A correction method for systematic error in metabolomic time-course data

The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for many cell culture applications. Although the increasing resolution of metabolic profiles has potential to reveal important details about cell culture metabolism, more detailed results are subject to greater influence from measurement and data processing error. A number of common errors, stemming from metabolite extraction and internal standard addition, take the form of a dilution effect, where all observed concentrations feature a constant deviation relative to the true values. We have developed a simple technique to deal with such errors. A nonparametric smoothing fit was applied to all metabolite concentrations, with percent deviations from the fit calculated for each observation. Taking the median of these percent deviations for each sample (across multiple compounds) allowed the estimation of a systematic bias in the relative concentration of all compounds – typical of a dilution error. To validate this method, we developed a general framework for simulating metabolomic experiments. The correction was applied to simulated data sets composed of 20-60 metabolites and 10-20 timepoints. Deviations as small as 2.5% were successfully identified, although greater accuracy was achieved when more data was available. Given the pronounced influence of a small concentration bias on metabolic flux calculation, we were also interested in the effect of similar measurement errors on Metabolic Flux Analysis (MFA). To this end, a Chinese Hamster Ovary (CHO) cell model was used to simulate a set of realistic flux profiles, which were then perturbed with measurement error. Despite the considerable impact of measurement error on flux estimation, the standard χ2-test was not able to identify erroneous data beyond the significance level. Our findings reinforce the need for validation at earlier stages of analysis in the development of rational strategies for metabolic engineering and media supplementatio

From Metabolite Concentration to Flux – A Systematic Assessment of Error in Cell Culture Metabolomics

The growing availability of genomic, transcriptomic, and metabolomic data has opened the door to the synthesis of multiple levels of information in biological research. As a consequence, there has been a push to analyze biological systems in a comprehensive manner through the integration of their interactions into mathematical models, with the process frequently referred to as “systems biology”. Despite the potential for this approach to greatly improve our knowledge of biological systems, the definition of mathematical relationships between different levels of information opens the door to diverse sources of error, requiring precise, unbiased quantification as well as robust validation methods. Failure to account for differences in uncertainty across multiple levels of data analysis may cause errors to drown out any useful outcomes of the synthesis. The application of a systems biology approach has been particularly important in metabolic modeling. There has been a concentrated effort to build models directly from genomic data and to incorporate as much of the metabolome as possible in the analysis. Metabolomic data collection has been expanded through the recent use of hydrogen Nuclear Magnetic Resonance (1H-NMR) spectroscopy for cell culture monitoring. However, the combination of uncertainty from model construction and measurement error from NMR (or other means of metabolomic) analysis complicates data interpretation. This thesis establishes the precision and accuracy of NMR spectroscopy in the context of cell cultivation while developing a methodology for assessing model error in Metabolic Flux Analysis (MFA). The analysis of cell culture media via NMR has been made possible by the development of specialized software for the “deconvolution” of complex spectra, however, the process is semi-qualitative. A human “profiler” is required to manually fit idealized peaks from a compound library to an observed spectra, where the quality of fit is often subject to considerable interpretation. Work presented in this thesis establishes baseline accuracy as approximately 2%-10% of the theoretical mean, with a relative standard deviation of 1.5% to 3%. Higher variabilities were associated primarily with profiling error, while lower variabilities were due in part to tube insertion (and the steps leading up to spectra acquisition). Although a human profiler contributed to overall uncertainty, the net impact did not make the deconvolution process prohibitively imprecise. Analysis was then expanded to consider solutions that are more representative of cell culture supernatant. The combination of metabolites at different concentration levels was efficiently represented by a Plackett-Burman experiment. The orthogonality of this design ensured that every level of metabolite concentration was combined with an equal number of high and low concentrations of all other variable metabolites, providing a worst-case scenario for variance estimation. Analysis of media-like mixtures revealed a median error and standard deviation to be approximately 10%, although estimating low metabolite concentrations resulted in a considerable loss of accuracy and precision in the presence of resonance overlap. Furthermore, an iterative regression process identified a number of cases where an increase in the concentration of one metabolite resulted in increased quantification error of another. More importantly, the analysis established a general methodology for estimating the quantification variability of media-specific metabolite concentrations. Subsequent application of NMR analysis to time-course data from cell cultivation revealed correlated deviations from calculated trends. Similar deviations were observed for multiple (chemically) unrelated metabolites, amounting to approximately 1%-10% of the metabolite’s concentration. The nature of these deviations suggested the cause to be inaccuracies in internal standard addition or quantification, resulting in a skew of all quantified metabolite concentrations within a sample by the same relative amount. Error magnitude was estimated by calculating the median relative deviation from a smoothing fit for all compounds at a give timepoint. A metabolite time-course simulation was developed to determine the frequency and magnitude of such deviations arising from typical measurement error (without added bias from incorrect internal standard addition). Multiple smoothing functions were tested on simulated time-courses and cubic spline regression was found to minimize the median relative deviation from measurement noise to approximately 2.5%. Based on these results, an iterative smoothing correction method was implemented to identify and correct median deviations greater than 2.5%, with both simulation and correction code released as the “metcourse” package for the R programming language. Finally, a t-test validation method was developed to assess the impact of measurement and model error on MFA, with a Chinese hamster ovary (CHO) cell model chosen as a case study. The standard MFA formulation was recast as a generalized least squares (GLS) problem, with calculated fluxes subject to a t-significance test. NMR data was collected for a CHO cell bioreactor run, with another set of data simulated directly from the model and perturbed by observed measurement error. The frequency of rejected fluxes in the simulated data (free of model error) was attributed to measurement uncertainty alone. The rejection of fluxes calculated from observed data as non-significant that were not rejected in the simulated data was attributed to a lack of model fit i.e. model error. Applying this method to the observed data revealed a considerable level of error that was not identified by traditional χ2 validation. Further simulation was carried out to assess the impact of measurement error and model structure, both of which were found to have a dramatic impact on statistical significance and calculation error that has yet to be addressed in the context of MFA

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. © 2016 The Author(s)

Construction and analysis of a genetically tuneable lytic phage display system

The Bacteriophage lambda 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 lambda 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 lambda Dam15, 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 lambda Dam15 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 lambda.UW Start-up funds; Drug Safety and Effectiveness Cross-Disciplinary Training (DSECT) Scholarship; Canadian Institute of Health Research (CIHR

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. (C) 2014 American Institute of Chemical EngineersNSERC ENGAGE; NSERC Strategic Network (MabNet); NSERC Canada Graduate Scholarshi

Additional file 3 of Identifying model error in metabolic flux analysis – a generalized least squares approach

SBML file. An SBML representation of the metabolic model. (XML 89 kb

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

International audienc

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

Summary: 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

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-¹H 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-¹H 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