Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called “errors-in-variables”. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.     

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only ''pair-wise interactions''. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the ''Apriori algorithm'' for deriving ''microbial association rules'' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various ''(subsets of) features/items'' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between ''subsets/subgroups'' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving ''microbial association rules'' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.     

Ecological rescue of host-microbial systems under environmental change

Beneficial mutations can promote persistence via evolutionary rescue in species experiencing environmental change. However, in long-lived organisms, the pace of evolution is often too slow relative to that of environmental change for evolutionary rescue to occur. Using a spatially implicit metacommunity model, we demonstrate how interactions between slow-growing hosts and their fast-growing microbiomes can promote persistence under rapid environmental change. We show that microbial mutualists can rescue their hosts by allowing them to persist under deteriorating environmental conditions. This form of mutualist-mediated ecological rescue can be jeopardized by competitively dominant microbial cheaters, which can destabilize host population dynamics and promote the risk of stochastic extinction. However, when microbial diversity is high, (meta)community-level interactions among multiple microbial species can buffer the disruptive effect of cheaters and give rise to a more potent form of ecological rescue mediated by the entire microbiome that promotes the abundance, stability, and persistence of the host in the face of environmental change. Our results address two critical problems associated with the viability of rescue in macroorganisms: the temporal mismatch between rapid environmental change and slow organismal response and the potential disruption of rescue by microbial cheaters.     

Non‐invasive monitoring of multiple wildlife health factors by fecal microbiome analysis

Fecal microbial biomarkers represent a less invasive alternative for acquiring information on wildlife populations than many traditional sampling methodologies. Our goal was to evaluate linkages between fecal microbiome communities in Rocky Mountain elk (Cervus canadensis) and four host factors including sex, age, population, and physical condition (body‐fat). We paired a feature‐selection algorithm with an LDA‐classifier trained on elk differential bacterial abundance (16S‐rRNA amplicon survey) to predict host health factors from 104 elk microbiomes across four elk populations. We validated the accuracy of the various classifier predictions with leave‐one‐out cross‐validation using known measurements. We demonstrate that the elk fecal microbiome can predict the four host factors tested. Our results show that elk microbiomes respond to both the strong extrinsic factor of biogeography and simultaneously occurring, but more subtle, intrinsic forces of individual body‐fat, sex, and age‐class. Thus, we have developed and described herein a generalizable approach to disentangle microbiome responses attributed to multiple host factors of varying strength from the same bacterial sequence data set. Wildlife conservation and management presents many challenges, but we demonstrate that non‐invasive microbiome surveys from scat samples can provide alternative options for wildlife population monitoring. We believe that, with further validation, this method could be broadly applicable in other species and potentially predict other measurements. Our study can help guide the future development of microbiome‐based monitoring of wildlife populations and supports hypothetical expectations found in host‐microbiome theory.     

Unexpected diversity among small-scale sample replicates of defined plant root compartments

Community assembly processes determine patterns of species distribution and abundance which are central to the ecology of microbiomes. When studying plant root microbiome assembly, it is typical to sample at the whole plant root system scale. However, sampling at these relatively large spatial scales may hinder the observability of intermediate processes. To study the relative importance of these processes, we employed millimetre-scale sampling of the cell elongation zone of individual roots. Both the rhizosphere and rhizoplane microbiomes were examined in fibrous and taproot model systems, represented by wheat and faba bean, respectively. Like others, we found that the plant root microbiome assembly is mainly driven by plant selection. However, based on variability between replicate millimetre-scale samples and comparisons with randomized null models, we infer that either priority effects during early root colonization or variable selection among replicate plant roots also determines root microbiome assembly.Subject terms: Soil microbiology, Microbial ecology     

Colliding and interacting microbiomes and microbial communities - consequences for human health

Living ‘things’ coexist with microorganisms, known as the microbiota/microbiome that provides essential physiological functions to its host. Despite this reliance, the microbiome is malleable and can be altered by several factors including birth-mode, age, antibiotics, nutrition, and disease. In this minireview, we consider how other microbiomes and microbial communities impact the host microbiome and the host through the concept of microbiome collisions (initial exposures) and interactions. Interactions include changes in host microbiome composition and functionality and/or host responses. Understanding the impact of other microbiomes and microbial communities on the microbiome and host are important considering the decline in human microbiota diversity in the developed world – paralleled by the surge of non-communicable, inflammatory-based diseases. Thus, surrounding ourselves with rich and diverse beneficial microbiomes and microbial communities to collide and interact with should help to diminish the loss in microbial diversity and protect from certain diseases. In the same vein, our microbiomes not only influence our health but potentially the health of those close to us. We also consider strategies for enhanced host microbiome collisions and interactions through the surrounding environment that ensure increased microbiome diversity and functionality contributing to enhanced symbiotic return to the host in terms of health benefit.     

Temporal and spatial dynamics in the apple flower microbiome in the presence of the phytopathogen Erwinia amylovora

Plant microbiomes have important roles in plant health and productivity. However, despite flowers being directly linked to reproductive outcomes, little is known about the microbiomes of flowers and their potential interaction with pathogen infection. Here, we investigated the temporal spatial dynamics of the apple stigma microbiome when challenged with a phytopathogen Erwinia amylovora, the causal agent of fire blight disease. We profiled the microbiome from the stigmas of individual flowers, greatly increasing the resolution at which we can characterize shifts in the composition of the microbiome. Individual flowers harbored unique microbiomes at the operational taxonomic unit level. However, taxonomic analysis of community succession showed a population gradually dominated by bacteria within the families Enterobacteriaceae and Pseudomonadaceae. Flowers inoculated with E. amylovora established large populations of the phytopathogen, with pathogen-specific gene counts of >3.0 × 10⁷ in 90% of the flowers. Yet, only 42% of inoculated flowers later developed fire blight symptoms. This reveals that pathogen abundance on the stigma is not sufficient to predict disease outcome. Our data demonstrate that apple flowers represent an excellent model in which to characterize how plant microbiomes establish, develop, and correlate with biological processes such as disease progression in an experimentally tractable plant organ.Subject terms: Microbial ecology, Non-coding RNAs     

Comparison of Microbiomes from Different Niches of Upper and Lower Airways in Children and Adolescents with Cystic Fibrosis

Changes in the airway microbiome may be important in the pathophysiology of chronic lung disease in patients with cystic fibrosis. However, little is known about the microbiome in early cystic fibrosis lung disease and the relationship between the microbiomes from different niches in the upper and lower airways. Therefore, in this cross-sectional study, we examined the relationship between the microbiome in the upper (nose and throat) and lower (sputum) airways from children with cystic fibrosis using next generation sequencing. Our results demonstrate a significant difference in both α and β-diversity between the nose and the two other sampling sites. The nasal microbiome was characterized by a polymicrobial community while the throat and sputum communities were less diverse and dominated by a few operational taxonomic units. Moreover, sputum and throat microbiomes were closely related especially in patients with clinically stable lung disease. There was a high inter-individual variability in sputum samples primarily due to a decrease in evenness linked to increased abundance of potential respiratory pathogens such as Pseudomonas aeruginosa. Patients with chronic Pseudomonas aeruginosa infection exhibited a less diverse sputum microbiome. A high concordance was found between pediatric and adult sputum microbiomes except that Burkholderia was only observed in the adult cohort. These results indicate that an adult-like lower airways microbiome is established early in life and that throat swabs may be a good surrogate in clinically stable children with cystic fibrosis without chronic Pseudomonas aeruginosa infection in whom sputum sampling is often not feasible.     

Human reproductive system microbiomes exhibited significantly different heterogeneity scaling with gut microbiome,but the intra-system scaling is invariant

Maintaining sexual reproduction in a highly competitive world is still one of the major mysteries of biology given the apparently high efficiency of asexual reproduction. Co-evolutionary theories such as the Red Queen hypothesis would suggest that the microbiomes in human reproductive systems, specifically the microbiomes contained in semen and vaginal fluids, should reach some level of homogeneity thanks to arguably the most conspicuous microbiome transmission between two sexes. The long-term sexual coevolution should favor the dynamic homogeneity or stability, which should also be beneficial for sexual reproduction such as sperm survival or fertilization on physiological/ecological time scale. We present a piece of quantitative evidence in the form of microbial community spatial heterogeneity to support the stability notion by analyzing three big datasets of the human vaginal, semen and gut microbiome. Methodologically, we applied a recent community-level extension to the classic Taylor's power law, which reached the rare status of ecological law and has found applications beyond biology. Both ecological and evolutionary theories, such as hologenome/holobiont and Red Queen, as well as consideration of first principles, would predict that microbiome transmissions between two sexes should have homogenizing effects on the composition and stability of the microbiomes in human reproductive systems, and therefore have similar variance structures. This is supported by the finding that the power law analysis revealed human vaginal and semen microbiomes exhibited the same scaling parameter size in their community spatial (inter-individual) heterogeneities, while the both exhibited significantly different heterogeneity scaling parameter size with the human gut microbiome.     

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.     

Microbiomes in Canidae

Because of their range expansion across North America, coyotes (Canis latrans) now occur sympatrically with numerous other predator species, including red foxes (Vulpes vulpes). This raises several interesting ecological questions, including if and how sympatry affects the diet and gut microbiomes of coyotes and red foxes. We examined the gut microbiomes of sympatric populations of coyotes and red foxes within two different National Parks in Virginia, USA, that differ in land use, vegetation, and anthropogenic disturbance: Prince William Forest Park (PRWI) and Manassas National Battlefield Park (MANA). From 2012 to 2017, scat samples from PRWI and MANA were collected and analyzed. Polymerase chain reaction (PCR) amplification of a region of the mitochondrial cytochrome‐b gene followed by restriction enzyme digestion of the PCR product was used to determine the origin of each scat sample. Next‐Generation DNA sequencing of a hypervariable 16S rRNA gene region was used to determine gut microbiome information about the scat samples. There was no evidence for a difference between the gut microbiomes of red foxes in either location, or for a difference between the gut microbiomes of red foxes at either location and coyotes at the location with lower human disturbance, PRWI. However, the gut microbiomes of coyotes at the location with higher anthropogenic disturbances, MANA, revealed a marked change from those found in red foxes at either location and from those in coyotes at the location with lower disturbances. The gut microbiomes of coyotes subjected to greater human impact may provide evidence of dysbiosis, indicative of increased physiological stress and reduced health. We discuss our observations in the context of understanding anthropogenic impacts on coyote and red fox interactions. Our results suggest that physiological stress in the form of human disturbance may play an important role in the composition of the gut microbiome of coyotes, which can affect their overall health.     

Characterization of the Upper Respiratory Tract Microbiomes of Patients with Pandemic H1N1 Influenza

The upper respiratory tract microbiome has an important role in respiratory health. Influenza A is a common viral infection that challenges that health, and a well-recognized sequela is bacterial pneumonia. Given this connection, we sought to characterize the upper respiratory tract microbiota of individuals suffering from the pandemic H1N1 influenza A outbreak of 2009 and determine if microbiome profiles could be correlated with patient characteristics. We determined the microbial profiles of 65 samples from H1N1 patients by cpn60 universal target amplification and sequencing. Profiles were examined at the phylum and nearest neighbor “species” levels using the characteristics of patient gender, age, originating health authority, sample type and designation (STAT/non-STAT). At the phylum level, Actinobacteria-, Firmicutes- and Proteobacteria-dominated microbiomes were observed, with none of the patient characteristics showing significant profile composition differences. At the nearest neighbor “species” level, the upper respiratory tract microbiomes were composed of 13-20 “species” and showed a trend towards increasing diversity with patient age. Interestingly, at an individual level, most patients had one to three organisms dominant in their microbiota. A limited number of discrete microbiome profiles were observed, shared among influenza patients regardless of patient status variables. To assess the validity of analyses derived from sequence read abundance, several bacterial species were quantified by quantitative PCR and compared to the abundance of cpn60 sequence read counts obtained in the study. A strong positive correlation between read abundance and absolute bacterial quantification was observed. This study represents the first examination of the upper respiratory tract microbiome using a target other than the 16S rRNA gene and to our knowledge, the first thorough examination of this microbiome during a viral infection.     

A common garden experiment with Porphyra umbilicalis (Rhodophyta) evaluates methods to study spatial differences in the macroalgal microbiome

While macroalgal microbiomes are the focus of many recent studies, there is little information about microbial spatial diversity across the thallus. Reliance on field material makes it difficult to discern whether recovered microbiomes belong to the host or its epiphytes, and technical comparisons of macroalgal samples for microbial studies are needed. Here, we use a common garden approach that avoids the problem of epiphytes, particularly at holdfasts, to examine the microbiome of Porphyra umbilicalis (strain Pum1). We used the V6 hypervariable region of the 16S rDNA with Illumina HiSeq sequencing and developed PNA clamps to block recovery of organelle V6 sequences. The common garden approach allowed us to determine differences in the microbiome at the holdfast versus blade margin. We found a notable increase in the relative abundance of Planctomycetes and Alphaproteobacteria at the holdfast, particularly of the possible symbiont Sulfitobacter sp. Nonadjacent 1.5 cm² samples of blade margin had microbiomes that were not statistically different. The most abundant phylum in the overall microbiome was Proteobacteria, followed by Bacteroidetes. Because phycologists often work in remote sites, we compared three stabilization and preparation techniques and found silica gel desiccation/bead‐beating and flash‐freezing/lyophilization/bead‐beating to be interchangeable. Core taxa (≥0.1% of sequences) across treatments were similar and accounted for ≥95% of all sequences. Finally, statistical conclusions for all comparisons were the same, regardless of which microbial community analysis tool was used: mothur or minimum entropy decomposition.     

Deep Illumina-Based Shotgun Sequencing Reveals Dietary Effects on the Structure and Function of the Fecal Microbiome of Growing Kittens

Background

Previously, we demonstrated that dietary protein:carbohydrate ratio dramatically affects the fecal microbial taxonomic structure of kittens using targeted 16S gene sequencing. The present study, using the same fecal samples, applied deep Illumina shotgun sequencing to identify the diet-associated functional potential and analyze taxonomic changes of the feline fecal microbiome.

Methodology & Principal Findings

Fecal samples from kittens fed one of two diets differing in protein and carbohydrate content (high–protein, low–carbohydrate, HPLC; and moderate-protein, moderate-carbohydrate, MPMC) were collected at 8, 12 and 16 weeks of age (n = 6 per group). A total of 345.3 gigabases of sequence were generated from 36 samples, with 99.75% of annotated sequences identified as bacterial. At the genus level, 26% and 39% of reads were annotated for HPLC- and MPMC-fed kittens, with HPLC-fed cats showing greater species richness and microbial diversity. Two phyla, ten families and fifteen genera were responsible for more than 80% of the sequences at each taxonomic level for both diet groups, consistent with the previous taxonomic study. Significantly different abundances between diet groups were observed for 324 genera (56% of all genera identified) demonstrating widespread diet-induced changes in microbial taxonomic structure. Diversity was not affected over time. Functional analysis identified 2,013 putative enzyme function groups were different (p<0.000007) between the two dietary groups and were associated to 194 pathways, which formed five discrete clusters based on average relative abundance. Of those, ten contained more (p<0.022) enzyme functions with significant diet effects than expected by chance. Six pathways were related to amino acid biosynthesis and metabolism linking changes in dietary protein with functional differences of the gut microbiome.

Conclusions

These data indicate that feline feces-derived microbiomes have large structural and functional differences relating to the dietary protein:carbohydrate ratio and highlight the impact of diet early in life.     

Microbiome engineering: Current applications and its future

Microbiomes exist in all ecosystems and are composed of diverse microbial communities. Perturbation to microbiomes brings about undesirable phenotypes in the hosts, resulting in diseases and disorders, and disturbs the balance of the associated ecosystems. Engineering of microbiomes can be used to modify structures of the microbiota and restore ecological balance. Consequently, microbiome engineering has been employed for improving human health and agricultural productivity. The importance and current applications of microbiome engineering, particularly in humans, animals, plants and soil is reviewed. Furthermore, we explore the challenges in engineering microbiome and the future of this field, thus providing perspectives and outlook of microbiome engineering.     

Social convergence of gut microbiomes in vampire bats

The ‘social microbiome’ can fundamentally shape the costs and benefits of group-living, but understanding social transmission of microbes in free-living animals is challenging due to confounding effects of kinship and shared environments (e.g. highly associated individuals often share the same spaces, food and water). Here, we report evidence for convergence towards a social microbiome among introduced common vampire bats, Desmodus rotundus, a highly social species in which adults feed only on blood, and engage in both mouth-to-body allogrooming and mouth-to-mouth regurgitated food sharing. Shotgun sequencing of samples from six zoos in the USA, 15 wild-caught bats from a colony in Belize and 31 bats from three colonies in Panama showed that faecal microbiomes were more similar within colonies than between colonies. To assess microbial transmission, we created an experimentally merged group of the Panama bats from the three distant sites by housing these bats together for four months. In this merged colony, we found evidence that dyadic gut microbiome similarity increased with both clustering and oral contact, leading to microbiome convergence among introduced bats. Our findings demonstrate that social interactions shape microbiome similarity even when controlling for past social history, kinship, environment and diet.     

Neutral Models of Microbiome Evolution

There has been an explosion of research on host-associated microbial communities (i.e.,microbiomes). Much of this research has focused on surveys of microbial diversities across a variety of host species, including humans, with a view to understanding how these microbiomes are distributed across space and time, and how they correlate with host health, disease, phenotype, physiology and ecology. Fewer studies have focused on how these microbiomes may have evolved. In this paper, we develop an agent-based framework to study the dynamics of microbiome evolution. Our framework incorporates neutral models of how hosts acquire their microbiomes, and how the environmental microbial community that is available to the hosts is assembled. Most importantly, our framework also incorporates a Wright-Fisher genealogical model of hosts, so that the dynamics of microbiome evolution is studied on an evolutionary timescale. Our results indicate that the extent of parental contribution to microbial availability from one generation to the next significantly impacts the diversity of microbiomes: the greater the parental contribution, the less diverse the microbiomes. In contrast, even when there is only a very small contribution from a constant environmental pool, microbial communities can remain highly diverse. Finally, we show that our models may be used to construct hypotheses about the types of processes that operate to assemble microbiomes over evolutionary time.