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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Two specific bacterial species detected in bladder urine, Atopobium vaginae and Finegoldia magna, are associated with preoperative urinary symptom severity in women undergoing POP/SUI surgery. The reservoir for Atopobium vaginae may be adjacent pelvic floor niches. This observation should be validated in a larger cohort to determine whether there is a microbiologic etiology for certain preoperative urinary symptoms.

BackgroundDomestic combustion of biomass fuels, such as wood, charcoal, crop residue and dung causes Household Air Pollution (HAP). These inhaled particulates affect more than half of the world’s population, causing respiratory problems such as infection and inflammatory lung disease. We examined whether the presence of black carbon in alveolar macrophages was associated with alterations in the lung microbiome in a Malawi population.MethodsBronchoalveolar lavage samples from 44 healthy adults were sequenced using 16S rDNA amplification to assess microbial diversity, richness and relative taxa abundance. Individuals were classified as high or low particulate exposure as determined by questionnaire and the percentage of black carbon within their alveolar macrophages.ResultsSubjects in the low and high particulate groups did not differ in terms of source of fuels used for cooking or lighting. There was no difference in alpha or beta diversity by particulate group. Neisseria and Streptococcus were significantly more abundant in samples from high particulate exposed individuals, and Tropheryma was found less abundant. Petrobacter abundance was higher in people using biomass fuel for household cooking and lighting, compared with exclusive use of electricity.ConclusionsHealthy adults in Malawi exposed to higher levels of particulates have higher abundances of potentially pathogenic bacteria (Streptococcus, Neisseria) within their lung microbiome. Domestic biomass fuel use was associated with an uncommon environmental bacterium (Petrobacter) associated with oil-rich niches.

The lipoxygenase isoform of 5-lipoxygenase (5-LOX) is reported to be overexpressed in human rheumatoid arthritis synovial tissue and involved in the progress of inflammatory arthritis. However, the detailed mechanism of how 5-lipoxygenase regulates the inflammatory response in arthritis synovial tissue is still unclear. The aim of this study was to investigate the involvement of lipoxygenase pathways in TNF-α-induced production of cytokines and chemokines. Human synovial fibroblasts from rheumatoid patients were used in this study. 5-LOX inhibitors and shRNA were used to examine the involvement of 5-LOX in TNF-α-induced cytokines and chemokines expression. The signaling pathways were examined by Western Blotting or immunofluorescence staining. The effect of 5-LOX inhibitor on TNF-α-induced chemokine expression and paw edema was also explored in vivo in C57BL/6 mice. Treatment with 5-LOX inhibitors significantly decreased TNF-α-induced pro-inflammatory mediators including interleukin-6 (IL-6) and monocyte chemo-attractant protein-1 (MCP-1) in human synovial fibroblasts. Knockdown of 5-LOX using shRNA exerted similar inhibitory effects. The abrogation of NF-κB activation was involved in the antagonizing effects of these inhibitors. Furthermore, 5-LOX inhibitor decreased TNF-α-induced up-regulation of serum MCP-1 level and paw edema in mouse model. Our results provide the evidence that the administration of 5-LOX inhibitors is able to ameliorate TNF-α-induced cytokine/chemokine release and paw edema, indicating that 5-LOX inhibitors may be developed for therapeutic treatment of inflammatory arthritis.