Explore the vast range of titles available.

ᅟ The advancement of DNA/RNA, proteins, and metabolite analytical platforms, combined with increased computing technologies, has transformed the field of microbial community analysis. This transformation is evident by the exponential increase in the number of publications describing the composition and structure, and sometimes function, of the microbial communities inhabiting the human body. This rapid evolution of the field has been accompanied by confusion in the vocabulary used to describe different aspects of these communities and their environments. The misuse of terms such as microbiome, microbiota, metabolomic, and metagenome and metagenomics among others has contributed to misunderstanding of many study results by the scientific community and the general public alike. A few review articles have previously defined those terms, but mainly as sidebars, and no clear definitions or use cases have been published. In this editorial, we aim to propose clear definitions of each of these terms, which we would implore scientists in the field to adopt and perfect.

Failure to predict and understand the causes of preterm birth, the leading cause of neonatal morbidity and mortality, have limited effective interventions and therapeutics. From a cohort of 2000 pregnant women, we performed a nested case control study on 107 well-phenotyped cases of spontaneous preterm birth (sPTB) and 432 women delivering at term. Using innovative Bayesian modeling of cervicovaginal microbiota, seven bacterial taxa were significantly associated with increased risk of sPTB, with a stronger effect in African American women. However, higher vaginal levels of β-defensin-2 lowered the risk of sPTB associated with cervicovaginal microbiota in an ethnicity-dependent manner. Surprisingly, even in Lactobacillus spp. dominated cervicovaginal microbiota, low β-defensin-2 was associated with increased risk of sPTB. These findings hold promise for diagnostics to accurately identify women at risk for sPTB early in pregnancy. Therapeutic strategies could include immune modulators and microbiome-based therapeutics to reduce this significant health burden. Here, Elovitz et al. investigate associations between cervicovaginal microbiota (CVM) and spontaneous preterm birth (sPTB) in a large cohort of African American and non-African American women, and find that CVM and local immune response early in pregnancy are associated with sPTB in an ethnicity-dependent manner.

Background High systemic estrogen levels contribute to breast cancer risk for postmenopausal women, whereas low levels contribute to osteoporosis risk. Except for obesity, determinants of non-ovarian systemic estrogen levels are undefined. We sought to identify members and functions of the intestinal microbial community associated with estrogen levels via enterohepatic recirculation. Methods Fifty-one epidemiologists at the National Institutes of Health, including 25 men, 7 postmenopausal women, and 19 premenopausal women, provided urine and aliquots of feces, using methods proven to yield accurate and reproducible results. Estradiol, estrone, 13 estrogen metabolites (EM), and their sum (total estrogens) were quantified in urine and feces by liquid chromatography/tandem mass spectrometry. In feces, β-glucuronidase and β-glucosidase activities were determined by realtime kinetics, and microbiome diversity and taxonomy were estimated by pyrosequencing 16S rRNA amplicons. Pearson correlations were computed for each log e estrogen level, log e enzymatic activity level, and microbiome alpha diversity estimate. For the 55 taxa with mean relative abundance of at least 0.1%, ordinal levels were created [zero, low (below median of detected sequences), high] and compared to log e estrogens, β-glucuronidase and β-glucosidase enzymatic activity levels by linear regression. Significance was based on two-sided tests with α=0.05. Results In men and postmenopausal women, levels of total urinary estrogens (as well as most individual EM) were very strongly and directly associated with all measures of fecal microbiome richness and alpha diversity (R≥0.50, P ≤0.003). These non-ovarian systemic estrogens also were strongly and significantly associated with fecal Clostridia taxa, including non- Clostridiales and three genera in the Ruminococcaceae family (R=0.57−0.70, P =0.03−0.002). Estrone, but not other EM, in urine correlated significantly with functional activity of fecal β-glucuronidase (R=0.36, P =0.04). In contrast, fecal β-glucuronidase correlated inversely with fecal total estrogens, both conjugated and deconjugated (R≤-0.47, P ≤0.01). Premenopausal female estrogen levels, which were collected across menstrual cycles and thus highly variable, were completely unrelated to fecal microbiome and enzyme parameters ( P ≥0.6). Conclusions Intestinal microbial richness and functions, including but not limited to β-glucuronidase, influence levels of non-ovarian estrogens via enterohepatic circulation. Thus, the gut microbial community likely affects the risk for estrogen-related conditions in older adults. Understanding how Clostridia taxa relate to systemic estrogens may identify targets for interventions. Trial registration Not applicable.

ObjectiveA history of periodontal disease and the presence of circulating antibodies to selected oral pathogens have been associated with increased risk of pancreatic cancer; however, direct relationships of oral microbes with pancreatic cancer have not been evaluated in prospective studies. We examine the relationship of oral microbiota with subsequent risk of pancreatic cancer in a large nested case–control study.DesignWe selected 361 incident adenocarcinoma of pancreas and 371 matched controls from two prospective cohort studies, the American Cancer Society Cancer Prevention Study II and the National Cancer Institute Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. From pre-diagnostic oral wash samples, we characterised the composition of the oral microbiota using bacterial 16S ribosomal RNA (16S rRNA) gene sequencing. The associations between oral microbiota and risk of pancreatic cancer, controlling for the random effect of cohorts and other covariates, were examined using traditional and L1-penalised least absolute shrinkage and selection operator logistic regression.ResultsCarriage of oral pathogens, Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans, were associated with higher risk of pancreatic cancer (adjusted OR for presence vs absence=1.60 and 95% CI 1.15 to 2.22; OR=2.20 and 95% CI 1.16 to 4.18, respectively). Phylum Fusobacteria and its genus Leptotrichia were associated with decreased pancreatic cancer risk (OR per per cent increase of relative abundance=0.94 and 95% CI 0.89 to 0.99; OR=0.87 and 95% CI 0.79 to 0.95, respectively). Risks related to these phylotypes remained after exclusion of cases that developed within 2 years of sample collection, reducing the likelihood of reverse causation in this prospective study.ConclusionsThis study provides supportive evidence that oral microbiota may play a role in the aetiology of pancreatic cancer.

Background Taxonomic profiles of vaginal microbial communities can be sorted into a discrete number of categories termed community state types (CSTs). This approach is advantageous because collapsing a hyper-dimensional taxonomic profile into a single categorical variable enables efforts such as data exploration, epidemiological studies, and statistical modeling. Vaginal communities are typically assigned to CSTs based on the results of hierarchical clustering of the pairwise distances between samples. However, this approach is problematic because it complicates between-study comparisons and because the results are entirely dependent on the particular set of samples that were analyzed. We sought to standardize and advance the assignment of samples to CSTs. Results We developed VALENCIA ( VA gina L community state typ E N earest C entro I d cl A ssifier), a nearest centroid-based tool which classifies samples based on their similarity to a set of reference centroids. The references were defined using a comprehensive set of 13,160 taxonomic profiles from 1975 women in the USA. This large dataset allowed us to comprehensively identify, define, and characterize vaginal CSTs common to reproductive age women and expand upon the CSTs that had been defined in previous studies. We validated the broad applicability of VALENCIA for the classification of vaginal microbial communities by using it to classify three test datasets which included reproductive age eastern and southern African women, adolescent girls, and a racially/ethnically and geographically diverse sample of postmenopausal women. VALENCIA performed well on all three datasets despite the substantial variations in sequencing strategies and bioinformatics pipelines, indicating its broad application to vaginal microbiota. We further describe the relationships between community characteristics (vaginal pH, Nugent score) and participant demographics (race, age) and the CSTs defined by VALENCIA. Conclusion VALENCIA provides a much-needed solution for the robust and reproducible assignment of vaginal community state types. This will allow unbiased analysis of both small and large vaginal microbiota datasets, comparisons between datasets and meta-analyses that combine multiple datasets. EVFyNWZ2oXD9YZVU-dNDhH Video abstract.

Background Clustering of sequences into operational taxonomic units (OTUs) and denoising methods are a mainstream stopgap to taxonomically classifying large numbers of 16S rRNA gene sequences. Environment-specific reference databases generally yield optimal taxonomic assignment. Results We developed SpeciateIT, a novel taxonomic classification tool which rapidly and accurately classifies individual amplicon sequences ( https://github.com/Ravel-Laboratory/speciateIT ). We also present vSpeciateDB, a custom reference database for the taxonomic classification of 16S rRNA gene amplicon sequences from vaginal microbiota. We show that SpeciateIT requires minimal computational resources relative to other algorithms and, when combined with vSpeciateDB, affords accurate species level classification in an environment-specific manner. Conclusions Herein, two resources with new and practical importance are described. The novel classification algorithm, SpeciateIT, is based on 7th order Markov chain models and allows for fast and accurate per-sequence taxonomic assignments (as little as 10 min for 10 7 sequences). vSpeciateDB, a meticulously tailored reference database, stands as a vital and pragmatic contribution. Its significance lies in the superiority of this environment-specific database to provide more species-resolution over its universal counterparts.

Background The human lung tissue microbiota remains largely uncharacterized, although a number of studies based on airway samples suggest the existence of a viable human lung microbiota. Here we characterized the taxonomic and derived functional profiles of lung microbiota in 165 non-malignant lung tissue samples from cancer patients. Results We show that the lung microbiota is distinct from the microbial communities in oral, nasal, stool, skin, and vagina, with Proteobacteria as the dominant phylum (60 %). Microbiota taxonomic alpha diversity increases with environmental exposures, such as air particulates, residence in low to high population density areas, and pack-years of tobacco smoking and decreases in subjects with history of chronic bronchitis. Genus Thermus is more abundant in tissue from advanced stage (IIIB, IV) patients, while Legionella is higher in patients who develop metastases. Moreover, the non-malignant lung tissues have higher microbiota alpha diversity than the paired tumors. Conclusions Our results provide insights into the human lung microbiota composition and function and their link to human lifestyle and clinical outcomes. Studies among subjects without lung cancer are needed to confirm our findings.

Analysis of metagenomic and metatranscriptomic data is complicated and typically requires extensive computational resources. Leveraging a curated reference database of genes encoded by members of the target microbiome can make these analyses more tractable. In this study, we assemble a comprehensive human vaginal non-redundant gene catalog (VIRGO) that includes 0.95 million non-redundant genes. The gene catalog is functionally and taxonomically annotated. We also construct a vaginal orthologous groups (VOG) from VIRGO. The gene-centric design of VIRGO and VOG provides an easily accessible tool to comprehensively characterize the structure and function of vaginal metagenome and metatranscriptome datasets. To highlight the utility of VIRGO, we analyze 1,507 additional vaginal metagenomes, and identify a high degree of intraspecies diversity within and across vaginal microbiota. VIRGO offers a convenient reference database and toolkit that will facilitate a more in-depth understanding of the role of vaginal microorganisms in women’s health and reproductive outcomes. Reference databases are essential for studies on host-microbiota interactions. Here, the authors present the construction of VIRGO, a human vaginal non-redundant gene catalog, which represents a comprehensive resource for taxonomic and functional profiling of vaginal microbiomes from metagenomic and metatranscriptomic datasets.

Background Vaginal bacterial communities dominated by Lactobacillus species are associated with a reduced risk of various adverse health outcomes. However, somewhat unexpectedly, many healthy women have microbiota that are not dominated by lactobacilli. To determine the factors that drive vaginal community composition we characterized the genetic composition and transcriptional activities of vaginal microbiota in healthy women. Results We demonstrate that the abundance of a species is not always indicative of its transcriptional activity and that impending changes in community composition can be predicted from metatranscriptomic data. Functional comparisons highlight differences in the metabolic activities of these communities, notably in their degradation of host produced mucin but not glycogen. Degradation of mucin by communities not dominated by Lactobacillus may play a role in their association with adverse health outcomes. Finally, we show that the transcriptional activities of L. crispatus , L. iners , and Gardnerella vaginalis vary with the taxonomic composition of the communities in which they reside. Notably, L. iners and G. vaginalis both demonstrate lower expression of their cholesterol-dependent cytolysins when co-resident with Lactobacillus spp. and higher expression when co-resident with other facultative and obligate anaerobes. The pathogenic potential of these species may depend on the communities in which they reside and thus could be modulated by interventional strategies. Conclusions Our results provide insight to the functional ecology of the vaginal microbiota, demonstrate the diagnostic potential of metatranscriptomic data, and reveal strategies for the management of these ecosystems.

The vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV). While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN) analysis of 16S rRNA gene sequence results, demographic and extensive questionnaire data, we describe both novel and previously documented associations between habits of women and their vaginal microbiome. The BN analysis approach shows promise in uncovering complex associations between disparate data types. Our findings based on this approach support published associations between specific microbiome members (e.g., Eggerthella, Gardnerella, Dialister, Sneathia and Ruminococcaceae), the Nugent score (a BV diagnostic) and vaginal pH (a risk symptom of BV). Additionally, we found that several microbiome members were directly connected to other risk symptoms of BV (such as vaginal discharge, odor, itch, irritation, and yeast infection) including L. jensenii, Corynebacteria, and Proteobacteria. No direct connections were found between the Nugent Score and risk symptoms of BV other than pH, indicating that the Nugent Score may not be the most useful criteria for assessment of clinical BV. We also found that demographics (i.e., age, ethnicity, previous pregnancy) were associated with the presence/absence of specific vaginal microbes. The resulting BN revealed several as-yet undocumented associations between birth control usage, menstrual hygiene practices and specific microbiome members. Many of these complex relationships were not identified using common analytical methods, i.e., ordination and PERMANOVA. While these associations require confirmatory follow-up study, our findings strongly suggest that future studies of the vaginal microbiome and vaginal pathologies should include detailed surveys of participants' sanitary, sexual and birth control habits, as these can act as confounders in the relationship between the microbiome and disease. Although the BN approach is powerful in revealing complex associations within multidimensional datasets, the need in some cases to discretize the data for use in BN analysis can result in loss of information. Future research is required to alleviate such limitations in constructing BN networks. Large sample sizes are also required in order to allow for the incorporation of a large number of variables (nodes) into the BN, particularly when studying associations between metadata and the microbiome. We believe that this approach is of great value, complementing other methods, to further our understanding of complex associations characteristic of microbiome research.