Ryan S. Senger

The transcriptional program underlying the physiology of clostridial sporulation

BackgroundClostridia are ancient soil organisms of major importance to human and animal health and physiology, cellulose degradation, and the production of biofuels from renewable resources. Elucidation of their sporulation program is critical for understanding important clostridial programs pertaining to their physiology and their industrial or environmental applications.ResultsUsing a sensitive DNA-microarray platform and 25 sampling timepoints, we reveal the genome-scale transcriptional basis of the Clostridium acetobutylicum sporulation program carried deep into stationary phase. A significant fraction of the genes displayed temporal expression in six distinct clusters of expression, which were analyzed with assistance from ontological classifications in order to illuminate all known physiological observations and differentiation stages of this industrial organism. The dynamic orchestration of all known sporulation sigma factors was investigated, whereby in addition to their transcriptional profiles, both in terms of intensity and differential expression, their activity was assessed by the average transcriptional patterns of putative canonical genes of their regulon. All sigma factors of unknown function were investigated by combining transcriptional data with predicted promoter binding motifs and antisense-RNA downregulation to provide a preliminary assessment of their roles in sporulation. Downregulation of two of these sigma factors, CAC1766 and CAP0167, affected the developmental process of sporulation and are apparently novel sporulation-related sigma factors.ConclusionThis is the first detailed roadmap of clostridial sporulation, the most detailed transcriptional study ever reported for a strict anaerobe and endospore former, and the first reported holistic effort to illuminate cellular physiology and differentiation of a lesser known organism.

Genome-Scale Model for Clostridium acetobutylicum: Part I. Metabolic Network Resolution and Analysis

A genome‐scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi‐automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L‐glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2‐oxoglutarate precursor. The semi‐automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network‐building algorithms. The proposed semi‐automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome‐scale model that correctly characterized the butyrate kinase knock‐out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock‐out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico. Biotechnol. Bioeng.

High-yield hydrogen production from biomass by in vitro metabolic engineering: Mixed sugars coutilization and kinetic modeling

Significance Hydrogen (H2) has great potential to be used to power passenger vehicles. One solution to these problems is to distribute and store renewable carbohydrate instead, converting it to hydrogen as required. In this work more than 10 purified enzymes were combined into artificial enzymatic pathways and a high yield from both glucose and xylose to hydrogen was achieved. Also, gaseous hydrogen can be separated from aqueous substrates easily, greatly decreasing product separation costs, and avoid reconcentrating sugar solutions. This study describes high-yield enzymatic hydrogen production from biomass sugars and an engineered reaction rate increase achieved through the use of kinetic modeling. Distributed hydrogen production based on evenly distributed less-costly biomass could accelerate the implementation of the hydrogen economy. The use of hydrogen (H2) as a fuel offers enhanced energy conversion efficiency and tremendous potential to decrease greenhouse gas emissions, but producing it in a distributed, carbon-neutral, low-cost manner requires new technologies. Herein we demonstrate the complete conversion of glucose and xylose from plant biomass to H2 and CO2 based on an in vitro synthetic enzymatic pathway. Glucose and xylose were simultaneously converted to H2 with a yield of two H2 per carbon, the maximum possible yield. Parameters of a nonlinear kinetic model were fitted with experimental data using a genetic algorithm, and a global sensitivity analysis was used to identify the enzymes that have the greatest impact on reaction rate and yield. After optimizing enzyme loadings using this model, volumetric H2 productivity was increased 3-fold to 32 mmol H2⋅L−1⋅h−1. The productivity was further enhanced to 54 mmol H2⋅L−1⋅h−1 by increasing reaction temperature, substrate, and enzyme concentrations—an increase of 67-fold compared with the initial studies using this method. The production of hydrogen from locally produced biomass is a promising means to achieve global green energy production.

Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

BackgroundSolventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications.ResultsWe present the first genome-scale metabolic model (i CM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate i CM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fishers exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed.ConclusionsMicrobial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. i CM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.

Effect of shear stress on intrinsic CHO culture state and glycosylation of recombinant tissue-type plasminogen activator protein

Shear stress in suspension culture was investigated as a possible manipulative parameter for the control of glycosylation of the recombinant tissue‐type plasminogen activator protein (r‐tPA) produced by recombinant Chinese hamster ovary (CHO) cell culture, grown in protein‐free media. Resulting fractions of partially glycosylated, Type II, and fully glycosylated, Type I, r‐tPA protein were monitored as a direct function of the shear characteristics of the culture environment. The shear‐induced response of CHO culture to levels of low shear stress, where exponential growth was not obtained, and to higher levels of shear stress, which resulted in extensive cell death, were examined through manipulation of the bioreactor stirring velocity. Both apparent and intrinsic cell growth, metabolite consumption, byproduct and r‐tPA production, and r‐tPA glycosylation, from a variable site‐occupancy standpoint, were monitored throughout. Kinetic analyses revealed a shear‐stress‐induced alteration of cellular homeostasis resulting in a nonlinear dependency of metabolic yield coefficients and an intrinsic cell lysis kinetic constant on shear stress. Damaging levels of shear stress were used to investigate the shear dependence of cell death and lysis, as well as the effects on the intrinsic growth rate of the culture. Kinetic models were also developed on the basis of the intrinsic state of the culture and compared to traditional models. Total r‐tPA production was maximized under moderate shear conditions, as was the viable CHO cell density of the culture. However, Type II r‐tPA production and the fraction of Type II glycoform production ratio was maximized under damaging levels of shear stress. Analyses of biomass production yield coefficients coupled with a plug‐flow reactor model of glycan addition in the endoplasmic reticulum (ER) were used to propose an overall mechanism of decreased r‐tPA protein site‐occupancy glycosylation with increasing shear stress. Decreased residence time of r‐tPA in the ER as a result of increased protein synthesis related to shear protection mechanisms is proposed to limit contact of site Asn184 with the membrane‐bound oligosaccharyltransferase enzyme in the ER.

Genome-Scale Model for Clostridium acetobutylicum: Part II. Development of Specific Proton Flux States and Numerically Determined Sub-Systems

A regulated genome‐scale model for Clostridium acetobutylicum ATCC 824 was developed based on its metabolic network reconstruction. To aid model convergence and limit the number of flux‐vector possible solutions (the size of the phenotypic solution space), modeling strategies were developed to impose a new type of constraint at the endo–exo‐metabolome interface. This constraint is termed the specific proton flux state, and its use enabled accurate prediction of the extracellular medium pH during vegetative growth of batch cultures. The specific proton flux refers to the influx or efflux of free protons (per unit biomass) across the cell membrane. A specific proton flux state encompasses a defined range of specific proton fluxes and includes all metabolic flux distributions resulting in a specific proton flux within this range. Effective simulation of time‐course batch fermentation required the use of independent flux balance solutions from an optimum set of specific proton flux states. Using a real‐coded genetic algorithm to optimize temporal bounds of specific proton flux states, we show that six separate specific proton flux states are required to model vegetative‐growth metabolism and accurately predict the extracellular medium pH. Further, we define the apparent proton flux stoichiometry per weak acids efflux and show that this value decreases from ∼3.5 mol of protons secreted per mole of weak acids at the start of the culture to ∼0 at the end of vegetative growth. Calculations revealed that when specific weak acids production is maximized in vegetative growth, the net proton exchange between the cell and environment occurs primarily through weak acids efflux (apparent proton flux stoichiometry is 1). However, proton efflux through cation channels during the early stages of acidogenesis was found to be significant. We have also developed the concept of numerically determined sub‐systems of genome‐scale metabolic networks here as a sub‐network with a one‐dimensional null space basis set. A numerically determined sub‐system was constructed in the genome‐scale metabolic network to study the flux magnitudes and directions of acetylornithine transaminase, alanine racemase, and D‐alanine transaminase. These results were then used to establish additional constraints for the genome‐scale model. Biotechnol. Bioeng.

Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

BackgroundGenome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico.ResultsA new method called “f lux b alance a nalysis with flux ratio s (FBrAtio)” was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (i CAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production.ConclusionsFBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

Biofuel production improvement with genome‐scale models: The role of cell composition

Genome‐scale models have developed into a vital tool for rational metabolic engineering. These models balance cofactors and energetic requirements and determine biosynthetic precursor availability in response to environmental and genetic perturbations. In particular, allocation of additional reducing power is an important strategy for engineering potential biofuel production from microbes. Many potential biofuel solvents induce biomolecular changes on the host organism that are not yet captured by genome‐scale models. Here, methods of construction for several biomass constituting equations are reviewed along with potential changes to cellular composition with potential biofuels exposure. The biomass constituting equations of potential host organisms with existing genome‐scale models are compared side‐by‐side to explore their evolution over the years and to explore differences that arise when these equations are compiled by different research groups. Genome‐scale model simulation results attempt to address and provide guidance for further research into: (i) whether inconsistencies in the biomass constituting equations are relevant to predictions of solvent production, (ii) what level of detail is necessary to accurately describe cellular composition, and (iii) future developments that may enable more accurate characterizations of biomolecular composition.

Phenotypic Profiling of Antibiotic Response Signatures in Escherichia coli Using Raman Spectroscopy

ABSTRACT Identifying the mechanism of action of new potential antibiotics is a necessary but time-consuming and costly process. Phenotypic profiling has been utilized effectively to facilitate the discovery of the mechanism of action and molecular targets of uncharacterized drugs. In this research, Raman spectroscopy was used to profile the phenotypic response of Escherichia coli to applied antibiotics. The use of Raman spectroscopy is advantageous because it is noninvasive, label free, and prone to automation, and its results can be obtained in real time. In this research, E. coli cultures were subjected to three times the MICs of 15 different antibiotics (representing five functional antibiotic classes) with known mechanisms of action for 30 min before being analyzed by Raman spectroscopy (using a 532-nm excitation wavelength). The resulting Raman spectra contained sufficient biochemical information to distinguish between profiles induced by individual antibiotics belonging to the same class. The collected spectral data were used to build a discriminant analysis model that identified the effects of unknown antibiotic compounds on the phenotype of E. coli cultures. Chemometric analysis showed the ability of Raman spectroscopy to predict the functional class of an unknown antibiotic and to identify individual antibiotics that elicit similar phenotypic responses. Results of this research demonstrate the power of Raman spectroscopy as a cellular phenotypic profiling methodology and its potential impact on antibiotic drug development research.

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.