Soil microbiome mediates positive plant diversity‐productivity relationships in late successional grassland species

Which processes drive the productivity benefits of biodiversity remain a critical, but unanswered question in ecology. We tested whether the soil microbiome mediates the diversity‐productivity relationships among late successional plant species. We found that productivity increased with plant richness in diverse soil communities, but not with low‐diversity mixtures of arbuscular mycorrhizal fungi or in pasteurised soils. Diversity‐interaction modelling revealed that pairwise interactions among species best explained the positive diversity‐productivity relationships, and that transgressive overyielding resulting from positive complementarity was only observed with the late successional soil microbiome, which was both the most diverse and exhibited the strongest community differentiation among plant species. We found evidence that both dilution/suppression from host‐specific pathogens and microbiome‐mediated resource partitioning contributed to positive diversity‐productivity relationships and overyielding. Our results suggest that re‐establishment of a diverse, late successional soil microbiome may be critical to the restoration of the functional benefits of plant diversity following anthropogenic disturbance.     

The microbiome of pest insects: it is not just bacteria

Insects are associated with multiple microbes that have been reported to influence various aspects of their biology. Most studies in insects, including pest species, focus on the bacterial communities of the microbiome even though the microbiome consists of members of many more kingdoms, which can also have large influence on the life history of insects. In this review, we present some key examples of how the different members of the microbiome, such as bacteria, fungi, viruses, archaea, and protozoa, affect the fitness and behavior of pest insects. Moreover, we argue that interactions within and among microbial groups are abundant and of great importance, necessitating the use of a community approach to study microbial–host interactions. We propose that the restricted focus on bacteria very likely hampers our understanding of the functioning and impact of the microbiome on the biology of pest insects. We close our review by highlighting a few open questions that can provide an in‐depth understanding of how other components of the microbiome, in addition to bacteria, might influence host performance, thus contributing to pest insect ecology.     

Full-repertoire comparison of the microscopic objects composing the human gut microbiome with sequenced and cultured communities

The study of the human gut microbiome is essential in microbiology and infectious diseases as specific alterations in the gut microbiome might be associated with various pathologies, such as chronic inflammatory disease, intestinal infection and colorectal cancer. To identify such dysregulations, several strategies are being used to create a repertoire of the microorganisms composing the human gut microbiome. In this study, we used the “microscomics” approach, which consists of creating an ultrastructural repertoire of all the cell-like objects composing stool samples from healthy donors using transmission electron microscopy (TEM). We used TEM to screen ultrathin sections of 8 resin-embedded stool samples. After exploring hundreds of micrographs, we managed to elaborate ultrastructural categories based on morphological criteria or features. This approach explained many inconsistencies observed with other techniques, such as metagenomics and culturomics. We highlighted the value of our culture-independent approach by comparing our microscopic images to those of cultured bacteria and those reported in the literature. This study helped to detect “minimicrobes” Candidate Phyla Radiation (CPR) for the first time in human stool samples. This “microscomics” approach is non-exhaustive but complements already existing approaches and adds important data to the puzzle of the microbiota.

     

An emerging field: Post-translational modification in microbiome

Post-translational modifications (PTMs) play an essential role in most biological processes. PTMs on human proteins have been extensively studied. Studies on bacterial PTMs are emerging, which demonstrate that bacterial PTMs are different from human PTMs in their types, mechanisms and functions. Few PTM studies have been done on the microbiome. Here, we reviewed several studied PTMs in bacteria including phosphorylation, acetylation, succinylation, glycosylation, and proteases. We discussed the enzymes responsible for each PTM and their functions. We also summarized the current methods used to study microbiome PTMs and the observations demonstrating the roles of PTM in the microbe-microbe interactions within the microbiome and their interactions with the environment or host. Although new methods and tools for PTM studies are still needed, the existing technologies have made great progress enabling a deeper understanding of the functional regulation of the microbiome. Large-scale application of these microbiome-wide PTM studies will provide a better understanding of the microbiome and its roles in the development of human diseases.     

Microbial co-occurrence relationships in the human microbiome

The healthy microbiota show remarkable variability within and among individuals. In addition to external exposures, ecological relationships (both oppositional and symbiotic) between microbial inhabitants are important contributors to this variation. It is thus of interest to assess what relationships might exist among microbes and determine their underlying reasons. The initial Human Microbiome Project (HMP) cohort, comprising 239 individuals and 18 different microbial habitats, provides an unprecedented resource to detect, catalog, and analyze such relationships. Here, we applied an ensemble method based on multiple similarity measures in combination with generalized boosted linear models (GBLMs) to taxonomic marker (16S rRNA gene) profiles of this cohort, resulting in a global network of 3,005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome. This network revealed strong niche specialization, with most microbial associations occurring within body sites and a number of accompanying inter-body site relationships. Microbial communities within the oropharynx grouped into three distinct habitats, which themselves showed no direct influence on the composition of the gut microbiota. Conversely, niches such as the vagina demonstrated little to no decomposition into region-specific interactions. Diverse mechanisms underlay individual interactions, with some such as the co-exclusion of Porphyromonaceae family members and Streptococcus in the subgingival plaque supported by known biochemical dependencies. These differences varied among broad phylogenetic groups as well, with the Bacilli and Fusobacteria, for example, both enriched for exclusion of taxa from other clades. Comparing phylogenetic versus functional similarities among bacteria, we show that dominant commensal taxa (such as Prevotellaceae and Bacteroides in the gut) often compete, while potential pathogens (e.g. Treponema and Prevotella in the dental plaque) are more likely to co-occur in complementary niches. This approach thus serves to open new opportunities for future targeted mechanistic studies of the microbial ecology of the human microbiome.     

Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients

Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, includ-ing KEGG orthologous groups and the evolutionary genealogy of genes:Non-supervised Ortholo-gous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 dia-betes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D.     

Extracting three-way gene interactions from microarray data

MOTIVATION: It is an important and difficult task to extract gene network information from high-throughput genomic data. A common approach is to cluster genes using pairwise correlation as a distance metric. However, pairwise correlation is clearly too simplistic to describe the complex relationships among real genes since co-expression relationships are often restricted to a specific set of biological conditions/processes. In this study, we described a three-way gene interaction model that captures the dynamic nature of co-expression relationship between a gene pair through the introduction of a controller gene. RESULTS: We surveyed 0.4 billion possible three-way interactions among 1000 genes in a microarray dataset containing 678 human cancer samples. To test the reproducibility and statistical significance of our results, we randomly split the samples into a training set and a testing set. We found that the gene triplets with the strongest interactions (i.e. with the smallest P-values from appropriate statistical tests) in the training set also had the strongest interactions in the testing set. A distinctive pattern of three-way interaction emerged from these gene triplets: depending on the third gene being expressed or not, the remaining two genes can be either co-expressed or mutually exclusive (i.e. expression of either one of them would repress the other). Such three-way interactions can exist without apparent pairwise correlations. The identified three-way interactions may constitute candidates for further experimentation using techniques such as RNA interference, so that novel gene network or pathways could be identified.     

Characterization of the rat oral microbiome and the effects of dietary nitrate

The nitrate–nitrite–NO pathway to nitric oxide (NO) production is a symbiotic pathway in mammals that is dependent on nitrate reducing oral commensal bacteria. Studies suggest that by contributing NO to the mammalian host, the oral microbiome helps maintain cardiovascular health. To begin to understand how changes in oral microbiota affect physiological functions such as blood pressure, we have characterized the Wistar rat nitrate reducing oral microbiome. Using 16S rRNA gene sequencing and analysis we compare the native Wistar rat tongue microbiome to that of healthy humans and to that of rats with sodium nitrate and chlorhexidine mouthwash treatments. We demonstrate that the rat tongue microbiome is less diverse than the human tongue microbiome, but that the physiological activity is comparable, as sodium nitrate supplementation significantly lowered diastolic blood pressure in Wistar rats and also lowers blood pressure (diastolic and systolic) in humans. We also show for the first time that sodium nitrate supplementation alters the abundance of specific bacterial species on the tongue. Our results suggest that the changes in oral nitrate reducing bacteria may affect nitric oxide availability and physiological functions such as blood pressure. Understanding individual changes in human oral microbiome may offer novel dietary approaches to restore NO availability and blood pressure.     

Host genetic variation impacts microbiome composition across human body sites

BackgroundThe composition of bacteria in and on the human body varies widely across human individuals, and has been associated with multiple health conditions. While microbial communities are influenced by environmental factors, some degree of genetic influence of the host on the microbiome is also expected. This study is part of an expanding effort to comprehensively profile the interactions between human genetic variation and the composition of this microbial ecosystem on a genome- and microbiome-wide scale.ResultsHere, we jointly analyze the composition of the human microbiome and host genetic variation. By mining the shotgun metagenomic data from the Human Microbiome Project for host DNA reads, we gathered information on host genetic variation for 93 individuals for whom bacterial abundance data are also available. Using this dataset, we identify significant associations between host genetic variation and microbiome composition in 10 of the 15 body sites tested. These associations are driven by host genetic variation in immunity-related pathways, and are especially enriched in host genes that have been previously associated with microbiome-related complex diseases, such as inflammatory bowel disease and obesity-related disorders. Lastly, we show that host genomic regions associated with the microbiome have high levels of genetic differentiation among human populations, possibly indicating host genomic adaptation to environment-specific microbiomes.ConclusionsOur results highlight the role of host genetic variation in shaping the composition of the human microbiome, and provide a starting point toward understanding the complex interaction between human genetics and the microbiome in the context of human evolution and disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0759-1) contains supplementary material, which is available to authorized users.     

Divergence across diet,time and populations rules out parallel evolution in the gut microbiomes of Trinidadian guppies

Diverse microbial consortia profoundly influence animal biology, necessitating an understanding of microbiome variation in studies of animal adaptation. Yet, little is known about such variability among fish, in spite of their importance in aquatic ecosystems. The Trinidadian guppy, Poecilia reticulata, is an intriguing candidate to test microbiome-related hypotheses on the drivers and consequences of animal adaptation, given the recent parallel origins of a similar ecotype across streams. To assess the relationships between the microbiome and host adaptation, we used 16S rRNA amplicon sequencing to characterize gut bacteria of two guppy ecotypes with known divergence in diet, life history, physiology and morphology collected from low-predation (LP) and high-predation (HP) habitats in four Trinidadian streams. Guts were populated by several recurring, core bacteria that are related to other fish associates and rarely detected in the environment. Although gut communities of lab-reared guppies differed from those in the wild, microbiome divergence between ecotypes from the same stream was evident under identical rearing conditions, suggesting host genetic divergence can affect associations with gut bacteria. In the field, gut communities varied over time, across streams and between ecotypes in a stream-specific manner. This latter finding, along with PICRUSt predictions of metagenome function, argues against strong parallelism of the gut microbiome in association with LP ecotype evolution. Thus, bacteria cannot be invoked in facilitating the heightened reliance of LP guppies on lower-quality diets. We argue that the macroevolutionary microbiome convergence seen across animals with similar diets may be a signature of secondary microbial shifts arising some time after host-driven adaptation.     

Diversity and temporal dynamics of primate milk microbiomes

Milk is inhabited by a community of bacteria and is one of the first postnatal sources of microbial exposure for mammalian young. Bacteria in breast milk may enhance immune development, improve intestinal health, and stimulate the gut‐brain axis for infants. Variation in milk microbiome structure (e.g., operational taxonomic unit [OTU] diversity, community composition) may lead to different infant developmental outcomes. Milk microbiome structure may depend on evolutionary processes acting at the host species level and ecological processes occurring over lactation time, among others. We quantified milk microbiomes using 16S rRNA high‐throughput sequencing for nine primate species and for six primate mothers sampled over lactation. Our data set included humans (Homo sapiens, Philippines and USA) and eight nonhuman primate species living in captivity (bonobo [Pan paniscus], chimpanzee [Pan troglodytes], western lowland gorilla [Gorilla gorilla gorilla], Bornean orangutan [Pongo pygmaeus], Sumatran orangutan [Pongo abelii], rhesus macaque [Macaca mulatta], owl monkey [Aotus nancymaae]) and in the wild (mantled howler monkey [Alouatta palliata]). For a subset of the data, we paired microbiome data with nutrient and hormone assay results to quantify the effect of milk chemistry on milk microbiomes. We detected a core primate milk microbiome of seven bacterial OTUs indicating a robust relationship between these bacteria and primate species. Milk microbiomes differed among primate species with rhesus macaques, humans and mantled howler monkeys having notably distinct milk microbiomes. Gross energy in milk from protein and fat explained some of the variations in microbiome composition among species. Microbiome composition changed in a predictable manner for three primate mothers over lactation time, suggesting that different bacterial communities may be selected for as the infant ages. Our results contribute to understanding ecological and evolutionary relationships between bacteria and primate hosts, which can have applied benefits for humans and endangered primates in our care.     

Mining virulence genes using metagenomics

When a bacterial genome is compared to the metagenome of an environment it inhabits, most genes recruit at high sequence identity. In free-living bacteria (for instance marine bacteria compared against the ocean metagenome) certain genomic regions are totally absent in recruitment plots, representing therefore genes unique to individual bacterial isolates. We show that these Metagenomic Islands (MIs) are also visible in bacteria living in human hosts when their genomes are compared to sequences from the human microbiome, despite the compartmentalized structure of human-related environments such as the gut. From an applied point of view, MIs of human pathogens (e.g. those identified in enterohaemorragic Escherichia coli against the gut metagenome or in pathogenic Neisseria meningitidis against the oral metagenome) include virulence genes that appear to be absent in related strains or species present in the microbiome of healthy individuals. We propose that this strategy (i.e. recruitment analysis of pathogenic bacteria against the metagenome of healthy subjects) can be used to detect pathogenicity regions in species where the genes involved in virulence are poorly characterized. Using this approach, we detect well-known pathogenicity islands and identify new potential virulence genes in several human pathogens.     

Geography,Host Genetics,and Cross‐Domain Microbial Networks Structure the Skin Microbiota of Fragmented Brazilian Atlantic Forest Frog Populations

The host‐associated microbiome plays a significant role in health. However, the roles of factors such as host genetics and microbial interactions in determining microbiome diversity remain unclear. We examined these factors using amplicon‐based sequencing of 175 Thoropa taophora frog skin swabs collected from a naturally fragmented landscape in southeastern Brazil. Specifically, we examined (1) the effects of geography and host genetics on microbiome diversity and structure; (2) the structure of microbial eukaryotic and bacterial co‐occurrence networks; and (3) co‐occurrence between microeukaryotes with bacterial OTUs known to affect growth of the fungal pathogen Batrachochytrium dendrobatidis (Bd). While bacterial alpha diversity varied by both site type and host MHC IIB genotype, microeukaryotic alpha diversity varied only by site type. However, bacteria and microeukaryote composition showed variation according to both site type and host MHC IIB genotype. Our network analysis showed the highest connectivity when both eukaryotes and bacteria were included, implying that ecological interactions may occur among domains. Lastly, anti‐Bd bacteria were not broadly negatively co‐associated with the fungal microbiome and were positively associated with potential amphibian parasites. Our findings emphasize the importance of considering both domains in microbiome research and suggest that for effective probiotic strategies for amphibian disease management, considering potential interactions among all members of the microbiome is crucial.     

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.     

Machine learning methods for microbiome studies

Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the development of sequencing technology, microbiome studies with large number of samples are eligible on an acceptable cost nowadays. Large samples allow analysis of more sophisticated modeling using machine learning approaches to study relationships between microbiome and various traits. This article provides an overview of machine learning methods for non-data scientists interested in the association analysis of microbiomes and host phenotypes. Once genomic feature of microbiome is determined, various analysis methods can be used to explore the relationship between microbiome and host phenotypes that include penalized regression, support vector machine (SVM), random forest, and artificial neural network (ANN). Deep neural network methods are also touched. Analysis procedure from environment setup to extract analysis results are presented with Python programming language.     

Allele frequency‐free inference of close familial relationships from genotypes or low‐depth sequencing data

Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non‐model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low‐depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low‐depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species.     

Skin microbiome: genomics-based insights into the diversity and role of skin microbes

Recent advances in DNA sequencing methodology have facilitated studies of human skin microbes that circumvent difficulties in isolating and characterizing fastidious microbes. Sequence-based approaches have identified a greater diversity of cutaneous bacteria than studies using traditional cultivation techniques. However, improved sequencing technologies and analytical methods are needed to study all skin microbes, including bacteria, archaea, fungi, viruses and mites, and how they interact with each other and their human hosts. This review discusses current skin microbiome research, with a primary focus on bacteria, and the challenges facing investigators striving to understand how skin microorganisms contribute to health and disease.     

Statistical Object Data Analysis of Taxonomic Trees from Human Microbiome Data

Human microbiome research characterizes the microbial content of samples from human habitats to learn how interactions between bacteria and their host might impact human health. In this work a novel parametric statistical inference method based on object-oriented data analysis (OODA) for analyzing HMP data is proposed. OODA is an emerging area of statistical inference where the goal is to apply statistical methods to objects such as functions, images, and graphs or trees. The data objects that pertain to this work are taxonomic trees of bacteria built from analysis of 16S rRNA gene sequences (e.g. using RDP); there is one such object for each biological sample analyzed. Our goal is to model and formally compare a set of trees. The contribution of our work is threefold: first, a weighted tree structure to analyze RDP data is introduced; second, using a probability measure to model a set of taxonomic trees, we introduce an approximate MLE procedure for estimating model parameters and we derive LRT statistics for comparing the distributions of two metagenomic populations; and third the Jumpstart HMP data is analyzed using the proposed model providing novel insights and future directions of analysis.     

Microbes,the gut and ankylosing spondylitis

It is increasingly clear that the interaction between host and microbiome profoundly affects health. There are 10 times more bacteria in and on our bodies than the total of our own cells, and the human intestine contains approximately 100 trillion bacteria. Interrogation of microbial communities by using classic microbiology techniques offers a very restricted view of these communities, allowing us to see only what we can grow in isolation. However, recent advances in sequencing technologies have greatly facilitated systematic and comprehensive studies of the role of the microbiome in human health and disease. Comprehensive understanding of our microbiome will enhance understanding of disease pathogenesis, which in turn may lead to rationally targeted therapy for a number of conditions, including autoimmunity.