The players may change but the game remains: network analyses of ruminal microbiomes suggest taxonomic differences mask functional similarity

By mapping translated metagenomic reads to a microbial metabolic network, we show that ruminal ecosystems that are rather dissimilar in their taxonomy can be considerably more similar at the metabolic network level. Using a new network bi-partition approach for linking the microbial network to a bovine metabolic network, we observe that these ruminal metabolic networks exhibit properties consistent with distinct metabolic communities producing similar outputs from common inputs. For instance, the closer in network space that a microbial reaction is to a reaction found in the host, the lower will be the variability of its enzyme copy number across hosts. Similarly, these microbial enzymes that are nearby to host nodes are also higher in copy number than are more distant enzymes. Collectively, these results demonstrate a widely expected pattern that, to our knowledge, has not been explicitly demonstrated in microbial communities: namely that there can exist different community metabolic networks that have the same metabolic inputs and outputs but differ in their internal structure.     

Genomic and mechanistic insights into the biodegradation of organic pollutants

Several new methodologies have enabled recent studies on the microbial biodegradation mechanisms of organic pollutants. Culture-independent techniques for analysis of the genetic and metabolic potential of natural and model microbial communities that degrade organic pollutants have identified new metabolic pathways and enzymes for aerobic and anaerobic degradation. Furthermore, structural studies of the enzymes involved have revealed the specificities and activities of key catabolic enzymes, such as dioxygenases. Genome sequencing of several biodegradation-relevant microorganisms have provided the first whole-genome insights into the genetic background of the metabolic capability and biodegradation versatility of these organisms. Systems biology approaches are still in their infancy, but are becoming increasingly helpful to unravel, predict and quantify metabolic abilities within particular organisms or microbial consortia.     

Industrial wastewater bioreactors: sources of novel microorganisms for biotechnology

Microorganisms exist in nature as members of complex, mixed communities. The microbial communities in industrial wastewater bioreactors can be used as model systems to study the evolution of new metabolic pathways in natural ecosystems. The evolution of microbial metabolic capability in these bioreactors is presumably analogous to phenomena that occur in natural ecosystems. The microorganisms in these bioreactors compete for different carbon sources and constantly have to evolve new metabolic capabilities for survival. Thus, industrial bioreactors should be a rich source of novel biocatalysts.     

Microbial metabolic exchange--the chemotype-to-phenotype link

The function of microbial interactions is to enable microorganisms to survive by establishing a homeostasis between microbial neighbors and local environments. A microorganism can respond to environmental stimuli using metabolic exchange-the transfer of molecular factors, including small molecules and proteins. Microbial interactions not only influence the survival of the microbes but also have roles in morphological and developmental processes of the organisms themselves and their neighbors. This, in turn, shapes the entire habitat of these organisms. Here we highlight our current understanding of metabolic exchange as well as the emergence of new technologies that are allowing us to eavesdrop on microbial conversations comprising dozens to hundreds of secreted metabolites that control the behavior, survival and differentiation of members of the community. The goal of the rapidly advancing field studying multifactorial metabolic exchange is to devise a microbial 'Rosetta stone' in order to understand the language by which microbial interactions are negotiated and, ultimately, to control the outcome of these conversations.     

成像质谱在微生物代谢产物研究中的应用

微生物代谢产物的结构和功能多样,对相邻微生物和环境会产生重要影响。传统的天然产物分离方法不能系统全面地监测单一或混合微生物样品中代谢物的合成和释放模式。成像质谱能够同时可视化观察从单一微生物菌落到复杂微生物群落的多个代谢产物的时空分布,可以用于发现重要的生物活性分子,观察微生物菌落的代谢交流,以及跟踪微生物之间相互竞争过程中代谢物的修饰等方面的研究。本文综述了成像质谱在微生物代谢产物研究中的最新进展,展望了该技术的应用前景。     

Dealing with complexity: evolutionary engineering and genome shuffling

Comparative analysis of the growing number of microbial genome sequences has shown a high plasticity of genomes and several mechanisms for the adaptation of microbial cells to changing environmental conditions have been discovered. By contrast, the underlying metabolic networks of microorganisms are under strict control and relatively rigid, which poses a significant challenge for rational metabolic engineering approaches. Recursive shuffling of whole genomes has recently been demonstrated as an effective new evolutionary whole-cell engineering approach for the rapid improvement of industrially important microbial phenotypes.     

Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our interest in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to develop high performance microbes that are capable of producing biofuels with very high efficiency in order to compete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolic engineering, which can be considered as metabolic engineering integrated with systems biology and synthetic biology, have been developed. Systems metabolic engineering allows successful development of microbes that are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodiesel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers by metabolic engineering and systems metabolic engineering approaches are reviewed with relevant example cases. It is expected that systems metabolic engineering will be employed as an essential strategy for the development of microbial strains for industrial applications.     

Assessing coral reefs on a pacific-wide scale using the microbialization score

The majority of the world's coral reefs are in various stages of decline. While a suite of disturbances (overfishing, eutrophication, and global climate change) have been identified, the mechanism(s) of reef system decline remain elusive. Increased microbial and viral loading with higher percentages of opportunistic and specific microbial pathogens have been identified as potentially unifying features of coral reefs in decline. Due to their relative size and high per cell activity, a small change in microbial biomass may signal a large reallocation of available energy in an ecosystem; that is the microbialization of the coral reef. Our hypothesis was that human activities alter the energy budget of the reef system, specifically by altering the allocation of metabolic energy between microbes and macrobes. To determine if this is occurring on a regional scale, we calculated the basal metabolic rates for the fish and microbial communities at 99 sites on twenty-nine coral islands throughout the Pacific Ocean using previously established scaling relationships. From these metabolic rate predictions, we derived a new metric for assessing and comparing reef health called the microbialization score. The microbialization score represents the percentage of the combined fish and microbial predicted metabolic rate that is microbial. Our results demonstrate a strong positive correlation between reef microbialization scores and human impact. In contrast, microbialization scores did not significantly correlate with ocean net primary production, local chla concentrations, or the combined metabolic rate of the fish and microbial communities. These findings support the hypothesis that human activities are shifting energy to the microbes, at the expense of the macrobes. Regardless of oceanographic context, the microbialization score is a powerful metric for assessing the level of human impact a reef system is experiencing.     

Differential utilization of carbon substrates by aggregate-associated and water-associated heterotrophic bacterial communities

The capacity to utilize carbon substrates is fundamental to the functioning of heterotrophic microbial communities in aquatic environments. Carbon-source utilization within the water column, however, is not a bulk property because microbial communities are patchily distributed on suspended organic aggregates (i.e., marine snow, marine aggregates, river aggregates, organic detritus, and bioflocs). In this study, Biolog Ecoplates were used to evaluate the metabolic capacity of heterotrophic bacterial communities associated with aggregates compared to communities in the surrounding water. Overall, aggregate-associated microbial communities demonstrated higher levels of metabolism, metabolic versatility, and functional redundancy, and a more consistent pattern of carbon-source utilization compared with water-associated communities. In addition, aggregate-associated communities more effectively exploited available resources, including representatives from several biochemical guilds and nitrogen-containing carbon sources. Within the aggregate-associated microbial community, metabolic activity was significantly higher in the presence of polymers, amino acids, and carbohydrates relative to amines and carboxylic acids. In comparison, metabolic activity of water-associated communities exceeded a threshold value for only two of the five guilds (polymers and carbohydrates) evaluated. These results suggest that compared with their free-living counterparts, aggregate-associated communities have a greater capacity to respond to a wider array of carbon inputs. Results also underscore the importance of targeting organic aggregates to better understand the role of microbial processes in ecosystem functioning.     

How will bioinformatics influence metabolic engineering?

Ten microbial genomes have been fully sequenced to date, and the sequencing of many more genomes is expected to be completed before the end of the century. The assignment of function to open reading frames (ORFs) is progressing, and for some genomes over 70% of functional assignments have been made. The majority of the assigned ORFs relate to metabolic functions. Thus, the complete genetic and biochemical functions of a number of microbial cells may be soon available. From a metabolic engineering standpoint, these developments open a new realm of possibilities. Metabolic analysis and engineering strategies can now be built on a sound genomic basis. An important question that now arises; how should these tasks be approached? Flux-balance analysis (FBA) has the potential to play an important role. It is based on the fundamental principle of mass conservation. It requires only the stoichiometric matrix, the metabolic demands, and some strain specific parameters. Importantly, no enzymatic kinetic data is required. In this article, we show how the genomically defined microbial metabolic genotypes can be analyzed by FBA. Fundamental concepts of metabolic genotype, metabolic phenotype, metabolic redundancy and robustness are defined and examples of their use given. We discuss the advantage of this approach, and how FBA is expected to find uses in the near future. FBA is likely to become an important analysis tool for genomically based approaches to metabolic engineering, strain design, and development.     

Metabolic potential and survival strategies of microbial communities across extreme temperature gradients on Deception Island volcano,Antarctica

Active volcanoes in Antarctica have remarkable temperature and geochemical gradients that could select for a wide variety of microbial adaptive mechanisms and metabolic pathways. Deception Island is a stratovolcano flooded by the sea, resulting in contrasting ecosystems such as permanent glaciers and active fumaroles, which creates steep gradients that have been shown to affect microbial diversity. In this study, we used shotgun metagenomics and metagenome-assembled genomes to explore the metabolic potentials and survival strategies of microbial communities along an extreme temperature gradient in fumarole and glacier sediments on Deception Island. We observed that communities from a 98 °C fumarole were significantly enriched in genes related to hyperthermophilic (e.g. reverse gyrase, GroEL/GroES and thermosome) and oxidative stress responses, as well as genes related to sulfate reduction, ammonification and carbon fixation. Communities from <80 °C fumaroles possessed more genes related osmotic, cold- and heat-shock responses, and diverse metabolic potentials, such as those related to sulfur oxidation and denitrification, while glacier communities showed abundant metabolic potentials mainly related to heterotrophy. Through the reconstruction of genomes, we were able to reveal the metabolic potentials and different survival strategies of underrepresented taxonomic groups, especially those related to Nanoarchaeota, Pyrodictiaceae and thermophilic ammonia-oxidizing archaeal lineages.     

Gene transfer, speciation, and the evolution of bacterial genomes.

Studies in microbial evolution have focused on the origin and vertical transmission of genetic variation within populations experiencing limited recombination. Genomic analyses have highlighted the importance of horizontal genetic transfer in shaping the composition of microbial genomes, providing novel metabolic capabilities, and catalyzing the diversification of bacterial lineages.     

Unravelling core microbial metabolisms in the hypersaline microbial mats of Shark Bay using high-throughput metagenomics

Modern microbial mats are potential analogues of some of Earth''s earliest ecosystems. Excellent examples can be found in Shark Bay, Australia, with mats of various morphologies. To further our understanding of the functional genetic potential of these complex microbial ecosystems, we conducted for the first time shotgun metagenomic analyses. We assembled metagenomic next-generation sequencing data to classify the taxonomic and metabolic potential across diverse morphologies of marine mats in Shark Bay. The microbial community across taxonomic classifications using protein-coding and small subunit rRNA genes directly extracted from the metagenomes suggests that three phyla Proteobacteria, Cyanobacteria and Bacteriodetes dominate all marine mats. However, the microbial community structure between Shark Bay and Highbourne Cay (Bahamas) marine systems appears to be distinct from each other. The metabolic potential (based on SEED subsystem classifications) of the Shark Bay and Highbourne Cay microbial communities were also distinct. Shark Bay metagenomes have a metabolic pathway profile consisting of both heterotrophic and photosynthetic pathways, whereas Highbourne Cay appears to be dominated almost exclusively by photosynthetic pathways. Alternative non-rubisco-based carbon metabolism including reductive TCA cycle and 3-hydroxypropionate/4-hydroxybutyrate pathways is highly represented in Shark Bay metagenomes while not represented in Highbourne Cay microbial mats or any other mat forming ecosystems investigated to date. Potentially novel aspects of nitrogen cycling were also observed, as well as putative heavy metal cycling (arsenic, mercury, copper and cadmium). Finally, archaea are highly represented in Shark Bay and may have critical roles in overall ecosystem function in these modern microbial mats.     

Overview of the rules of the microbial engagement in the gut microbiome: a step towards microbiome therapeutics

Human gut microbiome is a diversified, resilient, immuno-stabilized, metabolically active and physiologically essential component of the human body. Scientific explorations have been made to seek in-depth information about human gut microbiome establishment, microbiome functioning, microbiome succession, factors influencing microbial community dynamics and the role of gut microbiome in health and diseases. Extensive investigations have proposed the microbiome therapeutics as a futuristic medicine for various physiological and metabolic disorders. A comprehensive outlook of microbial colonization, host–microbe interactions, microbial adaptation, commensal selection and immuno-survivability is still required to catalogue the essential genetic and physiological features for the commensal engagement. Evolution of a structured human gut microbiome relies on the microbial flexibility towards genetic, immunological and physiological adaptation in the human gut. Key features for commensalism could be utilized in developing tailor-made microbiome-based therapy to overcome various physiological and metabolic disorders. This review describes the key genetics and physiological traits required for host–microbe interaction and successful commensalism to institute a human gut microbiome.     

Combinatorial genetic perturbation to refine metabolic circuits for producing biofuels and biochemicals

Recent advances in metabolic engineering have enabled microbial factories to compete with conventional processes for producing fuels and chemicals. Both rational and combinatorial approaches coupled with synthetic and systematic tools play central roles in metabolic engineering to create and improve a selected microbial phenotype. Compared to knowledge-based rational approaches, combinatorial approaches exploiting biological diversity and high-throughput screening have been demonstrated as more effective tools for improving various phenotypes of interest. In particular, identification of unprecedented targets to rewire metabolic circuits for maximizing yield and productivity of a target chemical has been made possible. This review highlights general principles and the features of the combinatorial approaches using various libraries to implement desired phenotypes for strain improvement. In addition, recent applications that harnessed the combinatorial approaches to produce biofuels and biochemicals will be discussed.     

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.     

Effects of introducing heterologous pathways on microbial metabolism with respect to metabolic optimality

Although optimality of microbial metabolism under genetic and environmental perturbations is well studied, the effects of introducing heterologous reactions on the overall metabolism are not well understood. This point is important in the field of metabolic engineering because heterologous reactions are more frequently introduced into various microbial hosts. The genome-scale metabolic simulations of Escherichia coli strains engineered to produce 1,4-butanediol, 1,3-propanediol, and amorphadiene suggest that microbial metabolism shows much different responses to the introduced heterologous reactions in a strain-specific manner than typical gene knockouts in terms of the energetic status (e.g., ATP and biomass generation) and chemical production capacity. The 1,4-butanediol and 1,3-propanediol producers showed greater metabolic optimality than the wild-type strains and gene knockout mutants for the energetic status, while the amorphadiene producer was metabolically less optimal. For the optimal chemical production capacity, additional gene knockouts were most effective for the strain producing 1,3-propanediol, but not for the one producing 1,4-butanediol. These observations suggest that strains having heterologous metabolic reactions have metabolic characteristics significantly different from those of the wild-type strain and single gene knockout mutants. Finally, comparison of the theoretically predicted and ¹³C-based flux values pinpoints pathways with non-optimal flux values, which can be considered as engineering targets in systems metabolic engineering strategies. To our knowledge, this study is the first attempt to quantitatively characterize microbial metabolisms with different heterologous reactions. The suggested potential reasons behind each strain’s different metabolic responses to the introduced heterologous reactions should be carefully considered in strain designs.