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.
The human microbiome project has shown a remarkable diversity of microbial ecology within the human body. The vaginal microbiota is unique in that in many women it is most often dominated by ...Lactobacillus species. However, in some women it lacks Lactobacillus spp. and is comprised of a wide array of strict and facultative anaerobes, a state that broadly correlates with increased risk for infection, disease, and poor reproductive and obstetric outcomes. Interestingly, the level of protection against infection can also vary by species and strains of Lactobacillus, and some species although dominant are not always optimal. This factors into the risk of contracting sexually transmitted infections and possibly influences the occurrence of resultant adverse reproductive outcomes such as tubal factor infertility. The composition and function of the vaginal microbiota appear to play an important role in pregnancy and fertility treatment outcomes and future research in this field will shed further translational mechanistic understanding onto the interplay of the vaginal microbiota with women's health and reproduction.
Vaginal microbiota form a mutually beneficial relationship with their host and have a major impact on health and disease. In recent years our understanding of vaginal bacterial community composition ...and structure has significantly broadened as a result of investigators using cultivation-independent methods based on the analysis of 16S ribosomal RNA (rRNA) gene sequences. In asymptomatic, otherwise healthy women, several kinds of vaginal microbiota exist, the majority often dominated by species of Lactobacillus, while others are composed of a diverse array of anaerobic microorganisms. Bacterial vaginosis is the most common vaginal condition and is vaguely characterized as the disruption of the equilibrium of the normal vaginal microbiota. A better understanding of normal and healthy vaginal ecosystems that is based on their true function and not simply on their composition would help better define health and further improve disease diagnostics as well as the development of more personalized regimens to promote health and treat diseases.
Most of what is known about fungi in the human vagina has come from culture-based studies and phenotypic characterization of single organisms. Though valuable, these approaches have masked the ...complexity of fungal communities within the vagina. The vaginal mycobiome has become an emerging field of study as genomics tools are increasingly employed and we begin to appreciate the role these fungal communities play in human health and disease. Though vastly outnumbered by its bacterial counterparts, fungi are important constituents of the vaginal ecosystem in many healthy women. Candida albicans, an opportunistic fungal pathogen, colonizes 20% of women without causing any overt symptoms, yet it is one of the leading causes of infectious vaginitis. Understanding its mechanisms of commensalism and patho-genesis are both essential to developing more effective therapies. Describing the interactions between Candida, bacteria (such as Lactobacillus spp.) and other fungi in the vagina is funda-mental to our characterization of the vaginal mycobiome.
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.
Fore-knowledge of the secondary metabolic potential of cultivated and previously uncultivated microorganisms can potentially facilitate the process of natural product discovery. By combining ...sequence-based knowledge with biochemical precedent, translated gene sequence data can be used to rapidly derive structural elements encoded by secondary metabolic gene clusters from microorganisms. These structural elements provide an estimate of the secondary metabolic potential of a given organism and a starting point for identification of potential lead compounds in isolation/structure elucidation campaigns. The accuracy of these predictions for a given translated gene sequence depends on the biochemistry of the metabolite class, similarity to known metabolite gene clusters, and depth of knowledge concerning its biosynthetic machinery. This chapter introduces methods for prediction of structural elements for two well-studied classes: modular polyketides and nonribosomally encoded peptides. A bioinformatics tool is presented for rapid preliminary analysis of these modular systems, and prototypical methods for converting these analyses into substructural elements are described.
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.
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 loge estrogen level, loge 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 loge estrogens, β-glucuronidase and β-glucosidase enzymatic activity levels by linear regression. Significance was based on two-sided tests with α=0.05.
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).
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.
A 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.
We 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.
Carriage of oral pathogens,
and
, 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
and its genus
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.
This study provides supportive evidence that oral microbiota may play a role in the aetiology of pancreatic cancer.
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.
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.
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.
Despite widespread use of antibiotics for the treatment of life-threatening infections and for research on the role of commensal microbiota, our understanding of their effects on the host is still ...very limited.
Using a popular mouse model of microbiota depletion by a cocktail of antibiotics, we analysed the effects of antibiotics by combining intestinal transcriptome together with metagenomic analysis of the gut microbiota. In order to identify specific microbes and microbial genes that influence the host phenotype in antibiotic-treated mice, we developed and applied analysis of the transkingdom network.
We found that most antibiotic-induced alterations in the gut can be explained by three factors: depletion of the microbiota; direct effects of antibiotics on host tissues and the effects of remaining antibiotic-resistant microbes. Normal microbiota depletion mostly led to downregulation of different aspects of immunity. The two other factors (antibiotic direct effects on host tissues and antibiotic-resistant microbes) primarily inhibited mitochondrial gene expression and amounts of active mitochondria, increasing epithelial cell death. By reconstructing and analysing the transkingdom network, we discovered that these toxic effects were mediated by virulence/quorum sensing in antibiotic-resistant bacteria, a finding further validated using in vitro experiments.
In addition to revealing mechanisms of antibiotic-induced alterations, this study also describes a new bioinformatics approach that predicts microbial components that regulate host functions and establishes a comprehensive resource on what, why and how antibiotics affect the gut in a widely used mouse model of microbiota depletion by antibiotics.
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