Microbial ecology to manage processes in environmental biotechnology

Microbial ecology and environmental biotechnology are inherently tied to each other. The concepts and tools of microbial ecology are the basis for managing processes in environmental biotechnology; and these processes provide interesting ecosystems to advance the concepts and tools of microbial ecology. Revolutionary advancements in molecular tools to understand the structure and function of microbial communities are bolstering the power of microbial ecology. A push from advances in modern materials along with a pull from a societal need to become more sustainable is enabling environmental biotechnology to create novel processes. How do these two fields work together? Five principles illuminate the way: (i) aim for big benefits; (ii) develop and apply more powerful tools to understand microbial communities; (iii) follow the electrons; (iv) retain slow-growing biomass; and (v) integrate, integrate, integrate.     

微生物生态学理论框架

微生物是生态系统的重要组成部分,直接或间接地参与所有的生态过程。微生物生态学是基于微生物群体的科学,利用微生物群体DNA/RNA等标志物,重点研究微生物群落构建、组成演变、多样性及其与环境的关系,在生态学理论的指导和反复模型拟合下由统计分析得出具有普遍意义的结论。其研究范围从基因尺度到全球尺度。分子生物学技术的发展,使人们可以直接从基因水平上考查其多样性,从而使得对微生物空间分布格局及其成因的深入研究成为可能。进而可以从方法学探讨微生物生物多样性、分布格局、影响机制及其对全球变化的响应等。在微生物生态学研究中,群落构建与演化、分布特征(含植物-微生物相互关系)、执行群体功能的机理(生物地球化学循环等)、对环境变化的响应与反馈机理是今后需要关注的重点领域。概述了微生物生态学的概念,并初步提出其理论框架,在对比宏观生态学基础理论和模型的基础上,分析微生物多样性的研究内容、研究方法和群落构建的理论机制,展望了今后研究的重点领域。     

Conceptualizing microbe–plasmid communities as complex adaptive systems

Plasmids shape microbial communities’ diversity, structure, and function. Nevertheless, we lack a mechanistic understanding of how community structure and dynamics emerge from local microbe–plasmid interactions and coevolution. Addressing this gap is challenging because multiple processes operate simultaneously at multiple levels of organization. For example, immunity operates between a plasmid and a cell, but incompatibility mechanisms regulate coexistence between plasmids. Conceptualizing microbe–plasmid communities as complex adaptive systems is a promising approach to overcoming these challenges. I illustrate how agent-based evolutionary modeling, extended by network analysis, can be used to quantify the relative importance of local processes governing community dynamics. These theoretical developments can advance our understanding of plasmid ecology and evolution, especially when combined with empirical data.     

环境微生物群落结构与功能多样性研究方法

微生物群落的结构及群落内种间相互作用是影响其生态功能的决定性因素。尽管微生物群落是地球生物化学循环的主要驱动者,但是由于传统的微生物培养方法只能分离约1%10%的环境微生物,对复杂的环境微生物群落结构和功能多样性了解甚少。元基因组学、单细胞分析和群落遗传学等方法的出现,及其与微生物学的交叉融合,使得人们能够从微生物群落组成、物种功能、种间相互作用和预测模型等方面分析微生物群落。重点综述了元基因组学、单细胞分析和群落遗传学等方法及其在环境微生物群落结构和功能多样性中的应用进展。     

Modeling with a view to target identification in metabolic engineering: A critical evaluation of the available tools

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Linking soil process and microbial ecology in freshwater wetland ecosystems

Soil microorganisms mediate many processes such as nitrification, denitrification, and methanogenesis that regulate ecosystem functioning and also feed back to influence atmospheric chemistry. These processes are of particular interest in freshwater wetland ecosystems where nutrient cycling is highly responsive to fluctuating hydrology and nutrients and soil gas releases may be sensitive to climate warming. In this review we briefly summarize research from process and taxonomic approaches to the study of wetland biogeochemistry and microbial ecology, and highlight areas where further research is needed to increase our mechanistic understanding of wetland system functioning. Research in wetland biogeochemistry has most often been focused on processes (e.g., methanogenesis), and less often on microbial communities or on populations of specific microorganisms of interest. Research on process has focused on controls over, and rates of, denitrification, methanogenesis, and methanotrophy. There has been some work on sulfate and iron transformations and wetland enzyme activities. Work to date indicates an important process level role for hydrology and soil nutrient status. The impact of plant species composition on processes is potentially critical, but is as yet poorly understood. Research on microbial communities in wetland soils has primarily focused on bacteria responsible for methanogenesis, denitrification, and sulfate reduction. There has been less work on taxonomic groups such as those responsible for nitrogen fixation, or aerobic processes such as nitrification. Work on general community composition and on wetland mycorrhizal fungi is particularly sparse. The general goal of microbial research has been to understand how microbial groups respond to the environment. There has been relatively little work done on the interactions among environmental controls over process rates, environmental constraints on microbial activities and community composition, and changes in processes at the ecosystem level. Finding ways to link process-based and biochemical or gene-based assays is becoming increasingly important as we seek a mechanistic understanding of the response of wetland ecosystems to current and future anthropogenic perturbations. We discuss the potential of new approaches, and highlight areas for further research.     

Towards Predictive Models of the Human Gut Microbiome

The intestinal microbiota is an ecosystem susceptible to external perturbations such as dietary changes and antibiotic therapies. Mathematical models of microbial communities could be of great value in the rational design of microbiota-tailoring diets and therapies. Here, we discuss how advances in another field, engineering of microbial communities for wastewater treatment bioreactors, could inspire development of mechanistic mathematical models of the gut microbiota. We review the state of the art in bioreactor modeling and current efforts in modeling the intestinal microbiota. Mathematical modeling could benefit greatly from the deluge of data emerging from metagenomic studies, but data-driven approaches such as network inference that aim to predict microbiome dynamics without explicit mechanistic knowledge seem better suited to model these data. Finally, we discuss how the integration of microbiome shotgun sequencing and metabolic modeling approaches such as flux balance analysis may fulfill the promise of a mechanistic model.     

Biophysical controls on cluster dynamics and architectural differentiation of microbial biofilms in contrasting flow environments

Ecology, with a traditional focus on plants and animals, seeks to understand the mechanisms underlying structure and dynamics of communities. In microbial ecology, the focus is changing from planktonic communities to attached biofilms that dominate microbial life in numerous systems. Therefore, interest in the structure and function of biofilms is on the rise. Biofilms can form reproducible physical structures (i.e. architecture) at the millimetre‐scale, which are central to their functioning. However, the spatial dynamics of the clusters conferring physical structure to biofilms remains often elusive. By experimenting with complex microbial communities forming biofilms in contrasting hydrodynamic microenvironments in stream mesocosms, we show that morphogenesis results in ‘ripple‐like’ and ‘star‐like’ architectures – as they have also been reported from monospecies bacterial biofilms, for instance. To explore the potential contribution of demographic processes to these architectures, we propose a size‐structured population model to simulate the dynamics of biofilm growth and cluster size distribution. Our findings establish that basic physical and demographic processes are key forces that shape apparently universal biofilm architectures as they occur in diverse microbial but also in single‐species bacterial biofilms.     

Microbial ecology in the age of genomics and metagenomics: concepts, tools, and recent advances

Microbial ecology examines the diversity and activity of micro-organisms in Earth's biosphere. In the last 20 years, the application of genomics tools have revolutionized microbial ecological studies and drastically expanded our view on the previously underappreciated microbial world. This review first introduces the basic concepts in microbial ecology and the main genomics methods that have been used to examine natural microbial populations and communities. In the ensuing three specific sections, the applications of the genomics in microbial ecological research are highlighted. The first describes the widespread application of multilocus sequence typing and representational difference analysis in studying genetic variation within microbial species. Such investigations have identified that migration, horizontal gene transfer and recombination are common in natural microbial populations and that microbial strains can be highly variable in genome size and gene content. The second section highlights and summarizes the use of four specific genomics methods (phylogenetic analysis of ribosomal RNA, DNA-DNA re-association kinetics, metagenomics, and micro-arrays) in analysing the diversity and potential activity of microbial populations and communities from a variety of terrestrial and aquatic environments. Such analyses have identified many unexpected phylogenetic lineages in viruses, bacteria, archaea, and microbial eukaryotes. Functional analyses of environmental DNA also revealed highly prevalent, but previously unknown, metabolic processes in natural microbial communities. In the third section, the ecological implications of sequenced microbial genomes are briefly discussed. Comparative analyses of prokaryotic genomic sequences suggest the importance of ecology in determining microbial genome size and gene content. The significant variability in genome size and gene content among strains and species of prokaryotes indicate the highly fluid nature of prokaryotic genomes, a result consistent with those from multilocus sequence typing and representational difference analyses. The integration of various levels of ecological analyses coupled to the application and further development of high throughput technologies are accelerating the pace of discovery in microbial ecology.     

Metabolic specialization and the assembly of microbial communities

Metabolic specialization is a general biological principle that shapes the assembly of microbial communities. Individual cell types rarely metabolize a wide range of substrates within their environment. Instead, different cell types often specialize at metabolizing only subsets of the available substrates. What is the advantage of metabolizing subsets of the available substrates rather than all of them? In this perspective piece, we argue that biochemical conflicts between different metabolic processes can promote metabolic specialization and that a better understanding of these conflicts is therefore important for revealing the general principles and rules that govern the assembly of microbial communities. We first discuss three types of biochemical conflicts that could promote metabolic specialization. Next, we demonstrate how knowledge about the consequences of biochemical conflicts can be used to predict whether different metabolic processes are likely to be performed by the same cell type or by different cell types. We then discuss the major challenges in identifying and assessing biochemical conflicts between different metabolic processes and propose several approaches for their measurement. Finally, we argue that a deeper understanding of the biochemical causes of metabolic specialization could serve as a foundation for the field of synthetic ecology, where the objective would be to rationally engineer the assembly of a microbial community to perform a desired biotransformation.     

基于宏组学方法认识微生物群落及其功能

进入后基因组学时代,测序技术飞速发展,测序成本明显下降,形成了涵盖宏基因组学、宏转录组学和宏蛋白质组学的宏组学技术,推动了对微生物群落的多样性、结构及潜在基因功能方面的深入研究。最近随着整合的宏组学技术的提出及应用,全面系统分析微生物群落动态变化及其代谢功能已成为可能,这将成为微生物生态学研究的新趋势。本文综述了宏组学在研究海洋湖泊、深海热泉、人体肠道、牛瘤胃生境、森林土壤与堆肥生境等环境中微生物群落的结构和功能方面的最新进展与成功应用案例。     

Big questions, small worlds: microbial model systems in ecology

Although many biologists have embraced microbial model systems as tools to address genetic and physiological questions, the explicit use of microbial communities as model systems in ecology has traditionally been more restricted. Here, we highlight recent studies that use laboratory-based microbial model systems to address ecological questions. Such studies have significantly advanced our understanding of processes that have proven difficult to study in field systems, including the genetic and biochemical underpinnings of traits involved in ecological interactions, and the ecological differences driving evolutionary change. It is the simplicity of microbial model systems that makes them such powerful tools for the study of ecology. Such simplicity enables the high degrees of experimental control and replication that are necessary to address many questions that are inaccessible through field observation or experimentation.     

Introducing SONS, a Tool for Operational Taxonomic Unit-Based Comparisons of Microbial Community Memberships and Structures

The recent advent of tools enabling statistical inferences to be drawn from comparisons of microbial communities has enabled the focus of microbial ecology to move from characterizing biodiversity to describing the distribution of that biodiversity. Although statistical tools have been developed to compare community structures across a phylogenetic tree, we lack tools to compare the memberships and structures of two communities at a particular operational taxonomic unit (OTU) definition. Furthermore, current tests of community structure do not indicate the similarity of the communities but only report the probability of a statistical hypothesis. Here we present a computer program, SONS, which implements nonparametric estimators for the fraction and richness of OTUs shared between two communities.     

Introducing SONS, a tool for operational taxonomic unit-based comparisons of microbial community memberships and structures

The recent advent of tools enabling statistical inferences to be drawn from comparisons of microbial communities has enabled the focus of microbial ecology to move from characterizing biodiversity to describing the distribution of that biodiversity. Although statistical tools have been developed to compare community structures across a phylogenetic tree, we lack tools to compare the memberships and structures of two communities at a particular operational taxonomic unit (OTU) definition. Furthermore, current tests of community structure do not indicate the similarity of the communities but only report the probability of a statistical hypothesis. Here we present a computer program, SONS, which implements nonparametric estimators for the fraction and richness of OTUs shared between two communities.     

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 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.     

Microbial communities across a hillslope‐riparian transect shaped by proximity to the stream,groundwater table,and weathered bedrock

Watersheds are important suppliers of freshwater for human societies. Within mountainous watersheds, microbial communities impact water chemistry and element fluxes as water from precipitation events discharge through soils and underlying weathered rock, yet there is limited information regarding the structure and function of these communities. Within the East River, CO watershed, we conducted a depth‐resolved, hillslope to riparian zone transect study to identify factors that control how microorganisms are distributed and their functions. Metagenomic and geochemical analyses indicate that distance from the East River and proximity to groundwater and underlying weathered shale strongly impact microbial community structure and metabolic potential. Riparian zone microbial communities are compositionally distinct, from the phylum down to the species level, from all hillslope communities. Bacteria from phyla lacking isolated representatives consistently increase in abundance with increasing depth, but only in the riparian zone saturated sediments we found Candidate Phyla Radiation bacteria. Riparian zone microbial communities are functionally differentiated from hillslope communities based on their capacities for carbon and nitrogen fixation and sulfate reduction. Selenium reduction is prominent at depth in weathered shale and saturated riparian zone sediments and could impact water quality. We anticipate that the drivers of community composition and metabolic potential identified throughout the studied transect will predict patterns across the larger watershed hillslope system.     

Human and Environmental Impacts on River Sediment Microbial Communities

Sediment microbial communities are responsible for a majority of the metabolic activity in river and stream ecosystems. Understanding the dynamics in community structure and function across freshwater environments will help us to predict how these ecosystems will change in response to human land-use practices. Here we present a spatiotemporal study of sediments in the Tongue River (Montana, USA), comprising six sites along 134 km of river sampled in both spring and fall for two years. Sequencing of 16S rRNA amplicons and shotgun metagenomes revealed that these sediments are the richest (∼65,000 microbial ‘species’ identified) and most novel (93% of OTUs do not match known microbial diversity) ecosystems analyzed by the Earth Microbiome Project to date, and display more functional diversity than was detected in a recent review of global soil metagenomes. Community structure and functional potential have been significantly altered by anthropogenic drivers, including increased pathogenicity and antibiotic metabolism markers near towns and metabolic signatures of coal and coalbed methane extraction byproducts. The core (OTUs shared across all samples) and the overall microbial community exhibited highly similar structure, and phylogeny was weakly coupled with functional potential. Together, these results suggest that microbial community structure is shaped by environmental drivers and niche filtering, though stochastic assembly processes likely play a role as well. These results indicate that sediment microbial communities are highly complex and sensitive to changes in land use practices.