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The natural variance of the Arabidopsis floral secondary metabolites.

ABSTRACT: Application of mass spectrometry-based metabolomics enables the detection of genotype-related natural variance in metabolism. Differences in secondary metabolite composition of flowers of 64 Arabidopsis thaliana (Arabidopsis) natural accessions, representing a considerable portion of the natural variation in this species are presented. The raw metabolomic data of the accessions and reference extracts derived from flavonoid knockout mutants have been deposited in the MetaboLights database. Additionally, summary tables of floral secondary metabolite data are presented in this article to enable efficient re-use of the dataset either in metabolomics cross-study comparisons or correlation-based integrative analysis of other metabolomic and phenotypic features such as transcripts, proteins and growth and flowering related phenotypes.

SUBMITTER: Tohge T

PROVIDER: S-EPMC5881409 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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The natural variance of the Arabidopsis floral secondary metabolites.

Tohge Takayuki T Borghi Monica M Fernie Alisdair R AR

Scientific data 20180403

Application of mass spectrometry-based metabolomics enables the detection of genotype-related natural variance in metabolism. Differences in secondary metabolite composition of flowers of 64 Arabidopsis thaliana (Arabidopsis) natural accessions, representing a considerable portion of the natural variation in this species are presented. The raw metabolomic data of the accessions and reference extracts derived from flavonoid knockout mutants have been deposited in the MetaboLights database. Additi ...[more]

PMID: 29611844

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Project description:Health organizations worldwide have warned that we are on the cusp of a "post-antibiotic era," necessitating new approaches to combat antibiotic resistant infections. One such approach is the development of antibiotic adjuvants, which have little or no inherent antibiotic activity at their active concentrations but instead potentiate the activity of antibiotics against antibiotic-resistant bacteria. Recently, we demonstrated that meridianin D, a natural product originally reported to have activity against Staphylococcus aureus and Mycobacterium tuberculosis, possesses the ability to reverse colistin resistance in colistin resistant bacteria. As most natural product screens typically involve screening for only certain activities (anticancer, antiviral, and antimicrobial are typical), we posited that the meridianin D discovery was not unique and there are potentially many natural products that have adjuvant activity. To explore this, the National Cancer Institute (NCI) Natural Product Library Set IV was screened for adjuvant activity using four classes of antibiotics (β-lactams, aminoglycosides, macrolides, and polymyxins) against three bacterial pathogens (methicillin-resistant Staphylococcus aureus (MRSA), Acinetobacter baumannii, and Klebsiella pneumoniae). Sixteen compounds suppressed β-lactam resistance in MRSA, five of which effected a 16-fold reduction in the oxacillin minimum inhibitory concentration (MIC). Two natural products effectively suppressed aminoglycoside resistance in both of the Gram-negative species tested, and no hits were observed with macrolides. In contrast, a larger number of natural product adjuvants were identified when screening against colistin-resistant strains of A. baumannii and K. pneumoniae. Nine compounds reduced the colistin MIC to its breakpoint or lower (up to a 1024-fold reduction). Clorobiocin, novobiocin, and prodigiosin were most effective, reducing the colistin MIC in K. pneumoniae strain B9 to 2 μg/mL at concentrations as low as 0.625, 2.5, and 1.25 μM, respectively. Restored sensitivity to colistin with these compounds does not appear to coincide with known mechanisms of colistin resistance.

Dataset Information

The natural variance of the Arabidopsis floral secondary metabolites.

Publications

The natural variance of the Arabidopsis floral secondary metabolites.

Similar Datasets

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