Inter- and intra-specific pan-genomes of Borrelia burgdorferi sensu lato: genome stability and adaptive radiation.

BACKGROUND: Lyme disease is caused by spirochete bacteria from the Borrelia burgdorferi sensu lato (B. burgdorferi s.l.) species complex. To reconstruct the evolution of B. burgdorferi s.l. and identify the genomic basis of its human virulence, we compared the genomes of 23 B. burgdorferi s.l. isolates from Europe and the United States, including B. burgdorferi sensu stricto (B. burgdorferi s.s., 14 isolates), B. afzelii (2), B. garinii (2), B. "bavariensis" (1), B. spielmanii (1), B. valaisiana (1), B. bissettii (1), and B. "finlandensis" (1). RESULTS: Robust B. burgdorferi s.s. and B. burgdorferi s.l. phylogenies were obtained using genome-wide single-nucleotide polymorphisms, despite recombination. Phylogeny-based pan-genome analysis showed that the rate of gene acquisition was higher between species than within species, suggesting adaptive speciation. Strong positive natural selection drives the sequence evolution of lipoproteins, including chromosomally-encoded genes 0102 and 0404, cp26-encoded ospC and b08, and lp54-encoded dbpA, a07, a22, a33, a53, a65. Computer simulations predicted rapid adaptive radiation of genomic groups as population size increases. CONCLUSIONS: Intra- and inter-specific pan-genome sizes of B. burgdorferi s.l. expand linearly with phylogenetic diversity. Yet gene-acquisition rates in B. burgdorferi s.l. are among the lowest in bacterial pathogens, resulting in high genome stability and few lineage-specific genes. Genome adaptation of B. burgdorferi s.l. is driven predominantly by copy-number and sequence variations of lipoprotein genes. New genomic groups are likely to emerge if the current trend of B. burgdorferi s.l. population expansion continues.

Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

MOTIVATION: Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. RESULTS: By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. AVAILABILITY: The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/. CONTACT: zliu@umm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.