Isotopic Steady-State Flux Analysis

Throughout the United States and the world, skin cancer is the most frequent form of cancer. Sonidegib (SNG) is a hedgehog inhibitor that has been used for skin cancer treatment. However, SNG has low bioavailability and is associated with resistance. The focus of this work is to enhance bioavailability, anti-tumor efficacy and targeting of SNG via developing ethosome gel as a potential treatment for skin cancer. SNG-loaded ethosomes formulation was prepared and characterized in vitro by %entrapment efficiency (%EE), vesicle size, morphology, %release and steady-state flux. The results showed that the prepared formulation was spherical nanovesicles with a %EE of 85.4 ± 0.57%, a particle size of 199.53 ± 4.51 nm and a steady-state flux of 5.58 ± 0.08 µg/cm2/h. In addition, SNG-loaded ethosomes formulation was incorporated into carbopol gel to study the anti-tumor efficacy, localization and bioavailability in vivo. Compared with oral SNG, the formulation showed 3.18 times higher relative bioavailability and consequently significant anti-tumor activity. In addition, this formulation showed a higher rate of SNG penetration in the skin’s deep layers and passive targeting in tumor cells. Briefly, SNG-loaded ethosome gel can produce desirable therapeutic benefits for treatment of skin cancer.

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Bayesian metabolic flux analysis reveals intracellular flux couplings

Bioinformatics ◽

10.1093/bioinformatics/btz315 ◽

2019 ◽

Vol 35 (14) ◽

pp. i548-i557 ◽

Cited By ~ 3

Author(s):

Markus Heinonen ◽

Maria Osmala ◽

Henrik Mannerström ◽

Janne Wallenius ◽

Samuel Kaski ◽

...

Keyword(s):

Steady State ◽

Metabolic Flux Analysis ◽

Metabolic Flux ◽

Reaction Network ◽

Reaction Rates ◽

Supplementary Information ◽

Flux Analysis ◽

Metabolic Reaction ◽

Metabolic Systems ◽

Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

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Measuring non-steady-state metabolic fluxes in starch-converting faecal microbiota in vitro

Beneficial Microbes ◽

10.3920/bm2010.0038 ◽

2010 ◽

Vol 1 (4) ◽

pp. 391-405 ◽

Cited By ~ 8

Author(s):

T. Binsl ◽

A. De Graaf ◽

K. Venema ◽

J. Heringa ◽

A. Maathuis ◽

...

Keyword(s):

Steady State ◽

Metabolic Flux ◽

Flux Distribution ◽

Computational Modelling ◽

Total Carbon ◽

Flux Analysis ◽

Small Contribution ◽

Faecal Microbiota ◽

Isotope Labelling

This paper explores human gut bacterial metabolism of starch using a combined analytical and computational modelling approach for metabolite and flux analysis. Non-steady-state isotopic labelling experiments were performed with human faecal microbiota in a well-established in vitro model of the human colon. After culture stabilisation, [U-13C] starch was added and samples were taken at regular intervals. Metabolite concentrations and 13C isotopomeric distributions were measured amongst other things for acetate, propionate and butyrate by mass spectrometry and NMR. The vast majority of metabolic flux analysis methods based on isotopomer analysis published to date are not applicable to metabolic non-steady-state experiments. We therefore developed a new ordinary differential equation-based representation of a metabolic model of human faecal microbiota to determine eleven metabolic parameters that characterised the metabolic flux distribution in the isotope labelling experiment. The feasibility of the model parameter quantification was demonstrated on noisy in silico data using a downhill simplex optimisation, matching simulated labelling patterns of isotopically labelled metabolites with measured metabolite and isotope labelling data. Using the experimental data, we determined an increasing net label influx from starch during the experiment from 94±1 µmol/l/min to 133±3 µmol/l/min. Only about 12% of the total carbon flux from starch reached propionate. Propionate production mainly proceeded via succinate with a small contribution via acrylate. The remaining flux from starch yielded acetate (35%) and butyrate (53%). Interpretation of 13C NMR multiplet signals further revealed that butyrate, valerate and caproate were mainly synthesised via cross-feeding, using acetate as a co-substrate. This study demonstrates for the first time that the experimental design and the analysis of the results by computational modelling allows the determination of time-resolved effects of nutrition on the flux distribution within human faecal microbiota in metabolic non-steady-state.

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