Development of novel ß2-adrenergic receptor agonists for the stimulation of glucose uptake - The importance of chirality and ring size of cyclic amines.

ß2-Adrenergic receptor (ß2AR) agonists have been reported to stimulate glucose uptake (GU) by skeletal muscle cells and are therefore highly interesting as a possible treatment for type 2 diabetes (T2D). The chirality of compounds often has a great impact on the activity of ß2AR agonists, although this has thus far not been investigated for GU. Here we report the GU for a selection of synthesized acyclic and cyclic ß-hydroxy-3-fluorophenethylamines. For the N-butyl and the N-(2-pentyl) compounds, the (R) and (R,R) (3d and 7e) stereoisomers induced the highest GU. When the compounds contained a saturated nitrogen containing 4- to 7-membered heterocycle, the (R,R,R) enantiomer of the azetidine (8a) and the pyrrolidine (9a) had the highest activity. Altogether, these results provide pivotal information for designing novel ß2AR agonist for the treatment of T2D.

High-throughput screening of a Corynebacterium glutamicum mutant library on genomic and metabolic level.

Due to impressive achievements in genomic research, the number of genome sequences has risen quickly, followed by an increasing number of genes with unknown or hypothetical function. This strongly calls for development of high-throughput methods in the fields of transcriptomics, proteomics and metabolomics. Of these platforms, metabolic profiling has the strongest correlation with the phenotype. We previously published a high-throughput metabolic profiling method for C. glutamicum as well as the automatic GC/MS processing software MetaboliteDetector. Here, we added a high-throughput transposon insertion determination for our C. glutamicum mutant library. The combination of these methods allows the parallel analysis of genotype/phenotype correlations for a large number of mutants. In a pilot project we analyzed the insertion points of 722 transposon mutants and found that 36% of the affected genes have unknown functions. This underlines the need for further information gathered by high-throughput techniques. We therefore measured the metabolic profiles of 258 randomly chosen mutants. The MetaboliteDetector software processed this large amount of GC/MS data within a few hours with a low relative error of 11.5% for technical replicates. Pairwise correlation analysis of metabolites over all genotypes showed dependencies of known and unknown metabolites. For a first insight into this large data set, a screening for interesting mutants was done by a pattern search, focusing on mutants with changes in specific pathways. We show that our transposon mutant library is not biased with respect to insertion points. A comparison of the results for specific mutants with previously published metabolic results on a deletion mutant of the same gene confirmed the concept of high-throughput metabolic profiling. Altogether the described method could be applied to whole mutant libraries and thereby help to gain comprehensive information about genes with unknown, hypothetical and known functions.

A method for enzyme quenching in microbial metabolome analysis successfully applied to gram-positive and gram-negative bacteria and yeast.

Although microbial metabolome analysis has now become a widely used method, no generally applicable quenching method has been published so far. Either the methods were established for only one defined organism or the metabolite coverage was quite low. In the current work, a novel, reliable, and robust quenching method for different types of organisms is described. Compared with the commonly used quenching procedure with 60% methanol (-50 degrees C), we obtained improved results for three examined organisms with different cell wall and membrane structures using a 40% ethanol/0.8% sodium chloride solution (-20 degrees C). Increased metabolite levels were achieved for 60-80% of all identified compounds. Moreover, the estimated standard error of the relative concentrations of 120-160 different substances was only 14+/-4% compared with 17+/-3% in unquenched samples and 24+/-7% in samples quenched with methanol for the different tested organisms.

MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis.

We have developed a new software, MetaboliteDetector, for the efficient and automatic analysis of GC/MS-based metabolomics data. Starting with raw MS data, the program detects and subsequently identifies potential metabolites. Moreover, a comparative analysis of a large number of chromatograms can be performed in either a targeted or nontargeted approach. MetaboliteDetector automatically determines appropriate quantification ions and performs an integration of single ion peaks. The analysis results can directly be visualized with a principal component analysis. Since the manual input is limited to absolutely necessary parameters, the program is also usable for the analysis of high-throughput data. However, the intuitive graphical user interface of MetaboliteDetector additionally allows for a detailed examination of a single GC/MS chromatogram including single ion chromatograms, recorded mass spectra, and identified metabolite spectra in combination with the corresponding reference spectra obtained from a reference library. MetaboliteDetector offers the ability to operate with highly resolved profile mass data. Finally, all analysis results can be exported to tab delimited tables. The features of MetaboliteDetector are demonstrated by the analysis of two experimental metabolomics data sets. MetaboliteDetector is freely available under the GNU public license (GPL) at http://metabolitedetector.tu-bs.de.