Metabolic shifts: a fitness perspective for microbial cell factories

Single-cell analysis reveals an active and heterotrophic microbiome in the Guaymas Basin deep subsurface with significant inorganic carbon fixation by heterotrophs

The marine subsurface is a long-term sink of atmospheric carbon dioxide with significant implications for climate on geologic timescales. Subsurface microbial cells can either enhance or reduce carbon sequestration in the subsurface, depending on their metabolic lifestyle. However, the activity of subsurface microbes is rarely measured. Here, we used nanoscale secondary ion mass spectrometry (nanoSIMS) to quantify anabolic activity in 3,203 individual cells from the thermally altered deep subsurface in the Guaymas Basin, Mexico (3-75 m below the seafloor, 0-14°C). We observed that a large majority of cells were active (83%-100%), although the rates of biomass generation were low, suggesting cellular maintenance rather than doubling. Mean single-cell activity decreased with increasing sediment depth and temperature and was most strongly correlated with porewater sulfate concentrations. Intracommunity heterogeneity in microbial activity decreased with increasing sediment depth and age. Using a dual-isotope labeling approach, we determined that all active cells analyzed were heterotrophic, deriving the majority of their cellular carbon from organic sources. However, we also detected inorganic carbon assimilation in these heterotrophic cells, likely via processes such as anaplerosis, and determined that inorganic carbon contributes at least 5% of the total biomass carbon in heterotrophs in this community. Our results demonstrate that the deep marine biosphere at Guaymas Basin is largely active and contributes to subsurface carbon cycling primarily by not only assimilating organic carbon but also fixing inorganic carbon. Heterotrophic assimilation of inorganic carbon may be a small yet significant and widespread underappreciated source of labile carbon in the global subsurface.

IMPORTANCE

The global subsurface is the largest reservoir of microbial life on the planet yet remains poorly characterized. The activity of life in this realm has implications for long-term elemental cycling, particularly of carbon, as well as how life survives in extreme environments. Here, we recovered cells from the deep subsurface of the Guaymas Basin and investigated the level and distribution of microbial activity, the physicochemical drivers of activity, and the relative significance of organic versus inorganic carbon to subsurface biomass. Using a sensitive single-cell assay, we found that the majority of cells are active, that activity is likely driven by the availability of energy, and that although heterotrophy is the dominant metabolism, both organic and inorganic carbon are used to generate biomass. Using a new approach, we quantified inorganic carbon assimilation by heterotrophs and highlighted the importance of this often-overlooked mode of carbon assimilation in the subsurface and beyond.

Polyhydroxyalkanoate production from rice straw hydrolysate: Insights into feast-famine dynamics and microbial community shifts

Activated sludge contains a versatile microbiome capable of converting wastes into valuable chemicals like polyhydroxyalkanoates (PHA). This study investigated the influence of repeated feast and famine phases on PHA production as well as the corresponding microbial population dynamics using waste activated sludge (WAS) as inoculum. Hydrolysate derived from rice straw was employed as a substrate for PHA production. The 16sRNA analysis results revealed that Corynebacteriaceae (40%), Bacillaceae (23%), and Pseudomonas (5%) were the primary contributors to PHA synthesis. Notably, Bacillaceae and Pseudomonas thrived in all the feast and famine phases. The achieved PHA concentration was 3.5 ± 0.2 g/L, and its structure and composition were assessed using Fourier Transform Infrared Spectroscopy (FTIR) and Nuclear Magnetic Resonance (NMR). The analysis revealed that the PHA consists of a copolymer of hydroxybutyrate (HB) and hydroxyvalerate (HV), specifically identified as Poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV).

The Evolution of Microbial Facilitation: Sociogenesis, Symbiogenesis, and Transition in Individuality

Metabolic cooperation is widespread, and it seems to be a ubiquitous and easily evolvable interaction in the microbial domain. Mutual metabolic cooperation, like syntrophy, is thought to have a crucial role in stabilizing interactions and communities, for example biofilms. Furthermore, cooperation is expected to feed back positively to the community under higher-level selection. In certain cases, cooperation can lead to a transition in individuality, when freely reproducing, unrelated entities (genes, microbes, etc.) irreversibly integrate to form a new evolutionary unit. The textbook example is endosymbiosis, prevalent among eukaryotes but virtually lacking among prokaryotes. Concerning the ubiquity of syntrophic microbial communities, it is intriguing why evolution has not lead to more transitions in individuality in the microbial domain. We set out to distinguish syntrophy-specific aspects of major transitions, to investigate why a transition in individuality within a syntrophic pair or community is so rare. We review the field of metabolic communities to identify potential evolutionary trajectories that may lead to a transition. Community properties, like joint metabolic capacity, functional profile, guild composition, assembly and interaction patterns are important concepts that may not only persist stably but according to thought-provoking theories, may provide the heritable information at a higher level of selection. We explore these ideas, relating to concepts of multilevel selection and of informational replication, to assess their relevance in the debate whether microbial communities may inherit community-level information or not.

Transcriptome Analysis Reveals Catabolite Control Protein A Regulatory Mechanisms Underlying Glucose-Excess or -Limited Conditions in a Ruminal Bacterium, Streptococcus bovis

Ruminants may suffer from rumen acidosis when fed with high-concentrate diets due to the higher proliferation and overproduction of lactate by Streptococcus bovis. The catabolite control protein A (CcpA) regulates the transcription of lactate dehydrogenase (ldh) and pyruvate formate-lyase (pfl) in S. bovis, but its role in response to different carbon concentrations remains unclear. To characterize the regulatory mechanisms of CcpA in S. bovis S1 at different levels of carbon, herein, we analyzed the transcriptomic and physiological characteristics of S. bovis S1 and its ccpA mutant strain grown in glucose-excess and glucose-limited conditions. A reduced growth rate and a shift in fermentation pattern from homofermentation to heterofermentation were observed under glucose-limited condition as compared to glucose-excess condition, in S. bovis S1. Additionally, the inactivation of ccpA significantly affected the growth and end metabolites in both conditions. For the glycolytic intermediate, fructose 1,6-bisphosphate (FBP), the concentration significantly reduced at lower glucose conditions; its concentration decreased significantly in the ccpA mutant strain. Transcriptomic results showed that about 46% of the total genes were differentially transcribed between the wild-type strain and ccpA mutant strain grown in glucose-excess conditions; while only 12% genes were differentially transcribed in glucose-limited conditions. Different glucose concentrations led to the differential expression of 38% genes in the wild-type strain, while only half of these were differentially expressed in the ccpA-knockout strain. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the substrate glucose concentration significantly affected the gene expression in histidine metabolism, nitrogen metabolism, and some carbohydrate metabolism pathways. The deletion of ccpA affected several genes involved in carbohydrate metabolism, such as glycolysis, pyruvate metabolism, fructose and mannose metabolism, as well as in fatty acid biosynthesis pathways in bacteria grown in glucose-excess conditions; this effect was attenuated under glucose-limited conditions. Overall, these findings provide new information on gene transcription and metabolic mechanisms associated with substrate glucose concentration and validate the important role of CcpA in the regulation of carbon metabolism in S. bovis S1 at differential glucose availability.

Metabolic history and metabolic fitness as drivers of anabolic heterogeneity in isogenic microbial populations

Microbial populations often display different degrees of heterogeneity in their substrate assimilation, that is, anabolic heterogeneity. It has been shown that nutrient limitations are a relevant trigger for this behaviour. Here we explore the dynamics of anabolic heterogeneity under nutrient replete conditions. We applied time-resolved stable isotope probing and nanoscale secondary ion mass spectrometry to quantify substrate assimilation by individual cells of Pseudomonas putida, P. stutzeri and Thauera aromatica. Acetate and benzoate at different concentrations were used as substrates. Anabolic heterogeneity was quantified by the cumulative differentiation tendency index. We observed two major, opposing trends of anabolic heterogeneity over time. Most often, microbial populations started as highly heterogeneous, with heterogeneity decreasing by various degrees over time. The second, less frequently observed trend, saw microbial populations starting at low or very low heterogeneity, and remaining largely stable over time. We explain these trends as an interplay of metabolic history (e.g. former growth substrate or other nutrient limitations) and metabolic fitness (i.e. the fine-tuning of metabolic pathways to process a defined growth substrate). Our results offer a new viewpoint on the intra-population functional diversification often encountered in the environment, and suggests that some microbial populations may be intrinsically heterogeneous.

Enhancing in silico strain design predictions through next generation metabolic modeling approaches

The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.

Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments

Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient-rich conditions, i.e., multiple available nitrogen sources, such as Lactococcus lactis. Here, we present a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose-limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model-based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient-rich environments and thus form targets of fitness improvement.

Seeding the idea of encapsulating a representative synthetic metagenome in a single yeast cell

Synthetic metagenomics could potentially unravel the complexities of microbial ecosystems by revealing the simplicity of microbial communities captured in a single cell. Conceptionally, a yeast cell carrying a representative synthetic metagenome could uncover the complexity of multi-species interactions, illustrated here with wine ferments.

Serial propagation in water-in-oil emulsions selects for Saccharomyces cerevisiae strains with a reduced cell size or an increased biomass yield on glucose

In S. cerevisiae and many other micro-organisms an increase in metabolic efficiency (i.e. ATP yield on carbon) is accompanied by a decrease in growth rate. From a fundamental point of view, studying these yield-rate trade-offs provides insight in for example microbial evolution and cellular regulation. From a biotechnological point of view, increasing the ATP yield on carbon might increase the yield of anabolic products. We here aimed to select S. cerevisiae mutants with an increased biomass yield. Serial propagation of individual cells in water-in-oil emulsions previously enabled the selection of lactococci with increased biomass yields, and adapting this protocol for yeast allowed us to enrich an engineered Crabtree-negative S. cerevisiae strain with a high biomass yield on glucose. When we started the selection with an S. cerevisiae deletion collection, serial propagation in emulsion enriched hxk2Δ and reg1Δ strains with an increased biomass yield on glucose. Surprisingly, a tps1Δ strain was highly abundant in both emulsion- and suspension-propagated populations. In a separate experiment we propagated a chemically mutagenized S. cerevisiae population in emulsion, which resulted in mutants with a higher cell number yield on glucose, but no significantly changed biomass yield. Genome analyses indicate that genes involved in glucose repression and cell cycle processes play a role in the selected phenotypes. The repeated identification of mutations in genes involved in glucose-repression indicates that serial propagation in emulsion is a valuable tool to study metabolic efficiency in S. cerevisiae.

Understanding gradients in industrial bioreactors

Gradients in industrial bioreactors have attracted substantial research attention since exposure to fluctuating environmental conditions has been shown to lead to changes in the metabolome, transcriptome as well as population heterogeneity in industrially relevant microorganisms. Such changes have also been found to impact key process parameters like the yield on substrate and the productivity. Hence, understanding gradients is important from both the academic and industrial perspectives. In this review the causes of gradients are outlined, along with their impact on microbial physiology. Quantifying the impact of gradients requires a detailed understanding of both fluid flow inside industrial equipment and microbial physiology. This review critically examines approaches used to investigate gradients including large-scale experimental work, computational methods and scale-down approaches. Avenues for future work have been highlighted, particularly the need for further coordinated development of both in silico and experimental tools which can be used to further the current understanding of gradients in industrial equipment.

A General Process-Based Model for Describing the Metabolic Shift in Microbial Cell Cultures

The metabolic shift between respiration and fermentation at high glucose concentration is a widespread phenomenon in microbial world, and it is relevant for the biotechnological exploitation of microbial cell factories, affecting the achievement of high-cell-densities in bioreactors. Starting from a model already developed for the yeast Saccharomyces cerevisiae, based on the System Dynamics approach, a general process-based model for two prokaryotic species of biotechnological interest, such as Escherichia coli and Bacillus subtilis, is proposed. The model is based on the main assumption that glycolytic intermediates act as central catabolic hub regulating the shift between respiratory and fermentative pathways. Furthermore, the description of a mixed fermentation with secondary by-products, characteristic of bacterial metabolism, is explicitly considered. The model also represents the inhibitory effect on growth and metabolism of self-produced toxic compounds relevant in assessing the late phases of high-cell density culture. Model simulations reproduced data from experiments reported in the literature with different strains of non-recombinant and recombinant E. coli and B. subtilis cultured in both batch and fed-batch reactors. The proposed model, based on simple biological assumptions, is able to describe the main dynamics of two microbial species of relevant biotechnological interest. It demonstrates that a reductionist System Dynamics approach to formulate simplified macro-kinetic models can provide a robust representation of cell growth and accumulation in the medium of fermentation by-products.

Escherichia coli metabolism under short-term repetitive substrate dynamics: adaptation and trade-offs

Background

Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions.

Results

In this study we applied a repetitive feast–famine regime in an aerobic Escherichia coli culture in a time-scale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast–famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast–famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/g_CDW/s) than reported during batch growth (3.3 μmol/g_CDW/s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions.

Conclusions

The adaptation of the physiology and metabolism of E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.

Evolution of mutualistic behaviour between Chlorella sorokiniana and Saccharomyces cerevisiae within a synthetic environment

Yeast and microalgae are microorganisms with widely diverging physiological and biotechnological properties. Accordingly, their fields of applications diverge: yeasts are primarily applied in processes related to fermentation, while microalgae are used for the production of high-value metabolites and green technologies such as carbon capture. Heterotrophic-autotrophic systems and synthetic ecology approaches have been proposed as tools to achieve stable combinations of such evolutionarily unrelated species. We describe an entirely novel synthetic ecology-based approach to evolve co-operative behaviour between winery wastewater isolates of the yeast Saccharomyces cerevisiae and microalga Chlorella sorokiniana. The data show that biomass production and mutualistic growth improved when co-evolved yeast and microalgae strains were paired together. Combinations of co-evolved strains displayed a range of phenotypes, including differences in amino acid profiles. Taken together, the results demonstrate that biotic selection pressures can lead to improved mutualistic growth phenotypes over relatively short time periods.

The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Living cells can express different metabolic pathways that support growth. The criteria that determine which pathways are selected in which environment remain unclear. One recurrent selection is overflow metabolism: the simultaneous usage of an ATP-efficient and -inefficient pathway, shown for example in Escherichia coli, Saccharomyces cerevisiae and cancer cells. Many models, based on different assumptions, can reproduce this observation. Therefore, they provide no conclusive evidence which mechanism is causing overflow metabolism. We compare the mathematical structure of these models. Although ranging from flux balance analyses to self-fabricating metabolism and expression models, we can rewrite all models into one standard form. We conclude that all models predict overflow metabolism when two, model-specific, growth-limiting constraints are hit. This is consistent with recent theory. Thus, identifying these two constraints is essential for understanding overflow metabolism. We list all imposed constraints by these models, so that they can hopefully be tested in future experiments.

Soluble electron acceptors affect bioluminescence from Shewanella woodyi

Shewanella woodyi cultures were used to correlate bioluminescence intensity with changes in the electrochemical potential of a saltwater medium using soluble electron acceptors. A relationship between the concentration of NaNO₃ or CoCl₂ to bioluminescence intensity was confirmed using aerobic cultures of S. woodyi at 20°C with glucose as the sole carbon source. In general, increasing the concentration of nitrate or Co(II) reduced the bioluminescence per cell, with complete luminescence being repressed at ≥5 mM nitrate and ≥0.5 mM Co(II). Results from cell viability fluorescent staining concluded that increasing the concentration of Co(II) or nitrate did not affect the overall viability of the cells when compared with cultures lacking Co(II) or nitrate. These data show that potentials of <0.2 V vs Normal Hydrogen Electrode (NHE) repress the luminescence from the cells, but the exact mechanism is unclear. Our results indicated that the luminescence intensity from S. woodyi could be systematically reduced using these two soluble electron acceptors, making S. woodyi a potential model bacterium for whole-cell luminescence bioelectrochemical sensor applications.

Evolutionary causes and consequences of metabolic division of labour: why anaerobes do and aerobes don't

Metabolic division of the labour of organic matter decomposition into several steps carried out by different types of microbes is typical for many anoxic - but not oxic environments. An explanation of this well-known pattern is proposed based on the combination of three key insights: (i) well-studied anoxic environments are high flux environments: they are only anoxic because their high organic matter influx leads to oxygen depletion; (ii) shorter, incomplete catabolic pathways provide the capacity for higher flux, but this capacity is only advantageous in high flux environments; (iii) longer, complete catabolic pathways have energetic happy ends but only with high redox potential electron acceptors. Thus, aerobic environments favour longer pathways. Bioreactors, in contrast, are high flux environments and therefore favour division of catabolic labour even if aeration keeps them aerobic; therefore, host strains and feeding strategies must be carefully engineered to resist this pull.

Revisiting the Crabtree/Warburg effect in a dynamic perspective: a fitness advantage against sugar-induced cell death

The mechanisms behind the Warburg effect in mammalian cells, as well as for the similar Crabtree effect in the yeast Saccharomyces cerevisiae, are still a matter of debate: why do cells shift from the energy-efficient respiration to the energy-inefficient fermentation at high sugar concentration?

This review reports on the strong similarities of these phenomena in both cell types, discusses the current ideas, and provides a novel interpretation of their common functional mechanism in a dynamic perspective. This is achieved by analysing another phenomenon, the sugar-induced-cell-death (SICD) occurring in yeast at high sugar concentration, to highlight the link between ATP depletion and cell death.

The integration between SICD and the dynamic functioning of the glycolytic process, suggests that the Crabtree/Warburg effect may be interpreted as the avoidance of ATP depletion in those conditions where glucose uptake is higher than the downstream processing capability of the second phase of glycolysis. It follows that the down-regulation of respiration is strategic for cell survival allowing the allocation of more resources to the fermentation pathway, thus maintaining the cell energetic homeostasis.

Modelling overflow metabolism in Escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways

Background

The formation of acetate by fast-growing Escherichia coli (E. coli) is a commonly observed phenomenon, often referred to as overflow metabolism. Among various studies that have been carried over decades, a recent work (Basan, M. et al. Nature528, 99–104, 2015) suggested and validated that it is the differential proteomic efficiencies in energy biogenesis between fermentation and respiration that lead to the production of acetate at rapid growth conditions, as the consequence of optimally allocating the limited proteomic resource. In the current work, we attempt to incorporate this newly developed proteome allocation theory into flux balance analysis (FBA) to capture quantitatively the extent of overflow metabolism in different E. coli strains.

Results

A concise constraint was introduced into a FBA-based model with three proteomic cost parameters to represent constrained allocation of proteome over two energy (respiration and fermentation) pathways and biomass synthesis. Linear relationships were shown to exist between the three proteomic cost parameters. Tests with three different strains revealed that the proteomic cost of fermentation was consistently lower than that of respiration. A slow-growing strain appeared to have a higher proteomic cost for biomass synthesis than fast-growing strains. Different assumed levels of carbon flowing into pentose phosphate pathway affected the absolute value of model parameters, but had no qualitative impact on the comparative proteomic costs. For the prediction of biomass yield, significant errors that occurred for one of the tested strains (ML308) were rectified by adjusting the cellular energy demand according to literature data.

Conclusions

With the aid of a concise proteome allocation constraint, our FBA-based model is able to quantitatively predict the onset and extent of the overflow metabolism in various E. coli strains. Such prediction is enabled by three linearly-correlated (as opposed to uniquely determinable) proteomic cost parameters. The linear relationships between these parameters, when determined using data from cell culturing experiments, render biologically meaningful comparative proteomic costs between fermentation and respiration pathways and between the biomass synthesis sectors of slow- and fast-growing species. Simultaneous prediction of acetate production and biomass yield in the overflow region requires the use of reliable cellular energy demand data.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0677-4) contains supplementary material, which is available to authorized users.

Phosphate and carbohydrate facilitate the formation of filamentous Salmonella enterica during osmotic stress

Salmonella enterica is a human pathogen that can produce filamentous cells in response to environmental stress. The molecular mediators and biosynthetic pathways that contribute to the formation of filamentous cells (>10 µm in length) during osmotic stress are mostly unknown. The comparison of filamentous and non-filamentous cells in this study was aided by the use of a filtration step to separate cell types. Osmotic stress caused an efflux of phosphate from cells, and the addition of phosphate and a carbohydrate to Luria broth with 7 % NaCl (LB-7NaCl) significantly increased the proportion of filamentous cells in the population (58 %). In addition to direct measurements of intracellular and extracellular phosphate concentrations, the relative abundance of the iraP transcript that is induced by phosphate limitation was monitored. Non-filamentous cells had a greater relative abundance of iraP transcript than filamentous cells. IraP also affects the stability of RpoS, which regulates the general stress regulon, and was detected in non-filamentous cells but not filamentous cells. Markers of metabolic pathways for the production of acetyl-CoA (pflB, encoding for pyruvate formate lyase) and fatty acids (fabH) that are essential to membrane biosynthesis were found in greater abundance in filamentous cells than non-filamentous cells. There were no differences in the DNA, protein and biomass levels in filamentous and non-filamentous cells after 48 h of incubation, although the filamentous cells produced significantly (P<0.05) more acetate. This study found that phosphate and carbohydrate enhanced the formation of filamentous cells during osmotic stress, and there were differences in key regulatory elements and markers of metabolic pathways in filamentous and non-filamentous S. enterica.

Using drug-loaded pH-responsive poly(4-vinylpyridine) microspheres as a new strategy for intelligent controlling of Lactobacillus plantarum contamination in bioethanol fermentation

Bioethanol fermentation is usually contaminated by bacteria, especially lactic acid bacteria (LAB), thereby leading to decrease of bioethanol yield and serious economic losses. Nisin is safer for controlling of bacterial contamination than antibiotics that are widely applied in industry. Moreover, in LAB contaminative bioethanol fermentation system, consistently decreased pH value provides opportunity to realize pH value responsive material-based release of anti-bacteria substances for intelligent and persistent controlling of bacterial contamination. In this study, nisin was embedded into pH-sensitive poly(4-vinylpyridine) (P4VP) microspheres synthesized by suspension polymerization to realize intelligent controlling of Lactobacillus plantarum contamination in bioethanol fermentation. Chloramphenicol with the highest antimicrobial activity and excellent stability was chosen as the model drug to be embedded into P4VP microspheres to test the drug release behavior. The drug release curve of chloramphenicol-loaded P4VP microspheres showed sustained and pH-responsive release properties. The diameters of the microspheres ranged from 40 to 100 µm. The encapsulation efficiency of nisin into P4VP microspheres was 47.67% and the drug-loading capacity of nisin was 2.5%. Nisin-loaded P4VP microspheres were added into the simulated contaminative fermentation system, and successfully reversed the decline of bioethanol yield secondary to L. plantarum contamination. The results in this study indicated that L. plantarum contamination in bioethanol fermentation could be effectively controlled by nisin-loaded P4VP microspheres.

A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering

BACKGROUND

The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing.

RESULTS

We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small example and demonstrate their relevance for metabolic engineering with realistic models of E. coli.

CONCLUSIONS

We develop a comprehensive mathematical framework for yield optimization in metabolic models. Our theory is particularly useful for the study and rational modification of cell factories designed under given yield and/or rate requirements.

The Evolution of gene regulation research in Lactococcus lactis

Lactococcus lactis is a major microbe. This lactic acid bacterium (LAB) is used worldwide in the production of safe, healthy, tasteful and nutritious milk fermentation products. Its huge industrial importance has led to an explosion of research on the organism, particularly since the early 1970s. The upsurge in the research on L. lactis coincided not accidentally with the advent of recombinant DNA technology in these years. The development of methods to take out and re-introduce DNA in L. lactis, to clone genes and to mutate the chromosome in a targeted way, to control (over)expression of proteins and, ultimately, the availability of the nucleotide sequence of its genome and the use of that information in transcriptomics and proteomics research have enabled to peek deep into the functioning of the organism. Among many other things, this has provided an unprecedented view of the major gene regulatory pathways involved in nitrogen and carbon metabolism and their overlap, and has led to the blossoming of the field of L. lactis systems biology. All of these advances have made L. lactis the paradigm of the LAB. This review will deal with the exciting path along which the research on the genetics of and gene regulation in L. lactis has trodden.

Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia

Resource scarcity is a common stress in nature and has a major impact on microbial physiology. This review highlights microbial acclimations to resource scarcity, focusing on resource investment strategies for chemoheterotrophs from the molecular level to the pathway level. Competitive resource allocation strategies often lead to a phenotype known as overflow metabolism; the resulting overflow byproducts can stabilize cooperative interactions in microbial communities and can lead to cross-feeding consortia. These consortia can exhibit emergent properties such as enhanced resource usage and biomass productivity. The literature distilled here draws parallels between in silico and laboratory studies and ties them together with ecological theories to better understand microbial stress responses and mutualistic consortia functioning.

Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield

Microbes may maximize the number of daughter cells per time or per amount of nutrients consumed. These two strategies correspond, respectively, to the use of enzyme-efficient or substrate-efficient metabolic pathways. In reality, fast growth is often associated with wasteful, yield-inefficient metabolism, and a general thermodynamic trade-off between growth rate and biomass yield has been proposed to explain this. We studied growth rate/yield trade-offs by using a novel modeling framework, Enzyme-Flux Cost Minimization (EFCM) and by assuming that the growth rate depends directly on the enzyme investment per rate of biomass production. In a comprehensive mathematical model of core metabolism in E. coli, we screened all elementary flux modes leading to cell synthesis, characterized them by the growth rates and yields they provide, and studied the shape of the resulting rate/yield Pareto front. By varying the model parameters, we found that the rate/yield trade-off is not universal, but depends on metabolic kinetics and environmental conditions. A prominent trade-off emerges under oxygen-limited growth, where yield-inefficient pathways support a 2-to-3 times higher growth rate than yield-efficient pathways. EFCM can be widely used to predict optimal metabolic states and growth rates under varying nutrient levels, perturbations of enzyme parameters, and single or multiple gene knockouts.

Effects of glucose availability in Lactobacillus sakei; metabolic change and regulation of the proteome and transcriptome

Effects of glucose availability were investigated in Lactobacillus sakei strains 23K and LS25 cultivated in anaerobic, glucose-limited chemostats set at high (D = 0.357 h-1) and low (D = 0.045 h-1) dilution rates. We observed for both strains a shift from homolactic towards more mixed acid fermentation when comparing high to low growth rates. However, this change was more pronounced for LS25 than for 23K, where dominating products were lactate>formate>acetate≥ethanol at both conditions. A multivariate approach was used for analyzing proteome and transcriptome data from the bacterial cultures, where the predictive power of the omics data was used for identifying features that can explain the differences in the end-product profiles. We show that the different degree of response to the same energy restriction revealed interesting strain specific regulation. An elevated formate production level during slow growth, more for LS25 than for 23K, was clearly reflected in correlating pyruvate formate lyase expression. With stronger effect for LS25, differential expression of the Rex transcriptional regulator and NADH oxidase, a target of Rex, indicated that maintainance of the cell redox balance, in terms of the NADH/NAD+ ratio, may be a key process during the metabolic change. The results provide a better understanding of different strategies that cells may deploy in response to changes in substrate availability.

Lactobacilli and pediococci as versatile cell factories - Evaluation of strain properties and genetic tools

This review discusses opportunities and bottlenecks for cell factory development of Lactic Acid Bacteria (LAB), with an emphasis on lactobacilli and pediococci, their metabolism and genetic tools. In order to enable economically feasible bio-based production of chemicals and fuels in a biorefinery, the choice of product, substrate and production organism is important. Currently, the most frequently used production hosts include Escherichia coli and Saccharomyces cerevisiae, but promising examples are available of alternative hosts such as LAB. Particularly lactobacilli and pediococci can offer benefits such as thermotolerance, an extended substrate range and increased tolerance to stresses such as low pH or high alcohol concentrations. This review will evaluate the properties and metabolism of these organisms, and provide an overview of their current biotechnological applications and metabolic engineering. We substantiate the review by including experimental results from screening various lactobacilli and pediococci for transformability, growth temperature range and ability to grow under biotechnologically relevant stress conditions. Since availability of efficient genetic engineering tools is a crucial prerequisite for industrial strain development, genetic tool development is extensively discussed. A range of genetic tools exist for Lactococcus lactis, but for other species of LAB like lactobacilli and pediococci such tools are less well developed. Whereas lactobacilli and pediococci have a long history of use in food and beverage fermentation, their use as platform organisms for production purposes is rather new. By harnessing their properties such as thermotolerance and stress resistance, and by using emerging high-throughput genetic tools, these organisms are very promising as versatile cell factories for biorefinery applications.

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach

Oxygenic photosynthesis dominates global primary productivity ever since its evolution more than three billion years ago. While many aspects of phototrophic growth are well understood, it remains a considerable challenge to elucidate the manifold dependencies and interconnections between the diverse cellular processes that together facilitate the synthesis of new cells. Phototrophic growth involves the coordinated action of several layers of cellular functioning, ranging from the photosynthetic light reactions and the electron transport chain, to carbon-concentrating mechanisms and the assimilation of inorganic carbon. It requires the synthesis of new building blocks by cellular metabolism, protection against excessive light, as well as diurnal regulation by a circadian clock and the orchestration of gene expression and cell division. Computational modeling allows us to quantitatively describe these cellular functions and processes relevant for phototrophic growth. As yet, however, computational models are mostly confined to the inner workings of individual cellular processes, rather than describing the manifold interactions between them in the context of a living cell. Using cyanobacteria as model organisms, this contribution seeks to summarize existing computational models that are relevant to describe phototrophic growth and seeks to outline their interactions and dependencies. Our ultimate aim is to understand cellular functioning and growth as the outcome of a coordinated operation of diverse yet interconnected cellular processes.

Resource allocation in metabolic networks: kinetic optimization and approximations by FBA

Based on recent theoretical results on optimal flux distributions in kinetic metabolic networks, we explore the congruences and differences between solutions of kinetic optimization problems and results obtained by constraint-based methods. We demonstrate that, for a certain resource allocation problem, kinetic optimization and standard flux balance analysis (FBA) give rise to qualitatively different results. Furthermore, we introduce a variant of FBA, called satFBA, whose predictions are in qualitative agreement with kinetic optimization.

Constructing kinetic models of metabolism at genome-scales: A review

Constraint-based modeling of biological networks (metabolism, transcription and signal transduction), although used successfully in many applications, suffer from specific limitations such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Kinetic models conversely overcome these shortcomings and enable dynamic analysis of biological systems for enhanced in silico hypothesis generation. Nonetheless, kinetic models also have limitations for modeling at genome-scales chiefly due to: (i) model non-linearity; (ii) computational tractability; (iii) parameter identifiability; (iv) estimability; and (v) uncertainty. In order to support further development of kinetic models as viable alternatives to constraint-based models, this review presents a brief description of the existing obstacles towards building genome-scale kinetic models. Specific kinetic modeling frameworks capable of overcoming these obstacles are covered in this review. The tractability and physiological feasibility of these models are discussed with the objective of using available in vivo experimental observations to define the model parameter space. Among the different methods discussed, Monte Carlo kinetic models of metabolism stand out as potentially tractable methods to model genome scale networks while also addressing in vivo parameter uncertainty.

Evolution across the Curriculum: Microbiology

An integrated understanding of microbiology and evolutionary biology is essential for students pursuing careers in microbiology and healthcare fields. In this Perspective, we discuss the usefulness of evolutionary concepts and an overall evolutionary framework for students enrolled in microbiology courses. Further, we propose a set of learning goals for students studying microbial evolution concepts. We then describe some barriers to microbial evolution teaching and learning and encourage the continued incorporation of evidence-based teaching practices into microbiology courses at all levels. Next, we review the current status of microbial evolution assessment tools and describe some education resources available for teaching microbial evolution. Successful microbial evolution education will require that evolution be taught across the undergraduate biology curriculum, with a continued focus on applications and applied careers, while aligning with national biology education reform initiatives. Journal of Microbiology & Biology Education.

Synthetic Ecology of Microbes: Mathematical Models and Applications

As the indispensable role of natural microbial communities in many aspects of life on Earth is uncovered, the bottom-up engineering of synthetic microbial consortia with novel functions is becoming an attractive alternative to engineering single-species systems. Here, we summarize recent work on synthetic microbial communities with a particular emphasis on open challenges and opportunities in environmental sustainability and human health. We next provide a critical overview of mathematical approaches, ranging from phenomenological to mechanistic, to decipher the principles that govern the function, dynamics and evolution of microbial ecosystems. Finally, we present our outlook on key aspects of microbial ecosystems and synthetic ecology that require further developments, including the need for more efficient computational algorithms, a better integration of empirical methods and model-driven analysis, the importance of improving gene function annotation, and the value of a standardized library of well-characterized organisms to be used as building blocks of synthetic communities.

Regulation of Lactobacillus plantarum contamination on the carbohydrate and energy related metabolisms of Saccharomyces cerevisiae during bioethanol fermentation

During the industrial bioethanol fermentation, Saccharomyces cerevisiae cells are often stressed by bacterial contaminants, especially lactic acid bacteria. Generally, lactic acid bacteria contamination can inhibit S. cerevisiae cell growth through secreting lactic acid and competing with yeast cells for micronutrients and living space. However, whether are there still any other influences of lactic acid bacteria on yeast or not? In this study, Lactobacillus plantarum ATCC 8014 was co-cultivated with S. cerevisiae S288c to mimic the L. plantarum contamination in industrial bioethanol fermentation. The contaminative L. plantarum-associated expression changes of genes involved in carbohydrate and energy related metabolisms in S. cerevisiae cells were determined by quantitative real-time polymerase chain reaction to evaluate the influence of L. plantarum on carbon source utilization and energy related metabolism in yeast cells during bioethanol fermentation. Contaminative L. plantarum influenced the expression of most of genes which are responsible for encoding key enzymes involved in glucose related metabolisms in S. cerevisiae. Specific for, contaminated L. plantarum inhibited EMP pathway but promoted TCA cycle, glyoxylate cycle, HMP, glycerol synthesis pathway, and redox pathway in S. cerevisiae cells. In the presence of L. plantarum, the carbon flux in S. cerevisiae cells was redistributed from fermentation to respiratory and more reducing power was produced to deal with the excess NADH. Moreover, L. plantarum contamination might confer higher ethanol tolerance to yeast cells through promoting accumulation of glycerol. These results also highlighted our knowledge about relationship between contaminative lactic acid bacteria and S. cerevisiae during bioethanol fermentation.

Metabolic shift of Escherichia coli under salt stress in the presence of glycine betaine

An important area of food safety focuses on bacterial survival and growth in unfavorable environments. In order to understand how bacteria adapt to stresses other than nutrient limitation in batch cultures, we need to develop mechanistic models of intracellular regulation and metabolism under stress. We studied the growth of Escherichia coli in minimal medium with added salt and different osmoprotectants. To characterize the metabolic efficiency with a robust parameter, we identified the optical density (OD) values at the inflection points of measured "OD versus time" growth curves and described them as a function of glucose concentration. We found that the metabolic efficiency parameter did not necessarily follow the trend of decreasing specific growth rate as the salt concentration increased. In the absence of osmoprotectant, or in the presence of proline, the metabolic efficiency decreased with increasing NaCl concentration. However, in the presence of choline or glycine betaine, it increased between 2 and 4.5% NaCl before declining at 5% NaCl and above. Microarray analysis of the transcriptional network and proteomics analysis with glycine betaine in the medium indicated that between 4.5 and 5% NaCl, the metabolism switched from aerobic to fermentative pathways and that the response to osmotic stress is similar to that for oxidative stress. We conclude that, although the growth rate appeared to decrease smoothly with increasing NaCl, the metabolic strategy of cells changed abruptly at a threshold concentration of NaCl.

Protein costs do not explain evolution of metabolic strategies and regulation of ribosomal content: does protein investment explain an anaerobic bacterial Crabtree effect?

Protein investment costs are considered a major driver for the choice of alternative metabolic strategies. We tested this premise in Lactococcus lactis, a bacterium that exhibits a distinct, anaerobic version of the bacterial Crabtree/Warburg effect; with increasing growth rates it shifts from a high yield metabolic mode [mixed-acid fermentation; 3 adenosine triphosphate (ATP) per glucose] to a low yield metabolic mode (homolactic fermentation; 2 ATP per glucose). We studied growth rate-dependent relative transcription and protein ratios, enzyme activities, and fluxes of L. lactis in glucose-limited chemostats, providing a high-quality and comprehensive data set. A three- to fourfold higher growth rate rerouted metabolism from acetate to lactate as the main fermentation product. However, we observed hardly any changes in transcription, protein levels and enzyme activities. Even levels of ribosomal proteins, constituting a major investment in cellular machinery, changed only slightly. Thus, contrary to the original hypothesis, central metabolism in this organism appears to be hardly regulated at the level of gene expression, but rather at the metabolic level. We conclude that L. lactis is either poorly adapted to growth at low and constant glucose concentrations, or that protein costs play a less important role in fitness than hitherto assumed.

Establishing Chlamydomonas reinhardtii as an industrial biotechnology host

Microalgae constitute a diverse group of eukaryotic unicellular organisms that are of interest for pure and applied research. Owing to their natural synthesis of value-added natural products microalgae are emerging as a source of sustainable chemical compounds, proteins and metabolites, including but not limited to those that could replace compounds currently made from fossil fuels. For the model microalga, Chlamydomonas reinhardtii, this has prompted a period of rapid development so that this organism is poised for exploitation as an industrial biotechnology platform. The question now is how best to achieve this? Highly advanced industrial biotechnology systems using bacteria and yeasts were established in a classical metabolic engineering manner over several decades. However, the advent of advanced molecular tools and the rise of synthetic biology provide an opportunity to expedite the development of C. reinhardtii as an industrial biotechnology platform, avoiding the process of incremental improvement. In this review we describe the current status of genetic manipulation of C. reinhardtii for metabolic engineering. We then introduce several concepts that underpin synthetic biology, and show how generic parts are identified and used in a standard manner to achieve predictable outputs. Based on this we suggest that the development of C. reinhardtii as an industrial biotechnology platform can be achieved more efficiently through adoption of a synthetic biology approach.

Bacterial economics: adaptation to stress conditions via stage-wise changes in the response mechanism

Common features of microbial adaptation are analysed with mathematical models and extended to stress conditions when the bacterial population declines before growing again. A parallel is drawn between bacterial and human communities in terms of non-mutation-based adaptation (acclimation) to stress. For a case study, the behaviour of Escherichia coli under osmotic stress, is detailed. It is suggested that stress modelling adaptation should be the focus of further developments in predictive food microbiology.

An evolutionary perspective on the Crabtree effect

The capability to ferment sugars into ethanol is a key metabolic trait of yeasts. Crabtree-positive yeasts use fermentation even in the presence of oxygen, where they could, in principle, rely on the respiration pathway. This is surprising because fermentation has a much lower ATP yield than respiration (2 ATP vs. approximately 18 ATP per glucose). While genetic events in the evolution of the Crabtree effect have been identified, the selective advantages provided by this trait remain controversial. In this review we analyse explanations for the emergence of the Crabtree effect from an evolutionary and game-theoretical perspective. We argue that an increased rate of ATP production is likely the most important factor behind the emergence of the Crabtree effect.

A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories

Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.

Metabolic states with maximal specific rate carry flux through an elementary flux mode

Specific product formation rates and cellular growth rates are important maximization targets in biotechnology and microbial evolution. Maximization of a specific rate (i.e. a rate expressed per unit biomass amount) requires the expression of particular metabolic pathways at optimal enzyme concentrations. In contrast to the prediction of maximal product yields, any prediction of optimal specific rates at the genome scale is currently computationally intractable, even if the kinetic properties of all enzymes are available. In the present study, we characterize maximal-specific-rate states of metabolic networks of arbitrary size and complexity, including genome-scale kinetic models. We report that optimal states are elementary flux modes, which are minimal metabolic networks operating at a thermodynamically-feasible steady state with one independent flux. Remarkably, elementary flux modes rely only on reaction stoichiometry, yet they function as the optimal states of mathematical models incorporating enzyme kinetics. Our results pave the way for the optimization of genome-scale kinetic models because they offer huge simplifications to overcome the concomitant computational problems.

Elucidating the adaptation and temporal coordination of metabolic pathways using in-silico evolution

Cellular metabolism, the interconversion of small molecules by chemical reactions, is a tightly coordinated process that requires integration of diverse environmental and intracellular cues. While for many organisms the topology of the network of metabolic reactions is increasingly known, the regulatory principles that shape the network's adaptation to diverse and changing environments remain largely elusive. To investigate the principles of metabolic adaptation and regulation in metabolic pathways, we propose a computational approach based on in-silico evolution. Rather than analyzing existing regulatory schemes, we let a population of minimal, prototypical metabolic cells evolve rate constants and appropriate regulatory schemes that allow for optimal growth in static and fluctuating environments. Applying our approach to a small, but already sufficiently complex, minimal system reveals intricate transitions between metabolic modes. These results have implications for trade-offs in resource allocation. Going from static to varying environments, we show that for fluctuating nutrient availability, active metabolic regulation results in a significantly increased overall rate of metabolism.

Enzyme allocation problems in kinetic metabolic networks: optimal solutions are elementary flux modes

The survival and proliferation of cells and organisms require a highly coordinated allocation of cellular resources to ensure the efficient synthesis of cellular components. In particular, the total enzymatic capacity for cellular metabolism is limited by finite resources that are shared between all enzymes, such as cytosolic space, energy expenditure for amino-acid synthesis, or micro-nutrients. While extensive work has been done to study constrained optimization problems based only on stoichiometric information, mathematical results that characterize the optimal flux in kinetic metabolic networks are still scarce. Here, we study constrained enzyme allocation problems with general kinetics, using the theory of oriented matroids. We give a rigorous proof for the fact that optimal solutions of the non-linear optimization problem are elementary flux modes. This finding has significant consequences for our understanding of optimality in metabolic networks as well as for the identification of metabolic switches and the computation of optimal flux distributions in kinetic metabolic networks.

Bacterial competition reveals differential regulation of the pks genes by Bacillus subtilis

Bacillus subtilis is adaptable to many environments in part due to its ability to produce a broad range of bioactive compounds. One such compound, bacillaene, is a linear polyketide/nonribosomal peptide. The pks genes encode the enzymatic megacomplex that synthesizes bacillaene. The majority of pks genes appear to be organized as a giant operon (>74 kb from pksC-pksR). In previous work (P. D. Straight, M. A. Fischbach, C. T. Walsh, D. Z. Rudner, and R. Kolter, Proc. Natl. Acad. Sci. U. S. A. 104:305-310, 2007, doi:10.1073/pnas.0609073103), a deletion of the pks operon in B. subtilis was found to induce prodiginine production by Streptomyces coelicolor. Here, colonies of wild-type B. subtilis formed a spreading population that induced prodiginine production from Streptomyces lividans, suggesting differential regulation of pks genes and, as a result, bacillaene. While the parent colony showed widespread induction of pks expression among cells in the population, we found the spreading cells uniformly and transiently repressed the expression of the pks genes. To identify regulators that control pks genes, we first determined the pattern of pks gene expression in liquid culture. We next identified mutations in regulatory genes that disrupted the wild-type pattern of pks gene expression. We found that expression of the pks genes requires the master regulator of development, Spo0A, through its repression of AbrB and the stationary-phase regulator, CodY. Deletions of degU, comA, and scoC had moderate effects, disrupting the timing and level of pks gene expression. The observed patterns of expression suggest that complex regulation of bacillaene and other antibiotics optimizes competitive fitness for B. subtilis.

Modeling challenges in the synthetic biology of secondary metabolism

The successful engineering of secondary metabolite production relies on the availability of detailed computational models of metabolism. In this brief review we discuss the types of models used for synthetic biology and their application for the engineering of metabolism. We then highlight some of the major modeling challenges, in particular the need to make informative model predictions based on incomplete and uncertain information. This issue is particularly pressing in the synthetic biology of secondary metabolism, due to the genetic diversity of microbial secondary metabolite producers, the difficulty of enzyme-kinetic characterization of the complex biosynthetic machinery, and the need for engineered pathways to function efficiently in heterologous hosts. We argue that an explicit quantitative consideration of the resulting uncertainty of metabolic models can lead to more informative predictions to guide the design of improved production hosts for bioactive secondary metabolites.