dc.contributor.author | Liu, Miaomiao | |
dc.contributor.author | Grkovic, Tanja | |
dc.contributor.author | Liu, Xueting | |
dc.contributor.author | Han, Jianying | |
dc.contributor.author | Zhang, Lixin | |
dc.contributor.author | Quinn, Ronald J | |
dc.date.accessioned | 2018-02-14T02:39:57Z | |
dc.date.available | 2018-02-14T02:39:57Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 2405-805X | |
dc.identifier.doi | 10.1016/j.synbio.2017.10.001 | |
dc.identifier.uri | http://hdl.handle.net/10072/369396 | |
dc.description.abstract | The growing number of sequenced microbial genomes has revealed a remarkably large number of secondary metabolite biosynthetic clusters for which the compounds are still unknown. The aim of the present work was to apply a strategy to detect newly induced natural products by cultivating microorganisms in different fermentation conditions. The metabolomic analysis of 4160 fractions generated from 13 actinomycetes under 32 different culture conditions was carried out by 1H NMR spectroscopy and multivariate analysis. The principal component analysis (PCA) of the 1H NMR spectra showed a clear discrimination between those samples within PC1 and PC2. The fractions with induced metabolites that are only produced under specific growth conditions was identified by PCA analysis. This method allows an efficient differentiation within a large dataset with only one fractionation step. This work demonstrates the potential of NMR spectroscopy in combination with metabolomic data analysis for the screening of large sets of fractions. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Elsevier | |
dc.relation.ispartofpagefrom | 276 | |
dc.relation.ispartofpageto | 286 | |
dc.relation.ispartofissue | 4 | |
dc.relation.ispartofjournal | Synthetic and Systems Biotechnology | |
dc.relation.ispartofvolume | 2 | |
dc.subject.fieldofresearch | Medicinal and biomolecular chemistry not elsewhere classified | |
dc.subject.fieldofresearch | Biochemistry and cell biology | |
dc.subject.fieldofresearch | Bioinformatics and computational biology | |
dc.subject.fieldofresearchcode | 340499 | |
dc.subject.fieldofresearchcode | 3101 | |
dc.subject.fieldofresearchcode | 3102 | |
dc.title | A systems approach using OSMAC, Log P and NMR fingerprinting: An approach to novelty | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
dcterms.license | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.description.version | Version of Record (VoR) | |
gro.faculty | Griffith Sciences, School of Natural Sciences | |
gro.rights.copyright | © 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Quinn, Ronald J. | |
gro.griffith.author | Liu, Miaomiao | |