Modeling microbial community structure and functional diversity across time and space

Microbial communities exhibit exquisitely complex structure. Many aspects of this complexity, from the number of species to the total number of interactions, are currently very difficult to examine directly. However, extraordinary efforts are being made to make these systems accessible to scientific investigation. While recent advances in high-throughput sequencing technologies have improved accessibility to the taxonomic and functional diversity of complex communities, monitoring the dynamics of these systems over time and space - using appropriate experimental design - is still expensive. Fortunately, modeling can be used as a lens to focus low-resolution observations of community dynamics to enable mathematical abstractions of functional and taxonomic dynamics across space and time. Here, we review the approaches for modeling bacterial diversity at both the very large and the very small scales at which microbial systems interact with their environments. We show that modeling can help to connect biogeochemical processes to specific microbial metabolic pathways.     

Microbial Community Metabolic Modeling: A Community Data‐Driven Network Reconstruction

Metabolic network modeling of microbial communities provides an in‐depth understanding of community‐wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high‐quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community‐level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium that was used to provide data needed for a community‐level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339–2345, 2016. © 2016 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals, Inc.     

Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel‐contaminated soil

Ecotoxicology is primarily concerned with predicting the effects of toxic substances on the biological components of the ecosystem. In remote, high latitude environments such as Antarctica, where field work is logistically difficult and expensive, and where access to adequate numbers of soil invertebrates is limited and response times of biota are slow, appropriate modeling tools using microbial community responses can be valuable as an alternative to traditional single‐species toxicity tests. In this study, we apply a Bayesian nonparametric model to a soil microbial data set acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica. We model community change in terms of OTUs (operational taxonomic units) in response to a range of total petroleum hydrocarbon (TPH) concentrations. The Shannon diversity of the microbial community, clustering of OTUs into groups with similar behavior with respect to TPH, and effective concentration values at level x, which represent the TPH concentration that causes x% change in the community, are presented. This model is broadly applicable to other complex data sets with similar data structure and inferential requirements on the response of communities to environmental parameters and stressors.     

Microbial Communities Can Be Described by Metabolic Structure: A General Framework and Application to a Seasonally Variable,Depth-Stratified Microbial Community from the Coastal West Antarctic Peninsula

Taxonomic marker gene studies, such as the 16S rRNA gene, have been used to successfully explore microbial diversity in a variety of marine, terrestrial, and host environments. For some of these environments long term sampling programs are beginning to build a historical record of microbial community structure. Although these 16S rRNA gene datasets do not intrinsically provide information on microbial metabolism or ecosystem function, this information can be developed by identifying metabolisms associated with related, phenotyped strains. Here we introduce the concept of metabolic inference; the systematic prediction of metabolism from phylogeny, and describe a complete pipeline for predicting the metabolic pathways likely to be found in a collection of 16S rRNA gene phylotypes. This framework includes a mechanism for assigning confidence to each metabolic inference that is based on a novel method for evaluating genomic plasticity. We applied this framework to 16S rRNA gene libraries from the West Antarctic Peninsula marine environment, including surface and deep summer samples and surface winter samples. Using statistical methods commonly applied to community ecology data we found that metabolic structure differed between summer surface and winter and deep samples, comparable to an analysis of community structure by 16S rRNA gene phylotypes. While taxonomic variance between samples was primarily driven by low abundance taxa, metabolic variance was attributable to both high and low abundance pathways. This suggests that clades with a high degree of functional redundancy can occupy distinct adjacent niches. Overall our findings demonstrate that inferred metabolism can be used in place of taxonomy to describe the structure of microbial communities. Coupling metabolic inference with targeted metagenomics and an improved collection of completed genomes could be a powerful way to analyze microbial communities in a high-throughput manner that provides direct access to metabolic and ecosystem function.     

Relating belowground microbial composition to the taxonomic,phylogenetic, and functional trait distributions of trees in a tropical forest

The complexities of the relationships between plant and soil microbial communities remain unresolved. We determined the associations between plant aboveground and belowground (root) distributions and the communities of soil fungi and bacteria found across a diverse tropical forest plot. Soil microbial community composition was correlated with the taxonomic and phylogenetic structure of the aboveground plant assemblages even after controlling for differences in soil characteristics, but these relationships were stronger for fungi than for bacteria. In contrast to expectations, the species composition of roots in our soil core samples was a poor predictor of microbial community composition perhaps due to the patchy, ephemeral, and highly overlapping nature of fine root distributions. Our ability to predict soil microbial composition was not improved by incorporating information on plant functional traits suggesting that the most commonly measured plant traits are not particularly useful for predicting the plot‐level variability in belowground microbial communities.     

NESTED AUTOMATED RIBOSOMAL INTERGENIC SPACER ANALYSIS: A RAPID AND ACCURATE METHOD FOR COMPARISON OF BACTERIAL COMMUNITY COMPOSITION

Nested automated ribosomal intergenic spacer analysis (ARISA) was used to examine the community structure of epilithic biofilms in freshwater streams experiencing different levels of human impact. This molecular fingerprinting technique generated reproducible profiles of bacterial community structure that varied significantly between stream sites. Nested ARISA was determined to be a cost-effective, high-throughput approach to assess bacterial community composition from very small sample volumes, requiring little sampling effort and without the need for taxonomic identification of individual organisms. In combination with multidimensional scaling, nested ARISA provides a rapid and sensitive method to carry out complex analyses of bacterial community structure.

PRACTICAL APPLICATIONS

Nested automated ribosomal intergenic spacer analysis (ARISA) provides a high-throughput molecular method with which to screen large numbers of environmental samples for differences in microbial community structure. This sensitive approach benefits assessments from small sample volumes or environments exhibiting reduced microbial biomass (both aquatic and terrestrial). Differences in bacterial community structure (obtained from ARISA profiles) could be used to characterize the impact of anthropogenic disturbance on freshwater systems, analogous to the current use of macroinvertebrate indicators of freshwater ecological health.     

水热增加下黑土细菌群落共生网络特征

黑土是有机质含量高且肥沃的土壤类型之一,气候变化会显著改变黑土中微生物群落的结构,同时影响群落间的潜在相互作用关系。[目的] 揭示水热增加对黑土中的细菌群落结构及潜在互作关系的影响。[方法] 基于土壤移置试验,采用16S rRNA高通量测序解析农田黑土（原位黑土、水热增加1和水热增加2）中的细菌群落结构对水热增加的响应;使用CoNet构建微生物群落共生网络,识别共生网络中的枢纽微生物;利用结构方程模型、相关性分析探究水热条件变化下土壤性质、微生物交互作用、多样性之间的直接、间接关系。[结果] 黑土中的微生物以疣微菌、变形杆菌、酸性杆菌和放线菌为主。水热增加下土壤微生物共生网络的拓扑性质发生显著变化,网络中表征微生物潜在竞争关系的负连线随着水热增加而显著增加。气候因素通过改变微生物潜在相互作用影响了群落水平分类多样性。物种竞争增强可能直接导致了土壤有机碳含量的降低。[结论] 水热增加会显著改变黑土中微生物之间的潜在交互作用,枢纽微生物的响应更加敏感。     

Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling

Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.     

青海湖主湖区与湖水淹没区的细菌群落结构差异分析

为对比青海湖与湖滨淹没区的微生物群落结构及多样性的差异, 利用16S rRNA高通量测序技术, 研究不同环境条件下水体的微生物群落组成的异同。结果表明: 青海湖主湖区及淹没区的细菌在分类门级水平上相对丰度最高的为变形菌门(Proteobacteria, 44.8%), 其次分别隶属于拟杆菌门[Bacteroidetes, (25.9%±7.8)%]、蓝细菌门[Cyanobacteria, (13.6%±5.4)%]、放线菌门[Actinobacteria, (7.54%±9)%]和柔壁菌门[Tenericutes, (3.32%±2)%]。淹没区整体微生物多样性显著高于主湖区水体。部分微生物分类属在两个湖区呈现显著的分布差异暗示这些细菌对于环境特征的适应性。节线藻在青海湖主湖的分布广泛显示其可能在高原咸水湖泊的碳氮循环过程中扮演着重要角色。研究对于深入了解栖居地如何塑造咸水水体微生物群落结构具有重要意义。     

Quantifying long‐term recurrence in planktonic microbial eukaryotes

How much temporal recurrence is present in microbial assemblages is still an unanswered ecological question. Even though marked seasonal changes have been reported for whole microbial communities, less is known on the dynamics and seasonality of individual taxa. Here, we aim at understanding microbial recurrence at three different levels: community, taxonomic group and operational taxonomic units (OTUs). For that, we focused on a model microbial eukaryotic community populating a long‐term marine microbial observatory using 18S rRNA gene data from two organismal size fractions: the picoplankton (0.2–3 µm) and the nanoplankton (3–20 µm). We have developed an index to quantify recurrence in particular taxa. We found that community structure oscillated systematically between two main configurations corresponding to winter and summer over the 10 years studied. A few taxonomic groups such as Mamiellophyceae or MALV‐III presented clear recurrence (i.e., seasonality), whereas 13%–19% of the OTUs in both size fractions, accounting for ~40% of the relative abundance, featured recurrent dynamics. Altogether, our work links long‐term whole community dynamics with that of individual OTUs and taxonomic groups, indicating that recurrent and non‐recurrent changes characterize the dynamics of microbial assemblages.     

Variable Cell Morphology Approach for Individual-Based Modeling of Microbial Communities

An individual-based, mass-spring modeling framework has been developed to investigate the effect of cell properties on the structure of biofilms and microbial aggregates through Lagrangian modeling. Key features that distinguish this model are variable cell morphology described by a collection of particles connected by springs and a mechanical representation of deformable intracellular, intercellular, and cell-substratum links. A first case study describes the colony formation of a rod-shaped species on a planar substratum. This case shows the importance of mechanical interactions in a community of growing and dividing rod-shaped cells (i.e., bacilli). Cell-substratum links promote formation of mounds as opposed to single-layer biofilms, whereas filial links affect the roundness of the biofilm. A second case study describes the formation of flocs and development of external filaments in a mixed-culture activated sludge community. It is shown by modeling that distinct cell-cell links, microbial morphology, and growth kinetics can lead to excessive filamentous proliferation and interfloc bridging, possible causes for detrimental sludge bulking. This methodology has been extended to more advanced microbial morphologies such as filament branching and proves to be a very powerful tool in determining how fundamental controlling mechanisms determine diverse microbial colony architectures.     

Variable Cell Morphology Approach for Individual-Based Modeling of Microbial Communities

An individual-based, mass-spring modeling framework has been developed to investigate the effect of cell properties on the structure of biofilms and microbial aggregates through Lagrangian modeling. Key features that distinguish this model are variable cell morphology described by a collection of particles connected by springs and a mechanical representation of deformable intracellular, intercellular, and cell-substratum links. A first case study describes the colony formation of a rod-shaped species on a planar substratum. This case shows the importance of mechanical interactions in a community of growing and dividing rod-shaped cells (i.e., bacilli). Cell-substratum links promote formation of mounds as opposed to single-layer biofilms, whereas filial links affect the roundness of the biofilm. A second case study describes the formation of flocs and development of external filaments in a mixed-culture activated sludge community. It is shown by modeling that distinct cell-cell links, microbial morphology, and growth kinetics can lead to excessive filamentous proliferation and interfloc bridging, possible causes for detrimental sludge bulking. This methodology has been extended to more advanced microbial morphologies such as filament branching and proves to be a very powerful tool in determining how fundamental controlling mechanisms determine diverse microbial colony architectures.     

Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays

Here we describe a quantitative PCR-based approach to estimating the relative abundances of major taxonomic groups of bacteria and fungi in soil. Primers were thoroughly tested for specificity, and the method was applied to three distinct soils. The technique provides a rapid and robust index of microbial community structure.     

Assessment of Soil Microbial Community Structure by Use of Taxon-Specific Quantitative PCR Assays

Here we describe a quantitative PCR-based approach to estimating the relative abundances of major taxonomic groups of bacteria and fungi in soil. Primers were thoroughly tested for specificity, and the method was applied to three distinct soils. The technique provides a rapid and robust index of microbial community structure.     

Lack of Congruence in Species Diversity Indices and Community Structures of Planktonic Groups Based on Local Environmental Factors

The importance of analyzing the determinants of biodiversity and community composition by using multiple trophic levels is well recognized; however, relevant data are lacking. In the present study, we investigated variations in species diversity indices and community structures of the plankton taxonomic groups–zooplankton, rotifers, ciliates, and phytoplankton–under a range of local environmental factors in pond ecosystems. For each planktonic group, we estimated the species diversity index by using linear models and analyzed the community structure by using canonical correspondence analysis. We showed that the species diversity indices and community structures varied among the planktonic groups and according to local environmental factors. The observed lack of congruence among the planktonic groups may have been caused by niche competition between groups with similar trophic guilds or by weak trophic interactions. Our findings highlight the difficulty of predicting total biodiversity within a system, based upon a single taxonomic group. Thus, to conserve the biodiversity of an ecosystem, it is crucial to consider variations in species diversity indices and community structures of different taxonomic groups, under a range of local conditions.     

Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

The human gut is inhabited by thousands of microbial species, most of which are still uncharacterized. Gut microbes have adapted to each other''s presence as well as to the host and engage in complex cross feeding. Constraint-based modeling has been successfully applied to predicting microbe-microbe interactions, such as commensalism, mutualism, and competition. Here, we apply a constraint-based approach to model pairwise interactions between 11 representative gut microbes. Microbe-microbe interactions were computationally modeled in conjunction with human small intestinal enterocytes, and the microbe pairs were subjected to three diets with various levels of carbohydrate, fat, and protein in normoxic or anoxic environments. Each microbe engaged in species-specific commensal, parasitic, mutualistic, or competitive interactions. For instance, Streptococcus thermophilus efficiently outcompeted microbes with which it was paired, in agreement with the domination of streptococci in the small intestinal microbiota. Under anoxic conditions, the probiotic organism Lactobacillus plantarum displayed mutualistic behavior toward six other species, which, surprisingly, were almost entirely abolished under normoxic conditions. This finding suggests that the anoxic conditions in the large intestine drive mutualistic cross feeding, leading to the evolvement of an ecosystem more complex than that of the small intestinal microbiota. Moreover, we predict that the presence of the small intestinal enterocyte induces competition over host-derived nutrients. The presented framework can readily be expanded to a larger gut microbial community. This modeling approach will be of great value for subsequent studies aiming to predict conditions favoring desirable microbes or suppressing pathogens.     

Divide and conquer: Spatial and temporal resource partitioning structures benthic cyanobacterial mats

Benthic cyanobacterial mats are increasing in abundance worldwide with the potential to degrade ecosystem structure and function. Understanding mat community dynamics is thus critical for predicting mat growth and proliferation and for mitigating any associated negative effects. Carbon, nitrogen, and sulfur cycling are the predominant forms of nutrient cycling discussed within the literature, while metabolic cooperation and viral interactions are understudied. Although many forms of nutrient cycling in mats have been assessed, the links between niche dynamics, microbial interactions, and nutrient cycling are not well described. Here, we present an updated review on how nutrient cycling and microbial community interactions in mats are structured by resource partitioning via spatial and temporal heterogeneity and succession. We assess community interactions and nutrient cycling at both intramat and metacommunity scales. Additionally, we present ideas and recommendations for research in this area, highlighting top-down control, boundary layers, and metabolic cooperation as important future directions.