Mapping the ecological networks of microbial communities

Emergent ecological patterns and modelling of gut microbiomes in health and in disease

Recent advancements in next-generation sequencing have revolutionized our understanding of the human microbiome. Despite this progress, challenges persist in comprehending the microbiome's influence on disease, hindered by technical complexities in species classification, abundance estimation, and data compositionality. At the same time, the existence of macroecological laws describing the variation and diversity in microbial communities irrespective of their environment has been recently proposed using 16s data and explained by a simple phenomenological model of population dynamics. We here investigate the relationship between dysbiosis, i.e. in unhealthy individuals there are deviations from the "regular" composition of the gut microbial community, and the existence of macro-ecological emergent law in microbial communities. We first quantitatively reconstruct these patterns at the species level using shotgun data, and addressing the consequences of sampling effects and statistical errors on ecological patterns. We then ask if such patterns can discriminate between healthy and unhealthy cohorts. Concomitantly, we evaluate the efficacy of different statistical generative models, which incorporate sampling and population dynamics, to describe such patterns and distinguish which are expected by chance, versus those that are potentially informative about disease states or other biological drivers. A critical aspect of our analysis is understanding the relationship between model parameters, which have clear ecological interpretations, and the state of the gut microbiome, thereby enabling the generation of synthetic compositional data that distinctively represent healthy and unhealthy individuals. Our approach, grounded in theoretical ecology and statistical physics, allows for a robust comparison of these models with empirical data, enhancing our understanding of the strengths and limitations of simple microbial models of population dynamics.

Nano- and microplastics drive the dynamic equilibrium of amoeba-associated bacteria and antibiotic resistance genes

As emerging pollutants, microplastics have become pervasive on a global scale, inflicting significant harm upon ecosystems. However, the impact of these microplastics on the symbiotic relationship between protists and bacteria remains poorly understood. In this study, we investigated the mechanisms through which nano- and microplastics of varying sizes and concentrations influence the amoeba-bacterial symbiotic system. The findings reveal that nano- and microplastics exert deleterious effects on the adaptability of the amoeba host, with the magnitude of these effects contingent upon particle size and concentration. Furthermore, nano- and microplastics disrupt the initial equilibrium in the symbiotic relationship between amoeba and bacteria, with nano-plastics demonstrating a reduced ability to colonize symbiotic bacteria within the amoeba host when compared to their microplastic counterparts. Moreover, nano- and microplastics enhance the relative abundance of antibiotic resistance genes and heavy metal resistance genes in the bacteria residing within the amoeba host, which undoubtedly increases the potential transmission risk of both human pathogens and resistance genes within the environment. In sum, the results presented herein provide a novel perspective and theoretical foundation for the study of interactions between microplastics and microbial symbiotic systems, along with the establishment of risk assessment systems for ecological environments and human health.

A novel hierarchical network-based approach to unveil the complexity of functional microbial genome

Biological networks serve a crucial role in elucidating intricate biological processes. While interspecies environmental interactions have been extensively studied, the exploration of gene interactions within species, particularly among individual microorganisms, is less developed. The increasing amount of microbiome genomic data necessitates a more nuanced analysis of microbial genome structures and functions. In this context, we introduce a complex structure using higher-order network theory, "Solid Motif Structures (SMS)", via a hierarchical biological network analysis of genomes within the same genus, effectively linking microbial genome structure with its function. Leveraging 162 high-quality genomes of Microcystis, a key freshwater cyanobacterium within microbial ecosystems, we established a genome structure network. Employing deep learning techniques, such as adaptive graph encoder, we uncovered 27 critical functional subnetworks and their associated SMSs. Incorporating metagenomic data from seven geographically distinct lakes, we conducted an investigation into Microcystis' functional stability under varying environmental conditions, unveiling unique functional interaction models for each lake. Our work compiles these insights into an extensive resource repository, providing novel perspectives on the functional dynamics within Microcystis. This research offers a hierarchical network analysis framework for understanding interactions between microbial genome structures and functions within the same genus.

Dietary Clostridium autoethanogenum protein has dose-dependent influence on the gut microbiota, immunity, inflammation and disease resistance of abalone Haliotis discus hannai

Clostridium autoethanogenum protein (CAP) is an eco-friendly protein source and has great application potential in aquafeeds. The present study aimed to investigate the effects of dietary CAP inclusion on the anti-oxidation, immunity, inflammation, disease resistance and gut microbiota of abalone Haliotis discus hannai after a 110-day feeding trial. Three isonitrogenous and isolipidic diets were formulated by adding 0 % (control), 4.10 % (CAP4.10) and 16.25 % (CAP16.25) of CAP, respectively. A total of 540 abalones with an initial mean body weight of 22.05 ± 0.19 g were randomly distributed in three groups with three replicates per group and 60 abalones per replicate. Results showed that the activities of superoxide dismutase and glutathione peroxidase in the cell-free hemolymph (CFH) were significantly decreased and the content of malondialdehyde in CFH was significantly increased in the CAP16.25 group. The diet with 4.1 % of CAP significantly increased the activities of lysozyme and acid phosphatase in CFH. The expressions of pro-inflammatory genes such as tumor necrosis factor-α (tnf-α), nuclear factor-κb (nf-κb) and toll-like receptor 4 (tlr4) in digestive gland were downregulated, and the expressions of anti-inflammatory genes such as β-defensin and mytimacin 6 in digestive gland were upregulated in the CAP4.10 group. Dietary CAP inclusion significantly decreased the cumulative mortality of abalone after the challenge test with Vibrio parahaemolyticus for 7 days. Dietary CAP inclusion changed the composition of gut microbiota of abalone. Besides, the balance of the ecological interaction network of bacterial genera in the intestine of abalone was enhanced by dietary CAP. The association analysis showed that two bacterial genera Ruegeria and Bacteroides were closely correlated with the inflammatory genes. In conclusion, the 4.10 % of dietary CAP enhanced the immunity and disease resistance as well as inhibited the inflammation of abalone. The 16.25 % of dietary CAP decreased the anti-oxidative capacity of abalone. The structure of the gut microbiota of abalone changed with dietary CAP levels.

Phylogeny structures species' interactions in experimental ecological communities

Species' traits and interactions are products of evolutionary history. Despite the long-standing hypothesis that closely related species possess similar traits, and thus experience stronger competition, measuring the effect of evolutionary history on the ecology of natural communities remains challenging. We propose a novel framework to test whether phylogeny influences patterns of coexistence and abundance of species assemblages. In our approach, phylogenetic trees are used to parameterize species' interactions, which in turn determine the abundance of species in a given assemblage. We use likelihoods to score models parameterized with a given phylogeny, and contrast them with models built using random trees, allowing us to test whether phylogenetic information helps to predict species' abundances. Our statistical framework reveals that interactions are indeed structured by phylogeny in a large set of experimental plant communities. Our results confirm that evolutionary history can help predict, and potentially manage or conserve, the structure and function of complex ecological communities.

Higher-order interactions and emergent properties of microbial communities: The power of synthetic ecology

Humans have long relied on microbial communities to create products, produce energy, and treat waste. The microbiota residing within our bodies directly impacts our health, while the soil and rhizosphere microbiomes influence the productivity of our crops. However, the complexity and diversity of microbial communities make them challenging to study and difficult to develop into applications, as they often exhibit the emergence of unpredictable higher-order phenomena. Synthetic ecology aims at simplifying complexity by constituting synthetic or semi-natural microbial communities with reduced diversity that become easier to study and analyze. This strategy combines methodologies that simplify existing complex systems (top-down approach) or build the system from its constituent components (bottom-up approach). Simplified communities are studied to understand how interactions among populations shape the behavior of the community and to model and predict their response to external stimuli. By harnessing the potential of synthetic microbial communities through a multidisciplinary approach, we can advance knowledge of ecological concepts and address critical public health, agricultural, and environmental issues more effectively.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Resource competition predicts assembly of gut bacterial communities in vitro

Microbial community dynamics arise through interspecies interactions, including resource competition, cross-feeding and pH modulation. The individual contributions of these mechanisms to community structure are challenging to untangle. Here we develop a framework to estimate multispecies niche overlaps by combining metabolomics data of individual species, growth measurements in spent media and mathematical models. We applied our framework to an in vitro model system comprising 15 human gut commensals in complex media and showed that a simple model of resource competition accounted for most pairwise interactions. Next, we built a coarse-grained consumer-resource model by grouping metabolomic features depleted by the same set of species and showed that this model predicted the composition of 2-member to 15-member communities with reasonable accuracy. Furthermore, we found that incorporation of cross-feeding and pH-mediated interactions improved model predictions of species coexistence. Our theoretical model and experimental framework can be applied to characterize interspecies interactions in bacterial communities in vitro.

Sparse species interactions reproduce abundance correlation patterns in microbial communities

During the last decades, macroecology has identified broad-scale patterns of abundances and diversity of microbial communities and put forward some potential explanations for them. However, these advances are not paralleled by a full understanding of the dynamical processes behind them. In particular, abundance fluctuations of different species are found to be correlated, both across time and across communities in metagenomic samples. Reproducing such correlations through appropriate population models remains an open challenge. The present paper tackles this problem and points to sparse species interactions as a necessary mechanism to account for them. Specifically, we discuss several possibilities to include interactions in population models and recognize Lotka-Volterra constants as a successful ansatz. For this, we design a Bayesian inference algorithm to extract sets of interaction constants able to reproduce empirical probability distributions of pairwise correlations for diverse biomes. Importantly, the inferred models still reproduce well-known single-species macroecological patterns concerning abundance fluctuations across both species and communities. Endorsed by the agreement with the empirically observed phenomenology, our analyses provide insights into the properties of the networks of microbial interactions, revealing that sparsity is a crucial feature.

Microeukaryotic plankton evolutionary constraints in a subtropical river explained by environment and bacteria along differing taxonomic resolutions

Microeukaryotic plankton communities are keystone components for keeping aquatic primary productivity. Currently, variations in microeukaryotic plankton diversity have often been explained by local ecological factors but not by evolutionary constraints. We used amplicon sequencing of 100 water samples across five years to investigate the ecological preferences of the microeukaryotic plankton community in a subtropical riverine ecosystem. We found that microeukaryotic plankton diversity was less associated with bacterial abundance (16S rRNA gene copy number) than bacterial diversity. Further, environmental effects exhibited a larger influence on microeukaryotic plankton community composition than bacterial community composition, especially at fine taxonomic levels. The evolutionary constraints of microeukaryotic plankton community increased with decreasing taxonomic resolution (from 97% to 91% similarity levels), but not significant change from 85% to 70% similarity levels. However, compared with the bacterial community, the evolutionary constraints were shown to be more affected by environmental variables. This study illustrated possible controlling environmental and bacterial drivers of microeukaryotic diversity and community assembly in a subtropical river, thereby indirectly reflecting on the quality status of the water environment by providing new clues on the microeukaryotic community assembly.

Competition-based control of the false discovery proportion

Recently, Barber and Candès laid the theoretical foundation for a general framework for false discovery rate (FDR) control based on the notion of "knockoffs." A closely related FDR control methodology has long been employed in the analysis of mass spectrometry data, referred to there as "target-decoy competition" (TDC). However, any approach that aims to control the FDR, which is defined as the expected value of the false discovery proportion (FDP), suffers from a problem. Specifically, even when successfully controlling the FDR at level α, the FDP in the list of discoveries can significantly exceed α. We offer FDP-SD, a new procedure that rigorously controls the FDP in the knockoff/TDC competition setup by guaranteeing that the FDP is bounded by α at a desired confidence level. Compared with the recently published framework of Katsevich and Ramdas, FDP-SD generally delivers more power and often substantially so in simulated and real data.

Mathematical models of cystic fibrosis as a systemic disease

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

Engineering the Maize Root Microbiome: A Rapid MoClo Toolkit and Identification of Potential Bacterial Chassis for Studying Plant-Microbe Interactions

Sustainably enhancing crop production is a global necessity to meet the escalating demand for staple crops while sustainably managing their associated carbon/nitrogen inputs. Leveraging plant-associated microbiomes is a promising avenue for addressing this demand. However, studying these communities and engineering them for sustainable enhancement of crop production have remained a challenge due to limited genetic tools and methods. In this work, we detail the development of the Maize Root Microbiome ToolKit (MRMTK), a rapid Modular Cloning (MoClo) toolkit that only takes 2.5 h to generate desired constructs (5400 potential plasmids) that replicate and express heterologous genes in Enterobacter ludwigii strain AA4 (Elu), Pseudomonas putida strain AA7 (Ppu), Herbaspirillum robiniae strain AA6 (Hro), Stenotrophomonas maltophilia strain AA1 (Sma), and Brucella pituitosa strain AA2 (Bpi), which comprise a model maize root synthetic community (SynCom). In addition to these genetic tools, we describe a highly efficient transformation protocol (107-109 transformants/μg of DNA) 1 for each of these strains. Utilizing this highly efficient transformation protocol, we identified endogenous Expression Sequences (ES; promoter and ribosomal binding sites) for each strain via genomic promoter trapping. Overall, MRMTK is a scalable and adaptable platform that expands the genetic engineering toolbox while providing a standardized, high-efficiency transformation method across a diverse group of root commensals. These results unlock the ability to elucidate and engineer plant-microbe interactions promoting plant growth for each of the 5 bacterial strains in this study.

A computational method to dissect colonization resistance of the gut microbiota against pathogens

The mammalian gut microbiome protects the host through colonization resistance (CR) against the incursion of exogenous and often harmful microorganisms, but identifying the exact microbes responsible for the gut microbiota-mediated CR against a particular pathogen remains a challenge. To address this limitation, we developed a computational method: generalized microbe-phenotype triangulation (GMPT). We first systematically validated GMPT using a classical population dynamics model in community ecology and demonstrated its superiority over baseline methods. We then tested GMPT on simulated data generated from the ecological network inferred from a real community (GnotoComplex microflora) and real microbiome data on two mouse studies on Clostridioides difficile infection. We demonstrated GMPT's ability to streamline the discovery of microbes that are potentially responsible for microbiota-mediated CR against pathogens. GMPT holds promise to advance our understanding of CR mechanisms and facilitate the rational design of microbiome-based therapies for preventing and treating enteric infections.

Resource competition can explain simplicity in microbial community assembly

Predicting the composition and diversity of communities is a central goal in ecology. While community assembly is considered hard to predict, laboratory microcosms often follow a simple assembly rule based on the outcome of pairwise competitions. This assembly rule predicts that a species that is excluded by another species in pairwise competition cannot survive in a multispecies community with that species. Despite the empirical success of this bottom-up prediction, its mechanistic origin has remained elusive. In this study, we elucidate how this simple pattern in community assembly can emerge from resource competition. Our geometric analysis of a consumer-resource model shows that trio community assembly is always predictable from pairwise outcomes when one species grows faster than another species on every resource. We also identify all possible trio assembly outcomes under three resources and find that only two outcomes violate the assembly rule. Simulations demonstrate that pairwise competitions accurately predict trio assembly with up to 100 resources and the assembly of larger communities containing up to twelve species. We then further demonstrate accurate quantitative prediction of community composition using the harmonic mean of pairwise fractions. Finally, we show that cross-feeding between species does not decrease assembly rule prediction accuracy. Our findings highlight that simple community assembly can emerge even in ecosystems with complex underlying dynamics.

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding

When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general 'interaction' models have been formulated, but what is currently lacking are models that link monoculture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate 'indifference'), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for monocultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model to constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from monoculture growth experiments.

Revealing proteome-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics

Functional redundancy is a key ecosystem property representing the fact that different taxa contribute to an ecosystem in similar ways through the expression of redundant functions. The redundancy of potential functions (or genome-level functional redundancy [Formula: see text]) of human microbiomes has been recently quantified using metagenomics data. Yet, the redundancy of expressed functions in the human microbiome has never been quantitatively explored. Here, we present an approach to quantify the proteome-level functional redundancy [Formula: see text] in the human gut microbiome using metaproteomics. Ultra-deep metaproteomics reveals high proteome-level functional redundancy and high nestedness in the human gut proteomic content networks (i.e., the bipartite graphs connecting taxa to functions). We find that the nested topology of proteomic content networks and relatively small functional distances between proteomes of certain pairs of taxa together contribute to high [Formula: see text] in the human gut microbiome. As a metric comprehensively incorporating the factors of presence/absence of each function, protein abundances of each function and biomass of each taxon, [Formula: see text] outcompetes diversity indices in detecting significant microbiome responses to environmental factors, including individuality, biogeography, xenobiotics, and disease. We show that gut inflammation and exposure to specific xenobiotics can significantly diminish the [Formula: see text] with no significant change in taxonomic diversity.

Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome

Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.

The underappreciated role of biotic factors in controlling the bloom ecology of potentially harmful microalgae in the Hudson-Raritan Bay

Despite widespread distribution of harmful algal blooms (HABs) and new and improved methods for detecting and quantifying them, no unifying ecological explanation has been found. Improved understanding depends upon local, ecological studies that include analysis of phytoplankton species data in relation to both abiotic and biotic parameters. Ecological network analysis was used to detect co-occurrence patterns among abiotic and biotic parameters in a long-term monitoring dataset (i.e., 2010-2021) from the eutrophic Hudson-Raritan Estuary (HRE) between the states of New York and New Jersey. The regular co-occurrence of potentially harmful bloom-forming species with companion species observed through microscopy was supported by the results of ecological network analysis, which showed that there were far more associations between HAB species and biotic parameters (∼95%) than abiotic parameters (∼5%). Temperature was the environmental variable that was most associated with HAB species throughout the estuary. The numerous network associations of HAB species with one another and with diatoms, dinoflagellates, and zooplankton highlight the complexity of planktonic food webs and interactions. Results also suggest that some taxa may play a central role in structuring the HRE plankton communities. These findings demonstrate that biotic associations play an underappreciated role in plankton structure and the value of examining the ecology of HAB species within the breadth of their biological communities. While network analysis does not fully explain and confirm complex associations among species, it does provide fresh insights and testable hypotheses to strengthen understanding and improve prediction.

Microbial coexistence in the rhizosphere and the promotion of plant stress resistance: A review

Plants can recruit soil microorganisms into the rhizosphere when experiencing various environmental stresses, including biotic (e.g., insect pests) and abiotic (e.g., heavy metal pollution, droughts, floods, and salinity) stresses. However, species coexistence in plant resistance has not received sufficient attention. Current research on microbial coexistence is only at the community scale, and there is a limited understanding of the interaction patterns between species, especially microbe‒microbe interactions. The relevant interaction patterns are limited to a few model strains. The coexisting microbial communities form a stable system involving complex nutritional competition, metabolic exchange, and even interdependent interactions. This pattern of coexistence can ultimately enhance plant stress tolerance. Hence, a systematic understanding of the coexistence pattern of rhizosphere microorganisms under stress is essential for the precise development and utilization of synthetic microbial communities and the achievement of efficient ecological control. Here, we integrated current analytical methods and introduced several new experimental methods to elucidate rhizosphere microbial coexistence patterns. Some advancements (e.g., network analysis, coculture experiments, and synthetic communities) that can be applied to plant stress resistance are also updated. This review aims to summarize the key role and potential application prospects of microbial coexistence in the resistance of plants to environmental stresses. Our suggestions, enhancing plant resistance with coexisting microbes, would allow us to gain further knowledge on plant-microbial and microbial-microbial functions, and facilitate translation to more effective measures.

Controlling the human microbiome

We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.

Small molecule modulation of microbiota: a systems pharmacology perspective

BACKGROUND

Microbes are associated with many human diseases and influence drug efficacy. Small-molecule drugs may revolutionize biomedicine by fine-tuning the microbiota on the basis of individual patient microbiome signatures. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional "one-drug-one-target-one-disease" process. A systematic pharmacology approach that would suppress multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution.

RESULTS

We construct a disease-centric signed microbe-microbe interaction network using curated microbe metabolite information and their effects on host. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of effect of microbes on human health and diseases. With a survey on the druggable and evolutionary space of microbe proteins, we find that 8-10% of them can be targeted by existing drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We demonstrate that drugs for diabetes can be the lead compounds for development of microbiota-targeted therapeutics. We further show that the potential drug targets that specifically exist in pathogenic microbes are periplasmic and cellular outer membrane proteins.

CONCLUSION

The systematic studies of the polypharmacological landscape of the microbiome network may open a new avenue for the small-molecule drug discovery of the microbiome. We believe that the application of systematic method on the polypharmacological investigation could lead to the discovery of novel drug therapies.

Species abundance correlations carry limited information about microbial network interactions

Unraveling the network of interactions in ecological communities is a daunting task. Common methods to infer interspecific interactions from cross-sectional data are based on co-occurrence measures. For instance, interactions in the human microbiome are often inferred from correlations between the abundances of bacterial phylogenetic groups across subjects. We tested whether such correlation-based methods are indeed reliable for inferring interaction networks. For this purpose, we simulated bacterial communities by means of the generalized Lotka-Volterra model, with variation in model parameters representing variability among hosts. Our results show that correlations can be indicative for presence of bacterial interactions, but only when measurement noise is low relative to the variation in interaction strengths between hosts. Indication of interaction was affected by type of interaction network, process noise and sampling under non-equilibrium conditions. The sign of a correlation mostly coincided with the nature of the strongest pairwise interaction, but this is not necessarily the case. For instance, under rare conditions of identical interaction strength, we found that competitive and exploitative interactions can result in positive as well as negative correlations. Thus, cross-sectional abundance data carry limited information on specific interaction types. Correlations in abundance may hint at interactions but require independent validation.

The bacteria in and on our body (the human microbiome) largely determine how our body functions, and whether we stay healthy or get sick. These bacteria do not live on their own, but interact among each other and with their human host. Finding out which bacteria interact with each other is cumbersome, but patterns of joint occurrence between species might provide a clue to their ecological dependencies. We investigated whether correlations in species abundance can be used for the purpose of ecological network reconstruction. We simulated different bacterial communities with known interactions according to a theoretical population model. After having collected virtual samples from our simulated data, we performed a correlation analysis and then compared the correlation network with our known interaction network. We found that correlations can be informative for underlying interactions, but ecological conclusions should be drawn carefully. An obvious limitation of correlation analysis is that direction of interaction cannot be recovered from co-occurrence data, making correlations insensitive for detection of asymmetric interactions. In addition, we found that competitive and exploitative interactions can induce positive as well as negative correlations. We recommend careful interpretation and validation when inferring networks from cross-sectional abundance data.

Root exudates and rhizosphere soil bacterial relationships of Nitraria tangutorum are linked to k-strategists bacterial community under salt stress

When plants are subjected to various biotic and abiotic stresses, the root system responds actively by secreting different types and amounts of bioactive compounds, while affects the structure of rhizosphere soil bacterial community. Therefore, understanding plant-soil-microbial interactions, especially the strength of microbial interactions, mediated by root exudates is essential. A short-term experiment was conducted under drought and salt stress to investigate the interaction between root exudates and Nitraria tangutorum rhizosphere bacterial communities. We found that drought and salt stress increased rhizosphere soil pH (9.32 and 20.6%) and electrical conductivity (1.38 and 11 times), respectively, while decreased organic matter (27.48 and 31.38%), total carbon (34.55 and 29.95%), and total phosphorus (20 and 28.57%) content of N. tangutorum rhizosphere soil. Organic acids, growth hormones, and sugars were the main differential metabolites of N. tangutorum under drought and salt stress. Salt stress further changed the N. tangutorum rhizosphere soil bacterial community structure, markedly decreasing the relative abundance of Bacteroidota as r-strategist while increasing that of Alphaproteobacteria as k-strategists. The co-occurrence network analysis showed that drought and salt stress reduced the connectivity and complexity of the rhizosphere bacterial network. Soil physicochemical properties and root exudates in combination with salt stress affect bacterial strategies and interactions. Our study revealed the mechanism of plant-soil-microbial interactions under the influence of root exudates and provided new insights into the responses of bacterial communities to stressful environments.

Modeling spatial interaction networks of the gut microbiota

How the gut microbiota is organized across space is postulated to influence microbial succession and its mutualistic relationships with the host. The lack of dynamic or perturbed abundance data poses considerable challenges for characterizing the spatial pattern of microbial interactions. We integrate allometric scaling theory, evolutionary game theory, and prey-predator theory into a unified framework under which quasi-dynamic microbial networks can be inferred from static abundance data. We illustrate that such networks can capture the full properties of microbial interactions, including causality, the sign of the causality, strength, and feedback loop, and are dynamically adaptive along spatial gradients, and context-specific, characterizing variability between individuals and within the same individual across time and space. We design and conduct a gut microbiota study to validate the model, characterizing key spatial determinants of the microbial differences between ulcerative colitis and healthy controls. Our model provides a sophisticated means of unraveling a complete atlas of how microbial interactions vary across space and quantifying causal relationships between such spatial variability and change in health state.

Impact of probiotic Limosilactobacillus reuteri DSM 17938 on amino acid metabolism in the healthy newborn mouse

We studied the effect of feeding a single probiotic Limosilactobacillus reuteri DSM 17938 (LR 17938) on the luminal and plasma levels of amino acids and their derivatives in the suckling newborn mouse, using gas chromatography and high-performance liquid chromatography. We found that LR 17938 increased the relative abundance of many amino acids and their derivatives in stool, while it simultaneously significantly reduced the plasma levels of three amino acids (serine, citrulline, and taurine). Many peptides and dipeptides were increased in stool and plasma, notably gamma-glutamyl derivatives of amino acids, following ingestion of the LR 17938. Gamma-glutamyl transformation of amino acids facilitates their absorption. LR 17938 significantly upregulated N-acetylated amino acids, the levels of which could be useful biomarkers in plasma and warrant further investigation. Specific fecal microbiota were associated with higher levels of fecal amino acids and their derivatives. Changes in luminal and circulating levels of amino acid derivatives, polyamines, and tryptophan metabolites may be mechanistically related to probiotic efficacy.

Predicting microbiome compositions from species assemblages through deep learning

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowing any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply our framework to data from in vitro and in vivo microbial communities, including ocean and soil microbiota, Drosophila melanogaster gut microbiota, and human gut and oral microbiota. We find that our framework learns to perform accurate out-of-sample predictions of complex community compositions from a small number of training samples. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

Limited Pairwise Synergistic and Antagonistic Interactions Impart Stability to Microbial Communities

One of the central goals of ecology is to explain and predict coexistence of species. In this context, microbial communities provide a model system where community structure can be studied in environmental niches and in laboratory conditions. A community of microbial population is stabilized by interactions between participating species. However, the nature of these stabilizing interactions has remained largely unknown. Theory and experiments have suggested that communities are stabilized by antagonistic interactions between member species, and destabilized by synergistic interactions. However, experiments have also revealed that a large fraction of all the interactions between species in a community are synergistic in nature. To understand the relative significance of the two types of interactions (synergistic vs. antagonistic) between species, we perform simulations of microbial communities with a small number of participating species using two frameworks—a replicator equation and a Lotka-Volterra framework. Our results demonstrate that synergistic interactions between species play a critical role in maintaining diversity in cultures. These interactions are critical for the ability of the communities to survive perturbations and maintain diversity. We follow up the simulations with quantification of the extent to which synergistic and antagonistic interactions are present in a bacterial community present in a soil sample. Overall, our results show that community stability is largely achieved with the help of synergistic interactions between participating species. However, we perform experiments to demonstrate that antagonistic interactions, in specific circumstances, can also contribute toward community stability.

Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks

Network-based assessments are important for disentangling complex microbial and microbial-host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka-Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.

Soft Statistical Mechanics for Biology

The multilevel organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism and the same hierarchical organization is in action for gene expression, tissue and organ architectures, and ecological systems.The still more common approach to such state of affairs is to think that causally relevant events originate from the lower level in the form of perturbations, that climb up the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such rigid bottom-up causative model is unable to offer realistic models of many biological phenomena.Complex network approach allows to uncover the nature of multilevel organization, but in order to operationally define the organization principles of biological systems, we need to go further and complement network approach with sensible measures of order and organization. These measures, while keeping their original physical meaning, must not impose theoretical premises not verifiable in biological frameworks. We will show here how relatively simple and largely hypothesis-free multidimensional statistics tools can satisfactorily meet these criteria.

Potential Role of Akkermansia muciniphila in Parkinson's Disease and Other Neurological/Autoimmune Diseases

The composition of the gut microbiota, including Akkermansia muciniphila (A. muciniphila), is altered in many neurological diseases and may be involved in the pathophysiological processes of Parkinson's disease (PD). A. muciniphila, a mucin-degrading bacterium, is a potential next-generation microbe that has anti-inflammatory properties and is responsible for keeping the body healthy. As the role of A. muciniphila in PD has become increasingly apparent, we discuss the potential link between A. muciniphila and various neurological diseases (including PD) in the current review.

Estuarine gradients dictate spatiotemporal variations of microbiome networks in the Chesapeake Bay

BACKGROUND

Annually reoccurring microbial populations with strong spatial and temporal variations have been identified in estuarine environments, especially in those with long residence time such as the Chesapeake Bay (CB). However, it is unclear how microbial taxa cooccurr and how the inter-taxa networks respond to the strong environmental gradients in the estuaries.

RESULTS

Here, we constructed co-occurrence networks on prokaryotic microbial communities in the CB, which included seasonal samples from seven spatial stations along the salinity gradients for three consecutive years. Our results showed that spatiotemporal variations of planktonic microbiomes promoted differentiations of the characteristics and stability of prokaryotic microbial networks in the CB estuary. Prokaryotic microbial networks exhibited a clear seasonal pattern where microbes were more closely connected during warm season compared to the associations during cold season. In addition, microbial networks were more stable in the lower Bay (ocean side) than those in the upper Bay (freshwater side). Multivariate regression tree (MRT) analysis and piecewise structural equation modeling (SEM) indicated that temperature, salinity and total suspended substances along with nutrient availability, particulate carbon and Chl a, affected the distribution and co-occurrence of microbial groups, such as Actinobacteria, Bacteroidetes, Cyanobacteria, Planctomycetes, Proteobacteria, and Verrucomicrobia. Interestingly, compared to the abundant groups (such as SAR11, Saprospiraceae and Actinomarinaceae), the rare taxa including OM60 (NOR5) clade (Gammaproteobacteria), Micrococcales (Actinobacteria), and NS11-12 marine group (Bacteroidetes) contributed greatly to the stability of microbial co-occurrence in the Bay. Modularity and cluster structures of microbial networks varied spatiotemporally, which provided valuable insights into the 'small world' (a group of more interconnected species), network stability, and habitat partitioning/preferences.

CONCLUSION

Our results shed light on how estuarine gradients alter the spatiotemporal variations of prokaryotic microbial networks in the estuarine ecosystem, as well as their adaptability to environmental disturbances and co-occurrence network complexity and stability.

Disentangling environmental effects in microbial association networks

BACKGROUND

Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not.

RESULTS

We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges-87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors.

CONCLUSIONS

To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses. Video abstract.

Microbial Interactions Drive Distinct Taxonomic and Potential Metabolic Responses to Habitats in Karst Cave Ecosystem

The geological role of microorganisms has been widely studied in the karst cave ecosystem. However, microbial interactions and ecological functions in such a dark, humid, and oligotrophic habitat have received far less attention, which is crucial to understanding cave biogeochemistry. Herein, microorganisms from weathered rock and sediment along the Heshang Cave depth were analyzed by random matrix theory-based network and Tax4Fun functional prediction. The results showed that although the cave microbial communities have spatial heterogeneity, differential habitats drove the community structure and diversity. Actinobacteria were predominant in weathered rock, whereas Proteobacteria dominated the sediment. The sediment communities presented significantly higher alpha diversities due to the relatively abundant nutrition from the outside by the intermittent stream. Consistently, microbial interactions in sediment were more complex, as visualized by more nodes and links. The abundant taxa presented more positive correlations with other community members in both of the two networks, indicating that they relied on promotion effects to adapt to the extreme environment. The keystones in weathered rock were mainly involved in the biodegradation of organic compounds, whereas the keystone Nitrospira in sediment contributed to carbon/nitrogen fixation. Collectively, these findings suggest that microbial interactions may lead to distinct taxonomic and functional communities in weathered rock and sediment in the subsurface Heshang Cave.

IMPORTANCE In general, the constant physicochemical conditions and limited nutrient sources over long periods in the subsurface support a stable ecosystem in karst cave. Previous studies on cave microbial ecology were mostly focused on community composition, diversity, and the relationship with local environmental factors. There are still many unknowns about the microbial interactions and functions in such a dark environment with little human interference. Two representative habitats, including weathered rock and sediment in Heshang Cave, were selected to give an integrated insight into microbial interactions and potential functions. The cooccurrence network, especially the subnetwork, was used to characterize the cave microbial interactions in detail. We demonstrated that abundant taxa primarily relied on promotion effects rather than inhibition effects to survive in Heshang Cave. Keystone species may play important metabolic roles in sustaining ecological functions. Our study provides improved understanding of microbial interaction patterns and community ecological functions in the karst cave ecosystem.

BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.

Characterizing the ecological interactions among microbial members is an important step towards understanding the structure and function of diverse microbial communities. Widely used correlation based approaches for inferring interactions from cross-sectional microbiome sequencing data are not able to predict the directionality of interactions, and their results may not be interpretable. We developed an expectation-maximization algorithm (BEEM-Static) that can infer directed interaction networks from cross-sectional data based on an ecological model. Our benchmarking results showed that BEEM-Static inferred presence and directionality of interactions accurately, while correlation based methods had performance slightly better than random guesses. In addition, BEEM-Static was robust to various types of noises using statistical filters to identify and remove data points violating its assumptions. Applying BEEM-Static to a large public dataset of human gut microbiomes, we were able to identify multiple stable equilibria with distinct ecological properties.

Untangling the complexity of priority effects in multispecies communities

The history of species immigration can dictate how species interact in local communities, thereby causing historical contingency in community assembly. Since immigration history is rarely known, these historical influences, or priority effects, pose a major challenge in predicting community assembly. Here, we provide a graph-based, non-parametric, theoretical framework for understanding the predictability of community assembly as affected by priority effects. To develop this framework, we first show that the diversity of possible priority effects increases super-exponentially with the number of species. We then point out that, despite this diversity, the consequences of priority effects for multispecies communities can be classified into four basic types, each of which reduces community predictability: alternative stable states, alternative transient paths, compositional cycles and the lack of escapes from compositional cycles to stable states. Using a neural network, we show that this classification of priority effects enables accurate explanation of community predictability, particularly when each species immigrates repeatedly. We also demonstrate the empirical utility of our theoretical framework by applying it to two experimentally derived assembly graphs of algal and ciliate communities. Based on these analyses, we discuss how the framework proposed here can help guide experimental investigation of the predictability of history-dependent community assembly.

An efficient and scalable top-down method for predicting structures of microbial communities

Modern applications involving multispecies microbial communities rely on the ability to predict structures of such communities in defined environments. The structures depend on pairwise and high-order interactions between species. To unravel these interactions, classical bottom-up approaches examine all possible species subcommunities. Such approaches are not scalable as the number of subcommunities grows exponentially with the number of species, n. Here we present a top-down method wherein the number of subcommunities to be examined grows linearly with n, drastically reducing experimental effort. The method uses steady-state data from leave-one-out subcommunities and mathematical modeling to infer effective pairwise interactions and predict community structures. The accuracy of the method increases with n, making it suitable for large communities. We established the method in silico and validated it against a five-species community from literature and an eight-species community cultured in vitro. Our method offers an efficient and scalable tool for predicting microbial community structures.

Structural identifiability of the generalized Lotka-Volterra model for microbiome studies

Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research, including predicting how populations change over time, determining how manipulations of microbiomes affect dynamics and designing synthetic microbiomes to perform tasks. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model. We used structural identifiability analyses to determine the extent to which a time series of relative abundances can be used to parametrize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Using synthetic data of a simple community for which we know the underlying structure, local practical identifiability analysis showed that modest amounts of both process and measurement error do not fundamentally affect these identifiability properties.

Fluctuation spectra of large random dynamical systems reveal hidden structure in ecological networks

Understanding the relationship between complexity and stability in large dynamical systems-such as ecosystems-remains a key open question in complexity theory which has inspired a rich body of work developed over more than fifty years. The vast majority of this theory addresses asymptotic linear stability around equilibrium points, but the idea of 'stability' in fact has other uses in the empirical ecological literature. The important notion of 'temporal stability' describes the character of fluctuations in population dynamics, driven by intrinsic or extrinsic noise. Here we apply tools from random matrix theory to the problem of temporal stability, deriving analytical predictions for the fluctuation spectra of complex ecological networks. We show that different network structures leave distinct signatures in the spectrum of fluctuations, and demonstrate the application of our theory to the analysis of ecological time-series data of plankton abundances.

Coexistence holes characterize the assembly and disassembly of multispecies systems

A central goal of ecological research has been to understand the limits on the maximum number of species that can coexist under given constraints. However, we know little about the assembly and disassembly processes under which a community can reach such a maximum number, or whether this number is in fact attainable in practice. This limitation is partly due to the challenge of performing experimental work and partly due to the lack of a formalism under which one can systematically study such processes. Here, we introduce a formalism based on algebraic topology and homology theory to study the space of species coexistence formed by a given pool of species. We show that this space is characterized by ubiquitous discontinuities that we call coexistence holes (that is, empty spaces surrounded by filled space). Using theoretical and experimental systems, we provide direct evidence showing that these coexistence holes do not occur arbitrarily-their diversity is constrained by the internal structure of species interactions and their frequency can be explained by the external factors acting on these systems. Our work suggests that the assembly and disassembly of ecological systems is a discontinuous process that tends to obey regularities.

Network analysis methods for studying microbial communities: A mini review

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Environmental connectivity controls diversity in soil microbial communities

Interspecific interactions are thought to govern the stability and functioning of microbial communities, but the influence of the spatial environment and its structural connectivity on the potential of such interactions to unfold remain largely unknown. Here we studied the effects on community growth and microbial diversity as a function of environmental connectivity, where we define environmental connectivity as the degree of habitat fragmentation preventing microbial cells from living together. We quantitatively compared growth of a naturally-derived high microbial diversity community from soil in a completely mixed liquid suspension (high connectivity) to growth in a massively fragmented and poorly connected environment (low connectivity). The low connectivity environment consisted of homogenously-sized miniature agarose beads containing random single or paired founder cells. We found that overall community growth was the same in both environments, but the low connectivity environment dramatically reduced global community-level diversity compared to the high connectivity environment. Experimental observations were supported by community growth modeling. The model predicts a loss of diversity in the low connectivity environment as a result of negative interspecific interactions becoming more dominant at small founder species numbers. Counterintuitively for the low connectivity environment, growth of isolated single genotypes was less productive than that of random founder genotype cell pairs, suggesting that the community as a whole profited from emerging positive interspecific interactions. Our work demonstrates the importance of environmental connectivity for growth of natural soil microbial communities, which aids future efforts to intervene in or restore community composition to achieve engineering and biotechnological objectives.

Manupriyam Dubey et al. use experimental systems with naturally derived soil microbiomes in liquid suspensions and encapsulated beads to compare community dynamics in well-connected and poorly connected environments. While their results show that microbial growth does not vary between conditions, they report that low connectivity led to reduced microbial diversity and suggest that these reductions in microbial diversity may be due to increased negative interspecific interactions.

Multi-Replicated Enrichment Communities as a Model System in Microbial Ecology

Recent advances in robotics and affordable genomic sequencing technologies have made it possible to establish and quantitatively track the assembly of enrichment communities in high-throughput. By conducting community assembly experiments in up to thousands of synthetic habitats, where the extrinsic sources of variation among replicates can be controlled, we can now study the reproducibility and predictability of microbial community assembly at different levels of organization, and its relationship with nutrient composition and other ecological drivers. Through a dialog with mathematical models, high-throughput enrichment communities are bringing us closer to the goal of developing a quantitative predictive theory of microbial community assembly. In this short review, we present an overview of recent research on this growing field, highlighting the connection between theory and experiments and suggesting directions for future work.

Interaction analyses based on growth parameters of GWAS between Escherichia coli and Staphylococcus aureus

To accurately explore the interaction mechanism between Escherichia coli and Staphylococcus aureus, we designed an ecological experiment to monoculture and co-culture E. coli and S. aureus. We co-cultured 45 strains of E. coli and S. aureus, as well as each species individually to measure growth over 36 h. We implemented a genome wide association study (GWAS) based on growth parameters (λ, R, A and s) to identify significant single nucleotide polymorphisms (SNPs) of the bacteria. Three commonly used growth regression equations, Logistic, Gompertz, and Richards, were used to fit the bacteria growth data of each strain. Then each equation's Akaike's information criterion (AIC) value was calculated as a commonly used information criterion. We used the optimal growth equation to estimate the four parameters above for strains in co-culture. By plotting the estimates for each parameter across two strains, we can visualize how growth parameters respond ecologically to environment stimuli. We verified that different genotypes of bacteria had different growth trajectories, although they were the same species. We reported 85 and 52 significant SNPs that were associated with interaction in E. coli and S. aureus, respectively. Many significant genes might play key roles in interaction, such as yjjW, dnaK, aceE, tatD, ftsA, rclR, ftsK, fepA in E. coli, and scdA, trpD, sdrD, SAOUHSC_01219 in S. aureus. Our study illustrated that there were multiple genes working together to affect bacterial interaction, and laid a solid foundation for the later study of more complex inter-bacterial interaction mechanisms.

Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combine metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotations, which we provide for experimental testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

The complexities of the diet-microbiome relationship: advances and perspectives

Personalised dietary modulation of the gut microbiota may be key to disease management. Current investigations provide a broad understanding of the impact of diet on the composition and activity of the gut microbiota, yet detailed knowledge in applying diet as an actionable tool remains limited. Further to the relative novelty of the field, approaches are yet to be standardised and extremely heterogeneous research outcomes have ensued. This may be related to confounders associated with complexities in capturing an accurate representation of both diet and the gut microbiota. This review discusses the intricacies and current methodologies of diet-microbial relations, the implications and limitations of these investigative approaches, and future considerations that may assist in accelerating applications. New investigations should consider improved collection of dietary data, further characterisation of mechanistic interactions, and an increased focus on -omic technologies such as metabolomics to describe the bacterial and metabolic activity of food degradation, together with its crosstalk with the host. Furthermore, clinical evidence with health outcomes is required before therapeutic dietary strategies for microbial amelioration can be made. The potential to reach detailed understanding of diet-microbiota relations may depend on re-evaluation, progression, and unification of research methodologies, which consider the complexities of these interactions.

History of petroleum disturbance triggering the depth-resolved assembly process of microbial communities in the vadose zone

Biogeochemical gradient forms in vadose zone, yet little is known about the assembly processes of microbial communities in this zone under petroleum disturbance. This study collected vadose zone soils at three sites with 0, 5, and 30 years of petroleum contamination to unravel the vertical microbial community successions and their assembly mechanisms. The results showed that petroleum hydrocarbons exhibited higher concentrations at the long-term contaminated site, showing negative impacts on some soil properties, retarding in the surface soils and decreasing along soil depth. Cultivable fraction of heterotrophic bacteria and microbial α-diversity decreased along depth in vadose zones with short-term/no contamination history, but exhibited an opposite trend with long-term contamination history. Petroleum contamination intensified the vertical heterogeneity of microbial communities based on the contamination time. Microbial co-occurrence network revealed the lowest species co-occurrence pattern at the long-term contaminated site. The distance-decay patterns and null model analysis together suggested distinct assembly mechanisms at three sites, where dispersal limitation (42-45%) was higher and variable and homogenizing selections were lower (37-38%) in vadose zones under petroleum disturbance than those in the uncontaminated vadose zone. Our findings help to better understand the subsurface biogeochemical cycles and bioremediation of petroleum-contaminated vadose zones.

Metabolic modelling approaches for describing and engineering microbial communities

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.