Multi-stability and the origin of microbial community types

Hysteretic and asynchronous regime shifts of bacterial and micro-eukaryotic communities driven by nutrient loading

Nutrient pollution is pervasive in many urban rivers, while restoration measures that reduce nutrient loading but fail to improve biological communities often lack effectiveness due to the indispensable role of biota, especially multi-taxa, in enhancing ecosystem stability and function. The investigation of the response patterns of multi-taxa to the nutrient loading in urban rivers is important for the recovery of biota structure and thus ecosystem function. However, little is known about the response patterns of multi-taxa and their impact on ecosystem structure and function in urban rivers. Here, the study, from the perspective of alternative stable states theory, showed the hysteretic response of both bacterial and micro-eukaryotic communities to nutrient loading based on the field investigation and environmental DNA metabarcoding. Bistability was shown to exist in both bacterial and micro-eukaryotic communities, demonstrating that the response of microbiota to nutrient loading was a regime shifts with hysteresis. Potential analysis then indicated that the increased nutrient loading drove regime shifts in the bacterial community and the micro-eukaryotic community towards a state dominated by anaerobic bacteria and benthic Bacillariophyta, respectively. High nutrient loading was found to reduce the relative abundance of metazoan, but increase that of eukaryotic algae, which made the trophic pyramid top-lighter and bottom-heavier, probably exacerbating the degradation of ecosystem function. It should be noted that, in response to the reduced nutrient loading, the recovery threshold of micro-eukaryotic communities (nutrient loading = ∼0.5) was lower than that of bacterial communities (nutrient loading = ∼1.2), demonstrating longer hysteresis of micro-eukaryotic communities. In addition, the markedly positive correlation between the status of microbial communities and N-related enzyme activities suggested the recovery of microbial communities probably will benefit the improvement of N-cycling functionality. The obtained results provide a deep insight into the collapse and recovery trajectories of multi-trophic microbiota to the nutrient loading gradient and their impact on the N transformation potential, therefore benefiting the restoration and management of urban rivers.

Revealing microbiota characteristics and predicting flavor-producing sub-communities in Nongxiangxing baijiu pit mud through metagenomic analysis and metabolic modeling

The microorganisms of the pit mud (PM) of Nongxiangxing baijiu (NXXB) have an important role in the synthesis of flavor substances, and they determine attributes and quality of baijiu. Herein, we utilize metagenomics and genome-scale metabolic models (GSMMs) to investigate the microbial composition, metabolic functions in PM microbiota, as well as to identify microorganisms and communities linked to flavor compounds. Metagenomic data revealed that the most prevalent assembly of bacteria and archaea was Proteiniphilum, Caproicibacterium, Petrimonas, Lactobacillus, Clostridium, Aminobacterium, Syntrophomonas, Methanobacterium, Methanoculleus, and Methanosarcina. The important enzymes ofPMwere in bothGH and GT familymetabolism. A total of 38 high-quality metagenome-assembled genomes (MAGs) were obtained, including those at the family level (n = 13), genus level (n = 17), and species level (n = 8). GSMMs of the 38 MAGs were then constructed. From the GSMMs, individual and community capabilities respectively were predicted to be able to produce 111 metabolites and 598 metabolites. Twenty-three predicted metabolites were consistent with the metabonomics detected flavors and served as targets. Twelve sub-community of were screened by cross-feeding of 38 GSMMs. Of them, Methanobacterium, Sphaerochaeta, Muricomes intestini, Methanobacteriaceae, Synergistaceae, and Caloramator were core microorganisms for targets in each sub-community. Overall, this study of metagenomic and target-community screening could help our understanding of the metabolite-microbiome association and further bioregulation of baijiu.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Cooperative growth in microbial communities is a driver of multistability

Microbial communities often exhibit more than one possible stable composition for the same set of external conditions. In the human microbiome, these persistent changes in species composition and abundance are associated with health and disease states, but the drivers of these alternative stable states remain unclear. Here we experimentally demonstrate that a cross-kingdom community, composed of six species relevant to the respiratory tract, displays four alternative stable states each dominated by a different species. In pairwise coculture, we observe widespread bistability among species pairs, providing a natural origin for the multistability of the full community. In contrast with the common association between bistability and antagonism, experiments reveal many positive interactions within and between community members. We find that multiple species display cooperative growth, and modeling predicts that this could drive the observed multistability within the community as well as non-canonical pairwise outcomes. A biochemical screening reveals that glutamate either reduces or eliminates cooperativity in the growth of several species, and we confirm that such supplementation reduces the extent of bistability across pairs and reduces multistability in the full community. Our findings provide a mechanistic explanation of how cooperative growth rather than competitive interactions can underlie multistability in microbial communities.

Sediment DNA Records the Critical Transition of Bacterial Communities in the Arid Lake

It is necessary to predict the critical transition of lake ecosystems due to their abrupt, non-linear effects on social-economic systems. Given the promising application of paleolimnological archives to tracking the historical changes of lake ecosystems, it is speculated that they can also record the lake's critical transition. We studied Lake Dali-Nor in the arid region of Inner Mongolia because of the profound shrinking the lake experienced between the 1300 s and the 1600 s. We reconstructed the succession of bacterial communities from a 140-cm-long sediment core at 4-cm intervals and detected the critical transition. Our results showed that the historical trajectory of bacterial communities from the 1200 s to the 2010s was divided into two alternative states: state1 from 1200 to 1300 s and state2 from 1400 to 2010s. Furthermore, in the late 1300 s, the appearance of a tipping point and critical slowing down implied the existence of a critical transition. By using a multi-decadal time series from the sedimentary core, with general Lotka-Volterra model simulations, local stability analysis found that bacterial communities were the most unstable as they approached the critical transition, suggesting that the collapse of stability triggers the community shift from an equilibrium state to another state. Furthermore, the most unstable community harbored the strongest antagonistic and mutualistic interactions, which may imply the detrimental role of interaction strength on community stability. Collectively, our study showed that sediment DNA can be used to detect the critical transition of lake ecosystems.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed bacterial communities in 880 CF sputum samples and developed microbiome descriptors to model community reorganization prior to and during 18 PEx. We identified two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx showed hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx displayed stochasticity and increased diversity. A simulation of antimicrobial treatment predicted better efficacy for hierarchically organized communities. This link between PEx type, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics

The interplay of stochastic and ecological processes that govern the establishment and persistence of host-associated microbial communities is not well understood. Here we illustrate the conceptual and practical advantages of fitting stochastic population dynamics models to multi-species bacterial time series data. We show how the stability properties, fluctuation regimes and persistence probabilities of human vaginal microbial communities can be better understood by explicitly accommodating three sources of variability in ecological stochastic models of multi-species abundances: 1) stochastic biotic and abiotic forces, 2) ecological feedback and 3) sampling error. Rooting our modeling tool in stochastic population dynamics modeling theory was key to apply standardized measures of a community's reaction to environmental variation that ultimately depends on the nature and intensity of the intra-specific and inter-specific interaction strengths. Using estimates of model parameters, we developed a Risk Prediction Monitoring (RPM) tool that estimates temporal changes in persistence probabilities for any bacterial group of interest. This method mirrors approaches that are often used in conservation biology in which a measure of extinction risks is periodically updated with any change in a population or community. Additionally, we show how to use estimates of interaction strengths and persistence probabilities to formulate hypotheses regarding the molecular mechanisms and genetic composition that underpin different types of interactions. Instead of seeking a definition of "dysbiosis" we propose to translate concepts of theoretical ecology and conservation biology methods into practical approaches for the management of human-associated bacterial communities.

A Mapping Review of the Pathogenesis of Peri-Implantitis: The Biofilm-Mediated Inflammation and Bone Dysregulation (BIND) Hypothesis

This mapping review highlights the need for a new paradigm in the understanding of peri-implantitis pathogenesis. The biofilm-mediated inflammation and bone dysregulation (BIND) hypothesis is proposed, focusing on the relationship between biofilm, inflammation, and bone biology. The close interactions between immune and bone cells are discussed, with multiple stable states likely existing between clinically observable definitions of peri-implant health and peri-implantitis. The framework presented aims to explain the transition from health to disease as a staged and incremental process, where multiple factors contribute to distinct steps towards a tipping point where disease is manifested clinically. These steps might be reached in different ways in different patients and may constitute highly individualised paths. Notably, factors affecting the underlying biology are identified in the pathogenesis of peri-implantitis, highlighting that disruptions to the host-microbe homeostasis at the implant-mucosa interface may not be the sole factor. An improved understanding of disease pathogenesis will allow for intervention on multiple levels and a personalised treatment approach. Further research areas are identified, such as the use of novel biomarkers to detect changes in macrophage polarisation and activation status, and bone turnover.

Decoupling the heterogeneity of sediment microbial communities along the urbanization gradients: A Bayesian-based approach

Comprehending the response of microbial communities in rivers along urbanization gradients to hydrologic characteristics and pollution sources is critical for effective watershed management. However, the effects of complex factors on riverine microbial communities remain poorly understood. Thus, we established a bacteria-based index of biotic integrity (Ba-IBI) to evaluate the microbial community heterogeneity of rivers along an urbanization gradient. To examine the response of Ba-IBI to multiple stressors, we employed a Bayesian network based on structural equation modeling (SEM-BN) and revealed the key control factors influencing Ba-IBI at different levels of urbanization. Our findings highlight that waterborne nutrients have the most significant direct impact on Ba-IBI (r = -0.563), with a particular emphasis on ammonia nitrogen, which emerged as the primary driver of microbial community heterogeneity in the Liuyang River basin. In addition, our study confirmed the substantial adverse effects of urbanization on river ecology, as urban land use had the greatest indirect effect on Ba-IBI (r = -0.460). Specifically, the discharge load from wastewater treatment plants (WWTP) was found to significantly negatively affect the Ba-IBI of the entire watershed. In the low urbanized watersheds, rice cultivation (RC) and concentrated animal feeding operations (CAFO) are key control factors, and an increase in their emissions can lead to a sharp decrease in Ba-IBI. In moderately urbanized watersheds, the Ba-IBI tended to decrease as the level of RC emissions increased, while in those with moderate RC emissions, an increase in point source emissions mitigated the negative impact of RC on Ba-IBI. In highly urbanized watersheds, Ba-IBI was not sensitive to changes in stressors. Overall, our study presents a novel approach by integrating Ba-IBI with multi-scenario analysis tools to assess the effects of multiple stressors on microbial communities in river sediments, providing valuable insights for more refined environmental decision-making.

Dam-induced flow alternations drive the regime shift towards a cyanobacteria-dominated microbiota state in the Yangtze River

While satisfying the demands of social and economic development, dams act as physical barriers affecting both abiotic and biotic factors in large rivers. These altered factors can interact with each other and gradually reshape the local ecosystem state. The reshaped state may spread downstream and affect ecosystem states on a large scale. However, the spread extent and characteristics of ecosystem states along large rivers remain understudied. To address this problem, alternative microbiota states and their responses to environmental conditions in the Yangtze River were investigated, considering the preponderance of alternative stable states theory in explaining the response of ecosystem states as well as the role of benthic microorganisms in indicating ecosystem states. In this study, flow discharge was identified as the main hydrological factor that clustered benthic microbiota into two types, and these two microbiota types were bistable and characterized by differential enrichment of the Cyanobacteria phylum. Potential analysis demonstrated that reducing flow discharge beneath a threshold (i.e., flow discharge < 12,900 m3/s) could shift benthic microbiotas to a state where benthic cyanobacteria would become the dominant species (the Microbiota State B). In the bistable region (i.e., 12,900 < flow discharge < 28,000 m3/s), both the ecological resilience and the contribution of deterministic process were found weak by relative potential depth calculations and neutral community modeling, suggesting that this region is susceptible to the microbiota state of its upstream and thus deserves more scientific attention to prevent the unfavorable state from spreading downstream. In addition, high denitrification potential at sites of the Microbiota State B was likely responsible for the low N:P ratio, further benefiting the dominance of N-fixing cyanobacteria. This study empirically showed the response of alternative microbiota states to flow gradients, and explored the distribution and characteristics of the microbiota states along the mainstream of the Yangtze River, therefore providing insights into environmental flow design and reservoir regulation of large rivers.

The collapse and re-establishment of stability regulate the gradual transition of bacterial communities from macrophytes- to phytoplankton-dominated types in a large eutrophic lake

Eutrophic lakes often exhibit two alternative types: macrophytes-dominated (MD) and phytoplankton-dominated (PD). However, the nature of bacterial community types that whether the transition from the MD to the PD types occurs in a gradual or abrupt manner remains hotly debated. Further, the theoretical recognition that stability regulates the transition of bacterial community types remains qualitative. To address these issues, we divided the transition of bacterial communities along a trophic gradient into 12 successional stages, ranging from the MD to the PD types. Results showed that 12 states were clustered into three distinct regimes: MD type, intermediate transitional type and PD type. Bacterial communities were not different between consecutive stages, suggesting that the transition of alternative types occurs in a continuous gradient. At the same time, the stability of bacterial communities was significantly lower in the intermediate type than in the MD or PD types, highlighting that the collapse and re-establishment of community stability regulate the transition. Further, our results showed that the high complexity of taxon interactions and strong stochastic processes disrupt the stability. Ultimately, this study enables deeper insights into understanding the alternative types of microbial communities in the view of community stability.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease (COPD). Intermittent pulmonary exacerbations (PEx) in these conditions are associated with lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed 880 near-daily CF sputum samples and developed non-standard microbiome descriptors to model community reorganization prior and during 18 PEx. We identified two communal microbial regimes with opposing ecology and dynamics. Whereas pathogen-governed dysbiosis showed hierarchical community organization and reduced diversity, anaerobic bloom dysbiosis displayed stochasticity and increased diversity. Microbiome organization modulated the relevance of pathogens and a simulation of antimicrobial treatment predicted better efficacy for hierarchically organized microbiota. This causal link between PEx, microbiome organization, and treatment success advances the development of personalized dysbiosis management in CF and, potentially, other obstructive lung diseases.

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.

Deciphering Interactions Within a 4-Strain Riverine Bacterial Community

The dynamics of a community of four planktonic bacterial strains isolated from river water was followed in R2 broth for 72 h in batch experiments. These strains were identified as Janthinobacterium sp., Brevundimonas sp., Flavobacterium sp. and Variovorax sp. 16S rRNA gene sequencing and flow cytometry analyses were combined to monitor the change in abundance of each individual strain in bi-cultures and quadri-culture. Two interaction networks were constructed that summarize the impact of the strains on each other's growth rate in exponential phase and carrying capacity in stationary phase. The networks agree on the absence of positive interactions but also show differences, implying that ecological interactions can be specific to particular growth phases. Janthinobacterium sp. was the fastest growing strain and dominated the co-cultures. However, its growth rate was negatively affected by the presence of other strains 10 to 100 times less abundant than Janthinobacterium sp. In general, we saw a positive correlation between growth rate and carrying capacity in this system. In addition, growth rate in monoculture was predictive of carrying capacity in co-culture. Taken together, our results highlight the necessity to take growth phases into account when measuring interactions within a microbial community. In addition, evidence that a minor strain can greatly influence the dynamics of a dominant one underlines the necessity to choose population models that do not assume a linear dependency of interaction strength to abundance of other species for accurate parameterization from such empirical data.

How nutrient loading leads to alternative stable states in microbially mediated N-cycle pathways: A new insight into bioavailable nitrogen removal in urban rivers

Excessive nutrients have disrupted pathways of microbial-mediated nitrogen (N) cycle in urban rivers and caused bioavailable N to accumulate in sediments, while remedial actions sometimes fail to recover degraded river ecosystems even when environmental quality has been improved. It is not sufficient to revert the ecosystem to its original healthy state by restoring the pre-degradation environmental conditions, as explained by alternative stable states theory. Understanding the recovery of disrupted N-cycle pathways from the perspective of alternative stable states theory can benefit effective river remediation. Previous studies have found alternative microbiota states in rivers; however, the existence and implications of alternative stable states in microbial-mediated N-cycle pathway remain unclear. Here, high-throughput sequencing and N-related enzyme activities measurement were combined in the field investigation to provide empirical evidence for the bi-stability in microbially mediated N-cycle pathways. According to the behavior of bistable ecosystems, the existence of alternative stable states in microbial-mediated N-cycle pathway have been shown, and nutrient loading, mainly total nitrogen and total phosphorus, were identified as key driver of regime shifts. In addition, potential analysis revealed that reducing nutrient loading shifted the N-cycle pathway to a desirable state characterized by high ammonification and nitrification, probably avoiding the accumulation of ammonia and organic N. It should be noted that the improvement of microbiota status can facilitate the recovery of the desirable pathway state according to the relationship between microbiota states and N-cycle pathway states. Keystone species, including Rhizobiales and Sphingomonadales, were discerned by network analysis, and the increase in their relative abundance may facilitate the improvement of microbiota status. The obtained results suggested that the nutrient reduction should be combined with microbiota management to benefit the bioavailable N removal in urban rivers, therefore providing a new insight into alleviating adverse effects of the nutrient loading on urban rivers.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

Multistability and hysteresis in states of oral microbiota: Is it impacting the dental clinical cohort studies?

INTRODUCTION

Microbiome from a "healthy cohort" is used as a reference for comparison to cases and intervention. However, the studies with cohort-based clinical research have not sufficiently accounted for the multistability in oral microbial community. The screening is limited to phenotypic features with marked variations in microbial genomic markers. Herein, we aimed to assess the stability of the oral microbiome across time from an intervention-free "healthy" cohort.

METHODS

We obtained 33 supragingival samples of 11 healthy participants from the biobank. For each participant, we processed one sample as baseline (T0) and two samples spaced at 1-month (T1) and 3-month (T2) intervals for 16S ribosomal RNA gene sequencing analysis.

RESULTS

We observed that taxonomic profiling had a similar pattern of dominant genera, namely, Rothia, Prevotella, and Hemophilus, at all time points. Shannon diversity revealed a significant increase from T0 (p < .05). Bray Curtis dissimilarity was significant (R = -.02, p < .01) within the cohort at each time point. Community stability had negative correlation to synchrony (r = -.739; p = .009) and variance (r = -.605; p = .048) of the species. Clustering revealed marked differences in the grouping patterns between the three time points. For all time points, the clusters presented a substantially dissimilar set of differentially abundant taxonomic and functional biomarkers.

CONCLUSION

Our observations indicate towards the presence of multistable states within the oral microbiome in an intervention-free healthy cohort. For a conclusive and meaningful long-term reference, dental clinical research should account for multistability in the personalized therapy approach to improve the identification and classification of reliable markers.

Ecological Stability Emerges at the Level of Strains in the Human Gut Microbiome

The human gut microbiome harbors substantial ecological diversity at the species level as well as at the strain level within species. In healthy hosts, species abundance fluctuations in the microbiome are thought to be stable, and these fluctuations can be described by macroecological laws. However, it is less clear how strain abundances change over time. An open question is whether individual strains behave like species themselves, exhibiting stability and following the macroecological relationships known to hold at the species level, or whether strains have different dynamics, perhaps due to the relatively close phylogenetic relatedness of cocolonizing lineages. Here, we analyze the daily dynamics of intraspecific genetic variation in the gut microbiomes of four healthy, densely longitudinally sampled hosts. First, we find that the overall genetic diversity of a large majority of species is stationary over time despite short-term fluctuations. Next, we show that fluctuations in abundances in approximately 80% of strains analyzed can be predicted with a stochastic logistic model (SLM), an ecological model of a population experiencing environmental fluctuations around a fixed carrying capacity, which has previously been shown to capture statistical properties of species abundance fluctuations. The success of this model indicates that strain abundances typically fluctuate around a fixed carrying capacity, suggesting that most strains are dynamically stable. Finally, we find that the strain abundances follow several empirical macroecological laws known to hold at the species level. Together, our results suggest that macroecological properties of the human gut microbiome, including its stability, emerge at the level of strains. IMPORTANCE To date, there has been an intense focus on the ecological dynamics of the human gut microbiome at the species level. However, there is considerable genetic diversity within species at the strain level, and these intraspecific differences can have important phenotypic effects on the host, impacting the ability to digest certain foods and metabolize drugs. Thus, to fully understand how the gut microbiome operates in times of health and sickness, its ecological dynamics may need to be quantified at the level of strains. Here, we show that a large majority of strains maintain stable abundances for periods of months to years, exhibiting fluctuations in abundance that can be well described by macroecological laws known to hold at the species level, while a smaller percentage of strains undergo rapid, directional changes in abundance. Overall, our work indicates that strains are an important unit of ecological organization in the human gut microbiome.

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.

Drivers and determinants of strain dynamics following fecal microbiota transplantation

Fecal microbiota transplantation (FMT) is a therapeutic intervention for inflammatory diseases of the gastrointestinal tract, but its clinical mode of action and subsequent microbiome dynamics remain poorly understood. Here we analyzed metagenomes from 316 FMTs, sampled pre and post intervention, for the treatment of ten different disease indications. We quantified strain-level dynamics of 1,089 microbial species, complemented by 47,548 newly constructed metagenome-assembled genomes. Donor strain colonization and recipient strain resilience were mostly independent of clinical outcomes, but accurately predictable using LASSO-regularized regression models that accounted for host, microbiome and procedural variables. Recipient factors and donor–recipient complementarity, encompassing entire microbial communities to individual strains, were the main determinants of strain population dynamics, providing insights into the underlying processes that shape the post-FMT gut microbiome. Applying an ecology-based framework to our findings indicated parameters that may inform the development of more effective, targeted microbiome therapies in the future, and suggested how patient stratification can be used to enhance donor microbiota colonization or the displacement of recipient microbes in clinical practice.

Gut microbiome dysbiosis is associated with increased mortality after solid organ transplantation

Organ transplantation is a life-saving treatment for patients with end-stage disease, but survival rates after transplantation vary considerably. There is now increasing evidence that the gut microbiome is linked to the survival of patients undergoing hematopoietic cell transplant, yet little is known about the role of the gut microbiome in solid organ transplantation. We analyzed 1370 fecal samples from 415 liver and 672 renal transplant recipients using shotgun metagenomic sequencing to assess microbial taxonomy, metabolic pathways, antibiotic resistance genes, and virulence factors. To quantify taxonomic and metabolic dysbiosis, we also analyzed 1183 age-, sex-, and body mass index-matched controls from the same population. In addition, a subset of 78 renal transplant recipients was followed longitudinally from pretransplantation to 24 months after transplantation. Our data showed that both liver and kidney transplant recipients suffered from gut dysbiosis, including lower microbial diversity, increased abundance of unhealthy microbial species, decreased abundance of important metabolic pathways, and increased prevalence and diversity of antibiotic resistance genes and virulence factors. These changes were found to persist up to 20 years after transplantation. Last, we demonstrated that the use of immunosuppressive drugs was associated with the observed dysbiosis and that the extent of dysbiosis was associated with increased mortality after transplantation. This study represents a step toward potential microbiome-targeted interventions that might influence the outcomes of recipients of solid organ transplantation.

Regime transition Shapes the Composition, Assembly Processes, and Co-occurrence Pattern of Bacterioplankton Community in a Large Eutrophic Freshwater Lake

At certain nutrient concentrations, shallow freshwater lakes are generally characterized by two contrasting ecological regimes with disparate patterns of biodiversity and biogeochemical cycles: a macrophyte-dominated regime (MDR) and a phytoplankton-dominated regime (PDR). To reveal ecological mechanisms that affect bacterioplankton along the regime shift, Illumina MiSeq sequencing of the 16S rRNA gene combined with a novel network clustering tool (Manta) were used to identify patterns of bacterioplankton community composition across the regime shift in Taihu Lake, China. Marked divergence in the composition and ecological assembly processes of bacterioplankton community was observed under the regime shift. The alpha diversity of the bacterioplankton community consistently and continuously decreased with the regime shift from MDR to PDR, while the beta diversity presents differently. Moreover, as the regime shifted from MDR to PDR, the contribution of deterministic processes (such as environmental selection) to the assembly of bacterioplankton community initially decreased and then increased again as regime shift from MDR to PDR, most likely as a consequence of differences in nutrient concentration. The topological properties, including modularity, transitivity and network diameter, of the bacterioplankton co-occurrence networks changed along the regime shift, and the co-occurrences among species changed in structure and were significantly shaped by the environmental variables along the regime transition from MDR to PDR. The divergent environmental state of the regimes with diverse nutritional status may be the most important factor that contributes to the dissimilarity of bacterioplankton community composition along the regime shift.

Quantifying the impact of ecological memory on the dynamics of interacting communities

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications.

Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity.

Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

An ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms adapt to their environment, modify it, and accumulate biotic and abiotic material. It may also emerge from phenotypic heterogeneity at the population level. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we use modeling to investigate how memory can influence the dynamics, composition, and stability landscape of communities. We incorporate long-term memory effects into a multi-species model recently introduced to investigate alternative stable states in microbial communities. We assess the impact of memory on key aspects of model behavior and further examine our findings using a model parameterized by empirical data from the human gut microbiota. Our approach for modeling long-term memory and studying its implications has the potential to improve our understanding of microbial community dynamics and ultimately our ability to predict, manipulate, and experimentally design microbial ecosystems. It could also be applied more broadly in the study of systems composed of interacting components.

Continental scale deciphering of microbiome networks untangles the phyllosphere homeostasis in tea plant

INTRODUCTION

Assembly and co-occurrence of the host co-evolved microbiota are essential ecological and evolutionary processes, which is not only crucial for managing individual plant fitness but also ecological function. However, understanding of the microbiome assembly and co-occurrence in higher plants is not well understood. The tea plant was shown to contribute the forest fitness due to the microbiome assembled in the phyllosphere; the landscape of microbiome assembly in the tea plants and its potential implication on phyllosphere homestasis still remains untangled.

OBJECTIVES

This study aimed to deciphering of the microbiome networks of the tea plants at a continental scale. It would provide fundamental insights into the factors driving the microbiome assembly, with an extended focus on the resilience towards the potential pathogen in the phyllosphere.

METHODS

We collected 225 samples from 45 locations spanning approximately 2000-km tea growing regions across China. By integration of high-throughput sequencing data, physicochemical properties profiling and bioinformatics analyses, we investigated continental scale microbiome assembly and co-occurrence in the tea plants. Synthetic assemblages, interaction assay and RT-qPCR were further implemented to analyze the microbial interaction indexed in phyllosphere.

RESULTS

A trade-off between stochastic and deterministic processes in microbiomes community assembly was highlighted. Assembly processes were dominated by deterministic processes in bulk and rhizosphere soils, and followed by stochastic processes in roots and leaves with amino acids as critical drivers for environmental selection. Sphingobacteria and Proteobacteria ascended from soils to leaves to sustain a core leaf taxa. The core taxa formed a close association with a prevalent foliar pathogen in the co-occurrence network and significantly attenuated the expression of a set of essential virulence genes in pathogen.

CONCLUSION

Our study unveils the mechanism underpinning microbiome assembly in the tea plants, and a potential implication of the microbiome-mediated resilience framework on the phyllosphere homeostasis.

The Self-Organization of Marine Microbial Networks under Evolutionary and Ecological Processes: Observations and Modeling

Simple Summary

The properties and structure of ecological networks in marine microbial communities determine ecosystem functions and stability; however, the principles of microbial network assemblages are poorly understood. In this study, we revealed the influences of species phylogeny and niches on the self-organization of marine microbial co-occurrence networks and provided a mathematical framework to simulate microbial network assemblages. Our results provide deep insights into network stability from the perspective of network assembly principles and not just network properties, such as complexity and modularity.

Abstract

Evolutionary and ecological processes are primary drivers of ecological network constrictions. However, the ways that these processes underpin self-organization and modularity in networks are poorly understood. Here, we performed network analyses to explore the evolutionary and ecological effects on global marine microbial co-occurrence networks across multiple network levels, including those of nodes, motifs, modules and whole networks. We found that both direct and indirect species interactions were evolutionarily and ecologically constrained across at least four network levels. Compared to ecological processes, evolutionary processes generally showed stronger long-lasting effects on indirect interactions and dominated the network assembly of particle-associated communities in spatially homogeneous environments. Regarding the large network path distance, the contributions of either processes to species interactions generally decrease and almost disappear when network path distance is larger than six. Accordingly, we developed a novel mathematical model based on scale-free networks by considering the joint effects of evolutionary and ecological processes. We simulated the self-organization of microbial co-occurrence networks and found that long-lasting effects increased network stability via decreasing link gain or loss. Overall, these results revealed that evolutionary and ecological processes played key roles in the self-organization and modularization of microbial co-occurrence networks.

Convergency and Stability Responses of Bacterial Communities to Salinization in Arid and Semiarid Areas: Implications for Global Climate Change in Lake Ecosystems

Climate change has given rise to salinization and nutrient enrichment in lake ecosystems of arid and semiarid areas, which have posed the bacterial communities not only into an ecotone in lake ecosystems but also into an assemblage of its own unique biomes. However, responses of bacterial communities to climate-related salinization and nutrient enrichment remain unclear. In September 2019, this study scrutinized the turnover of bacterial communities along gradients of increasing salinity and nutrient by a space-for-time substitution in Xinjiang Uyghur Autonomous Region, China. We find that salinization rather than nutrient enrichment primarily alters bacterial communities. The homogenous selection of salinization leads to convergent response of bacterial communities, which is revealed by the combination of a decreasing β-nearest taxon index (βNTI) and a pronounced negative correlation between niche breadth and salinity. Furthermore, interspecific interactions within bacterial communities significantly differed among distinct salinity levels. Specifically, mutualistic interactions showed an increase along the salinization. In contrast, topological parameters show hump-shaped curves (average degree and density) and sunken curves (modularity, density, and average path distance), the extremums of which all appear in the high-brackish environment, hinting that bacterial communities are comparatively stable at freshwater and brine environments but are unstable in moderately high-brackish lake.

Temporal variability in quantitative human gut microbiome profiles and implications for clinical research

While clinical gut microbiota research is ever-expanding, extending reference knowledge of healthy between- and within-subject gut microbiota variation and its drivers remains essential; in particular, temporal variability is under-explored, and a comparison with cross-sectional variation is missing. Here, we perform daily quantitative microbiome profiling on 713 fecal samples from 20 Belgian women over six weeks, combined with extensive anthropometric measurements, blood panels, dietary data, and stool characteristics. We show substantial temporal variation for most major gut genera; we find that for 78% of microbial genera, day-to-day absolute abundance variation is substantially larger within than between individuals, with up to 100-fold shifts over the study period. Diversity, and especially evenness indicators also fluctuate substantially. Relative abundance profiles show similar but less pronounced temporal variation. Stool moisture, and to a lesser extent diet, are the only significant host covariates of temporal microbiota variation, while menstrual cycle parameters did not show significant effects. We find that the dysbiotic Bact2 enterotype shows increased between- and within-subject compositional variability. Our results suggest that to increase diagnostic as well as target discovery power, studies could adopt a repeated measurement design and/or focus analysis on community-wide microbiome descriptors and indices.

How environmental stress leads to alternative microbiota states in a river ecosystem: A new insight into river restoration

Catastrophic shifts in river ecosystems can abruptly degrade their structures and functions, often reducing the efficacy of traditional remediation targeting physicochemical properties. Alternative stable states theory can not only explain this phenomenon but also provide a new insight into river restoration; however, little is known about the existence and implications of alternative stable states in a river. Considering the important role of benthic microbiota in sustaining river ecosystem structures and functions, ecological theory and high-throughput sequencing were combined to firstly investigate multi-stability in microbial communities and its relationship with environmental factors in river sediments. The Nanjing reach of the Yangtze River was selected as the study area because of its huge spatial heterogeneity and varying degrees of pollution. Bimodal distributions combined with temporal variations of microbiota status provided direct evidence of bistability by showing the instability at the intermediate. In addition, environmental stress, particularly concentrations of NH₄⁺-N and NO₃^--N, was identified as an important driver of alternative microbiota states from the perspectives of the behavior of bistable ecosystems. Comparison of α-diversity indices and network properties between two alternative microbiota states revealed that the diversity and co-occurrence pattern of microbial communities will be high if they are settled in favorable environments (i.e., comprehensive sediment quality identification index > 3.7). Key taxa, including Clostridiales, Nitrospirales and Myxococcales, were discerned by combining LEfSe and network analysis, and their strong interspecies interactions were believed to be an important factor in triggering alternative microbiota states. This study suggests alternative stable states theory should be considered in river remediation to better understand the response of river ecosystems to environmental stress and the effect of hysteresis, benefiting the implementation of effective monitoring and restoration strategies in a river of urban area.

Two community types occur in gut microbiota of large-sample wild plateau pikas (Ochotona curzoniae)

Studies on large-sample gut microbial sequencing data indicate that gut microbiota can be divided into multiple community types; different community types may influence the community function and ecosystem service. However, the knowledge on the classification, diversity, interaction, and assembling of microbial community types in the gut of wild animals is still insufficient. Here, we used pika gut microbiota data as an example to study the microbial community types in large-sample sequencing dataset. Cecal microbial communities from 118 wild plateau pika (Ochotona curzoniae) individuals at 5 elevational regions on the Qinghai-Tibet Plateau were analyzed. Our results show that pika gut microbiota can be separated into 2 community types (Cluster I and Cluster II). Cluster I was mainly distributed on the high-elevation regions with more than 3694 m and was most dominated by Firmicutes. Cluster II was from the low-elevation areas (lower than 3580 m), and was predominated by Bacteroidetes. Cluster I had a higher community alpha-diversity and predicted functional diversity than Cluster II, and the beta-diversity and predicted functional profiles of these 2 clusters were significantly different. Network analysis revealed that there were more complex interactions between Cluster I, which had enhanced influence on the co-occurrences of other microbes in the bacterial community when compared to Cluster II. Phylogenetic analysis found that the environmental filtering in the Cluster I was stronger than Cluster II. The assemblages of pika gut bacterial communities were determined mainly by deterministic processes, while the relative importance of deterministic processes accounted for more percentages in the Cluster I than Cluster II. Our results demonstrated that 2 gut microbial community types in pikas had distinct diversity patterns and ecological functions. Current methods are also helpful for identifying gut community types and the related mechanisms behind gut microbiota types in large-sample sequencing data of wild animals.

Multi-stable bacterial communities exhibit extreme sensitivity to initial conditions

Microbial communities can have dramatically different compositions even among similar environments. This might be due to the existence of multiple alternative stable states, yet there exists little experimental evidence supporting this possibility. Here, we gathered a large collection of absolute population abundances capturing population dynamics in one- to four-strain communities of soil bacteria with a complex life cycle in a feast-or-famine environment. This dataset led to several observations: (i) some pairwise competitions resulted in bistability with a separatrix near a 1:1 initial ratio across a range of population densities; (ii) bistability propagated to multi-stability in multispecies communities; and (iii) replicate microbial communities reached different stable states when starting close to initial conditions separating basins of attraction, indicating finite-sized regions where the dynamics are unpredictable. The generalized Lotka-Volterra equations qualitatively captured most competition outcomes but were unable to quantitatively recapitulate the observed dynamics. This was partly due to complex and diverse growth dynamics in monocultures that ranged from Allee effects to nonmonotonic behaviors. Overall, our results highlight that multi-stability might be generic in multispecies communities and, combined with ecological noise, can lead to unpredictable community assembly, even in simple environments.

C. elegans: A biosensor for host-microbe interactions

Microbes are an integral part of life on this planet. Microbes and their hosts influence each other in an endless dance that shapes how the meta-organism interacts with its environment. Although great advances have been made in microbiome research over the past 20 years, the mechanisms by which both hosts and their microbes interact with each other and the environment are still not well understood. The nematode Caenorhabditis elegans has been widely used as a model organism to study a remarkable number of human-like processes. Recent evidence shows that the worm is a powerful tool to investigate in fine detail the complexity that exists in microbe-host interactions. By combining the large array of genetic tools available for both organisms together with deep phenotyping approaches, it has been possible to uncover key effectors in the complex relationship between microbes and their hosts. In this perspective, we survey the literature for insightful discoveries in the microbiome field using the worm as a model. We discuss the latest conceptual and technological advances in the field and highlight the strengths that make C. elegans a valuable biosensor tool for the study of microbe-host interactions.

A resilience concept based on system functioning: A dynamical systems perspective

We introduce a new framework for resilience, which is traditionally understood as the ability of a system to absorb disturbances and maintain its state, by proposing a shift from a state-based to a system functioning-based approach to resilience, which takes into account that several different coexisting stable states could fulfill the same functioning. As a consequence, not every regime shift, i.e., transition from one stable state to another, is associated with a lack or loss of resilience. We emphasize the importance of flexibility-the ability of a system to shift between different stable states while still maintaining system functioning. Furthermore, we provide a classification of system responses based on the phenomenological properties of possible disturbances, including the role of their timescales. Therefore, we discern fluctuations, shocks, press disturbances, and trends as possible disturbances. We distinguish between two types of mechanisms of resilience: (i) tolerance and flexibility, which are properties of the system, and (ii) adaptation and transformation, which are processes that alter the system's tolerance and flexibility. Furthermore, we discuss quantitative methods to investigate resilience in model systems based on approaches developed in dynamical systems theory.

Three-Species Lotka-Volterra Model with Respect to Caputo and Caputo-Fabrizio Fractional Operators

In this paper, we apply the concept of fractional calculus to study three-dimensional Lotka-Volterra differential equations. We incorporate the Caputo-Fabrizio fractional derivative into this model and investigate the existence of a solution. We discuss the uniqueness of the solution and determine under what conditions the model offers a unique solution. We prove the stability of the nonlinear model and analyse the properties, considering the non-singular kernel of the Caputo-Fabrizio operator. We compare the stability conditions of this system with respect to the Caputo-Fabrizio operator and the Caputo fractional derivative. In addition, we derive a new numerical method based on the Adams-Bashforth scheme. We show that the type of differential operators and the value of orders significantly influence the stability of the Lotka-Volterra system and numerical results demonstrate that different fractional operator derivatives of the nonlinear population model lead to different dynamical behaviors.

Multistability and Reversibility of Aerobic Granular Sludge Microbial Communities Upon Changes From Simple to Complex Synthetic Wastewater and Back

Aerobic granular sludge (AGS) is a promising alternative wastewater treatment to the conventional activated sludge system allowing space and energy saving. Basic understanding of AGS has mainly been obtained using simple wastewater containing acetate and propionate as carbon source. Yet, the aspect and performances of AGS grown in such model systems are different from those obtained in reactor treating real wastewater. The impact of fermentable and hydrolyzable compounds on already formed AGS was assessed separately by changing the composition of the influent from simple wastewater containing volatile fatty acids to complex monomeric wastewater containing amino acids and glucose, and then to complex polymeric wastewater containing also starch and peptone. The reversibility of the observed changes was assessed by changing the composition of the wastewater from complex monomeric back to simple. The introduction of fermentable compounds in the influent left the settling properties and nutrient removal performance unchanged, but had a significant impact on the bacterial community. The proportion of Gammaproteobacteria diminished to the benefit of Actinobacteria and the Saccharibateria phylum. On the other hand, the introduction of polymeric compounds altered the settling properties and denitrification efficiency, but induced smaller changes in the bacterial community. The changes induced by the wastewater transition were only partly reversed. Seven distinct stables states of the bacterial community were detected during the 921 days of experiment, four of them observed with the complex monomeric wastewater. The transitions between these states were not only caused by wastewater changes but also by operation failures and other incidences. However, the nutrient removal performance and settling properties of the AGS were globally maintained due to the functional redundancy of its bacterial community.

Stochastic colonization of hosts with a finite lifespan can drive individual host microbes out of equilibrium

Macroorganisms are inhabited by microbial communities that often change through the lifespan of an individual. One of the factors contributing to this change is colonization from the environment. The colonization of initially microbe-free hosts is particularly interesting, as their microbiome depends entirely on microbes of external origin. We present a mathematical model of this process with a particular emphasis on the effect of ecological drift and a finite host lifespan. Our results indicate the host lifespan becomes especially relevant for short-living organisms (e.g. Caenorhabditis elegans, Drosophila melanogaster, and Danio rerio). In this case, alternative microbiome states (often called enterotypes), the coexistence of microbe-free and colonized hosts, and a reduced probability of colonization can be observed in our model. These results unify multiple reported observations around colonization and suggest that no selective or deterministic drivers are necessary to explain them.

Microbial communities are prevalent not only in the environment but also in hosts. Although the drivers of environmental microbiomes have been studied extensively, less is known about the drivers distinguishing a host environment. Recent experimental observations have highlighted the influence of ecological drift in hosts with short lifespan, including model organisms like C. elegans, D. melanogaster and D. rerio. We have developed a theoretical model to study the effect of a finite host lifespan on relevant observables of the microbiome, including the microbial load, probability of colonization of a microbial taxon, and distribution of microbiome composition in a host population. Although we focus on a case free of any selection, our results indicate the possible coexistence of hosts with alternative microbiome composition, and to a larger extent the coexistence of colonized and microbe-free hosts. A quantitative description is provided.

Coexisting mechanisms of bacterial community are changeable even under similar stable conditions in a chemostat culture

The coexisting mechanism of a synthetic bacterial community (SBC) was investigated to better understand how to manage microbial communities. The SBC was constructed with three kinds of phenol-utilizing bacteria, Pseudomonas sp. LAB-08, Comamonas testosteroni R2, and Cupriavidus sp. P-10, under chemostat conditions supplied with phenol as a sole carbon and energy source. Population densities of all strains were monitored by real-time quantitative PCR (qPCR) targeting the gene encoding the large subunit of phenol hydroxylase. Although the supply of phenol was stopped to allow perturbation in the SBC, all of the strains coexisted and the degradation of phenol was maintained for more than 800 days. The qPCR analyses showed that strains LAB-08 and R2 became dominant simultaneously, whereas strain P-10 was a minor population. This phenomenon was observed before and after the phenol-supply stoppage. The kinetic parameters for phenol of the SBC changed before and after the phenol-supply stoppage, which suggests a change in functional roles of strains in the SBC. Transcriptional levels of phenol hydroxylase and catechol dioxygenases of three strains were monitored by reverse-transcription qPCR (RT-qPCR). The RT-qPCR analyses revealed that all strains shared phenol and survived independently before the phenol-supply stoppage. After the stoppage, strain P-10 would incur the cost for degradation of phenol and catechol, whereas strains LAB-08 and R2 seemed to be cheaters using metabolites, indicating the development of the metabolic network. These results indicated that it is important for the management and redesign of microbial communities to understand the metabolism of bacterial communities.

Metabolic multistability and hysteresis in a model aerobe-anaerobe microbiome community

Major changes in the microbiome are associated with health and disease. Some microbiome states persist despite seemingly unfavorable conditions, such as the proliferation of aerobe-anaerobe communities in oxygen-exposed environments in wound infections or small intestinal bacterial overgrowth. Mechanisms underlying transitions into and persistence of these states remain unclear. Using two microbial taxa relevant to the human microbiome, we combine genome-scale mathematical modeling, bioreactor experiments, transcriptomics, and dynamical systems theory to show that multistability and hysteresis (MSH) is a mechanism describing the shift from an aerobe-dominated state to a resilient, paradoxically persistent aerobe-anaerobe state. We examine the impact of changing oxygen and nutrient regimes and identify changes in metabolism and gene expression that lead to MSH and associated multi-stable states. In such systems, conceptual causation-correlation connections break and MSH must be used for analysis. Using MSH to analyze microbiome dynamics will improve our conceptual understanding of stability of microbiome states and transitions between states.

Image-based quantification of soil microbial dead zones induced by nitrogen fertilization

Microbial communities in agricultural soils underpin many ecosystem services including the maintenance of soil structure, food production, water purification and carbon storage. However, the impact of fertilization on the health of microbial communities is not well understood. This study investigates the spatial and temporal dynamics of nitrogen (N) transport away from a fertilizer granule with pore scale resolution. Specifically, we examined how soil structure and moisture content influence fertilizer derived N movement through the soil pore network and the subsequent impact of on soil microbial communities. We develop a mathematical model to describe N transport and reactions in soil at the pore-scale. Using X-ray Computed Tomography scans, we reconstructed a microscale description of a soil-pore geometry as a computational mesh. Solving two-phase water/air model produced pore-scale water distributions at 15, 30 and 70% water-filled pore volume. The N-speciation model considered ammonium (NH₄⁺), nitrate (NO₃^-) and dissolved organic N (DON), and included N immobilization, ammonification and nitrification processes, as well as diffusion in soil solution. We simulated the dissolution of a fertilizer pellet and a pore scale N cycle at three different water saturations. To aid interpretation of the model results, microbial activity at a range of N concentrations was measured. The model showed that the diffusion and concentration of N in water films is critically dependent upon soil moisture and N species. We predict that the maximum NH₄⁺ and NO₃^- concentrations in soil solution around the pellet under dry conditions are in the order of 1 × 10³ and 1 × 10⁴ mol m^-3 respectively, and under wet conditions 2 × 10² and 1 × 10³ mol m^-3, respectively. Supporting experimental evidence suggests that these concentrations would be sufficient to reduce microbial activity in the short-term in the zone immediately around the fertilizer pellet (ranging from 0.9 to 3.8 mm), causing a major loss of soil biological functioning. This model demonstrates the importance of pore-scale processes in regulating N movement and their interactions with the soil microbiome.

From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes

Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.

Microbial Ecology of Methanotrophy in Streams Along a Gradient of CH4 Availability

Despite the recognition of streams and rivers as sources of methane (CH₄) to the atmosphere, the role of CH₄ oxidation (MOX) in these ecosystems remains poorly understood to date. Here, we measured the kinetics of MOX in stream sediments of 14 sites to resolve the ecophysiology of CH₄ oxidizing bacteria (MOB) communities. The streams cover a gradient of land cover and associated physicochemical parameter and differed in stream- and porewater CH₄ concentrations. Michealis–Menten kinetic parameter of MOX, maximum reaction velocity (V_max), and CH₄ concentration at half V_max (K_S) increased with CH₄ supply. K_S values in the micromolar range matched the CH₄ concentrations measured in shallow stream sediments and indicate that MOX is mostly driven by low-affinity MOB. 16S rRNA gene sequencing identified MOB classified as Methylococcaceae and particularly Crenothrix. Their relative abundance correlated with pmoA gene counts and MOX rates, underscoring their pivotal role as CH₄ oxidizers in stream sediments. Building on the concept of enterotypes, we identify two distinct groups of co-occurring MOB. While there was no taxonomic difference among the members of each cluster, one cluster contained abundant and common MOB, whereas the other cluster contained rare operational taxonomic units (OTUs) specific to a subset of streams. These integrated analyses of changes in MOB community structure, gene abundance, and the corresponding ecosystem process contribute to a better understanding of the distal controls on MOX in streams.

Applying Differential Neural Networks to Characterize Microbial Interactions in an Ex Vivo Gastrointestinal Gut Simulator

The structure of mixed microbial cultures—such as the human gut microbiota—is influenced by a complex interplay of interactions among its community members. The objective of this study was to propose a strategy to characterize microbial interactions between particular members of the community occurring in a simulator of the human gastrointestinal tract used as the experimental system. Four runs were carried out separately in the simulator: two of them were fed with a normal diet (control system), and two more had the same diet supplemented with agave fructans (fructan-supplemented system). The growth kinetics of Lactobacillus spp., Bifidobacterium spp., Salmonella spp., and Clostridium spp. were assessed in the different colon sections of the simulator for a nine-day period. The time series of microbial concentrations were used to estimate specific growth rates and pair-wise interaction coefficients as considered by the generalized Lotka-Volterra (gLV) model. A differential neural network (DNN) composed of a time-adaptive set of differential equations was applied for the nonparametric identification of the mixed microbial culture, and an optimization technique was used to determine the interaction parameters, considering the DNN identification results and the structure of the gLV model. The assessment of the fructan-supplemented system showed that microbial interactions changed significantly after prebiotics administration, demonstrating their modulating effect on microbial interactions. The strategy proposed here was applied satisfactorily to gain quantitative and qualitative knowledge of a broad spectrum of microbial interactions in the gut community, as described by the gLV model. In the future, it may be utilized to study microbial interactions within mixed cultures using other experimental approaches and other mathematical models (e.g., metabolic models), which will yield crucial information for optimizing mixed microbial cultures to perform certain processes—such as environmental bioremediation or modulation of gut microbiota—and to predict their dynamics.

Hysteretic response of Microbial Eukaryotic Communities to Gradually Decreased Nutrient Concentrations in Eutrophic Water

External environments to microbial eukaryotic communities often change gradually with time. However, whether the responses of microbial eukaryotic communities to these gradually changed environments are continuous or hysteretic and the mechanisms underlying these responses are largely unknown. Here, we used a microcosm to investigate the temporal variation of microbial eukaryotic communities with the gradually decreased nutrient concentrations (nitrogen and phosphorus). We found the differences of microbial eukaryotic community composition and species richness between the control and treatment groups were low during the days 0 to 12, although the nutrient concentrations decreased rapidly during this period in treatment group. However, these differences were clear during the days 14 to 18, although the nutrient concentrations decreased slowly during this period in treatment group. The mechanisms for these results are that the strong homogenous selection (perhaps due to the biotic factors) during the days 8 to 10 in treatment group might enhance the stability of microbial eukaryotic communities. However, the continuously decreased nutrient concentrations weakened the homogenous selection and promoted the strength of environmental filtering, and therefore resulted in the distinct change of microbial eukaryotic communities during the days 14 to 18 in treatment group. Fungi, Chlorophyta and Chrysophyta which associated with the nutrient removal played important roles in this hysteretic change of microbial eukaryotic communities. Overall, our findings suggest that disentangling the non-linear response of communities to gradual environmental changes is essential for understanding ecosystem restoration and degradation in future.

Emerging Priorities for Microbiome Research

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Multistability and regime shifts in microbial communities explained by competition for essential nutrients

Microbial communities routinely have several possible species compositions or community states observed for the same environmental parameters. Changes in these parameters can trigger abrupt and persistent transitions (regime shifts) between such community states. Yet little is known about the main determinants and mechanisms of multistability in microbial communities. Here, we introduce and study a consumer-resource model in which microbes compete for two types of essential nutrients each represented by multiple different metabolites. We adapt game-theoretical methods of the stable matching problem to identify all possible species compositions of such microbial communities. We then classify them by their resilience against three types of perturbations: fluctuations in nutrient supply, invasions by new species, and small changes of abundances of existing ones. We observe multistability and explore an intricate network of regime shifts between stable states in our model. Our results suggest that multistability requires microbial species to have different stoichiometries of essential nutrients. We also find that a balanced nutrient supply promotes multistability and species diversity, yet make individual community states less stable.

Temporal stability of the rumen microbiota in beef cattle, and response to diet and supplements

BACKGROUND

Dietary intake is known to be a driver of microbial community dynamics in ruminants. Beef cattle go through a finishing phase that typically includes very high concentrate ratios in their feed, with consequent effects on rumen metabolism including methane production. This longitudinal study was designed to measure dynamics of the rumen microbial community in response to the introduction of high concentrate diets fed to beef cattle during the finishing period. A cohort of 50 beef steers were fed either of two basal diet formulations consisting of approximately 10:90 or 50:50 forage:concentrate ratios respectively. Nitrate and oil rich supplements were also added either individually or in combination. Digesta samples were taken at time points over ~ 200 days during the finishing period of the cattle to measure the adaptation to the basal diet and long-term stability of the rumen microbiota.

RESULTS

16S rRNA gene amplicon libraries were prepared from 313 rumen digesta samples and analysed at a depth of 20,000 sequences per library. Bray Curtis dissimilarity with analysis of molecular variance (AMOVA) revealed highly significant (p < 0.001) differences in microbiota composition between cattle fed different basal diets, largely driven by reduction of fibre degrading microbial groups and increased relative abundance of an unclassified Gammaproteobacteria OTU in the high concentrate fed animals. Conversely, the forage-based diet was significantly associated with methanogenic archaea. Within basal diet groups, addition of the nitrate and combined supplements had lesser, although still significant, impacts on microbiota dissimilarity compared to pre-treatment time points and controls. Measurements of the response and stability of the microbial community over the time course of the experiment showed continuing adaptation up to 25 days in the high concentrate groups. After this time point, however, no significant variability was detected.

CONCLUSIONS

High concentrate diets that are typically fed to finishing beef cattle can have a significant effect on the microbial community in the rumen. Inferred metabolic activity of the different microbial communities associated with each of the respective basal diets explained differences in methane and short chain fatty acid production between cattle. Longitudinal sampling revealed that once adapted to a change in diet, the rumen microbial community remains in a relatively stable alternate state.

Species-wide Metabolic Interaction Network for Understanding Natural Lignocellulose Digestion in Termite Gut Microbiota

The structural complexity of lignocellulosic biomass hinders the extraction of cellulose, and it has remained a challenge for decades in the biofuel production process. However, wood-feeding organisms like termite have developed an efficient natural lignocellulolytic system with the help of specialized gut microbial symbionts. Despite having an enormous amount of high-throughput metagenomic data, specific contributions of each individual microbe to achieve this lignocellulolytic functionality remains unclear. The metabolic cross-communication and interdependence that drives the community structure inside the gut microbiota are yet to be explored. We have contrived a species-wide metabolic interaction network of the termite gut-microbiome to have a system-level understanding of metabolic communication. Metagenomic data of Nasutitermes corniger have been analyzed to identify microbial communities in different gut segments. A comprehensive metabolic cross-feeding network of 205 microbes and 265 metabolites was developed using published experimental data. Reconstruction of inter-species influence network elucidated the role of 37 influential microbes to maintain a stable and functional microbiota. Furthermore, in order to understand the natural lignocellulose digestion inside N. corniger gut, the metabolic functionality of each influencer was assessed, which further elucidated 15 crucial hemicellulolytic microbes and their corresponding enzyme machinery.

Regime Shifts in a Phage-Bacterium Ecosystem and Strategies for Its Control

The competition between bacteria often involves both nutrients and phage predators and may give rise to abrupt regime shifts between the alternative stable states characterized by different species compositions. While such transitions have been previously studied in the context of competition for nutrients, the case of phage-induced bistability between competing bacterial species has not been considered yet. Here we demonstrate a possibility of regime shifts in well-mixed phage-bacterium ecosystems. In one of the bistable states, the fast-growing bacteria competitively exclude the slow-growing ones by depleting their common nutrient. Conversely, in the second state, the slow-growing bacteria with a large burst size generate such a large phage population that the other species cannot survive. This type of bistability can be realized as the competition between a strain of bacteria protected from phage by abortive infection and another strain with partial resistance to phage. It is often desirable to reliably control the state of microbial ecosystems, yet bistability significantly complicates this task. We discuss successes and limitations of one control strategy in which one adds short pulses to populations of individual species. Our study proposes a new type of phage therapy, where introduction of the phage is supplemented by the addition of a partially resistant host bacteria.IMPORTANCE Phage-microbe communities play an important role in human health as well as natural and industrial environments. Here we show that these communities can assume several alternative species compositions separated by abrupt regime shifts. Our model predicts these regime shifts in the competition between bacterial strains protected by two different phage defense mechanisms: abortive infection/CRISPR and partial resistance. The history dependence caused by regime shifts greatly complicates the task of manipulation and control of a community. We propose and study a successful control strategy via short population pulses aimed at inducing the desired regime shifts. In particular, we predict that a fast-growing pathogen could be eliminated by a combination of its phage and a slower-growing susceptible host.