Chronic exposure to stress is associated with increased incidence of depression, generalized anxiety, and PTSD. However, stress induces vulnerability to such disorders only in a sub-population of ...individuals, as others remain resilient. Inflammation has emerged as a putative mechanism for promoting stress vulnerability. Using a rodent model of social defeat, we have previously shown that rats with short-defeat latencies (SL/vulnerable rats) show increased anxiety- and depression-like behaviors, and these behaviors are mediated by inflammation in the ventral hippocampus. The other half of socially defeated rats show long-latencies to defeat (LL/resilient) and are similar to controls. Because gut microbiota are important activators of inflammatory substances, we assessed the role of the gut microbiome in mediating vulnerability to repeated social defeat stress. We analyzed the fecal microbiome of control, SL/vulnerable, and LL/resilient rats using shotgun metagenome sequencing and observed increased expression of immune-modulating microbiota, such as Clostridia, in SL/vulnerable rats. We then tested the importance of gut microbiota to the SL/vulnerable phenotype. In otherwise naive rats treated with microbiota from SL/vulnerable rats, there was higher microglial density and IL-1β expression in the vHPC, and higher depression-like behaviors relative to rats that received microbiota from LL/resilient rats, non-stressed control rats, or vehicle-treated rats. However, anxiety-like behavior during social interaction was not altered by transplant of the microbiome of SL/vulnerable rats into non-stressed rats. Taken together, the results suggest the gut microbiome contributes to the depression-like behavior and inflammatory processes in the vHPC of stress vulnerable individuals.
Motivation: The Nearest Alignment Space Termination (NAST) tool is commonly used in sequence-based microbial ecology community analysis, but due to the limited portability of the original ...implementation, it has not been as widely adopted as possible. Python Nearest Alignment Space Termination (PyNAST) is a complete reimplementation of NAST, which includes three convenient interfaces: a Mac OS X GUI, a command-line interface and a simple application programming interface (API). Results: The availability of PyNAST will make the popular NAST algorithm more portable and thereby applicable to datasets orders of magnitude larger by allowing users to install PyNAST on their own hardware. Additionally because users can align to arbitrary template alignments, a feature not available via the original NAST web interface, the NAST algorithm will be readily applicable to novel tasks outside of microbial community analysis. Availability: PyNAST is available at http://pynast.sourceforge.net. Contact: rob.knight@colorado.edu
IMPORTANCE: Establishment of the infant microbiome has lifelong implications on health and immunity. Gut microbiota of breastfed compared with nonbreastfed individuals differ during infancy as well ...as into adulthood. Breast milk contains a diverse population of bacteria, but little is known about the vertical transfer of bacteria from mother to infant by breastfeeding. OBJECTIVE: To determine the association between the maternal breast milk and areolar skin and infant gut bacterial communities. DESIGN, SETTING, AND PARTICIPANTS: In a prospective, longitudinal study, bacterial composition was identified with sequencing of the 16S ribosomal RNA gene in breast milk, areolar skin, and infant stool samples of 107 healthy mother-infant pairs. The study was conducted in Los Angeles, California, and St Petersburg, Florida, between January 1, 2010, and February 28, 2015. EXPOSURES: Amount and duration of daily breastfeeding and timing of solid food introduction. MAIN OUTCOMES AND MEASURES: Bacterial composition in maternal breast milk, areolar skin, and infant stool by sequencing of the 16S ribosomal RNA gene. RESULTS: In the 107 healthy mother and infant pairs (median age at the time of specimen collection, 40 days; range, 1-331 days), 52 (43.0%) of the infants were male. Bacterial communities were distinct in milk, areolar skin, and stool, differing in both composition and diversity. The infant gut microbial communities were more closely related to an infant’s mother’s milk and skin compared with a random mother (mean difference in Bray-Curtis distances, 0.012 and 0.014, respectively; P < .001 for both). Source tracking analysis was used to estimate the contribution of the breast milk and areolar skin microbiomes to the infant gut microbiome. During the first 30 days of life, infants who breastfed to obtain 75% or more of their daily milk intake received a mean (SD) of 27.7% (15.2%) of the bacteria from breast milk and 10.3% (6.0%) from areolar skin. Bacterial diversity (Faith phylogenetic diversity, P = .003) and composition changes were associated with the proportion of daily breast milk intake in a dose-dependent manner, even after the introduction of solid foods. CONCLUSIONS AND RELEVANCE: The results of this study indicate that bacteria in mother’s breast milk seed the infant gut, underscoring the importance of breastfeeding in the development of the infant gut microbiome.
Diabetes is a risk factor for periodontitis, an inflammatory bone disorder and the greatest cause of tooth loss in adults. Diabetes has a significant impact on the gut microbiota; however, studies in ...the oral cavity have been inconclusive. By 16S rRNA sequencing, we show here that diabetes causes a shift in oral bacterial composition and, by transfer to germ-free mice, that the oral microbiota of diabetic mice is more pathogenic. Furthermore, treatment with IL-17 antibody decreases the pathogenicity of the oral microbiota in diabetic mice; when transferred to recipient germ-free mice, oral microbiota from IL-17-treated donors induced reduced neutrophil recruitment, reduced IL-6 and RANKL, and less bone resorption. Thus, diabetes-enhanced IL-17 alters the oral microbiota and renders it more pathogenic. Our findings provide a mechanistic basis to better understand how diabetes can increase the risk and severity of tooth loss.
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•Diabetes increases periodontal bone resorption and tooth loss in mice•Diabetic mice have increased periodontal inflammation and IL-17 levels•Diabetic oral microbiota induces periodontitis in wild-type germ-free recipients•Blocking IL-17 reduces the pathogenic effect of diabetic oral microbiota
Diabetes increases periodontal disease, a major cause of tooth loss. Xiao et al. demonstrated, by transfer to germ-free mice, that oral microbiota from diabetic mice induced more periodontal inflammation and bone loss than microbiota from normal mice. Diabetes increased the pathogenicity of the oral microbiota through an IL-17-mediated mechanism.
The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. ...PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA.
We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (ω2). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study.
Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and ...the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.
Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate.
These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.
Defining the biogeography of bacterial populations in human body habitats is a high priority for understanding microbial-host relationships in health and disease. The healthy lung was traditionally ...considered sterile, but this notion has been challenged by emerging molecular approaches that enable comprehensive examination of microbial communities. However, studies of the lung are challenging due to difficulties in working with low biomass samples.
Our goal was to use molecular methods to define the bacterial microbiota present in the lungs of healthy individuals and assess its relationship to upper airway populations.
We sampled respiratory flora intensively at multiple sites in six healthy individuals. The upper tract was sampled by oral wash and oro-/nasopharyngeal swabs. Two bronchoscopes were used to collect samples up to the glottis, followed by serial bronchoalveolar lavage and lower airway protected brush. Bacterial abundance and composition were analyzed by 16S rDNA Q-PCR and deep sequencing.
Bacterial communities from the lung displayed composition indistinguishable from the upper airways, but were 2 to 4 logs lower in biomass. Lung-specific sequences were rare and not shared among individuals. There was no unique lung microbiome.
In contrast to other organ systems, the respiratory tract harbors a homogenous microbiota that decreases in biomass from upper to lower tract. The healthy lung does not contain a consistent distinct microbiome, but instead contains low levels of bacterial sequences largely indistinguishable from upper respiratory flora. These findings establish baseline data for healthy subjects and sampling approaches for sequence-based analysis of diseases.
Gut microbiota metabolites may be important for host health, yet few studies investigate the correlation between human gut microbiome and production of fecal metabolites and their impact on the ...plasma metabolome. Since gut microbiota metabolites are influenced by diet, we performed a longitudinal analysis of the impact of three divergent diets, vegan, omnivore, and a synthetic enteral nutrition (EEN) diet lacking fiber, on the human gut microbiome and its metabolome, including after a microbiota depletion intervention. Omnivore and vegan, but not EEN, diets altered fecal amino acid levels by supporting the growth of Firmicutes capable of amino acid metabolism. This correlated with relative abundance of a sizable number of fecal amino acid metabolites, some not previously associated with the gut microbiota. The effect on the plasma metabolome, in contrast, were modest. The impact of diet, particularly fiber, on the human microbiome influences broad classes of metabolites that may modify health.
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•The gut microbiome on a fiber-free diet differs from that of an omnivore or vegan•The lack of dietary fiber slows microbiome recovery after an ecological stress•Dietary effects on Firmicutes alter carbohydrate and amino acid gut metabolites•The effect of diet-based microbiota metabolites on the plasma metabolome is modest
Tanes et al. show that consumption of a fiber-free diet shifts the human gut microbiome toward metabolism of simple carbohydrates. Following an ecological stress, omnivore and vegan diets, but not the fiber-free diet, support growth of Firmicutes capable of carbohydrate and amino acid metabolism, altering fecal amino acid level.
Fecal microbiota transplantation (FMT) is a successful therapeutic strategy for treating recurrent Clostridioides difficile infection. Despite remarkable efficacy, implementation of FMT therapy is ...limited and the mechanism of action remains poorly understood. Here, we demonstrate a critical role for the immune system in supporting FMT using a murine C. difficile infection system. Following FMT, Rag1 heterozygote mice resolve C. difficile while littermate Rag1
mice fail to clear the infection. Targeted ablation of adaptive immune cell subsets reveal a necessary role for CD4
Foxp3
T-regulatory cells, but not B cells or CD8
T cells, in FMT-mediated resolution of C. difficile infection. FMT non-responsive mice exhibit exacerbated inflammation, impaired engraftment of the FMT bacterial community and failed restoration of commensal bacteria-derived secondary bile acid metabolites in the large intestine. These data demonstrate that the host's inflammatory immune status can limit the efficacy of microbiota-based therapeutics to treat C. difficile infection.
Background & Aims The gut microbiota is a complex and densely populated community in a dynamic environment determined by host physiology. We investigated how intestinal oxygen levels affect the ...composition of the fecal and mucosally adherent microbiota. Methods We used the phosphorescence quenching method and a specially designed intraluminal oxygen probe to dynamically quantify gut luminal oxygen levels in mice. 16S ribosomal RNA gene sequencing was used to characterize the microbiota in intestines of mice exposed to hyperbaric oxygen, human rectal biopsy and mucosal swab samples, and paired human stool samples. Results Average P o2 values in the lumen of the cecum were extremely low (<1 mm Hg). In altering oxygenation of mouse intestines, we observed that oxygen diffused from intestinal tissue and established a radial gradient that extended from the tissue interface into the lumen. Increasing tissue oxygenation with hyperbaric oxygen altered the composition of the gut microbiota in mice. In human beings, 16S ribosomal RNA gene analyses showed an increased proportion of oxygen-tolerant organisms of the Proteobacteria and Actinobacteria phyla associated with rectal mucosa, compared with feces. A consortium of asaccharolytic bacteria of the Firmicute and Bacteroidetes phyla, which primarily metabolize peptones and amino acids, was associated primarily with mucus. This could be owing to the presence of proteinaceous substrates provided by mucus and the shedding of the intestinal epithelium. Conclusions In an analysis of intestinal microbiota of mice and human beings, we observed a radial gradient of microbes linked to the distribution of oxygen and nutrients provided by host tissue.
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