A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns

Microbial Diversity for Biotechnology 2014

High-throughput sequencing of small-subunit (SSU) rRNA genes has revolutionized understanding of microbial communities and facilitated investigations into ecological dynamics at unprecedented scales. Such extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain a substantial proportion of unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. Indeed, these novel taxonomic lineages are associated with so-called microbial "dark matter," which is the genomic potential of these lineages. Unfortunately, characterization beyond "unclassified" is challenging due to relatively short read lengths and large data set sizes. Here we demonstrate how mining of phylogenetically novel sequences from microbial ecosystems can be automated using SSUnique, a software pipeline that filters unclassified and/or rare operational taxonomic units (OTUs) from 16S rRNA gene sequence libraries by screening against consensus structural models for SSU rRNA. Phylogenetic position is inferred against a reference data set, and additional characterization of novel clades is also included, such as targeted probe/primer design and mining of assembled metagenomes for genomic context. We show how SSUnique reproduced a previous analysis of phylogenetic novelty from an Arctic tundra soil and demonstrate the recovery of highly novel clades from data sets associated with both the Earth Microbiome Project (EMP) and Human Microbiome Project (HMP). We anticipate that SSUnique will add to the expanding computational toolbox supporting high-throughput sequencing approaches for the study of microbial ecology and phylogeny. IMPORTANCE Extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain many unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. This novelty is poorly explored in standard workflows, which narrows the breadth and discovery potential of such studies. Here we present the SSUnique analysis pipeline, which will promote the exploration of unclassified diversity in microbiome research and, importantly, enable the discovery of substantial novel taxonomic lineages through the analysis of a large variety of existing data sets.

The Human Microbiome Project, Earth Microbiome Project, and next-generation sequencing have advanced novel genome association, host genetic linkages, and pathogen identification. The microbiome is the sum of the microbes, their genetic information, and their ecological niche. This study will describe how millions of bacteria in the gut affect the human body in health and disease. The gut microbiome changes in relation with age, with an increase in Bacteroidetes and Firmicutes. Host and environmental factors affecting the gut microbiome are diet, drugs, age, smoking, exercise, and host genetics. In addition, changes in the gut microbiome may affect the local gut immune system and systemic immune system. In this study, we discuss how the microbiome may affect the metabolism of healthy subjects or may affect the pathogenesis of metabolism-generating metabolic diseases. Due to the high number of publications on the argument, from a methodologically point of view, we decided to select the best papers published in referred journals in the last 3 years. Then we selected the previously published papers. The major goals of our study were to elucidate which microbiome and by which pathways are related to healthy and disease conditions.

Microorganisms represent the majority of life on earth, populating a wide range of niches on its surface, underground, in the oceans, in the atmosphere, and both on and inside all multicellular organisms. This “microbiome” will clearly play a critical role as humans struggle to deal with society's major challenges—health care, agriculture, energy, and the environment. As one example, the human gut microbiome contributes 36% of the small molecules that are found in human blood, and it also plays a major role in creating susceptibility to certain human diseases. In recent years, a variety of microbial communities have been characterized through such efforts as the Human Microbiome Project and the Earth Microbiome Project. But mapping these trillions upon trillions of microbes and analyzing the vast amounts of data that are accumulating will require new integrative approaches aimed at understanding how microorganisms function and are interrelated.

The relationship between humans and microbes is attracting the scientific interest since the creation of the first lens by Leeuwenhoek (Fogg, 1969). As years went by, the understanding of the nature and importance of microbes became more apparent however, to date, for the broad audience, the understanding of the microbial roles in our life has been restricted to those causing diseases. Recent advances in the microbiome field, mostly boosted by the new DNA sequencing approaches, brought novel insights linking the human health and the microbes living in close contact with us (Loman and Pallen, 2015). Even though, a big effort from the non-scientific community to access these discoveries it is still necessary. The educational process has been undergoing remarkable changes in the forms of transmission and assimilation of knowledge. “Science”—as we know—is very recent in our history, being responsible for generating knowledge and technologies that profoundly impact the organization and the whole life of individuals. This book (Knight and Buhler, 2015) bridges the scientific knowledge and the general public with outstanding clarity. It uses an accessible language and everyday examples to explain complex concepts of microbial ecology and taxonomy. Moreover, it presents new discoveries regarding the role of microbes on diseases previously thought unrelated to them, like asthma, autism, and depression and links the gut microbes with our behavior. Chapter 1 - This chapter shows how diverse microbes are and the metabolic importance of this diversity. It describes the microbial community present in different skin areas, in the nose and lungs, in the mouth and stomach, intestines, and genitals. Chapter 2 - Describes how we acquire our first microbes from our mother and explains the agents influencing the microbial community shifts along our lives, in response to the food we eat, the contact we have with the environment and with pets, and the use of antibiotics. It ends discussing the links between microbiota diversity and health conditions, and the need for further studies in this area. Chapter 3 - Links diseases like obesity, allergies, asthma, kwashiorkor malnutrition, and irritable bowel disease to the presence or absence of specific microbial groups and the life stages when we are exposed to them. The authors use several research papers showing evidences that the microbes have an enormous impact on our health. Chapter 4 - Focuses on the influences that gut microbes have on our brain; it shows impacts on mood, behavior and/or psychological disorders. It presents several examples of experiments using mice with anxiety disorders or with induced autism, treated with probiotics and altering their gut microbiota, resulting in an alleviation of symptoms. Furthermore, the authors discuss the current state of art on the application of these treatment strategies to potentially cure human disorders. Experiments involving probiotics to help treating irritable bowel disease and celiac disease in infants are shown, and their relation to depression and several psychological effects are raised. Chapter 5 - Starts driving the reader attention to the plasticity of our microbiome and raises the possibility of manipulating and enhancing it. To discuss this subject, the authors use a lawn as example, showing its whole biodiversity and how it grows. Later on, the authors introduce concepts of prebiotics—substances that enhance and benefit our microbes, and probiotics—microbes that live in and benefit the human body. Moreover, they talk about the risks of the microbe ingestion without the proper knowledge, discuss about the benefits brought by vaccines and consider a future, where we could use vaccines for diseases like depression, anxiety or other mind disorders. Chapter 6 - Begins telling a story about the worries that people have when they think about vaccines and how comfortable they are about having antibiotics. This chapter explains the differences between these two issues, elucidating the effects on our whole microbiota and the risks of antibiotic misuse, like not having it for the corrected time, as prescribed. Chapter 7 and Appendix - makes predictions about the future of the microbiome studies and the direct benefits it can bring to people. Furthermore, it introduces the three most famous American microbiome efforts—American Gut Project, Earth Microbiome Project and the Human Microbiome Project—and invites people to have their gut microbiomes described by the former.

Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a ‘taxonomy to tree' approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/.

SummaryPhylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization.Availability and implementation phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize.Supplementary information Supplementary data are available at Bioinformatics online.

If you are not an astronomer (amateur or otherwise), looking at the stars can be an exercise in frustration. Where are the constellations? The planets? A telescope doesn’t necessarily help: you can see things more clearly, but without knowing where to look and what to look for, you can’t tell what you are seeing.

Analyses of the taxonomic diversity associated with the human microbiome continue to be an area of great importance. The study of the nature and extent of the commonly shared taxa (“core”), versus those less prevalent, establishes a baseline for comparing healthy and diseased groups by quantifying the variation among people, across body habitats and over time. The National Institutes of Health (NIH) sponsored Human Microbiome Project (HMP) has provided an unprecedented opportunity to examine and better define what constitutes the taxonomic core within and across body habitats and individuals through pyrosequencing-based profiling of 16S rRNA gene sequences from oral, skin, distal gut (stool), and vaginal body habitats from over 200 healthy individuals. A two-parameter model is introduced to quantitatively identify the core taxonomic members of each body habitat’s microbiota across the healthy cohort. Using only cutoffs for taxonomic ubiquity and abundance, core taxonomic members were identified for each of the 18 body habitats and also for the 4 higher-level body regions. Although many microbes were shared at low abundance, they exhibited a relatively continuous spread in both their abundance and ubiquity, as opposed to a more discretized separation. The numbers of core taxa members in the body regions are comparatively small and stable, reflecting the relatively high, but conserved, interpersonal variability within the cohort. Core sizes increased across the body regions in the order of: vagina, skin, stool, and oral cavity. A number of “minor” oral taxonomic core were also identified by their majority presence across the cohort, but with relatively low and stable abundances. A method for quantifying the difference between two cohorts was introduced and applied to samples collected on a second visit, revealing that over time, the oral, skin, and stool body regions tended to be more transient in their taxonomic structure than the vaginal body region.

Purpose: Characterization of the respiratory tract bacterial microbiome is in its infancy when compared to the gut microbiota. To limit bias mandates a robust methodology. Specific amplification of the hypervariable (V) region of the 16SrRNA gene is a crucial step. Differences in accuracy exist for one V region to another depending on the sampled environment. We aimed to assess the impact of the primer sequences targeting the V4 region currently used for gut microbiota studies in respiratory samples. Materials and methods: The original 515 F-806R primer pair targets the V4 region of the 16SrRNA gene. We compared two different 515 F-806R primer pairs before Illumina 250 paired-end sequencing for bacterial microbiome analyses of respiratory samples from critically-ill ventilated patients. “S-V4” for “Stringent V4” primer pair is used in two ongoing international projects “the Integrative Human microbiome project (iHMP)” and “the Earth microbiome project (EMP).” “R-V4” for “Relaxed V4” primer pair has been modified to reduce biases against specific environmental taxa. The optimal method was determined by concordance with conventional microbiology. Results: Twenty samples from three patients who developed a ventilator-associated pneumonia (VAP) and four who did not (control ventilated patients) were sequenced. Highly different results were obtained. “S-V4” provided the best agreement with the conventional microbiology for endotracheal aspirate: 89% as compared to 56% for “R-V4.” The main difference related to poor Enterobacteriaceae detection with “R-V4” primers. Conclusions: Accuracy of the bacterial lung microbiome composition was highly dependent on the primers used for amplification of the 16 s rRNA hypervariable sequence. This work validates for future lung microbiome studies the use of the 515 F-806R “S-V4” primer pair associated to Illumina® MiSeq paired-end sequencing.

The features of soil microbiome may be an universal and very sensitive indicator of soil state used for optimization and biologization of agriculture systems.However, this approach to the matter requires a preliminary analysis of microbiomes composition in different types of soils.An analogical taxonomic investigations presented difficult task formerly and took considerable material and time expenditures.The introduction to molecular ecology of the new progeny methods of sequencing permits to increase both a number of revealed microorganism species and analyzed ecotops.The authors made the primary analysis of microbial associations with the use of pyrosequencing of soil metagenome.For the study, the collection of soils from different regions of Russia (19 samples) and also from the Crimea (Ukraine, 1 sample) was created.The bacteria from phylas of Proteobacteria (up 59.3 %), Actinobacteria (up 55.4 %), Acidobacteria (up 26.5 %), Verrucomicrobia (up 13.6 %), Bacteroidetes (up 10.5 %), Firmicutes (up 8.2 %), Gemmatimonadetes (up 6.9 %), Chloroflexi (up 5.7 %) and archaea from Crenarchaeota phyla were dominating in microbial associations.The comparison of taxonomic structure of microbial associations indicates that physiochemical factors (acidity and moisture of soil) have a more influence on prokaryote biodiversity than other factors (for example, type of soil or sampling point).So the soils from south regions with lesser moisture contain more the actinobacteria, when the moister north soils contain mainly the proteobacteria.The soils with low pH are characterized by a raise of acidobacteria percent.

Microbial ecology has witnessed tremendous progress over the last decade empowered by meta-omics approaches and innovations in DNA/RNA sequencing as well as high-resolution mass spectrometry. In this climate, the rise of meta-omics projects (Raes, 2011) such as MetaHIT and the Human Microbiome Project, Tara Oceans, the Global Ocean Sampling Expedition and the Earth Microbiome Project aiming at unraveling the structure and function of specific microbiomes in different habitats was observed. Now that massive data generation is no longer science fiction, the bottleneck shifts to computational analysis (Falony et al., 2015).

Metagenomics, as a culture-independent approach, enables the exploration of complex heterogeneous microbial communities under natural conditions by massive sequencing of community-specific DNA. Metagenomic data sets, derived from various environments, provide new insights into microbial life. Large-scale projects like the Human Microbiome Project or the Earth Microbiome Project emphasize the increasing importance of metagenomics for biomedical and ecosystem research. However, such projects are currently challenging bioinformatics due to the explosive increase in sequencing data. New computationally efficient and statistically adequate methods are required to answer the essential questions “Who is in there?” and “What are they doing?”. In this thesis, I developed the Mixture-of-Pathways (MoP) model and Tax4Fun approach. Both methods link the taxonomic profile to a set of pre-computed reference profiles to predict the metabolic repertoire of the microbial community. Since the taxonomic profile is normally estimated to answer the question “Who is in there?”, the further use of the taxonomic profile avoids additional costs for answering the question “What are they doing?”. Tax4Fun is specifically designed for the output of 16S rRNA analysis pipelines using the SILVA database as reference, whereas the MoP model is especially conceived for metagenome sequence data and provides a robust statistical basis to describe the metabolic potential of a microbial community. The adequate metabolic modeling of metagenomes provides a concise summary of the functional variation of metagenomes across many samples, enabling the identification of relevant metabolic differences in comparative analyses. For comparative metagenomics, the identification of similar metagenomes to a newly obtained dataset is of growing importance. For an efficient large-scale identification of closely related metagenomes within a database retrieval context, I conducted a detailed evaluation of a k-nearest-neighbor search utilizing different biological feature profiles and metrics. I demonstrated that different features and metrics can be chosen for a convenient interpretation of results in terms of the underlying features. The integration of the k-nearest-neighbor search into metagenome annotation and comparison systems is beneficial to automatically identify additional metagenomes for comparative analyses as well as to detect mislabeled or contaminated datasets by unexpected neighboring habitat labels. The MoP approach and k-nearest-neighbor search are available to the scientific community as part of the CoMet-Universe web server application. Additionally, the MoP and Tax4Fun approach are provided as R Package.

β3-Adrenoceptor Agonist for the Treatment of Bladder Dysfunction in Children: A Systematic Review and Meta-Analysis.

Many systemic therapies used for moderate-to-severe atopic dermatitis (AD) have immunosuppressive properties and necessitate laboratory screening and monitoring, adding to the treatment burden. Previous dupilumab studies in adults, adolescents and children aged 6–11 years with moderate-to-severe AD showed no clinically meaningful adverse changes in laboratory parameters. Here we evaluate hematology and chemistry laboratory safety data for dupilumab-treated children aged 6 months to 5 years with moderate-to-severe atopic dermatitis. Patients aged 6 months to 5 years with inadequately controlled moderate-to-severe AD were enrolled in LIBERTY AD PRESCHOOL (NCT03346434 part B), a randomized, double-blind placebo-controlled phase 3 study. 162 patients were randomized to either dupilumab 200/300 mg every 4 weeks (q4w; N = 83; baseline weight ≥5 <15 kg: 200 mg; ≥15 to <30 kg: 300 mg) or placebo (N = 79) for 16 weeks. From Day –14, all patients initiated standardized treatment with low-potency topical corticosteroids. Laboratory data was collected at baseline, weeks 4 and 16. At baseline, mean (SD) counts of hematology parameters were similar in both treatment groups: haemoglobin (dupilumab: 129.4 gL−1 [12]; placebo: 127.2 gL−1 [11.4]), lymphocyte (dupilumab: 4.6 × 109 L−1 [1.8]; placebo: 4.5 × 109 L−1 [1.7]), basophil (dupilumab: 0.07 × 109 L−1 [0.03]; placebo: 0.07 × 109 L−1 [0.04]), platelet (dupilumab: 397.7 × 109 L−1 [103.2]; placebo: 385.6 × 109 L−1 [112.9]) and eosinophils (dupilumab: 1.1 × 109 L−1 [0.7]; placebo: 1.1 × 109 L−1 [0.7]). Mean (SD) haemoglobin count in the dupilumab (128.4 × gL−1 [11]) and placebo groups (128.2 × gL−1 [11.2]), lymphocyte count in the dupilumab (4.20 × 109 L−1 [2.06]) and placebo groups (4.29 × 109 L−1 [1.52]) and basophil count in the dupilumab (0.07 × 109 L−1 [0.04]) and placebo groups (0.06 × 109 L−1 [0.03]) remained with the normal reference range for this population at week 16. The mean change (SD) in platelet count at week 16 was −16.3 × 109 L−1 (78.5) in the dupilumab group and +17.4 × 109 L−1 (106.6) in the placebo group. In the dupilumab treatment group, the mean eosinophil count increased at week 4 (mean change from baseline [SD]; + 0.48 × 109 L−1 [1.8]) and trended downward by week 16 (+0.31 × 109 L−1 [1.4]) while minimal changes were noted in the placebo group at week 4 (0.1 × 109 L−1 [0.7]) and week 16 (−0.2 × 109 L−1 [0.7]). The values for creatine kinase, alkaline phosphatase, lactate dehydrogenase, blood urea nitrogen, albumin and protein at week 16 remained within the normal reference range in all treatment groups. Two patients in the dupilumab 200/300 mg q4w arm of this study reported treatment-emergent adverse events of severe and moderate eosinophilia. Neither event was associated with clinical symptoms nor led to the discontinuation of the study treatment. No clinically meaningful changes in hematology and chemistry parameters in children aged 6 months to 5 years with moderate-to-severe AD were seen with 16 weeks of dupilumab treatment. These data demonstrate that, as with adults, adolescents and older children, routine laboratory monitoring is unnecessary in this younger population. Dupilumab was generally well tolerated with an acceptable safety profile.