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Metabolomics-Guided Discovery and Characterization of Five New Cyclic

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Metabolomics-Guided Discovery and Characterization of Five New Cyclic Justus-Liebig-Universität Gießen Department 08 - Biology & Chemistry Dissertation Metabolomics-Guided Discovery and Characterization of five n ew Cyclic Lipopeptides from Freshwater Isolate Pseudomonas sp. Michael MARNER Gießen, March 2020 1 st examiner: PD Dr. Jens Glaeser 2 nd examiner: Prof. Dr. Peter Hammann I Contents 1 Abstract1 2 Introduction2 2.1 Antibiotic Resistance . .2 2.2 Natural product research and Metabolomics . .3 3 Developement and Evaluation of a Metabolomics platform6 3.1 Introduction . .6 3.2 Material and Methods . 12 3.2.1 Cultivation of bacteria . 12 3.2.2 Extract preparation . 13 3.2.3 Bioactivity assessment . 14 3.2.4 Analytics . 15 3.2.5 Data bucketing and visualization . 16 3.2.6 Variable dereplication via molecular networking . 16 3.3 Results . 18 3.3.1 Bioactivity . 18 3.3.2 Chemical diversity assessment and automatic annotation . 19 3.3.3 Molecular networking and variable dereplication . 25 3.3.4 Linking bioactivity to causative agent . 30 3.4 Discussion . 38 3.4.1 Metabolomics . 38 3.4.2 Bioactivity . 41 4 Bioprospecting and characterization of the bacterial community of Lake Stechlin 44 4.1 Intoduction . 44 4.2 Material and Methods . 45 4.2.1 Sampling of microorganisms from Lake Stechlin . 45 4.2.2 Sample preparation . 46 4.2.3 Cell enumeration via fluorescence microscopy . 47 4.2.4 Microbiome analysis . 47 4.2.5 Cultivation and conservation . 49 4.2.6 Bioactivity assessment via quick supernatant lux assay . 49 4.2.7 Phylogenetic identification based on 16S rRNA gene se- quencing . 50 4.3 Results . 51 4.3.1 Microbiome analysis . 51 4.3.2 Cultivation and bioactivity assessment . 55 4.4 Discussion . 59 4.4.1 Microbiome analysis . 59 4.4.2 Cultivation and Bioactivity assessment . 62 5 Metabolomics-guided discovery of new cyclic lipopeptides from Pseu- domonas sp. with anti-Gram-negative activity 65 5.1 Abstract . 65 5.2 Introduction . 65 III 5.3 Materials and Methods . 66 5.3.1 Isolation Pseudomonas sp. FhG100052 . 66 5.3.2 Bioactivity assessment . 66 5.3.3 Screening for chemical novelty . 67 5.3.4 Genome sequencing and biosynthetic gene cluster annotation 68 5.3.5 Optimization of production . 69 5.3.6 Purification of compounds . 69 5.3.7 Structure elucidation using NMR . 70 5.3.8 Determination of absolute configuration . 70 5.3.9 Optical rotation . 71 5.4 Results . 72 5.4.1 Bioactivity of crude extract . 72 5.4.2 Molecular Network cluster analysis . 72 5.4.3 Optimization of production . 73 5.4.4 Compound purification and structure elucidation . 75 5.4.5 Optical rotation . 84 5.4.6 Genome analysis and biosynthetic gene cluster identification 84 5.4.7 Minimum inhibitory concentrations . 86 5.5 Discussion . 89 5.5.1 Bioactivity . 89 5.5.2 Biosynthesis . 91 6 Perspective 94 7 Supplements 116 7.1 Metabolomics platform . 116 7.1.1 Data processing . 116 7.1.2 Bucket intensities . 121 7.1.3 Molecular networking cluster . 122 7.2 Sampling Lake Stechlin . 128 7.3 Microbiome analysis . 129 7.4 Bioluminescence Assay layout . 130 7.5 µ-fractionation FhG100052 extract C.albicans ........... 131 7.6 MIC determination Assay layout . 132 7.7 steABC flanking regions . 133 7.8 Optimization of CLP production . 134 7.8.1 Media variation . 134 7.8.2 Gas exchange . 135 7.8.3 Incubation period . 137 7.9 Stechlisins Marfey’s Analysis . 138 7.10 Stechlisins MS/MS fragmentation . 140 7.11 Stechlisins NMR spectra . 142 7.11.1 Stechlisin B2 . 142 7.11.2 Stechlisin C3 . 151 7.11.3 Stechlisin D3 . 160 7.11.4 Tensin . 169 7.11.5 Stechlisin E2 . 178 7.11.6 Stechlisin F . 187 8 Project contributions 196 IV 9 Declaration of Originality/Eigenständigkeitserklärung 197 10 Acknowledgments 198 List of Figures 1 Recalculation of Line spectra schematic . .7 2 MS/MS spectral vector comparison schematic . 10 3 Example of a MS/MS network schematic . 11 4 Metabolomics platform: Bioactivity Actinobacteria . 19 5 Metabolomics platform: Principle component analysis of Acti- nobacteria bucket table . 20 6 Metabolomics platform: Metabolic Heatmap of Actinobacteria bucket table . 23 7 Metabolomics platform: MS-networking ClusterA Echoside A . 26 9 Metabolomics platform: MS-networking ClusterB - Resomycins . 26 10 Metabolomics platform: MS-networking ClusterC - Anguinomycines 26 8 Metabolomics platform: MS-Network overview . 27 11 Metabolomics platform: MS-networking Cluster D - Naphtocyclinones 29 12 Metabolomics platform: MS-networking Cluster E - Scopafungines 30 13 Metabolomics platform: µ-fractionation of ST101789 in 5315 . 31 14 Metabolomics platform: µ-fractionation of ST106693 in 5315 . 33 15 Metabolomics platform: µ-fractionation ST107645 . 35 16 Metabolomics platform: µ-fractionation ST107165 . 37 17 Lake Stechlin: Krona chart of plankton associated bacterial com- munity . 53 18 Lake Stechlin: Krona chart of bacterial community in lake water . 54 19 Lake Stechlin: Phyla distribution associated to plankton and in water 55 20 Lake Stechlin: OTU distribution associated to plankton and in water 56 21 Lake Stechlin: Stechlin cultivates exhibiting bioactivity against E. coli DH5α ............................... 57 22 Lake Stechlin: Dereplication of Aerobactin in FhG100039 extracts 58 23 Stechlisins: Molecular Network cluster analysis of FhG100052 . 73 24 Stechlisins: Media optimization for increased CLP production . 74 25 Stechlisins: Structure and absolute configuration of isolated Stech- lisins and Tensin . 81 26 Stechlisins: Proposed structures of minor compounds based on MS/MS . 83 27 Stechlisins: Phylogenetic analysis aminoacid sequences of A-domain of steABC ............................... 85 28 Stechlisins: Biosynthetic gene cluster and proposed biosynthesis of Stechlisins . 86 S1 Metabolomics platform: Custom script used for LCMS data processing117 S2 Metabolomics platform: Custom script used for metabolic heatmap generation . 118 S3 Metabolomics platform: Absolute intensity of selected buckets . 121 S4 Metabolomics platform: MS-networking Cluster - Conglobatin . 122 S5 Metabolomics platform: Echoside A Reference comparison . 124 V S6 Metabolomics platform: Anguinomycin A Reference comparison . 125 S7 Metabolomics platform: β-Naphthocyclinone-epoxide Reference comparison . 126 S8 Metabolomics platform: Scopafungin Reference comparison . 127 S9 Lake Stechlin: Biomass maxima Lake Stechlin . 128 S10 Lake Stechlin: Phyla distribution in percent . 129 S11 Lake Stechlin: Bioluminescene assay layout . 130 S12 Lake Stechlin: µ-fractionation of FhG10052 extract . 131 S13 MIC plate design . 132 S14 Stechlisins: steABC flanking regions . 133 S15 Stechlisins: Optitimization of C-source and cultivation duration . 134 S16 Influence of culture vessel on CLP biosynthesis . 135 S17 Influence of culture vessel on cell density . 136 S18 Stechlisins: Kinetics of Tensin biosynthesis by strain FhG100052 . 137 S19 Stechlisins: Marfey’s derivatization . 138 S20 Stechlisins: Marfey’s derivatization continued . 139 S21 Stechlisins: Proposed structures of minor Stechlisins based on MS/MS141 S22 NMR spectra Stechlisin B2 . 142 S31 NMR spectra Stechlisin C3 . 151 S40 NMR spectra Stechlisin D3 . 160 S49 NMR spectra Tensin . 169 S58 NMR spectra Stechlisin E2 . 178 S67 NMR spectra Stechlisin F . 187 VI 1 Abstract In order to fill the discovery void of novel anti Gram-negative substances, a selection of metabolomic tools was established for industrial routine application. The herein described platform was tested with a set of Streptomycetes strains, before environmental isolates were analyzed. First, two methods for quality control and chemical diversity assessment were compared. It appeared that a vector space model reduces the effect of outliers, compared to a principle component analysis, and is thereby most suitable to describe heterogeneous data such as metabolome comparisons. Second, the pipeline of data generation yielded unsupervised annotations of many literature known as well as structurally related, yet not described, molecules (variable dereplication) in the bacterial compound mixtures. The majority of compounds was detected by both applied methods, mass spectrometry (MS) based bucket annotation and tandem MS based molecular networking. In the following, the survey for new anti Gram negative compound producers was extended to environmental isolates. In that sense, the potential of the bacterial community of Lake Stechlin (Brandenburg, Germany) was evaluated. Different sample types, namely plankton associated and free living microorganisms, were retrieved from the lake and analyzed on the basis of their microbiome composition. The observed OTU distribution pattern was comparable to previously published observations, thus was considered to genuinely reflect the natural bacterial composition of the lake. The different samples types were shown contain distinct bacterial communities, thus made a great overall biodiversity available for further experiments. Prioritization of isolated bacteria from Lake Stechlin was carried out by cell free su- pernatant assays against E.coli DH5α. A metabolom analysis led to the discovery of a undescribed group of cyclic lipopeptides (CLPs) in the culture broth of Pseu- domonas sp. FhG100052. Culture condition optimization facilitated the isolation and subsequent structure elucidation of the five new compounds. Characterization comprised extensive MS/MS and NMR experiments in combination with Marfey’s analysis. The data were in agreement with in silico analysis of the corresponding biosynthetic gene cluster (BGC). The new compounds resemble members of the Amphisin group [147] as they are constructed of a 3-hydroxy fatty acid linked to the N-terminus of an undecapeptide core. Most strikingly, the length of the incorporated fatty acid seems to define the moderate growth inhibitory effects against the Gram negative pathogen Moraxella catarrhalis FH6810 as observed by MIC values ranging from no inhibition (> 128 µg/mL) to 4 µg/mL. 1 2 Introduction 2.1 Antibiotic Resistance Globally, infectious diseases remain top ten killers of humans. In 2016, lower respiratory tract infections, diarrheal diseases and tuberculosis claimed about 6 million lives, translating to 10 % of all death worldwide [133]). In the near future, emergence and spread of antimicrobial resistance (AMR) among pathogenic bacteria might intensify.
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