Huaiying Lin

The Human Skin Microbiome Associates with the Outcome of and Is Influenced by Bacterial Infection

ABSTRACT 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. IMPORTANCE Human skin is home to a diverse community of microorganisms, collectively known as the skin microbiome. Some resident bacteria are thought to protect the skin from infection by outcompeting pathogens for resources or by priming the immune systems response to invaders. However, the influence of the skin microbiome on the susceptibility to or protection from infection has not been prospectively evaluated in humans. We characterized the skin microbiome before, during, and after experimental inoculation of the arm with Haemophilus ducreyi in matched volunteers who subsequently resolved the infection or formed abscesses. Our results suggest that the preinfection microbiomes of pustule formers and resolvers have distinct community structures which change in response to the progression of H. ducreyi infection to abscess formation. Human skin is home to a diverse community of microorganisms, collectively known as the skin microbiome. Some resident bacteria are thought to protect the skin from infection by outcompeting pathogens for resources or by priming the immune systems response to invaders. However, the influence of the skin microbiome on the susceptibility to or protection from infection has not been prospectively evaluated in humans. We characterized the skin microbiome before, during, and after experimental inoculation of the arm with Haemophilus ducreyi in matched volunteers who subsequently resolved the infection or formed abscesses. Our results suggest that the preinfection microbiomes of pustule formers and resolvers have distinct community structures which change in response to the progression of H. ducreyi infection to abscess formation.

Effect of Advanced HIV Infection on the Respiratory Microbiome

RATIONALE Previous work found the lung microbiome in healthy subjects infected with HIV was similar to that in uninfected subjects. We hypothesized the lung microbiome from subjects infected with HIV with more advanced disease would differ from that of an uninfected control population. OBJECTIVES To measure the lung microbiome in an HIV-infected population with advanced disease. METHODS 16s RNA gene sequencing was performed on acellular bronchoalveolar lavage (BAL) fluid from 30 subjects infected with HIV with advanced disease (baseline mean CD4 count, 262 cells/mm(3)) before and up to 3 years after starting highly active antiretroviral therapy (HAART) and compared with 22 uninfected control subjects. MEASUREMENTS AND MAIN RESULTS The lung microbiome in subjects infected with HIV with advanced disease demonstrated decreased alpha diversity (richness and diversity) and greater beta diversity compared with uninfected BAL. Differences improved with HAART, but still persisted up to 3 years after starting therapy. Population dispersion in the group infected with HIV was significantly greater than in the uninfected cohort and declined after treatment. There were differences in the relative abundance of some bacteria between the two groups at baseline and after 1 year of therapy. After 1 year on HAART, HIV BAL contained an increased abundance of Prevotella and Veillonella, bacteria previously associated with lung inflammation. CONCLUSIONS The lung microbiome in subjects infected with HIV with advanced disease is altered compared with an uninfected population both in diversity and bacterial composition. Differences remain up to 3 years after starting HAART. We speculate an altered lung microbiome in HIV infection may contribute to chronic inflammation and lung complications seen in the HAART era.

A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy

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.

Cutaneous Burn Injury Promotes Shifts in the Bacterial Microbiome in Autologous Donor Skin: Implications for Skin Grafting Outcomes

Introduction: The cutaneous microbiome maintains skin barrier function, regulates inflammation, and stimulates wound-healing responses. Burn injury promotes an excessive activation of the cutaneous and systemic immune response directed against commensal and invading pathogens. Skin grafting is the primary method of reconstructing full-thickness burns, and wound infection continues to be a significant complication. Methods: In this study, the cutaneous bacterial microbiome was evaluated and subsequently compared to patient outcomes. Three different full-thickness skin specimens were assessed: control skin from non-burned subjects; burn margin from burn patients; and autologous donor skin from the same cohort of burn patients. Results: We observed that skin bacterial community structure of burn patients was significantly altered compared with control patients. We determined that the unburned autologous donor skin from burn patients exhibits a microbiome similar to that of the burn margin, rather than unburned controls, and that changes in the cutaneous microbiome statistically correlate with several post-burn complications. We established that Corynebacterium positively correlated with burn wound infection, while Staphylococcus and Propionibacterium negatively correlated with burn wound infection. Both Corynebacterium and Enterococcus negatively correlated with the development of sepsis. Conclusions: This study identifies distinct differences in the cutaneous microbiome between burn subjects and unburned controls, and ascertains that select bacterial taxa significantly correlate with several comorbid complications of burn injury. These preliminary data suggest that grafting donor skin exhibiting bacterial dysbiosis may augment infection and/or graft failure and sets the foundation for more in-depth and mechanistic analyses in presumably “healthy” donor skin from patients requiring skin grafting procedures.

Urinary symptoms are associated with certain urinary microbes in urogynecologic surgical patients

Introduction and hypothesisPersistent and de novo symptoms decrease satisfaction after urogynecologic surgery. We investigated whether the preoperative bladder microbiome is associated with urinary symptoms prior to and after urogynecologic surgery.MethodsOne hundred twenty-six participants contributed responses to the validated OABq symptom questionnaire. Catheterized (bladder) urine samples and vaginal and perineal swabs were collected immediately preoperatively. Bacterial DNA in the urine samples and swabs was sequenced and classified.ResultsPreoperative symptom severity was significantly worse in sequence-positive patients. Higher OABq Symptom Severity (OABqSS) scores (more symptomatic) were associated with higher abundance in bladder urine of two bacterial species: Atopobium vaginae and Finegoldia magna. The presence of Atopobium vaginae in bladder urine also was correlated with its presence in either the vagina or perineum.ConclusionsTwo 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.

A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions

By incorporating prior information on disease prevalence, Bayes classifiers have the potential to estimate disease probability better than other common machine-learning methods. Thus, it is important to develop Bayes classifiers specifically tailored for microbiome data. Our method shows higher classification accuracy than the only existing Bayesian classifier and the popular random forest method, and thus provides an alternative option for using microbial compositions for disease diagnosis. ABSTRACT Dysbiosis of microbial communities is associated with various human diseases, raising the possibility of using microbial compositions as biomarkers for disease diagnosis. We have developed a Bayes classifier by modeling microbial compositions with Dirichlet-multinomial distributions, which are widely used to model multicategorical count data with extra variation. The parameters of the Dirichlet-multinomial distributions are estimated from training microbiome data sets based on maximum likelihood. The posterior probability of a microbiome sample belonging to a disease or healthy category is calculated based on Bayes’ theorem, using the likelihood values computed from the estimated Dirichlet-multinomial distribution, as well as a prior probability estimated from the training microbiome data set or previously published information on disease prevalence. When tested on real-world microbiome data sets, our method, called DMBC (for Dirichlet-multinomial Bayes classifier), shows better classification accuracy than the only existing Bayesian microbiome classifier based on a Dirichlet-multinomial mixture model and the popular random forest method. The advantage of DMBC is its built-in automatic feature selection, capable of identifying a subset of microbial taxa with the best classification accuracy between different classes of samples based on cross-validation. This unique ability enables DMBC to maintain and even improve its accuracy at modeling species-level taxa. The R package for DMBC is freely available at https://github.com/qunfengdong/DMBC . IMPORTANCE By incorporating prior information on disease prevalence, Bayes classifiers have the potential to estimate disease probability better than other common machine-learning methods. Thus, it is important to develop Bayes classifiers specifically tailored for microbiome data. Our method shows higher classification accuracy than the only existing Bayesian classifier and the popular random forest method, and thus provides an alternative option for using microbial compositions for disease diagnosis.