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Background Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. Results We implemented the software package IPO (‘Isotopologue Parameter Optimization’) which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable 13 C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. Conclusions IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO .

Ageing constitutes the most important risk factor for all major chronic ailments, including malignant, cardiovascular and neurodegenerative diseases. However, behavioural and pharmacological interventions with feasible potential to promote health upon ageing remain rare. Here we report the identification of the flavonoid 4,4′-dimethoxychalcone (DMC) as a natural compound with anti-ageing properties. External DMC administration extends the lifespan of yeast, worms and flies, decelerates senescence of human cell cultures, and protects mice from prolonged myocardial ischaemia. Concomitantly, DMC induces autophagy, which is essential for its cytoprotective effects from yeast to mice. This pro-autophagic response induces a conserved systemic change in metabolism, operates independently of TORC1 signalling and depends on specific GATA transcription factors. Notably, we identify DMC in the plant Angelica keiskei koidzumi , to which longevity- and health-promoting effects are ascribed in Asian traditional medicine. In summary, we have identified and mechanistically characterised the conserved longevity-promoting effects of a natural anti-ageing drug. Although ageing is the most important risk factor for chronic ailments, effective interventions remain rare. Here, the authors identify the flavonoid 4,4’-dimethoxychalcone and demonstrate that it extends lifespan and promotes health in multiple organisms by inducing autophagy.

PurposeAccurate methods to determine dermal pharmacokinetics are important to increase the rate of clinical success in topical drug development. We investigated in an in vivo pig model whether the unbound drug concentration in the interstitial fluid as determined by dermal open flow microperfusion (dOFM) is a more reliable measure of dermal exposure compared to dermal biopsies for seven prescription or investigational drugs. In addition, we verified standard dOFM measurement using a recirculation approach and compared dosing frequencies (QD versus BID) and dose strengths (high versus low drug concentrations).MethodsDomestic pigs were topically administered seven different drugs twice daily in two studies. On day 7, drug exposures in the dermis were assessed in two ways: (1) dOFM provided the total and unbound drug concentrations in dermal interstitial fluid, and (2) clean punch biopsies after heat separation provided the total concentrations in the upper and lower dermis.ResultsdOFM showed sufficient intra-study precision to distinguish interstitial fluid concentrations between different drugs, dose frequencies and dose strengths, and had good reproducibility between studies. Biopsy concentrations showed much higher and more variable values. Standard dOFM measurements were consistent with values obtained with the recirculation approach.ConclusionsdOFM pig model is a robust and reproducible method to directly determine topical drug concentration in dermal interstitial fluid. Dermal biopsies were a less reliable measure of dermal exposure due to possible contributions from drug bound to tissue and drug associated with skin appendages.

Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data.

Background Unlike xylose-converting natural yeasts, recombinant strains of Saccharomyces cerevisiae expressing the same xylose assimilation pathway produce under anaerobic conditions xylitol rather than ethanol from xylose at low specific xylose conversion rates. Despite intense research efforts over the last two decades, differences in these phenotypes cannot be explained by current metabolic and kinetic models. To improve our understanding how metabolic flux of xylose carbon to ethanol is controlled, we developed a novel kinetic model based on enzyme mechanisms and applied quantitative metabolite profiling together with enzyme activity analysis to study xylose-to-ethanol metabolisms of Candida tenuis CBS4435 (q xylose = 0.10 g/gdc/h, 25 °C; Y ethanol = 0.44 g/g; Y xylitol = 0.09 g/g) and the recombinant S. cerevisiae strain BP000 (q xylose = 0.07 g/gdc/h, 30 °C; Y ethanol = 0.24 g/g; Y xylitol = 0.43 g/g), both expressing the same xylose reductase (XR), comprehensively. Results Results from strain-to-strain metabolic control analysis indicated that activity levels of XR and the maximal flux capacity of the upper glycolysis (UG; both ≥ tenfold higher in CBS4435) contributed predominantly to phenotype differentiation while reactions from the oxidative pentose phosphate pathway played minor roles. Intracellular metabolite profiles supported results obtained from kinetic modeling and indicated a positive correlation between pool sizes of UG metabolites and carbon flux through the UG. For CBS4435, fast carbon flux through the UG could be associated with an allosteric control of 6-phosphofructokinase (PFK) activity by fructose 6-phosphate. The ability of CBS4435 to keep UG metabolites at high levels could be explained by low glycerol 3-phosphate phosphatase (GPP, 17-fold lower in CBS4435) and high XR activities. Conclusions By applying a systems biology approach in which we combined results obtained from metabolic control analysis based on kinetic modeling with data obtained from quantitative metabolite profiling and enzyme activity analyses, we could provide new insights into metabolic and kinetic interactions contributing to the control of carbon flux from xylose to ethanol. Supported by evidences presented two new targets, PFK and GPP, could be identified that aside from XR play pivotal roles in phenotype differentiation. Design of efficient and fast microbial ethanol producers in the future can certainly benefit from results presented in this study.