Metabolic flux network and analysis of fermentative hydrogen production

Fermentative hydrogen production (FHP) has received a great R & D interest in recent decades, as it offers a potential means of producing H₂ from a variety of renewable resources, even wastewater via a low energy continuous process. Various extracellular metabolites including ethanol, acetate, butyrate and lactate can be produced during the fermentation, building a complex metabolic network of the FHP. Except for the recognition of its complexity, the metabolic flux network has not been well understood. Studies on biochemical reactions and metabolic flux network associated with the FHP in anaerobic fermentation system have only been drawn attention in recent years. This review summarizes the biochemical reactions taking place in the metabolic network of FHP. We discuss how the key operation factors influence metabolism in the FHP process. Recently developed and applied technologies for metabolic flux analysis have been described. Future studies on the metabolic network to enhance fermentative hydrogen production by strict anaerobes are recommended. It is expected that this review can provide useful information in terms of fundamental knowledge and update technology for scientists and research engineers in the field of biological hydrogen production.     

普洱茶后发酵过程中微生物的研究进展

对有关普洱茶后发酵生产与微生物的关系、微生物的种类、微生物对普洱茶品质的影响以及主要微生物的生长特性等方面的研究工作进行综述。指出加强普洱茶后发酵过程中微生物基础研究的必要性,提出建立普洱茶后发酵菌物库,重视对影响普洱茶品质的菌物开展系统研究的建议。     

Using Bioconductor Package BiGGR for Metabolic Flux Estimation Based on Gene Expression Changes in Brain

Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer’s disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.     

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.     

Phenotypic landscape of Saccharomyces cerevisiae during wine fermentation: evidence for origin-dependent metabolic traits

The species Saccharomyces cerevisiae includes natural strains, clinical isolates, and a large number of strains used in human activities. The aim of this work was to investigate how the adaptation to a broad range of ecological niches may have selectively shaped the yeast metabolic network to generate specific phenotypes. Using 72 S. cerevisiae strains collected from various sources, we provide, for the first time, a population-scale picture of the fermentative metabolic traits found in the S. cerevisiae species under wine making conditions. Considerable phenotypic variation was found suggesting that this yeast employs diverse metabolic strategies to face environmental constraints. Several groups of strains can be distinguished from the entire population on the basis of specific traits. Strains accustomed to growing in the presence of high sugar concentrations, such as wine yeasts and strains obtained from fruits, were able to achieve fermentation, whereas natural yeasts isolated from "poor-sugar" environments, such as oak trees or plants, were not. Commercial wine yeasts clearly appeared as a subset of vineyard isolates, and were mainly differentiated by their fermentative performances as well as their low acetate production. Overall, the emergence of the origin-dependent properties of the strains provides evidence for a phenotypic evolution driven by environmental constraints and/or human selection within S. cerevisiae.     

Metabolic engineering of Bacillus subtilis fueled by systems biology: Recent advances and future directions

By combining advanced omics technology and computational modeling, systems biologists have identified and inferred thousands of regulatory events and system-wide interactions of the bacterium Bacillus subtilis, which is commonly used both in the laboratory and in industry. This dissection of the multiple layers of regulatory networks and their interactions has provided invaluable information for unraveling regulatory mechanisms and guiding metabolic engineering. In this review, we discuss recent advances in the systems biology and metabolic engineering of B. subtilis and highlight current gaps in our understanding of global metabolism and global pathway engineering in this organism. We also propose future perspectives in the systems biology of B. subtilis and suggest ways that this approach can be used to guide metabolic engineering. Specifically, although hundreds of regulatory events have been identified or inferred via systems biology approaches, systematic investigation of the functionality of these events in vivo has lagged, thereby preventing the elucidation of regulatory mechanisms and further rational pathway engineering. In metabolic engineering, ignoring the engineering of multilayer regulation hinders metabolic flux redistribution. Post-translational engineering, allosteric engineering, and dynamic pathway analyses and control will also contribute to the modulation and control of the metabolism of engineered B. subtilis, ultimately producing the desired cellular traits. We hope this review will aid metabolic engineers in making full use of available systems biology datasets and approaches for the design and perfection of microbial cell factories through global metabolism optimization.     

Performance,genomic rearrangements,and signatures of adaptive evolution: Lessons from fermentative yeasts

The capacity of some yeasts to extract energy from single sugars, generating CO₂ and ethanol (=fermentation), even in the presence of oxygen, is known as the Crabtree effect. This phenomenon represents an important adaptation as it allowed the utilization of the ecological niche given by modern fruits, an abundant source of food that emerged in the terrestrial environment in the Cretaceous. However, identifying the evolutionary events that triggered fermentative capacity in Crabtree‐positive species is challenging, as microorganisms do not leave fossil evidence. Thus, key innovations should be inferred based only on traits measured under culture conditions. Here, we reanalyzed data from a common garden experiment where several proxies of fermentative capacity were recorded in Crabtree‐positive and Crabtree‐negative species, representing yeast phylogenetic diversity. In particular, we applied the “lasso‐OU” algorithm which detects points of adaptive shifts, using traits that are proxies of fermentative performance. We tested whether multiple events or a single event explains the actual fermentative capacity of yeasts. According to the lasso‐OU procedure, evolutionary changes in the three proxies of fermentative capacity that we considered (i.e., glycerol production, ethanol yield, and respiratory quotient) are consistent with a single evolutionary episode (a whole‐genomic duplication, WGD), instead of a series of small genomic rearrangements. Thus, the WGD appears as the key event behind the diversification of fermentative yeasts, which by increasing gene dosage, and maximized their capacity of energy extraction for exploiting the new ecological niche provided by single sugars.     

Expanding the concepts and tools of metabolic engineering to elucidate cancer metabolism

The metabolic engineer's toolbox, comprising stable isotope tracers, flux estimation and analysis, pathway identification, and pathway kinetics and regulation, among other techniques, has long been used to elucidate and quantify pathways primarily in the context of engineering microbes for producing small molecules of interest. Recently, these tools are increasingly finding use in cancer biology due to their unparalleled capacity for quantifying intracellular metabolism of mammalian cells. Here, we review basic concepts that are used to derive useful insights about the metabolism of tumor cells, along with a number of illustrative examples highlighting the fundamental contributions of these methods to elucidating cancer cell metabolism. This area presents unique opportunities for metabolic engineering to expand its portfolio of applications into the realm of cancer biology and help develop new cancer therapies based on a new class of metabolically derived targets. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Predicting the physiological role of circadian metabolic regulation in the green alga Chlamydomonas reinhardtii

Although the number of reconstructed metabolic networks is steadily growing, experimental data integration into these networks is still challenging. Based on elementary flux mode analysis, we combine sequence information with metabolic pathway analysis and include, as a novel aspect, circadian regulation. While minimizing the need of assumptions, we are able to predict changes in the metabolic state and can hypothesise on the physiological role of circadian control in nitrogen metabolism of the green alga Chlamydomonas reinhardtii.     

Acetate‐containing substrate mixtures improve recombinant protein secretion in Schizosaccharomyces pombe

Recombinant protein secretion in yeasts poses a burden to the metabolism of the host cell. Consequently, unfavorable cultivation conditions during strain screening or process development can lead to limitations in the energy and carbon metabolism of the cell, constraining the cell's ability to secrete the protein of interest. Recently, we demonstrated that improving cultivation conditions by using substrate mixtures of glycerol and acetate strongly elevated secretion of the homologous model protein maltase in the fission yeast Schizosaccharomyces pombe. In this work, we investigated if these previous findings were also applicable to the expression of recombinant proteins. Strains were constructed secreting either green fluorescent protein or a fluorescent single‐chain antibody fragment. These strains were cultivated under fermentative and respiratory growth conditions on glucose as sole carbon source and on mixtures of glucose/acetate and glycerol/acetate. We observed an increase in the specific secretion of both recombinant proteins by 1.8‐ and 3.8‐fold, respectively. This clearly demonstrates that the proper choice of process conditions and the applied carbon sources have a significant impact on the secretion of at least two recombinant proteins in S. pombe allowing an improved production of the protein of interest.     

Functional traits determine heterospecific use of risk‐related social information in forest birds of tropical South‐East Asia

In birds and mammals, mobbing calls constitute an important form of social information that can attract numerous sympatric species to localized mobbing aggregations. While such a response is thought to reduce the future predation risk for responding species, there is surprisingly little empirical evidence to support this hypothesis. One way to test the link between predation risk reduction and mobbing attraction involves testing the relationship between species’ attraction to mobbing calls and the functional traits that define their vulnerability to predation risk. Two important traits known to influence prey vulnerability include relative prey‐to‐predator body size ratio and the overlap in space use between predator and prey; in combination, these measures strongly influence prey accessibility, and therefore their vulnerability, to predators. Here, we combine community surveys with behavioral experiments of a diverse bird assemblage in the lowland rainforest of Sumatra to test whether the functional traits of body mass (representing body size) and foraging height (representing space use) can predict species’ attraction to heterospecific mobbing calls. At four forest sites along a gradient of forest degradation, we characterized the resident bird communities using point count and mist‐netting surveys, and determined the species groups attracted to standardized playbacks of mobbing calls produced by five resident bird species of roughly similar body size and foraging height. We found that (1) a large, diverse subcommunity of bird species was attracted to the mobbing calls and (2) responding species (especially the most vigorous respondents) tended to be (a) small (b) mid‐storey foragers (c) with similar trait values as the species producing the mobbing calls. Our findings from the relatively lesser known bird assemblages of tropical Asia add to the growing evidence for the ubiquity of heterospecific information networks in animal communities, and provide empirical support for the long‐standing hypothesis that predation risk reduction is a major benefit of mobbing information networks.     

Discovering Functional Modules across Diverse Maize Transcriptomes Using COB,the Co-Expression Browser

Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB), which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks.     

The dimensionality of ecological networks

How many dimensions (trait‐axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high‐quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large‐scale community structure.     

Environments that induce synthetic microbial ecosystems

Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications.     

Metabolic Proximity in the Order of Colonization of a Microbial Community

Microbial biofilms are often composed of multiple bacterial species that accumulate by adhering to a surface and to each other. Biofilms can be resistant to antibiotics and physical stresses, posing unresolved challenges in the fight against infectious diseases. It has been suggested that early colonizers of certain biofilms could cause local environmental changes, favoring the aggregation of subsequent organisms. Here we ask whether the enzyme content of different microbes in a well-characterized dental biofilm can be used to predict their order of colonization. We define a metabolic distance between different species, based on the overlap in their enzyme content. We next use this metric to quantify the average metabolic distance between neighboring organisms in the biofilm. We find that this distance is significantly smaller than the one observed for a random choice of prokaryotes, probably reflecting the environmental constraints on metabolic function of the community. More surprisingly, this metabolic metric is able to discriminate between observed and randomized orders of colonization of the biofilm, with the observed orders displaying smaller metabolic distance than randomized ones. By complementing these results with the analysis of individual vs. joint metabolic networks, we find that the tendency towards minimal metabolic distance may be counter-balanced by a propensity to pair organisms with maximal joint potential for synergistic interactions. The trade-off between these two tendencies may create a “sweet spot” of optimal inter-organism distance, with possible broad implications for our understanding of microbial community organization.     

COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions

Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.