Protein-SIP enables time-resolved analysis of the carbon flux in a sulfate-reducing,benzene-degrading microbial consortium

Benzene is a major contaminant in various environments, but the mechanisms behind its biodegradation under strictly anoxic conditions are not yet entirely clear. Here we analyzed a benzene-degrading, sulfate-reducing enrichment culture originating from a benzene-contaminated aquifer by a metagenome-based functional metaproteomic approach, using protein-based stable isotope probing (protein-SIP). The time-resolved, quantitative analysis of carbon fluxes within the community supplied with either ¹³C-labeled benzene or ¹³C-labeled carbonate yielded different functional groups of organisms, with their peptides showing specific time dependencies of ¹³C relative isotope abundance indicating different carbon utilization. Through a detailed analysis of the mass spectrometric (MS) data, it was possible to quantify the utilization of the initial carbon source and the metabolic intermediates. The functional groups were affiliated to Clostridiales, Deltaproteobacteria and Bacteroidetes/Chlorobi. The Clostridiales-related organisms were involved in benzene degradation, putatively by fermentation, and additionally used significant amounts of carbonate as a carbon source. The other groups of organisms were found to perform diverse functions, with Deltaproteobacteria degrading fermentation products and Bacteroidetes/Chlorobi being putative scavengers feeding on dead cells. A functional classification of identified proteins supported this allocation and gave further insights into the metabolic pathways and the interactions between the community members. This example shows how protein-SIP can be applied to obtain temporal and phylogenetic information about functional interdependencies within microbial communities.     

Are pathogenic bacteria just looking for food? Metabolism and microbial pathogenesis

It is interesting to speculate that the evolutionary drive for microbes to develop pathogenic characteristics was to access the nutrient resources that animals provided. Animal environments that pathogens colonize have likely driven the evolution of new bacterial characteristics to maximize these new nutritional opportunities. This review focuses on genomic and functional aspects of pathogen metabolism that allow efficient utilization of nutrient resources provided by animals. Similar to genes encoding specific virulence traits, genes encoding metabolic functions have been horizontally acquired by pathogens to provide a selective advantage in host tissues. Selective advantage in host tissues can also be gained by loss of function mutations that alter metabolic capabilities. Greater understanding of bacterial metabolism within host tissues should be important for increased understanding of host-pathogen interactions and the development of future therapeutic strategies.     

Effect of Long-Term Starvation on the Survival,Recovery, and Carbon Utilization Profiles of a Bovine Escherichia coli O157:H7 Isolate from New Zealand

The ability to maintain a dual lifestyle of colonizing the ruminant gut and surviving in nonhost environments once shed is key to the success of Escherichia coli O157:H7 as a zoonotic pathogen. Both physical and biological conditions encountered by the bacteria are likely to change during the transition between host and nonhost environments. In this study, carbon starvation at suboptimal temperatures in nonhost environments was simulated by starving a New Zealand bovine E. coli O157:H7 isolate in phosphate-buffered saline at 4 and 15°C for 84 days. Recovery of starved cells on media with different nutrient availabilities was monitored under aerobic and anaerobic conditions. We found that the New Zealand bovine E. coli O157:H7 isolate was able to maintain membrane integrity and viability over 84 days and that the level of recovery depended on the nutrient level of the recovery medium as well as the starvation temperature. In addition, a significant difference in carbon utilization was observed between starved and nonstarved cells.     

Patterns of Endemism and Habitat Selection in Coalbed Microbial Communities

Microbially produced methane, a versatile, cleaner-burning alternative energy resource to fossil fuels, is sourced from a variety of natural and engineered ecosystems, including marine sediments, anaerobic digesters, shales, and coalbeds. There is a prevailing interest in developing environmental biotechnologies to enhance methane production. Here, we use small-subunit rRNA gene sequencing and metagenomics to better describe the interplay between coalbed methane (CBM) well conditions and microbial communities in the Alberta Basin. Our results show that CBM microbial community structures display patterns of endemism and habitat selection across the Alberta Basin, consistent with observations from other geographical locations. While some phylum-level taxonomic patterns were observed, relative abundances of specific taxonomic groups were localized to discrete wells, likely shaped by local environmental conditions, such as coal rank and depth-dependent physicochemical conditions. To better resolve functional potential within the CBM milieu, a metagenome from a deep volatile-bituminous coal sample was generated. This sample was dominated by Rhodobacteraceae genotypes, resolving a near-complete population genome bin related to Celeribacter sp. that encoded metabolic pathways for the degradation of a wide range of aromatic compounds and the production of methanogenic substrates via acidogenic fermentation. Genomic comparisons between the Celeribacter sp. population genome and related organisms isolated from different environments reflected habitat-specific selection pressures that included nitrogen availability and the ability to utilize diverse carbon substrates. Taken together, our observations reveal that both endemism and metabolic specialization should be considered in the development of biostimulation strategies for nonproductive wells or for those with declining productivity.     

Metabolic-network-driven analysis of bacterial ecological strategies

Background  

The growth-rate of an organism is an important phenotypic trait, directly affecting its ability to survive in a given environment. Here we present the first large scale computational study of the association between ecological strategies and growth rate across 113 bacterial species, occupying a variety of metabolic habitats. Genomic data are used to reconstruct the species' metabolic networks and habitable metabolic environments. These reconstructions are then used to investigate the typical ecological strategies taken by organisms in terms of two basic species-specific measures: metabolic variability - the ability of a species to survive in a variety of different environments; and co-habitation score vector - the distribution of other species that co-inhabit each environment.     

Constraint-Based Model of Shewanella oneidensis MR-1 Metabolism: A Tool for Data Analysis and Hypothesis Generation

Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox) stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i) used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii) developed an approach to identify cycles (such as futile cycles and circulations), (iii) classified how reaction usage affects cellular growth, (iv) predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v) used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a systems level.     

Heterogeneous composition of key metabolic gene clusters in a vent mussel symbiont population

Chemosynthetic symbiosis is one of the successful systems for adapting to a wide range of habitats including extreme environments, and the metabolic capabilities of symbionts enable host organisms to expand their habitat ranges. However, our understanding of the adaptive strategies that enable symbiotic organisms to expand their habitats is still fragmentary. Here, we report that a single-ribotype endosymbiont population in an individual of the host vent mussel, Bathymodiolus septemdierum has heterogeneous genomes with regard to the composition of key metabolic gene clusters for hydrogen oxidation and nitrate reduction. The host individual harbours heterogeneous symbiont subpopulations that either possess or lack the gene clusters encoding hydrogenase or nitrate reductase. The proportions of the different symbiont subpopulations in a host appeared to vary with the environment or with the host''s development. Furthermore, the symbiont subpopulations were distributed in patches to form a mosaic pattern in the gill. Genomic heterogeneity in an endosymbiont population may enable differential utilization of diverse substrates and confer metabolic flexibility. Our findings open a new chapter in our understanding of how symbiotic organisms alter their metabolic capabilities and expand their range of habitats.     

Memory and Fitness Optimization of Bacteria under Fluctuating Environments

Bacteria prudently regulate their metabolic phenotypes by sensing the availability of specific nutrients, expressing the required genes for their metabolism, and repressing them after specific metabolites are depleted. It is unclear, however, how genetic networks maintain and transmit phenotypic states between generations under rapidly fluctuating environments. By subjecting bacteria to fluctuating carbon sources (glucose and lactose) using microfluidics, we discover two types of non-genetic memory in Escherichia coli and analyze their benefits. First, phenotypic memory conferred by transmission of stable intracellular lac proteins dramatically reduces lag phases under cyclical fluctuations with intermediate timescales (1–10 generations). Second, response memory, a hysteretic behavior in which gene expression persists after removal of its external inducer, enhances adaptation when environments fluctuate over short timescales (<1 generation). Using a mathematical model we analyze the benefits of memory across environmental fluctuation timescales. We show that memory mechanisms provide an important class of survival strategies in biology that improve long-term fitness under fluctuating environments. These results can be used to understand how organisms adapt to fluctuating levels of nutrients, antibiotics, and other environmental stresses.     

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

Background  

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees.     

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.     

Evolution under Fluctuating Environments Explains Observed Robustness in Metabolic Networks

A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was “apparent,” in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further, they suggest that organisms living under stable environments should display lower robustness in their metabolic networks, and that robustness should decrease upon switching to more stable environments.     

碱蓬属植物耐盐机理研究进展

碱蓬属(Suaeda)植物是一类典型的真盐生植物,属于重要的盐生植物资源,全球广泛分布.人们已经对20种碱蓬属植物进行了观察和盐胁迫实验,研究了不同器官或组织的生理生化特征及其对盐胁迫的反应,并基于这些研究分析了盐胁迫的应答机制.叶片肉质化、细胞内离子区域化、渗透调节物质增加和抗氧化系统能力增强是碱蓬属植物响应和适应盐胁迫的重要方式和途径.但迄今为止的研究工作尚有一定的局限性,主要包括:研究工作主要集中在植物地上部分,而对植物地下部分的研究较少;多是少数生物学指标或生理学现象的单独观察,而缺乏对生理代谢过程的整体和综合分析;针对某种碱蓬的独立分析较多,而与近缘种的比较研究较少;植物对中性盐胁迫的反应研究较多,而对碱性盐的研究较少.为进一步系统阐明碱蓬属植物的耐盐机制,今后的工作应注重碱蓬属植物响应和适应盐胁迫的信号网络和调控机制研究,基于系统生物学研究思路,采用现代组学技术探索该属植物响应盐胁迫的由复杂信号网络调控的特殊生理特征和特异代谢途径.     

Phenomenological Model for Predicting the Catabolic Potential of an Arbitrary Nutrient

The ability of microbial species to consume compounds found in the environment to generate commercially-valuable products has long been exploited by humanity. The untapped, staggering diversity of microbial organisms offers a wealth of potential resources for tackling medical, environmental, and energy challenges. Understanding microbial metabolism will be crucial to many of these potential applications. Thermodynamically-feasible metabolic reconstructions can be used, under some conditions, to predict the growth rate of certain microbes using constraint-based methods. While these reconstructions are powerful, they are still cumbersome to build and, because of the complexity of metabolic networks, it is hard for researchers to gain from these reconstructions an understanding of why a certain nutrient yields a given growth rate for a given microbe. Here, we present a simple model of biomass production that accurately reproduces the predictions of thermodynamically-feasible metabolic reconstructions. Our model makes use of only: i) a nutrient''s structure and function, ii) the presence of a small number of enzymes in the organism, and iii) the carbon flow in pathways that catabolize nutrients. When applied to test organisms, our model allows us to predict whether a nutrient can be a carbon source with an accuracy of about 90% with respect to in silico experiments. In addition, our model provides excellent predictions of whether a medium will produce more or less growth than another () and good predictions of the actual value of the in silico biomass production.