Atrial natriuretic peptide signaling co-regulates lipid metabolism and ventricular conduction system gene expression in the embryonic heart.

It has been shown that atrial natriuretic peptide (ANP) and its high affinity receptor (NPRA) are involved in the formation of ventricular conduction system (VCS). Inherited genetic variants in fatty acid oxidation (FAO) genes are known to cause conduction abnormalities in newborn children. Although the effect of ANP on energy metabolism in noncardiac cell types is well documented, the role of lipid metabolism in VCS cell differentiation via ANP/NPRA signaling is not known. In this study, histological sections and primary cultures obtained from E11.5 mouse ventricles were analyzed to determine the role of metabolic adaptations in VCS cell fate determination and maturation. Exogenous treatment of E11.5 ventricular cells with ANP revealed a significant increase in lipid droplet accumulation, FAO and higher expression of VCS marker Cx40. Using specific inhibitors, we further identified PPARγ and FAO as critical downstream regulators of ANP-mediated regulation of metabolism and VCS formation.

Mechanism of action and therapeutic route for a muscular dystrophy caused by a genetic defect in lipid metabolism.

CHKB encodes one of two mammalian choline kinase enzymes that catalyze the first step in the synthesis of the membrane phospholipid phosphatidylcholine. In humans and mice, inactivation of the CHKB gene (Chkb in mice) causes a recessive rostral-to-caudal muscular dystrophy. Using Chkb knockout mice, we reveal that at no stage of the disease is phosphatidylcholine level significantly altered. We observe that in affected muscle a temporal change in lipid metabolism occurs with an initial inability to utilize fatty acids for energy via mitochondrial ß-oxidation resulting in shunting of fatty acids into triacyglycerol as the disease progresses. There is a decrease in peroxisome proliferator-activated receptors and target gene expression specific to Chkb-/- affected muscle. Treatment of Chkb-/- myocytes with peroxisome proliferator-activated receptor agonists enables fatty acids to be used for ß-oxidation and prevents triacyglyerol accumulation, while simultaneously increasing expression of the compensatory choline kinase alpha (Chka) isoform, preventing muscle cell injury.

A mouse model of inherited choline kinase ß-deficiency presents with specific cardiac abnormalities and a predisposition to arrhythmia.

The CHKB gene encodes choline kinase ß, which catalyzes the first step in the biosynthetic pathway for the major phospholipid phosphatidylcholine. Homozygous loss-of-function variants in human CHKB are associated with a congenital muscular dystrophy. Dilated cardiomyopathy is present in some CHKB patients and can cause heart failure and death. Mechanisms underlying a cardiac phenotype due to decreased CHKB levels are not well characterized. We determined that there is cardiac hypertrophy in Chkb-/- mice along with a decrease in left ventricle size, internal diameter, and stroke volume compared with wildtype and Chkb+/- mice. Unlike wildtype mice, 60% of the Chkb+/- and all Chkb-/- mice tested displayed arrhythmic events when challenged with isoproterenol. Lipidomic analysis revealed that the major change in lipid level in Chkb+/- and Chkb-/- hearts was an increase in the arrhythmogenic lipid acylcarnitine. An increase in acylcarnitine level is also associated with a defect in the ability of mitochondria to use fatty acids for energy and we observed that mitochondria from Chkb-/- hearts had abnormal cristae and inefficient electron transport chain activity. Atrial natriuretic peptide (ANP) is a hormone produced by the heart that protects against the development of heart failure including ventricular conduction defects. We determined that there was a decrease in expression of ANP, its receptor NPRA, as well as ventricular conduction system markers in Chkb+/- and Chkb-/- mice.

Detection of highly overlapping peaks via adaptive apodization.

Accurate peak detection is an essential component of many NMR tasks such as peak alignment, compound identification, and global spectral deconvolution. However, current peak detection approaches are generally limited by their ability to deal with spectral overlap, which has a deleterious effect on downstream data processing. In this work, we present the use of an adaptive apodization strategy that improves the detection of highly overlapping peaks. Sensitivity enhancement is used to identify general regions of interest and resolution enhancement is used to separate overlapping peaks, with parameters for both calculated directly from the data. Further limits on peak width help to reduce false positives. The method proposed in this work has been implemented in an open-source R package called rnmrfind that is available for download on GitHub (https://github.com/ssokolen/rnmrfind). A set of default parameters have been chosen to provide effective peak detection while keeping false positives to a minimum; however, application-specific tuning is possible through the modification of minimum peak width at half height (in Hz) and noise cutoff threshold (as a factor of estimated standard deviation). Comparison to existing packages rNMR and speaq on a series of 1H NMR spectra demonstrates improved peak resolution with little to no apparent drawbacks.

Quantifying the impact of cell culture media on CHO cell growth and protein production.

In recombinant protein production, cell culture media development and optimization is typically seen as a useful strategy to increase titer and cell density, reduce by-products, as well as improve product quality (with cell density and titer often serving as the primary reported outcome of media studies). However, despite the large number of media optimization studies, there have been few attempts to comprehensively assess the overall effectiveness of media additives. The aim of this review is therefore both to document published media optimization studies over the last twenty years (in the context of Chinese hamster ovary cell recombinant production) and quantitatively estimate the impact of this media optimization on cell culture performance. In considering 78 studies, we have identified 238 unique media components that have been supplemented over the last 20 years. Among these additives, trace elements stood out as having a positive impact on cell density while nucleotides show potential for increasing titer, with commercial supplements benefiting both. However, we also identified that the impact of specific additives is far more variable than often perceived. With relatively few media studies considering multiple cell lines or multiple basal media, teasing out consistent and general trends becomes a considerable challenge. By extracting cell density and titer values from all of the reviewed studies, we were able to build a mixed-effect model capable of estimating the relative impact of additives, cell line, product type, basal medium, cultivation method (flask or reactor), and feeding strategy (batch or fed-batch). Overall, additives only accounted for 3% of the variation in cell density and 1% of the variation in titer. Similarly, the impact of basal media was also relatively modest, at 10% for cell density and 0% for titer. Cell line, product type, and feeding strategy were all found to have more impact. These results emphasize the need for media studies to consider more factors to ensure that reported observations can be generalized and further developed.

Robust 1D NMR lineshape fitting using real and imaginary data in the frequency domain.

Quantitative NMR is intrinsically dependent on precise, accurate, and robust peak area calculation. In this work, we demonstrate how the use of complex-valued peak descriptions can improve peak fitting in the frequency domain - incorporating phase and baseline correction as well as apodization while working with commonly used Fourier-transformed data. The method has been implemented in an open source R package called rnmrfit that is available for download on GitHub (https://github.com/ssokolen/rnmrfit). Application to real data suggests that this approach can also result in dramatically higher precision than can be achieved with existing software. Simulation data indicates that coefficients of variation below 0.1% can be readily achieved at signal to noise (SNR) ratios of approximately 100. The use of complex-valued data in the frequency domain is demonstrated as a relatively simple and effective means of improving peak fitting for quantitative NMR analysis.

Identifying model error in metabolic flux analysis - a generalized least squares approach.

BACKGROUND: The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit. RESULTS: In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range). CONCLUSIONS: The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.

An omics approach to rational feed: Enhancing growth in CHO cultures with NMR metabolomics and 2D-DIGE proteomics.

Expression of recombinant proteins exerts stress on cell culture systems, affecting the expression of endogenous proteins, and contributing to the depletion of nutrients and accumulation of waste metabolites. In this work, 2D-DIGE proteomics was employed to analyze differential expression of proteins following stable transfection of a Chinese Hamster Ovary (CHO) cell line to constitutively express a heavy-chain monoclonal antibody. Thirty-four proteins of significant differential expression were identified and cross-referenced with cellular functions and metabolic pathways to identify points of cell stress. Subsequently, 1D-(1)H NMR metabolomics experiments analyzed cultures to observe nutrient depletion and waste metabolite accumulations to further examine these cell stresses and pathways. From among fifty metabolites tracked in time-course, eight were observed to be completely depleted from the production media, including: glucose, glutamine, proline, serine, cystine, asparagine, choline, and hypoxanthine, while twenty-three excreted metabolites were also observed to accumulate. The differentially expressed proteins, as well as the nutrient depletion and accumulation of these metabolites corresponded with upregulated pathways and cell systems related to anaplerotic TCA-replenishment, NADH/NADPH replenishment, tetrahydrofolate cycle C1 cofactor conversions, limitations to lipid synthesis, and redox modulation. A nutrient cocktail was assembled to improve the growth medium and alleviate these cell stresses to achieve a ∼75% improvement to peak cell densities.

Disruption of the Reductive 1,3-Propanediol Pathway Triggers Production of 1,2-Propanediol for Sustained Glycerol Fermentation by Clostridium pasteurianum.

UNLABELLED: Crude glycerol, the major by-product of biodiesel production, is an attractive bioprocessing feedstock owing to its abundance, low cost, and high degree of reduction. In line with the advent of the biodiesel industry, Clostridium pasteurianum has gained prominence as a result of its unique capacity to convert waste glycerol into n-butanol, a high-energy biofuel. However, no efforts have been directed at abolishing the production of 1,3-propanediol (1,3-PDO), the chief competing product of C. pasteurianum glycerol fermentation. Here, we report rational metabolic engineering of C. pasteurianum for enhanced n-butanol production through inactivation of the gene encoding 1,3-PDO dehydrogenase (dhaT). In spite of current models of anaerobic glycerol dissimilation, culture growth and glycerol utilization were unaffected in the dhaT disruption mutant (dhaT::Ll.LtrB). Metabolite characterization of the dhaT::Ll.LtrB mutant revealed an 83% decrease in 1,3-PDO production, encompassing the lowest C. pasteurianum 1,3-PDO titer reported to date (0.58 g liter(-1)). With 1,3-PDO formation nearly abolished, glycerol was converted almost exclusively to n-butanol (8.6 g liter(-1)), yielding a high n-butanol selectivity of 0.83 g n-butanol g(-1) of solvents compared to 0.51 g n-butanol g(-1) of solvents for the wild-type strain. Unexpectedly, high-performance liquid chromatography (HPLC) analysis of dhaT::Ll.LtrB mutant culture supernatants identified a metabolite peak consistent with 1,2-propanediol (1,2-PDO), which was confirmed by nuclear magnetic resonance (NMR). Based on these findings, we propose a new model for glycerol dissimilation by C. pasteurianum, whereby the production of 1,3-PDO by the wild-type strain and low levels of both 1,3-PDO and 1,2-PDO by the engineered mutant balance the reducing equivalents generated during cell mass synthesis from glycerol. IMPORTANCE: Organisms from the genus Clostridium are perhaps the most notable native cellular factories, owing to their vast substrate utilization range and equally diverse variety of metabolites produced. The ability of C. pasteurianum to sustain redox balance and glycerol fermentation despite inactivation of the 1,3-PDO pathway is a testament to the exceptional metabolic flexibility exhibited by clostridia. Moreover, identification of a previously unknown 1,2-PDO-formation pathway, as detailed herein, provides a deeper understanding of fermentative glycerol utilization in clostridia and will inform future metabolic engineering endeavors involving C. pasteurianum To our knowledge, the C. pasteurianum dhaT disruption mutant derived in this study is the only organism that produces both 1,2- and 1,3-PDOs. Most importantly, the engineered strain provides an excellent platform for highly selective production of n-butanol from waste glycerol.

A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation.

BACKGROUND: The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. RESULTS: Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and systematic errors. To detect systematic errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify systematic errors as small as 2.5 % under a wide range of conditions. CONCLUSION: Both the simulation framework and error correction method represent examples of time-course analysis that can be applied to further developments in (1)H-NMR methodology and the more general application of quantitative metabolomics.

Metabolomic analysis of human fecal microbiota: a comparison of feces-derived communities and defined mixed communities.

The extensive impact of the human gut microbiota on its human host calls for a need to understand the types of communication that occur among the bacteria and their host. A metabolomics approach can provide a snapshot of the microbe-microbe interactions occurring as well as variations in the microbes from different hosts. In this study, metabolite profiles from an anaerobic continuous stirred-tank reactors (CSTR) system supporting the growth of several consortia of bacteria representative of the human gut were established and compared. Cell-free supernatant samples were analyzed by 1D (1)H nuclear magnetic resonance (NMR) spectroscopy, producing spectra representative of the metabolic activity of a particular community at a given time. Using targeted profiling, specific metabolites were identified and quantified on the basis of NMR analyses. Metabolite profiles discriminated each bacterial community examined, demonstrating that there are significant differences in the microbiota metabolome between each cultured community. We also found unique compounds that were identifying features of individual bacterial consortia. These findings are important because they demonstrate that metabolite profiles of gut microbial ecosystems can be constructed by targeted profiling of NMR spectra. Moreover, examination of these profiles sheds light on the type of microbes present in the gut and their metabolic interactions.

Treating cell culture media with UV irradiation against adventitious agents: minimal impact on CHO performance.

Sterility of cell culture media is an important concern in biotherapeutic processing. In large scale biotherapeutic production, a unit contamination of cell culture media can have costly effects. Ultraviolet (UV) irradiation is a sterilization method effective against bacteria and viruses while being non-thermal and non-adulterating in its mechanism of action. This makes UV irradiation attractive for use in sterilization of cell culture media. The objective of this study was to evaluate the effect of UV irradiation of cell culture media in terms of chemical composition and the ability to grow cell cultures in the treated media. The results showed that UV irradiation of commercial cell culture media at relevant disinfection doses impacted the chemical composition of the media with respect to several carboxylic acids, and to a minimal extent, amino acids. The cumulative effect of these changes, however, did not negatively influence the ability to culture Chinese Hamster Ovary cells, as evaluated by cell viability, growth rate, and protein titer measurements in simple batch growth compared with the same cells cultured in control media exposed to visible light.

Profiling convoluted single-dimension proton NMR spectra: a Plackett-Burman approach for assessing quantification error of metabolites in complex mixtures with application to cell culture.

Single-dimension hydrogen, or proton, nuclear magnetic resonance spectroscopy (1D-(1)H NMR) has become an attractive option for characterizing the full range of components in complex mixtures of small molecular weight compounds due to its relative simplicity, speed, spectral reproducibility, and noninvasive sample preparation protocols compared to alternative methods. One challenge associated with this method is the overlap of NMR resonances leading to "convoluted" spectra. While this can be mitigated through "targeted profiling", there is still the possibility of increased quantification error. This work presents the application of a Plackett-Burman experimental design for the robust estimation of precision and accuracy of 1D-(1)H NMR compound quantification in synthetic mixtures, with application to mammalian cell culture supernatant. A single, 20 sample experiment was able to provide a sufficient estimate of bias and variability at different metabolite concentrations. Two major sources of bias were identified: incorrect interpretation of singlet resonances and the quantification of resonances from protons in close proximity to labile protons. Furthermore, decreases in measurement accuracy and precision could be observed with decreasing concentration for a small fraction of the components as a result of their particular convolution patterns. Finally, the importance of a priori concentration estimates is demonstrated through the example of interpreting acetate metabolite trends from a bioreactor cultivation of Chinese hamster ovary cells expressing a recombinant antibody.

Construction and analysis of a genetically tuneable lytic phage display system.

The Bacteriophage λ capsid protein gpD has been used extensively for fusion polypeptides that can be expressed from plasmids in Escherichia coli and remain soluble. In this study, a genetically controlled dual expression system for the display of enhanced green fluorescent protein (eGFP) was developed and characterized. Wild-type D protein (gpD) expression is encoded by λ Dam15 infecting phage particles, which can only produce a functional gpD protein when translated in amber suppressor strains of E. coli in the absence of complementing gpD from a plasmid. However, the isogenic suppressors vary dramatically in their ability to restore functional packaging to λDam15, imparting the first dimension of decorative control. In combination, the D-fusion protein, gpD::eGFP, was supplied in trans from a multicopy temperature-inducible expression plasmid, influencing D::eGFP expression and hence the availability of gpD::eGFP to complement for the Dam15 mutation and decorate viable phage progeny. Despite being the worst suppressor, maximal incorporation of gpD::eGFP into the λDam15 phage capsid was imparted by the SupD strain, conferring a gpDQ68S substitution, induced for plasmid expression of pD::eGFP. Differences in size, fluorescence and absolute protein decoration between phage preparations could be achieved by varying the temperature of and the suppressor host carrying the pD::eGFP plasmid. The effective preparation with these two variables provides a simple means by which to manage fusion decoration on the surface of phage λ.

Graphical analysis of flow cytometer data for characterizing controlled fluorescent protein display on λ phage.

As native virus particles typically cannot be resolved using a flow cytometer, the general practice is to use fluorescent dyes to label the particles. In this work, an attempt was made to use a common commercial flow cytometer to characterize a phage display strategy that allows for controlled levels of protein display, in this case, eGFP. To achieve this characterization, a number of data processing steps were needed to ensure that the observed phenomena were indeed capturing differences in the phages produced. Phage display of eGFP resulted in altered side scatter and fluorescence profile, and sub-populations could be identified within what would otherwise be considered uniform populations. Surprisingly, this study has found that side scatter may be used in the future to characterize the display of nonfluorescent proteins.

Rapid and cost-effective baculovirus sample preparation method as a viable alternative to conventional preparation for quantitative real-time PCR.

The increasing use of the baculovirus expression vector system (BEVS) has generated significant interest into techniques for quantifying baculovirus stocks. One method involves the use of quantitative real-time polymerase chain reaction (PCR). This study investigated simplifying baculovirus sample preparation for quantitative Real Time PCR to provide an alternative to current kit-based preparation methods. To achieve this goal, combinations of freeze/thaw cycles and Triton X-100 treatment were investigated. A treatment with only Triton X-100 was found to be sufficient to provide a simple, rapid and cheap alternative to kit-based preparation methods. This study also examined other factors such as primer choice to further examine the process of baculovirus quantitation by qPCR.

Co-expression vs. co-infection using baculovirus expression vectors in insect cell culture: Benefits and drawbacks.

The baculovirus expression vector system (BEVS) is a versatile and powerful platform for protein expression in insect cells. With the ability to approach similar post-translational modifications as in mammalian cells, the BEVS offers a number of advantages including high levels of expression as well as an inherent safety during manufacture and of the final product. Many BEVS products include proteins and protein complexes that require expression from more than one gene. This review examines the expression strategies that have been used to this end and focuses on the distinguishing features between those that make use of single polycistronic baculovirus (co-expression) and those that use multiple monocistronic baculoviruses (co-infection). Three major areas in which researchers have been able to take advantage of co-expression/co-infection are addressed, including compound structure-function studies, insect cell functionality augmentation, and VLP production. The core of the review discusses the parameters of interest for co-infection and co-expression with time of infection (TOI) and multiplicity of infection (MOI) highlighted for the former and the choice of promoter for the latter. In addition, an overview of modeling approaches is presented, with a suggested trajectory for future exploration. The review concludes with an examination of the gaps that still remain in co-expression/co-infection knowledge and practice.

Getting more from cell size distributions: establishing more accurate biovolumes by estimating viable cell populations.

Current approaches for cell size distribution modeling are attempting to describe the behavior of the entire distribution with respect to time. Although some advances have been made in this area, the modeling process requires a large number of culture-specific parameters and an a priori assumption of the distribution nature (Poisson, Gaussian, etc.). In this work, we propose a deconvolution of the distribution into size ranges and an iterative regression process with respect to a single culture variable, such as viability. Following this approach, two example applications are outlined using data collected with a Coulter Counter Multisizer. In the first, traditional biovolume measurements are corrected to account for the noneven distribution of nonviable cells. These corrections amount to an average increase of 7-65% in the calculated biovolume from 24 to 72 h postinfection and are expected to aid in the development of a new basis for nutrient consumption postinfection. In the second example, viability is predicted from the cell size distribution using both linear and exponential regressions. Differences between predicted and measured viabilities were found to be normally distributed with means of 0.4% and 0% as well as standard deviations of 7.6% and 8.1% for linear and exponential regression, respectively. Although only viability relationships were tested, our approach yielded significant results for both applications, allowing the possibility for further development.