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Additional file 1. Supplementary Tables, Figures and Text.

Additional file 4. Review history.

Additional file 16: Figure S9. Oligotyping entropy profile for OTU41. X axis is the position in the alignment to OTU41 consensus sequence. Y axis is the Shannon Entropy of each position in the sequences that were clustered into OTU41. Position 183 was the only position in the OTU41 alignment that was above an entropy of 0.4. The background highlights the major oligotypes based on the top 6 most entropic positions, which represent the G and A alleles at position 183.

Additional file 9: Figure S2. Analysis of WMS data confirms that cranberry beverage consumption does not change overall gut microbiome composition. a) WMS-based taxonomic profiles displaying the phylum-level composition of the microbial population indicate that the composition did not change over time or due to cranberry consumption. The sample order was sorted by the relative abundance of Firmicutes. b) The species richness, based on WMS data, did not change significantly with cranberry beverage consumption. c) A comparison of all samples at the 16S OTU level, using principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarities, indicated that the samples from the cranberry cohort did not cluster into a specific group, and the trajectories from week 0 to week 24 (shown by arrows) were scattered, indicating no common shift in microbial composition after 24 weeks of cranberry or placebo treatment. The first two principal components (PCo1 and PCo2) accounted for 14.8 and 10.8% of the variability, respectively.

Additional file 8: Figure S1. Additional analysis of diversity using 16S data. a) Shannon diversity index, a measure of ɑ-diversity, is not significantly different between cranberry and placebo cohort (p > 0.5) b) β-diversity of longitudinal samples from the same subject is significantly lower than that between samples from different subjects (p = 1.6 × 10− 20); c) β-diversity of samples from the placebo cohort versus those from the cranberry cohort do not differ significantly; p = 0.51).

Metagenomic sequencing is a powerful tool for examining the diversity and complexity of microbial communities. Most widely used tools for taxonomic profiling of metagenomic sequence data allow for a species-level overview of the composition. However, individual strains within a species can differ greatly in key genotypic and phenotypic characteristics, such as drug resistance, virulence and growth rate. Therefore, the ability to resolve microbial communities down to the level of individual strains within a species is critical to interpreting metagenomic data for clinical and environmental applications, where identifying a particular strain, or tracking a particular strain across a set of samples, can help aid in clinical diagnosis and treatment, or in characterizing yet unstudied strains across novel environmental locations. Recently published approaches have begun to tackle the problem of resolving strains within a particular species in metagenomic samples. In this review, we present an overview of these new algorithms and their uses, including methods based on assembly reconstruction and methods operating with or without a reference database. While existing metagenomic analysis methods show reasonable performance at the species and higher taxonomic levels, identifying closely related strains within a species presents a bigger challenge, due to the diversity of databases, genetic relatedness, and goals when conducting these analyses. Selection of which metagenomic tool to employ for a specific application should be performed on a case-by case basis as these tools have strengths and weaknesses that affect their performance on specific tasks. A comprehensive benchmark across different use case scenarios is vital to validate performance of these tools on microbial samples. Because strain-level metagenomic analysis is still in its infancy, development of more fine-grained, high-resolution algorithms will continue to be in demand for the future.

Modern industrial agriculture depends on high-density cultivation of genetically similar crop plants, creating favorable conditions for the emergence of novel pathogens with increased fitness in managed compared with ecologically intact settings. Here, we present the genome sequence of six strains of the cucurbit bacterial wilt pathogen Erwinia tracheiphila (Enterobacteriaceae) isolated from infected squash plants in New York, Pennsylvania, Kentucky, and Michigan. These genomes exhibit a high proportion of recent horizontal gene acquisitions, invasion and remarkable amplification of mobile genetic elements, and pseudogenization of approximately 20% of the coding sequences. These genome attributes indicate that E. tracheiphila recently emerged as a host-restricted pathogen. Furthermore, chromosomal rearrangements associated with phage and transposable element proliferation contribute to substantial differences in gene content and genetic architecture between the six E. tracheiphila strains and other Erwinia species. Together, these data lead us to hypothesize that E. tracheiphila has undergone recent evolution through both genome decay (pseudogenization) and genome expansion (horizontal gene transfer and mobile element amplification). Despite evidence of dramatic genomic changes, the six strains are genetically monomorphic, suggesting a recent population bottleneck and emergence into E. tracheiphila’s current ecological niche., Genome Biology and Evolution, 8 (3), ISSN:1759-6653

Erwinia tracheiphila is one of the most economically important pathogens of cucumbers, melons, squashes, pumpkins, and gourds in the northeastern and midwestern United States, yet its molecular pathology remains uninvestigated. Here, we report the first draft genome sequence of an E. tracheiphila strain isolated from an infected wild gourd (Cucurbita pepo subsp. texana) plant. The genome assembly consists of 7 contigs and includes a putative plasmid and at least 20 phage and prophage elements., Genome Announcements, 3 (3), ISSN:2169-8287