BJOC - Biosynthesis and function of secondary metabolites

Clear reasons exist for studying secondary metabolites. Many have been found to have use in industry and medicine. Indeed, six of the twenty most commonly prescribed medications for humans are of fungal origin. These metabolites have been subjected to combinatorial chemistry following growth in selective media. Some metabolites are toxic to humans and other animals. Yet others can modify the growth and metabolism of plants. Interestingly, the most important secondary metabolites seem to be synthesised from one or a combination of three biosynthetic pathways: polyketides arising from Acetyl Coenzyme A, mevalonate pathway that also arises from Acetyl Coenzyme A, and from amino acids. In addition, genes for the synthesis of some important secondary metabolites are found clustered together, and expression of the cluster appears to be induced by one or a few global regulators.

The Biosynthesis Of Secondary Metabolites.

Biosynthesis of plant secondary metabolites - Genome

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EV volumes were calculated using the NTA data i.e., using the EV diameters for calculating the sphere volumes in each size class, which were multiplied by the particle numbers in each size class. Subsequently, the EV volumes of all size classes were summed to yield the total volume of the EVs applied to the metabolomics analysis. Metabolite concentrations inside the EVs were calculated by dividing the mole amount of metabolites with the total volumes of EVs in the metabolomics samples. The metabolite concentrations of platelets were calculated similarly using the average platelet volume of 8 fl (normal range 7.2-11.7 fl in Demirin , 2011 []) and 1 x 107 platelets/sample. The contribution of uEV-derived metabolites to urine was calculated by dividing the mole amount of individual metabolites in the control uEV samples with the urine volume from which the EVs were isolated and comparing this concentration value to the concentration of the same metabolites measured from the urine filtrates. Loss of EVs during isolation was not taken into account in these calculations. Human Metabolome Database (HMDB) and EVpedia (accessed 12.8.2016) were searched for reported EV-resident enzymes and transporters linked to the common EV metabolites. For assigning the subcellular localizations of the common EV metabolites, HMDB and Small Molecule Pathway Database (SMPDB) were used. The statistics module and metabolite set enrichment analysis through over representation analysis in the Metaboanalyst 3.0 software were employed to analyze the metabolomics data. The MS-compound panel of 102 metabolites (Table ) was used as a reference library and pathways with ≥ 2 metabolites present in all control samples of either EV type were included. For comparisons of population means, the input data was log-transformed, normalized to the EV-derived parameters or to concentrations of other metabolites from the same panel as indicated. When comparing two population means, differences between the groups were tested for statistical significance with non-parametric Wilcoxon rank test or student´s t-test with equal variance. For the comparisons of three population means, one-way ANOVA with Fischer's LSD as the post-hoc analysis was used.

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Characterization of the urinary EV samples applied to metabolomics by western blotting and Nanoparticle tracking analysis. A. Western blotting of the urinary EV samples in the metabolomics study with EV-markers demonstrated significant variation in EV quantity from different donors. Two out of three samples from prostate cancer patients of Helsinki Urological Biobank project (HUB.1-3) obtained before prostatectomy (pre) contained more EVs than the samples from the same patients after prostatectomy (post) or from healthy controls. B. The urinary EV concentrations in these samples measured by nanoparticle tracking analysis and by quantification of CD9 band optical density (OD) from the western blot correlated well. C. Size distribution of the urinary EVs applied to metabolomics was obtained by nanoparticle tracking analysis showing that the EV sizes did not vary much between samples (N = 8).

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Biosynthesis and regulation of secondary metabolites in microorganisms

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We also used western blotting and NTA to validate the individual uEV samples applied to the metabolomics i.e. the samples from three healthy controls and three Pca patients (HUB.1-3) from whom samples were obtained before and after prostatectomy (Table ). However, all of the post-prostatectomy sample of HUB.1, where only 10 ml of urine was available for EV isolation, was applied to metabolomics and not analyzed by NTA and western blotting in order to save the EV material and increase the probability of successful metabolomics. Western blotting of CD9, CD63 and CD59 gave similar staining pattern, with the most intensive staining observed in HUB.1 and HUB.3 pre-prostatectomy samples (Fig. A). The same samples also contained the highest EV concentrations by NTA (Table ). Both the western blotting and NTA suggested highly varying concentrations of uEVs in the urine samples from different donors: the levels ranged one order of magnitude from 2.1 x 108 to 2.6 x 109 per ml of urine. Quantified values of CD9 from the western blotting correlated well with the NTA measurements yielding an R2 value of 0.966 (Fig. B). The size distributions of uEVs in the individual samples by NTA did not vary much (Fig. C). All samples contained over 85% (87-94%) of small, 10 EVs per ml of platelet concentrate and a mean diameter of 152 ± 6 nm (Fig. C). In conclusion, the quality control showed that our EV preparations contained EVs of sufficient purity in quantities that could be measured by NTA or by CD9 optical density.

A genomics-guided study of the biosynthesis of microbial secondary metabolites

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