BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism.

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

Safety and clinical impact of FEES - results of the FEES-registry.

BACKGROUND: At present, the flexible endoscopic evaluation of swallowing (FEES) is one of the most commonly used methods for the objective assessment of swallowing. This multicenter trial prospectively collected data on the safety of FEES and also assessed the impact of this procedure on clinical dysphagia management. METHODS: Patients were recruited in 23 hospitals in Germany and Switzerland from September 2014 to May 2017. Patient characteristics, professional affiliation of the FEES examiners (physicians or speech and language therapists), side-effects and cardiorespiratory parameters, severity of dysphagia and clinical consequences of FEES were documented. RESULTS: 2401 patients, mean age 69.8 (14.6) years, 42.3% women, were included in the FEES-registry. The most common main diagnosis was stroke (61%), followed by Parkinson's disease (6.5%). FEES was well tolerated by patients. Complications were reported in 2% of examinations, were all self-limited and resolved without sequelae and showed no correlation to the endoscopist's previous experience. In more than 50% of investigations FEES led to changes of feeding strategies, in the majority of cases an upgrade of oral diet was possible. DISCUSSION: This study confirmed that FEES, even when performed by less experienced clinicians is a safe and well tolerated procedure and significantly impacts on the patients' clinical course. Implementation of a FEES-service in different clinical settings may improve dysphagia care. TRIAL REGISTRATION: ClinicalTrials.gov NCT03037762, registered January 31st 2017.