Dynamic metabolic flux analysis (DMFA): a framework for determining fluxes at metabolic non-steady state

Metabolic flux analysis (MFA) is a key tool for measuring in vivo metabolic fluxes in systems at metabolic steady state. Here, we present a new method for dynamic metabolic flux analysis (DMFA) of systems that are not at metabolic steady state. The advantages of our DMFA method are: (1) time-series of metabolite concentration data can be applied directly for estimating dynamic fluxes, making data smoothing and estimation of average extracellular rates unnecessary; (2) flux estimation is achieved without integration of ODEs, or iterations; (3) characteristic metabolic phases in the fermentation data are identified automatically by the algorithm, rather than selected manually/arbitrarily. We demonstrate the application of the new DMFA framework in three example systems. First, we evaluated the performance of DMFA in a simple three-reaction model in terms of accuracy, precision and flux observability. Next, we analyzed a commercial glucose-limited fed-batch process for 1,3-propanediol production. The DMFA method accurately captured the dynamic behavior of the fed-batch fermentation and identified characteristic metabolic phases. Lastly, we demonstrate that DMFA can be used without any assumed metabolic network model for data reconciliation and detection of gross measurement errors using carbon and electron balances as constraints.     

Parallel labeling experiments with [U-(13)C]glucose validate E. coli metabolic network model for (13)C metabolic flux analysis

(13)C-metabolic flux analysis (MFA) is a widely used method for measuring intracellular metabolic fluxes in living cells. (13)C MFA relies on several key assumptions: (1) the assumed metabolic network model is complete, in that it accounts for all significant enzymatic and transport reactions; (2) (13)C-labeling measurements are accurate and precise; and (3) enzymes and transporters do not discriminate between (12)C- and (13)C-labeled metabolites. In this study, we tested these inherent assumptions of (13)C MFA for wild-type E. coli by parallel labeling experiments with [U-(13)C]glucose as tracer. Cells were grown in six parallel cultures in custom-constructed mini-bioreactors, starting from the same inoculum, on medium containing different mixtures of natural glucose and fully labeled [U-(13)C]glucose, ranging from 0% to 100% [U-(13)C]glucose. Macroscopic growth characteristics of E. coli showed no observable kinetic isotope effect. The cells grew equally well on natural glucose, 100% [U-(13)C]glucose, and mixtures thereof. (13)C MFA was then used to determine intracellular metabolic fluxes for several metabolic network models: an initial network model from literature; and extended network models that accounted for potential dilution effects of isotopic labeling. The initial network model did not give statistically acceptable fits and produced inconsistent flux results for the parallel labeling experiments. In contrast, an extended network model that accounted for dilution of intracellular CO(2) by exchange with extracellular CO(2) produced statistically acceptable fits, and the estimated metabolic fluxes were consistent for the parallel cultures. This study illustrates the importance of model validation for (13)C MFA. We show that an incomplete network model can produce statistically unacceptable fits, as determined by a chi-square test for goodness-of-fit, and return biased metabolic fluxes. The validated metabolic network model for E. coli from this study can be used in future investigations for unbiased metabolic flux measurements.     

MAGI: Methylation analysis using genome information

By incorporating annotation information into the analysis of next-generation sequencing DNA methylation data, we provide an improvement in performance over current testing procedures. Methylation analysis using genome information (MAGI) is applicable for both unreplicated and replicated data, and provides an effective analysis for studies with low sequencing depth. When compared with current tests, the annotation-informed tests provide an increase in statistical power and offer a significance-based interpretation of differential methylation.     

Principles and approach to developing mammalian cell culture media for high cell density perfusion process leveraging established fed‐batch media

Perfusion medium was successfully developed based on our fed‐batch platform basal and feed media. A systematic development approach was undertaken by first optimizing the ratios of fed‐batch basal and feed media followed by targeted removal of unnecessary and redundant components. With this reduction in components, the medium could then be further concentrated by 2× to increase medium depth. The medium osmolality was also optimized where we found ～360 mOsm/kg was desirable resulting in a residual culture osmolality of ～300 mOsm/kg for our cell lines. Further building on this, the amino acids Q, E, N, and D were rebalanced to reduce lactate and ammonium levels, and increase the cell‐specific productivity without compromising on cell viability while leaving viable cell density largely unaffected. Further modifications were also made by increasing certain important vitamin and lipid concentrations, while eliminating other unnecessary vitamins. Overall, an effective perfusion medium was developed with all components remaining in the formulation understood to be important and their concentrations increased to improve medium depth. The critical cell‐specific perfusion rate using this medium was then established for a cell line of interest to be 0.075 nL/cell‐day yielding 1.2 g/L‐day at steady state. This perfusion process was then successfully scaled up to a 100 L single‐use bioreactor with an ATF6 demonstrating similar performance as a 2 L bioreactor with an ATF2. Large volume handling challenges in our fed‐batch facility were overcome by developing a liquid medium version of the powder medium product contained in custom totes for plug‐and‐play use with the bioreactor. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:891–901, 2017     

A highly conserved nuclear gene for low-level phylogenetics: elongation factor-1 alpha recovers morphology-based tree for heliothine moths

Molecular systematists need increased access to nuclear genes. Highly conserved, low copy number protein-encoding nuclear genes have attractive features for phylogenetic inference but have heretofore been applied mostly to very ancient divergences. By virtue of their synonymous substitutions, such genes should contain a wealth of information about lower-level taxonomic relationships as well, with the advantage that amino acid conservatism makes both alignment and primer definition straightforward. We tested this postulate for the elongation factor-1 alpha (EF-1 alpha) gene in the noctuid moth subfamily Heliothinae, which has probably diversified since the middle Tertiary. We sequenced 1,240 bp in 18 taxa representing heliothine groupings strongly supported by previous morphological and allozyme studies. The single most parsimonious gene tree and the neighbor-joining tree for all nucleotides show almost complete concordance with the morphological tree. Homoplasy and pairwise divergence levels are low, transition/transversion ratios are high, and phylogenetic information is spread evenly across gene regions. The EF-1 alpha gene and presumably other highly conserved genes hold much promise for phylogenetics of Tertiary age eukaryote groups.