases and target genes) that were previously annotated as proteins of unknown functions or nonspecific functions.

SUBMITTER: Ye Y

PROVIDER: S-EPMC4159848 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Identification of diversity-generating retroelements in human microbiomes.

Ye Yuzhen Y

International journal of molecular sciences 20140815 8

Diversity-generating retroelements (DGRs) are a unique family of retroelements that confer selective advantages to their hosts by accelerating the evolution of target genes through a specialized, error-prone, reverse transcription process. First identified in a Bordetella phage (BPP-1), which mediates the phage tropism specificity by generating variability in an involved gene, DGRs were predicted to be present in a larger collection of viral and bacterial species. A minimal DGR system is compris ...[more]

PMID: 25196521

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Project description:The human microbiome contributes significantly to the genetic content of the human body. Genetic and environmental factors help shape the microbiome, and as such, the microbiome can be unique to an individual. Previous studies have demonstrated the potential to use microbiome profiling for forensic applications; however, a method has yet to identify stable features of skin microbiomes that produce high classification accuracies for samples collected over reasonably long time intervals. A novel approach is described here to classify skin microbiomes to their donors by comparing two feature types: Propionibacterium acnes pangenome presence/absence features and nucleotide diversities of stable clade-specific markers. Supervised learning was used to attribute skin microbiomes from 14 skin body sites from 12 healthy individuals sampled at three time points over a >2.5-year period with accuracies of up to 100% for three body sites. Feature selection identified a reduced subset of markers from each body site that are highly individualizing, identifying 187 markers from 12 clades. Classification accuracies were compared in a formal model testing framework, and the results of this analysis indicate that learners trained on nucleotide diversity perform significantly better than those trained on presence/absence encodings. This study used supervised learning to identify individuals with high accuracy and associated stable features from skin microbiomes over a period of up to almost 3 years. These selected features provide a preliminary marker panel for future development of a robust and reproducible method for skin microbiome profiling for forensic human identification.IMPORTANCE A novel approach is described to attribute skin microbiomes, collected over a period of >2.5 years, to their individual hosts with a high degree of accuracy. Nucleotide diversities of stable clade-specific markers with supervised learning were used to classify skin microbiomes from a particular individual with up to 100% classification accuracy for three body sites. Attribute selection was used to identify 187 genetic markers from 12 clades which provide the greatest differentiation of individual skin microbiomes from 14 skin sites. This study performs skin microbiome profiling from a supervised learning approach and obtains high classification accuracy for samples collected from individuals over a relatively long time period for potential application to forensic human identification.

Dataset Information

Identification of diversity-generating retroelements in human microbiomes.

Publications

Identification of diversity-generating retroelements in human microbiomes.

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