SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree.

Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significant consensus response (in terms of operational taxonomic unit abundance) to the intervention. We present the R software package SigTree, a collection of flexible tools that make use of meta-analysis methods and regular expressions to identify and visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting for multiple comparisons

Lignin engineering in field-grown poplar trees affects the endosphere bacterial microbiome

Cinnamoyl-CoA reductase (CCR), an enzyme central to the lignin bio-synthetic pathway, represents a promising biotechnological target to reduce lignin levels and to improve the commercial viability of lignocellulosic biomass. However, silencing of the CCR gene results in considerable flux changes of the general and monolignol-specific lignin pathways, ultimately leading to the accumulation of various extractable phenolic compounds in the xylem. Here, we evaluated host genotype-dependent effects of field-grown, CCR-down-regulated poplar trees (Populus tremula x Populus alba) on the bacterial rhizosphere microbiome and the endosphere microbiome, namely the microbiota present in roots, stems, and leaves. Plant-associated bacteria were isolated from all plant compartments by selective isolation and enrichment techniques with specific phenolic carbon sources (such as ferulic acid) that are up-regulated in CCR-deficient poplar trees. The bacterial microbiomes present in the endosphere were highly responsive to the CCR-deficient poplar genotype with remarkably different metabolic capacities and associated community structures compared with the WT trees. In contrast, the rhizosphere microbiome of CCR-deficient and WT poplar trees featured highly overlapping bacterial community structures and metabolic capacities. We demonstrate the host genotype modulation of the plant microbiome by minute genetic variations in the plant genome. Hence, these interactions need to be taken into consideration to understand the full consequences of plant metabolic pathway engineering and its relation with the environment and the intended genetic improvement

Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other “omics” disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials

Microbial communities in sediment from Zostera marina patches, but not the Z. marina leaf or root microbiomes, vary in relation to distance from patch edge

© 2017 Ettinger et al. Background. Zostera marina (also known as eelgrass) is a foundation species in coastal and marine ecosystems worldwide and is a model for studies of seagrasses (a paraphyletic group in the order Alismatales) that include all the known fully submerged marine angiosperms. In recent years, there has been a growing appreciation of the potential importance of the microbial communities (i.e., microbiomes) associated with various plant species. Here we report a study of variation in Z. marina microbiomes from a field site in Bodega Bay, CA. Methods. We characterized and then compared the microbial communities of root, leaf and sediment samples (using 16S ribosomal RNA gene PCR and sequencing) and associated environmental parameters from the inside, edge and outside of a single subtidal Z. marina patch. Multiple comparative approaches were used to examine associations between microbiome features (e.g., diversity, taxonomic composition) and environmental parameters and to compare sample types and sites. Results. Microbial communities differed significantly between sample types (root, leaf and sediment) and in sediments from different sites (inside, edge, outside). Carbon:Nitrogen ratio and eelgrass density were both significantly correlated to sediment community composition. Enrichment of certain taxonomic groups in each sample type was detected and analyzed in regard to possible functional implications (especially regarding sulfur metabolism). Discussion. Our results are mostly consistent with prior work on seagrass associated microbiomes with a few differences and additional findings. From a functional point of view, the most significant finding is that many of the taxa that differ significantly between sample types and sites are closely related to ones commonly associated with various aspects of sulfur and nitrogen metabolism. Though not a traditional model organism, we believe that Z. marina can become a model for studies of marine plantmicrobiome interactions

Análise metagenómica do microbioma da saliva de pacientes com doença pulmonar obstrutiva crónica

Mestrado em Biologia Molecular e CelularMicrobiome is a community of microorganisms living in a particular environment that englobes all microorganisms with their genes and environmental interactions. The human microbiome plays a pivotal role in human physiology and metabolism being associated to development, nutrition, immunity, and resistance to pathogens and has recognized implications for health and disease. Chronic Obstructive Pulmonary Disease (COPD) is a pulmonary disease characterized by persistent and progressive and nonreversible airflow obstruction. The role of bacteria as a potential pathogenic and etiologic factor in COPD has been a topic of debate for many years. It is thought that lung colonization by particular bacterial strains, in patients with COPD, is responsible for the chronic bronchitis phenotype, increased risk of exacerbations, and loss of lung function. Even though saliva is one of the most easily collectable samples, few studies have been conducted to characterize the saliva microbiome in patients with COPD and even fewer to identify biomarkers that might be informative for disease onset and progression. The aim of this study was to implement the methodology to study the saliva microbiome in patients suffering with COPD, to understand the dynamics of saliva microbiome in the setting of an exacerbation and how the microbiome evolve after that. For that a metagenomic approach was carried out, using the sequencing of the 16S rRNA gene, to analyze 17 samples from 7 patients with COPD, collected at 3 different time points, i.e. at exacerbation, 2 weeks after exacerbation, and at clinical full recovery. In this study, we found microbial shifts in the samples collected at different time points. We also detected high sample variability, especially between samples collected from different individuals. These results suggest that saliva might me a good source of biomarkers for COPD management and may represent an improvement to the implementation of personalized medicine in this population. However, more and larger studies must be conducted.Microbioma é definido como sendo uma comunidade de microrganismos presente num dado ambiente, que engloba todos os microorganismos com seus genes e interações ambientais. O microbioma humano desempenha um papel importante na fisiologia humana e no seu metabolismo, estando associado ao desenvolvimento, nutrição, imunidade e resistência a agentes patogénicos com implicações na saúde e doença. A doença pulmonar obstrutiva crónica (DPOC) é uma doença pulmonar caracterizada por uma obstrução das vias aéreas persistente progressiva e não reversível. O papel das bactérias como potencial fator patogénico e etiológico na DPOC tem sido tema de debate nos últimos anos. Pensa-se que a colonização dos pulmões por determinadas bactérias, em pacientes com DPOC, é responsável pelo aumento do risco de exacerbações e perda de função pulmonar. Embora a saliva seja uma das amostras mais facilmente recolhida, são ainda poucos os estudos para caracterizar o microbioma da saliva em pacientes com DPOC, e ainda menos para identificar nele biomarcadores informativos sobre o diagnóstico e progressão desta doença. O objetivo deste estudo foi implementar a metodologia que permita estudar o microbioma da saliva em pacientes com DPOC, compreender a dinâmica do microbioma da saliva no contexto de uma exacerbação e como o microbioma evolui depois disso. Para isso, utilizou-se uma abordagem metagenómica utilizando a sequenciação do gene 16S rRNA, para analisar 17 amostras de 7 pacientes com DPOC, recolhidas em 3 momentos diferentes, i.e. em exacerbação, 2 semanas após a exacerbação e após recuperação clínica. Neste estudo foram encontradas e serão descritas diferenças na composição microbiana das amostras colhidas em tempos diferentes. Verificou-se também uma grande variabilidade nos resultados, com grandes diferenças entre as amostras colhidas de diferentes pacientes. Estes resultados sugerem que a saliva pode ser uma boa fonte de biomarcadores para a DPOC e poderá representar um avanço para a implementação da medicina personalizada nesta população. No entanto mais estudos com amostras alargadas são ainda necessários. Contudo, mais estudos deverão ser realizados

Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny

Scleractinian corals' microbial symbionts influence host health, yet how coral microbiomes assembled over evolution is not well understood. We survey bacterial and archaeal communities in phylogenetically diverse Australian corals representing more than 425 million years of diversification. We show that coral microbiomes are anatomically compartmentalized in both modern microbial ecology and evolutionary assembly. Coral mucus, tissue, and skeleton microbiomes differ in microbial community composition, richness, and response to host vs. environmental drivers. We also find evidence of coral-microbe phylosymbiosis, in which coral microbiome composition and richness reflect coral phylogeny. Surprisingly, the coral skeleton represents the most biodiverse coral microbiome, and also shows the strongest evidence of phylosymbiosis. Interactions between bacterial and coral phylogeny significantly influence the abundance of four groups of bacteria-including Endozoicomonas-like bacteria, which divide into host-generalist and host-specific subclades. Together these results trace microbial symbiosis across anatomy during the evolution of a basal animal lineage

Assessing Microbial Diversity Through Nucleotide Variation

Microbes are the most abundant and most diverse form of life on Earth, constituting the largest portion of the total biomass of the entire planet. They are present in every niche in nature, including very extreme environments, and they govern biogeochemical transformations in ecosystems. The human body is home to a diverse assemblage of microbial species as well. In fact, the number of microbial cells in the gastrointestinal tract, oral cavity, skin, airway passages and urogenital system is approximately an order of magnitude greater than the number of cells that make up the human body itself, and changes in the composition and relative abundance of these microbial communities are highly associated with intestinal and respiratory disorders and diseases of the skin and mucus membranes. In the early 1990\u27s, cultivation-­‐independent methods, especially those based on PCR-­‐amplification and sequences of phylogenetically informative 16S rRNA genes, made it possible to assess the composition of microbial species in natural environments, advances in high-­‐throughput sequencing technologies in recent years have increased sequencing capacity and microbial detection by orders of magnitude. However, the effectiveness of current computational methods available to analyze the vast amounts of sequence data is poor and investigating the diversity within microbial communities remains challenging. In addition to offering an easy-­‐to-­‐use visualization and statistical analysis framework for microbial community analyses, the study described herein aims to present a biologically relevant computational approach for assessing microbial diversity at finer scales of microbial communities through nucleotide variation in 16S rRNA genes

Bioconductor workflow for microbiome data analysis: from raw reads to community analyses [version 1; referees: 2 approved]

High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to quantify and compare abundance levels or microbial composition of communities in different conditions. The sequencing reads have to be denoised and assigned to the closest taxa from a reference database. Common approaches use a notion of 97% similarity and normalize the data by subsampling to equalize library sizes. In this paper, we show that statistical models allow more accurate abundance estimates. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, including both parameteric and nonparametric methods. We provide examples of using the R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests, partial least squares and linear models as well as nonparametric testing using community networks and the ggnetwork package

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity