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Microbiome
Microbiome and Metagenomics Research
Curated by David Guttman
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Establishment of the bacterial fecal community during the first month of life in

Clinics (Sao Paulo). 2012;67(2):113-23.

OBJECTIVE:

The establishment of the intestinal microbiota in newborns is a critical period with possible long-term consequences for human health. In this research, the development of the fecal microbiota of a group of exclusively breastfed neonates living in low socio-economic conditions in the city of São Paulo, Brazil, during the first month of life, was studied.

METHODS:

Fecal samples were collected from ten neonates on the second, seventh, and 30th days after birth. One of the neonates underwent antibiotic therapy. Molecular techniques were used for analysis; DNA was extracted from the samples, and 16S rRNA libraries were sequenced and phylogenetically analyzed after construction. A real-time polymerase chain reaction (PCR) was performed on the samples taken from the 30th day to amplify DNA from Bifidobacterium sp.

RESULTS:

The primary phylogenetic groups identified in the samples were Escherichia and Clostridium. Staphylococcus was identified at a low rate. Bifidobacterium sp. was detected in all of the samples collected on the 30th day. In the child who received antibiotics, a reduction in anaerobes and Escherichia, which was associated with an overgrowth of Klebsiella, was observed throughout the experimental period.

CONCLUSION:

The observed pattern of Escherichia predominance and reduced Staphylococcus colonization is in contrast with the patterns observed in neonates living in developed countries.

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PLoS ONE: Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics

PLoS ONE: Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics | Microbiome | Scoop.it
Abstract Top
We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct ‘metacommunities’, and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the ‘evidence framework’ (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the ‘Anna Karenina principle (AKP)’ applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community.
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Dmitry Alexeev's curator insight, December 25, 2012 3:49 AM

i wonder if model explains some of the abnormalties we observed earlier

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Commensal microbiota and myelin autoantigen cooperate to trigger autoimmune demyelination

Commensal microbiota and myelin autoantigen cooperate to trigger autoimmune demyelination | Microbiome | Scoop.it

Active multiple sclerosis lesions show inflammatory changes suggestive of a combined attack by autoreactive T and B lymphocytes against brain white matter. These pathogenic immune cells derive from progenitors that are normal, innocuous components of the healthy immune repertoire but become autoaggressive upon pathological activation. The stimuli triggering this autoimmune conversion have been commonly attributed to environmental factors, in particular microbial infection. However, using the relapsing-remitting mouse model of spontaneously developing experimental autoimmune encephalomyelitis, here we show that the commensal gut flora-in the absence of pathogenic agents-is essential in triggering immune processes, leading to a relapsing-remitting autoimmune disease driven by myelin-specific CD4(+) T cells. We show further that recruitment and activation of autoantibody-producing B cells from the endogenous immune repertoire depends on availability of the target autoantigen, myelin oligodendrocyte glycoprotein (MOG), and commensal microbiota. Our observations identify a sequence of events triggering organ-specific autoimmune disease and these processes may offer novel therapeutic targets.

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PLoS ONE: Analysis of the Lung Microbiome in the “Healthy” Smoker and in COPD

PLoS ONE: Analysis of the Lung Microbiome in the “Healthy” Smoker and in COPD | Microbiome | Scoop.it

Abstract

Although culture-independent techniques have shown that the lungs are not sterile, little is known about the lung microbiome in chronic obstructive pulmonary disease (COPD). We used pyrosequencing of 16S amplicons to analyze the lung microbiome in two ways: first, using bronchoalveolar lavage (BAL) to sample the distal bronchi and air-spaces; and second, by examining multiple discrete tissue sites in the lungs of six subjects removed at the time of transplantation. We performed BAL on three never-smokers (NS) with normal spirometry, seven smokers with normal spirometry (“heathy smokers”, HS), and four subjects with COPD (CS). Bacterial 16 s sequences were found in all subjects, without significant quantitative differences between groups. Both taxonomy-based and taxonomy-independent approaches disclosed heterogeneity in the bacterial communities between HS subjects that was similar to that seen in healthy NS and two mild COPD patients. The moderate and severe COPD patients had very limited community diversity, which was also noted in 28% of the healthy subjects. Both approaches revealed extensive membership overlap between the bacterial communities of the three study groups. No genera were common within a group but unique across groups. Our data suggests the existence of a core pulmonary bacterial microbiome that includes Pseudomonas, Streptococcus, Prevotella, Fusobacterium, Haemophilus, Veillonella, and Porphyromonas. Most strikingly, there were significant micro-anatomic differences in bacterial communities within the same lung of subjects with advanced COPD. These studies are further demonstration of the pulmonary microbiome and highlight global and micro-anatomic changes in these bacterial communities in severe COPD patients.

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Comparison of normalization methods for construction of large, multiplex amplicon pools for next-generation sequencing.

Comparison of normalization methods for construction of large, multiplex amplicon pools for next-generation sequencing. | Microbiome | Scoop.it

Constructing mixtures of tagged or bar-coded DNAs for sequencing is an important requirement for the efficient use of next-generation sequencers in applications where limited sequence data are required per sample. There are many applications in which next-generation sequencing can be used effectively to sequence large mixed samples; an example is the characterization of microbial communities where <or=1,000 sequences per samples are adequate to address research questions. Thus, it is possible to examine hundreds to thousands of samples per run on massively parallel next-generation sequencers. However, the cost savings for efficient utilization of sequence capacity is realized only if the production and management costs associated with construction of multiplex pools are also scalable. One critical step in multiplex pool construction is the normalization process, whereby equimolar amounts of each amplicon are mixed. Here we compare three approaches (spectroscopy, size-restricted spectroscopy, and quantitative binding) for normalization of large, multiplex amplicon pools for performance and efficiency. We found that the quantitative binding approach was superior and represents an efficient scalable process for construction of very large, multiplex pools with hundreds and perhaps thousands of individual amplicons included. We demonstrate the increased sequence diversity identified with higher throughput. Massively parallel sequencing can dramatically accelerate microbial ecology studies by allowing appropriate replication of sequence acquisition to account for temporal and spatial variations. Further, population studies to examine genetic variation, which require even lower levels of sequencing, should be possible where thousands of individual bar-coded amplicons are examined in parallel.

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Topographical Continuity of Bacterial Populations in the Healthy Human Respiratory Tract

Topographical Continuity of Bacterial Populations in the Healthy Human Respiratory Tract | Microbiome | Scoop.it

Abstract

Rationale: 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.

Objectives: 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.

Methods: 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.

Measurements and Main Results: 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.

Conclusions: 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.

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Is the gut microbiota a new factor contributing to ob... [J Obes. 2012] - PubMed - NCBI

Is the gut microbiota a new factor contributing to ob... [J Obes. 2012] - PubMed - NCBI | Microbiome | Scoop.it

Abstract

The gut microbiota refers to the trillions of microorganisms residing in the intestine and is integral in multiple physiological processes of the host. Recent research has shown that gut bacteria play a role in metabolic disorders such as obesity, diabetes, and cardiovascular diseases. The mechanisms by which the gut microbiota affects metabolic diseases are by two major routes: (1) the innate immune response to the structural components of bacteria (e.g., lipopolysaccharide) resulting in inflammation and (2) bacterial metabolites of dietary compounds (e.g., SCFA from fiber), which have biological activities that regulate host functions. Gut microbiota has evolved with humans as a mutualistic partner, but dysbiosis in a form of altered gut metagenome and collected microbial activities, in combination with classic genetic and environmental factors, may promote the development of metabolic disorders. This paper reviews the available literature about the gut microbiota and aforementioned metabolic disorders and reveals the gaps in knowledge for future study.

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