A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome

Meconium microbiota in naturally delivered canine puppies

BACKGROUND

Microbial colonization during early life has a pivotal impact on the host health, shaping immune and metabolic functions, but little is known about timing and features of this process in dogs. The objectives of this study were to characterize the first step of intestinal microbiota development in naturally delivered canine puppies and to investigate its relationship with the maternal bacterial flora, using traditional culture and molecular analyses. Sixty puppies of two breeds, Appenzeller Cattle Dog (n = 3 dams) and Lagotto Romagnolo (n = 6), housed in the same breeding kennel, were included in the study. Swabs were collected in duplicate (for culture and for molecular analysis) from the dams' vagina and rectum at the end of parturition, from puppies' rectum, before maternal care, and from the environment (floor of the nursery and parturition box).

RESULTS

93.3% meconium samples showed bacterial growth, limited to a few colonies in 57.0% of cases. High growth was detected for Enterococcus faecalis, which was the most frequently isolated bacterium. The genus Enterococcus was one of the most represented in the dams' rectum and vagina (88.9% and 55.6%, respectively). The genera Staphylococcus, Enterococcus, Escherichia and Proteus were also often isolated in meconium but were usually present in maternal samples as well, together with ubiquitous bacteria (Acinetobacter, Psychrobacter). In the environmental samples, just a few bacterial species were found, all with low microbial load. Additionally, bacteria of the phyla Proteobacteria, Firmicutes, and Actinobacteria were identified in meconium through molecular analysis, confirming the culture results and the early colonization of the newborn gut. Maternal, meconium and environmental samples had similar alpha diversity, while beta-diversity showed differences among families (i.e. a dam and her litter), and association indexes revealed a significant correlation between family members and between sample origin, suggesting a strong contribution of the maternal flora to the initial seeding of the canine neonatal gut and a strong individual dam imprint.

CONCLUSION

This study showed that the meconium of vaginally delivered puppies has its own microbiota immediately after birth, and that it is shaped by the dam, which gives a specific imprint to her litter.

Life at the borderlands: microbiomes of interfaces critical to One Health

Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.

Statistically learning the functional landscape of microbial communities

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes-analogues to fitness landscapes-that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

Factors Affecting Gut Microbiota of Puppies from Birth to Weaning

The review described the most important factors affecting the development of the intestinal microbiota in puppies from birth to weaning. The health and well-being of the microbiome in puppies is influenced by the type of parturition, the maternal microbiota, and the diet of the mother, directly or indirectly. The isolation of bacteria in dogs from the placenta, fetal fluids, and fetuses suggests that colonization could occur before birth, although this is still a matter of debate. Accordingly, newborn puppies could harbor bacteria that could be of maternal origin and that could influence microbial colonization later in life. However, the long-term impacts on health and the clinical significance of this transfer is not yet clear and needs to be investigated. The same maternal bacteria were found in puppies that were born vaginally and in those delivered via cesarean section. Potentially, the relationship between the type of parturition and the colonization of the microbiome will influence the occurrence of diseases, since it can modulate the gut microbiome during early life. In addition, puppies' gut microbiota becomes progressively more similar to adult dogs at weaning, as a consequence of the transition from milk to solid food that works together with behavioral factors. A number of researches have investigated the effects of diet on the gut microbiota of dogs, revealing that dietary interference may affect the microbial composition and activity through the production of short-chain fatty acids and vitamins. These compounds play a fundamental role during the development of the fetus and the initial growth of the puppy. The composition of the diet fed during pregnancy to the bitches is also an important factor to consider for the health of newborns. As far as it is known, the effects of the type of parturition, the maternal microbiota, and the diet on the microbial colonization and the long-term health of the dogs deserve further studies. Definitely, longitudinal studies with a larger number of dogs will be required to assess a causal link between microbiome composition in puppies and diseases in adult dogs.

Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy

The human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine-learning-based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing or shotgun metagenomics. However, several properties of microbial sequence-based studies hinder machine learning (ML), including non-uniform representation, a small number of samples compared with the dimension of each sample, and sparsity of the data, with the majority of taxa present in a small subset of samples. We show here using a graph representation that the cladogram structure is as informative as the taxa frequency. We then suggest a novel method to combine information from different taxa and improve data representation for ML using microbial taxonomy. iMic (image microbiome) translates the microbiome to images through an iterative ordering scheme, and applies convolutional neural networks to the resulting image. We show that iMic has a higher precision in static microbiome gene sequence-based ML than state-of-the-art methods. iMic also facilitates the interpretation of the classifiers through an explainable artificial intelligence (AI) algorithm to iMic to detect taxa relevant to each condition. iMic is then extended to dynamic microbiome samples by translating them to movies.

Spatial scales of competition and a growth-motility trade-off interact to determine bacterial coexistence

The coexistence of competing species is a long-lasting puzzle in evolutionary ecology research. Despite abundant experimental evidence showing that the opportunity for coexistence decreases as niche overlap increases between species, bacterial species and strains competing for the same resources are commonly found across diverse spatially heterogeneous habitats. We thus hypothesized that the spatial scale of competition may play a key role in determining bacterial coexistence, and interact with other mechanisms that promote coexistence, including a growth-motility trade-off. To test this hypothesis, we let two Pseudomonas putida strains compete at local and regional scales by inoculating them either in a mixed droplet or in separate droplets in the same Petri dish, respectively. We also created conditions that allow the bacterial strains to disperse across abiotic or fungal hyphae networks. We found that competition at the local scale led to competitive exclusion while regional competition promoted coexistence. When competing in the presence of dispersal networks, the growth-motility trade-off promoted coexistence only when the strains were inoculated in separate droplets. Our results provide a mechanism by which existing laboratory data suggesting competitive exclusion at a local scale is reconciled with the widespread coexistence of competing bacterial strains in complex natural environments with dispersal.

Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.

A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome

MOTIVATION

PERMANOVA is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence-absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias but at the potential costs of information loss and the introduction of a stochastic component into the analysis.

RESULTS

Here, we develop a non-stochastic approach to PERMANOVA presence-absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix and averaging the F-statistic. Our simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease in which samples from case participants have systematically smaller library sizes than samples from control participants.

AVAILABILITY AND IMPLEMENTATION

We have implemented all the approaches described above, including the function for calculating the analytical average of the squared or unsquared distance matrix, in our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Neonatal Programming of Microbiota Composition: A Plausible Idea That Is Not Supported by the Evidence

Underpinning the theory "developmental origins of health and disease" (DOHaD), evidence is accumulating to suggest that the risks of adult disease are in part programmed by exposure to environmental factors during the highly plastic "first 1,000 days of life" period. An elucidation of the mechanisms involved in this programming is challenging as it would help developing new strategies to promote adult health. The intestinal microbiome is proposed as a long-lasting memory of the neonatal environment. This proposal is supported by indisputable findings such as the concomitance of microbiota assembly and the first 1,000-day period, the influence of perinatal conditions on microbiota composition, and the impact of microbiota composition on host physiology, and is based on the widely held but unconfirmed view that the microbiota is long-lastingly shaped early in life. In this review, we examine the plausibility of the gut microbiota being programmed by the neonatal environment and evaluate the evidence for its validity. We highlight that the capacity of the pioneer bacteria to control the implantation of subsequent bacteria is supported by both theoretical principles and statistical associations, but remains to be demonstrated experimentally. In addition, our critical review of the literature on the long-term repercussions of selected neonatal modulations of the gut microbiota indicates that sustained programming of the microbiota composition by neonatal events is unlikely. This does not exclude the microbiota having a role in DOHaD due to a possible interaction with tissue and organ development during the critical windows of neonatal life.

Drivers of ecological assembly in the hindgut of Atlantic Cod fed a macroalgal supplemented diet

It is difficult to disentangle the many variables (e.g. internal or external cues and random events) that shape the microbiota in the gastrointestinal tract of any living species. Ecological assembly processes applied to microbial communities can elucidate these drivers. In our study, farmed Atlantic cod (Gadus morhua) were fed a diet of 10% macroalgae supplement (Ulva rigida [ULVA] or Ascophyllum nodosum [ASCO] or a non-supplemented control diet [CTRL]) over 12 weeks. We determined the influence of ecological assembly processes using a suite of null-modelling tools. We observed dissimilarity in the abundance of common OTUs over time, which was driven by deterministic assembly. The CTRL samples showed selection as a critical assembly process. While dispersal limitation was a driver of the gut microbiome for fish fed the macroalgae supplemented diet at Week 12 (i.e., ASCO and ULVA). Fish from the ASCO grouping diverged into ASCO_N (normal) and ASCO_LG (lower growth), where ASCO_LG individuals found the diet unpalatable. The recruitment of new taxa overtime was altered in the ASCO_LG fish, with the gut microbiome showing phylogenetic underdispersion (nepotistic species recruitment). Finally, the gut microbiome (CTRL and ULVA) showed increasing robustness to taxonomic disturbance over time and lower functional redundancy. This study advances our understanding of the ecological assembly and succession in the hindgut of juvenile Atlantic cod across dietary treatments. Understanding the processes driving ecological assembly in the gut microbiome, in fish research specifically, could allow us to manipulate the microbiome for improved health or resilience to disease for improved aquaculture welfare and production.

Twenty Important Research Questions in Microbial Exposure and Social Equity

Social and political policy, human activities, and environmental change affect the ways in which microbial communities assemble and interact with people. These factors determine how different social groups are exposed to beneficial and/or harmful microorganisms, meaning microbial exposure has an important socioecological justice context. Therefore, greater consideration of microbial exposure and social equity in research, planning, and policy is imperative. Here, we identify 20 research questions considered fundamentally important to promoting equitable exposure to beneficial microorganisms, along with safeguarding resilient societies and ecosystems. The 20 research questions we identified span seven broad themes, including the following: (i) sociocultural interactions; (ii) Indigenous community health and well-being; (iii) humans, urban ecosystems, and environmental processes; (iv) human psychology and mental health; (v) microbiomes and infectious diseases; (vi) human health and food security; and (vii) microbiome-related planning, policy, and outreach. Our goal was to summarize this growing field and to stimulate impactful research avenues while providing focus for funders and policymakers.

Long-term dynamics of the human oral microbiome during clinical disease progression

BACKGROUND

Oral microbiome dysbiosis is linked to overt inflammation of tooth-supporting tissues, leading to periodontitis, an oral condition that can cause tooth and bone loss. Microbiome dysbiosis has been described as a disruption in the symbiotic microbiota composition's stability that could adversely affect the host's health status. However, the precise microbiome dynamics that lead to dysbiosis and the progression of the disease are largely unknown. The objective of our study was to investigate the long-term dynamics of periodontitis progression and its connection to dysbiosis.

RESULTS

We studied three different teeth groups: sites that showed disease progression, sites that remained stable during the study, and sites that exhibited a cyclic deepening followed by spontaneous recovery. Time-series analysis revealed that communities followed a characteristic succession of bacteria clusters. Stable and fluctuating sites showed high asynchrony in the communities (i.e., different species responding dissimilarly through time) and a reordering of the communities where directional changes dominated (i.e., sample distance increases over time) in the stable sites but not in the fluctuating sites. Progressing sites exhibited low asynchrony and convergence (i.e., samples distance decreases over time). Moreover, new species were more likely to be recruited in stable samples if a close relative was not recruited previously. In contrast, progressing and fluctuating sites followed a neutral recruitment model, indicating that competition between closely related species is a significant component of species-species interactions in stable samples. Finally, periodontal treatment did not select similar communities but stabilized α-diversity, centered the abundance of different clusters to the mean, and increased community rearrangement.

CONCLUSIONS

Here, we show that ecological principles can define dysbiosis and explain the evolution and outcomes of specific microbial communities of the oral microbiome in periodontitis progression. All sites showed an ecological succession in community composition. Stable sites were characterized by high asynchrony, a reordering of the communities where directional changes dominated, and new species were more likely to be recruited if a close relative was not recruited previously. Progressing sites were characterized by low asynchrony, community convergence, and a neutral model of recruitment. Finally, fluctuating sites were characterized by high asynchrony, community convergence, and a neutral recruitment model.

Exploration of marine bacterioplankton community assembly mechanisms during chemical dispersant and surfactant-assisted oil biodegradation

Assembly processes in marine microbial communities amended with crude oil and chemical dispersant are poorly understood and even more so when biosurfactants are used. We set up a microcosm experiment in which microbiome structure was analyzed using 16S rRNA gene amplicon sequencing and six null models to better understand and quantify the mechanisms and patterns controlling the assembly of a marine crude oil degrading microbial community in the presence of chemical dispersant or rhamnolipid biosurfactant. Although each null model quantifies different aspects of the community assembly, there was a general agreement that neither purely stochastic nor purely deterministic processes dominated the microbial communities, and their influence was variable over time. Determinism was dominant in the early phase of incubation, while stochasticity was prevalent in the middle and late stages. There was faster recruitment of phylogenetically distant species in the dispersant-amended community compared to oil-only or rhamnolipid-amended communities. This analysis provides important insights of how chemical dispersants and rhamnolipid influence microbial communities' dynamics and identified which groups may be excluded-an important consideration for biodegradation process and oil spill response.

Multi-omics data integration considerations and study design for biological systems and disease

With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

Assessment of phylo-functional coherence along the bacterial phylogeny and taxonomy

In this report we use available curated phylogenies, taxonomy, and genome annotations to assess the phylogenetic and gene content similarity associated with each different taxon and taxonomic rank. Subsequently, we employ the same data to assess the frontiers of functional coherence along the bacterial phylogeny. Our results show that within-group phylogenetic and gene content similarity of taxa in the same rank are not homogenous, and that these values show extensive overlap between ranks. Functional coherence along the 16S rRNA gene-based phylogeny was limited to 44 particular nodes presenting large variations in phylogenetic depth. For instance, the deep subtree affiliated to class Actinobacteria presented functional coherence, while the shallower family Enterobacteriaceae-affiliated subtree did not. On the other hand, functional coherence along the genome-based phylogeny delimited deep subtrees affiliated to phyla Actinobacteriota, Deinococcota, Chloroflexota, Firmicutes, and a subtree containing the rest of the bacterial phyla. The results presented here can be used to guide the exploration of results in many microbial ecology and evolution research scenarios. Moreover, we provide dedicated scripts and files that can be used to continue the exploration of functional coherence along the bacterial phylogeny employing different parameters or input data (https://git.io/Jec5U).

Bacterial associations in the healthy human gut microbiome across populations

In a microbial community, associations between constituent members play an important role in determining the overall structure and function of the community. The human gut microbiome is believed to play an integral role in host health and disease. To understand the nature of bacterial associations at the species level in healthy human gut microbiomes, we analyzed previously published collections of whole-genome shotgun sequence data, totaling over 1.6 Tbp, generated from 606 fecal samples obtained from four different healthy human populations. Using a Random Forest Classifier, we identified 202 signature bacterial species that were prevalent in these populations and whose relative abundances could be used to accurately distinguish between the populations. Bacterial association networks were constructed with these signature species using an approach based on the graphical lasso. Network analysis revealed conserved bacterial associations across populations and a dominance of positive associations over negative associations, with this dominance being driven by associations between species that are closely related either taxonomically or functionally. Bacterial species that form network modules, and species that constitute hubs and bottlenecks, were also identified. Functional analysis using protein families suggests that much of the taxonomic variation across human populations does not foment substantial functional or structural differences.

Temporal changes in the gut microbiota in farmed Atlantic cod (Gadus morhua) outweigh the response to diet supplementation with macroalgae

BACKGROUND

Aquaculture successfully meets global food demands for many fish species. However, aquaculture production of Atlantic cod (Gadus morhua) is just 2.5% of total market production. For cod farming to be a viable economic venture specific challenges on how to increase growth, health and farming productivity need to be addressed. Feed ingredients play a key role here. Macroalgae (seaweeds) have been suggested as a functional feed supplement with both health and economic benefits for terrestrial farmed animals and fish. The impact of such dietary supplements to cod gut integrity and microbiota, which contribute to overall fish robustness is unknown. The objective of this study was to supplement the diet of juvenile Atlantic cod with macroalgae and determine the impacts on fish condition and growth, gut morphology and hindgut microbiota composition (16S rRNA amplicon sequencing). Fish were fed one of three diets: control (no macroalgal inclusion), 10% inclusion of either egg wrack (Ascophyllum nodosum) or sea lettuce (Ulva rigida) macroalgae in a 12-week trial.

RESULTS

The results demonstrated there was no significant difference in fish condition, gut morphology or hindgut microbiota between the U. rigida supplemented fish group and the control group at any time-point. This trend was not observed with the A. nodosum treatment. Fish within this group were further categorised as either 'Normal' or 'Lower Growth'. 'Lower Growth' individuals found the diet unpalatable resulting in reduced weight and condition factor combined with an altered gut morphology and microbiome relative to the other treatments. Excluding this group, our results show that the hindgut microbiota was largely driven by temporal pressures with the microbial communities becoming more similar over time irrespective of dietary treatment. The core microbiome at the final time-point consisted of the orders Vibrionales (Vibrio and Photobacterium), Bacteroidales (Bacteroidetes and Macellibacteroides) and Clostridiales (Lachnoclostridium).

CONCLUSIONS

Our study indicates that U. rigida macroalgae can be supplemented at 10% inclusion levels in the diet of juvenile farmed Atlantic cod without any impact on fish condition or hindgut microbial community structure. We also conclude that 10% dietary inclusion of A. nodosum is not a suitable feed supplement in a farmed cod diet.

Functional Gastrointestinal Disorders and the Microbiome-What Is the Best Strategy for Moving Microbiome-based Therapies for Functional Gastrointestinal Disorders into the Clinic?

There have been numerous human studies reporting associations between the intestinal microbiome and functional gastrointestinal disorders (FGIDs), and independently animal studies have explored microbiome-driven mechanisms underlying FGIDs. However, there is often a disconnect between human and animal studies, which hampers translation of microbiome findings to the clinic. Changes in the microbiota composition of patients with FGIDs are generally subtle, whereas changes in microbial function, reflected in the fecal metabolome, appear to be more precise indicators of disease subtype-specific mechanisms. Although we have made significant progress in characterizing the microbiome, to effectively translate microbiome science in a timely manner, we need concurrent and iterative longitudinal studies in humans and animals to determine the precise microbial functions that can be targeted to address specific pathophysiological processes in FGIDs. A systems approach integrating multiple data layers rather than evaluating individual data layers of symptoms, physiological changes, or -omics data in isolation will allow for validation of mechanistic insights from animal studies while also allowing new discovery. Patient stratification for clinical trials based on functional microbiome alterations and/or pathophysiological measurements may allow for more accurate determination of efficacy of individual microbiome-targeted interventions designed to correct an underlying abnormality. In this review, we outline current approaches and knowledge, and identify gaps, to provide a potential roadmap for accelerating translation of microbiome science toward microbiome-targeted personalized treatments for FGIDs.