Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale.

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms.

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

Building kinetic models for metabolic engineering.

Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this review we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline.

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

Risk Factors Associated with the Development of Transaminitis in Oxandrolone-Treated Adult Burn Patients.

Oxandrolone has proven benefits in thermal burn injury and has become a standard of care. Transaminitis is the most frequent side effect of oxandrolone use, although no risk factors have been identified that increase the risk of transaminitis. The objective was to evaluate the frequency of transaminitis while on oxandrolone and to identify risk factors leading to an increased risk of transaminitis in adult burn patients. This multicenter retrospective risk factor analysis compared two patient groups with and without occurrence of transaminitis, which was detected by an aspartate aminotransferase (AST) or alanine aminotransferase (ALT) >100 mg/dL. Secondary outcomes included percentage increase from baseline for AST/ALT, length of stay, and mortality. After univariable analysis, a multivariable logistic regression analysis was performed to detect possible risk factors leading to transaminitis. A total of 309 patients were included, with transaminitis occurring in 128 patients (41.4%) after 13 (interquartile range [IQR] 8-23) days on oxandrolone. After multivariable analysis, age (odds ratio [OR] 0.91; 95% confidence interval [CI] 0.84-0.99 for a 5-year increase in age), intravenous vasopressor use (OR 1.85; 95% CI 1.05-3.27), and amiodarone use (OR 2.51; 95% CI 1.09-5.77) were independent predictors of transaminitis, controlling for TBSA%. Transaminitis was not significantly associated with length of stay or mortality after adjusting for age and TBSA%. We conclude that patients who are younger and have concurrent amiodarone or vasopressor use have the highest risk of developing oxandrolone induced transaminitis and should be monitored closely.

A Randomized Pharmacokinetic and Pharmacodynamic Evaluation of Every 8-Hour and 12-Hour Dosing Strategies of Vancomycin and Cefepime in Neurocritically ill Patients.

PURPOSE: Neurocritically ill patients have clinically significant alterations in pharmacokinetic parameters of renally eliminated medications that may result in subtherapeutic plasma and cerebrospinal fluid antibiotic concentrations. METHODS: We conducted a prospective randomized open-label study of adult neurocritically ill patients treated with vancomycin and cefepime. Vancomycin 15 mg/kg and cefepime 2 g were dosed at every-8- or 12-hour intervals. The primary outcomes were the achievement of pharmacodynamic (PD) targets related to time of unbound drug above minimum inhibitory concentrations (MIC) for 60% or more of the dosing interval (fT > MIC ≥ 60%) for ß-lactams and ratio of 24-hour area under the curve (AUC):MIC of 400 or greater for vancomycin. RESULTS: Twenty patients were included in the study. They were divided equally between the every-12-hour and every-8-hour dosing groups. Patients (mean age 51.8 ± 11 yrs) were primarily male (60%) and white (95%), and most had an admission diagnosis of intracranial hemorrhage (80%). Compared with the every-12-hour group, the every-8-hour vancomycin group achieved target trough concentrations (higher than 15 µg/ml) significantly more frequently at initial measurement (0% vs 80%, p<0.01) and at 7-10 days (0% vs 90%, p=0.045) and achieved PD targets more frequently at increasing MICs. Similarly, compared with every-12-hour dosing, the every-8-hour cefepime dosing strategy significantly increased PD target attainment (fT > MIC ≥ 60%) at an MIC of 8 µg/ml (20% vs 70%, p=0.02). CONCLUSIONS: This study demonstrated that more frequent dosing of vancomycin and cefepime is required to achieve optimal PD targets in adult neurocritically ill patients. The need for increased total daily doses is potentially secondary to the development of augmented renal clearance.

Evaluation of Early Dexmedetomidine Addition to the Standard of Care for Severe Alcohol Withdrawal in the ICU: A Retrospective Controlled Cohort Study.

PURPOSE: This study evaluated the impact of dexmedetomidine (DEX) administration on benzodiazepine (BZD) requirements in intensive care unit (ICU) patients experiencing alcohol withdrawal syndrome (AWS). METHODS: This trial included adults admitted to the ICU for >24 hours for AWS. Early DEX was defined as receiving DEX within 60 hours of hospital admission. The primary outcome was 12-hour BZD requirement from the inflection point or DEX initiation. Secondary outcomes included 24-hour BZD requirements, symptom control, ICU and hospital length of stay, and incidence and duration of mechanical ventilation. Safety outcomes included incidence of bradycardia and hypotension. RESULTS: Twenty patients receiving DEX were matched to 22 control patients. The mean 12-hour change in BZD requirement was significantly different for DEX versus control (-20 vs -8.3 mg, P = .0455) with a trend toward significance at 24 hours (-29.6 vs -11 mg, P = .06). No significant differences were noted in other secondary outcomes. Patients receiving DEX experienced significantly more bradycardia than controls (35% vs 0%, P < .01) but not hypotension. CONCLUSIONS: This study suggests DEX is associated with a reduction in BZD requirement when utilized as adjunctive therapy for AWS. A larger prospective trial is needed to evaluate the clinical impact of DEX for AWS.