@phdthesis{Langhammer2018, author = {Langhammer, Romy}, title = {Metabolomic Imaging for Human Prostate Cancer Detection using MR Spectroscopy at 7T}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-165772}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2018}, abstract = {BACKGROUND. Prostate cancer (PCa) remains a major health concern in men of the Western World. However, we still lack effective diagnostic tools a) for an effective screening with both high sensitivity and specificity, b) to guide biopsies and avoid histology sampling errors and c) to predict tumor aggressiveness in order to avoid overtreatment. Therefore, a more reliable, highly cancer-specific and ideally in vivo approach is needed. The present study has been designed in order to further develop and test the method of "metabolomic imaging" using magnetic resonance spectroscopy (MRS) at 7T to address those challenges. METHODS. Thirty whole prostates with biopsy-proven PCa were in vitro analyzed with a 7T human MR scanner. A voxel grid containing the spectral information was overlaid with the MR image of the middle transverse cross-sectional plane of each case. Subsequent histopathological evaluation of the prostate specimen followed. After the spectral output was processed, all voxels were compared with a metabolomic PCa profile, which had been established within a preliminary study, in order to create a metabolomic map indicating MRS cancer-suspicious regions. Those regions were compared with the histologically identified tumor lesions regarding location. RESULTS. Sixty-one percent of the histological cancer lesions were detected by metabolomic imaging. Among the cases with PCa on the examined slice, 75\% were identified as cancerous. None of the tested features significantly differed between detected and undetected cancer lesions. A defined "Malignancy Index" (MI) significantly differentiated between MRS-suspicious lesions corresponding with a histological cancer lesion and benign lesions (p = 0.006) with an overall accuracy of 70\%. The MI furthermore showed a positive correlation with the Gleason grade (p = 0.021). CONCLUSION. A new approach within PCa diagnostics was developed with spectral analysis including the whole measureable metabolome - referred to as "metabolomics" - rather than focusing on single metabolites. The MI facilitates precise tumor detection and may additionally serve as a marker for tumor aggressiveness. Metabolomic imaging might contribute to a highly cancer-specific in vivo diagnostic protocol for PCa.}, subject = {Prostatakrebs}, language = {en} } @phdthesis{Cheng2017, author = {Cheng, Cheng}, title = {Metabolomics and dereplication-based isolation of novel bioactive natural products from marine sponge-associated actinomycetes}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-136587}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2017}, abstract = {Marine sponge-associated actinomycetes are considered as promising source for the discovery of novel biologically active compounds. Metabolomics coupled multivariate analysis can efficiently reduce the chemical redundancy of re-isolating known compounds at the very early stage of natural product discovery. This Ph.D. project aimed to isolate biologically active secondary metabolites from actinomycetes associated with different Mediterranean sponges with the assistance of metabolomics tools to implement a rapid dereplication and chemically distinct candidate targeting for further up-scaling compounds isolation. This study first focused on the recovery of actinomycetes from marine sponges by various cultivation efforts. Twelve different media and two separate pre-treatments of each bacterial extract were designed and applied to facilitate actinomycete diversity and richness. A total of 64 actinomycetes were isolated from 12 different marine sponge species. The isolates were affiliated to 23 genera representing 8 different suborders based on nearly full-length 16S rRNA gene sequencing. Four putatively novel species belonging to the genera Geodermatophilus, Microlunatus, Rhodococcus, and Actinomycetospora were identified based on a sequence similarity <98.5\% to validly described 16S rRNA gene sequences. 20\% of the isolated actinomycetes was shown to exhibit diverse biological properties, including antioxidant, anti-Bacillus sp., anti-Aspergillus sp., and antitrypanosomal activities. The metabolomics approaches combined with the bioassay results identified two candidate strains Streptomyces sp. SBT348 and Streptomyces sp. SBT345 for further up-scaling cultivation and compounds isolation. Four compounds were isolated from Streptomyces sp. SBT348. Three of these compounds including the new cyclic dipeptide petrocidin A were previously highlighted in the metabolomics analyses, corroborating the feasibility of metabolomics approaches in novel compounds discovery. These four compounds were also tested against two pathogen microorganisms since the same activities were shown in their crude extract in the preliminary bioassay screening, however none of them displayed the expected activities, which may ascribe to the insufficient amount obtained. Streptomyces sp. SBT345 yielded 5 secondary metabolites, three of which were identified as new natural products, namely strepthonium A, ageloline A and strepoxazine A. Strepthonium A inhibited the production of Shiga toxin produced by enterohemorrhagic Escherichia coli at a concentration of 80 μM, without interfering with the bacterial growth. Ageloline A exhibited antioxidant activity and inhibited the inclusion of Chlamydia trachomatis with an IC50 value of 9.54 ± 0.36 μM. Strepoxazine A displayed antiproliferative property towards human promyelocytic HL-60 cells with an IC50 value of 16 μg/ml. 11 These results highlighted marine sponges as a rich source for novel actinomycetes and further exhibited the significance of marine sponge-associated actinomycetes as promising producers of novel biologically active compounds. The chemometrics coupled metabolomics approach also demonstrated its feasibility and efficacy in natural product discovery.}, subject = {Actinomyces}, language = {en} }