BackgroundSpecies-level classification for 16S rRNA gene sequences remains a serious challenge for microbiome researchers, because existing taxonomic classification tools for 16S rRNA gene sequences either do not provide species-level classification, or their classification results are unreliable. The unreliable results are due to the limitations in the existing methods which either lack solid probabilistic-based criteria to evaluate the confidence of their taxonomic assignments, or use nucleotide k-mer frequency as the proxy for sequence similarity measurement.ResultsWe have developed a method that shows significantly improved species-level classification results over existing methods. Our method calculates true sequence similarity between query sequences and database hits using pairwise sequence alignment. Taxonomic classifications are assigned from the species to the phylum levels based on the lowest common ancestors of multiple database hits for each query sequence, and further classification reliabilities are evaluated by bootstrap confidence scores. The novelty of our method is that the contribution of each database hit to the taxonomic assignment of the query sequence is weighted by a Bayesian posterior probability based upon the degree of sequence similarity of the database hit to the query sequence. Our method does not need any training datasets specific for different taxonomic groups. Instead only a reference database is required for aligning to the query sequences, making our method easily applicable for different regions of the 16S rRNA gene or other phylogenetic marker genes.ConclusionsReliable species-level classification for 16S rRNA or other phylogenetic marker genes is critical for microbiome research. Our software shows significantly higher classification accuracy than the existing tools and we provide probabilistic-based confidence scores to evaluate the reliability of our taxonomic classification assignments based on multiple database matches to query sequences. Despite its higher computational costs, our method is still suitable for analyzing large-scale microbiome datasets for practical purposes. Furthermore, our method can be applied for taxonomic classification of any phylogenetic marker gene sequences. Our software, called BLCA, is freely available at https://github.com/qunfengdong/BLCA.

Rationale: Previous work found the lung microbiome in healthy subjects infected with HIV was similar to that in uninfected subjects.

The influence of the skin microbiota on host susceptibility to infectious agents is largely unexplored. The skin harbors diverse bacterial species that may promote or antagonize the growth of an invading pathogen. We developed a human infection model for Haemophilus ducreyi in which human volunteers are inoculated on the upper arm. After inoculation, papules form and either spontaneously resolve or progress to pustules. To examine the role of the skin microbiota in the outcome of H. ducreyi infection, we analyzed the microbiomes of four dose-matched pairs of “resolvers” and “pustule formers” whose inoculation sites were swabbed at multiple time points. Bacteria present on the skin were identified by amplification and pyrosequencing of 16S rRNA genes. Nonmetric multidimensional scaling (NMDS) using Bray-Curtis dissimilarity between the preinfection microbiomes of infected sites showed that sites from the same volunteer clustered together and that pustule formers segregated from resolvers (P = 0.001, permutational multivariate analysis of variance [PERMANOVA]), suggesting that the preinfection microbiomes were associated with outcome. NMDS using Bray-Curtis dissimilarity of the endpoint samples showed that the pustule sites clustered together and were significantly different than the resolved sites (P = 0.001, PERMANOVA), suggesting that the microbiomes at the endpoint differed between the two groups. In addition to H. ducreyi, pustule-forming sites had a greater abundance of Proteobacteria, Bacteroidetes, Micrococcus, Corynebacterium, Paracoccus, and Staphylococcus species, whereas resolved sites had higher levels of Actinobacteria and Propionibacterium species. These results suggest that at baseline, resolvers and pustule formers have distinct skin bacterial communities which change in response to infection and the resultant immune response.

OBJECTIVES: Women have a 20% risk of developing a urinary tract infection (UTI) following urogynecologic surgery. This study assessed the association of post-operative UTI with bacteria in pre-operative samples of catheterized urine. METHODS: Immediately before surgery, vaginal swabs, perineal swabs and catheterized urine samples were collected and the V4 region of the 16S rRNA gene was sequenced. The cohort was dichotomized in two ways: 1) standard day-of-surgery urine culture result (positive/negative) and 2) occurrence of post-operative UTI (positive/negative). Characteristics of the bladder, vaginal and perineal microbiomes were assessed to identify factors associated with post-operative UTI. RESULTS: 87% of the 104 POP/UI surgical patients were white; the mean age was 57 years. The most common genus was Lactobacillus with a mean relative abundance of 39.91% in catheterized urine, 53.88% in vaginal swabs, and 30.28% in perineal swabs. Two distinct clusters, based on dispersion of catheterized urine (i.e., bladder) microbiomes, had highly significant (p<2.2e-16) differences in age, microbes and post-operative UTI risk. Post-operative UTI was most associated with the bladder microbiome; microbes in adjacent pelvic floor niches also contributed to UTI risk. UTI risk was associated with depletion of Lactobacillus iners and enrichment of a diverse mixture of uropathogens. CONCLUSIONS: Post-operative UTI risk appears associated with pre-operative bladder microbiome composition, where an abundance of L. iners appears to protect against post-operative UTI.

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