Background: Pattern-oriented chemical profiling is increasingly being used to characterize the phytochemical composition of herbal medicines for quality control purposes. Ideally, a fingerprint of the biological effects should complement the chemical fingerprint. For ethical and practical reasons it is not possible to test each herbal extract in laboratory animals or humans. What is needed is a test system consisting of an organism with relevant biology and complexity that can serve as a surrogate in vitro system. The purpose of this study was to test the hypothesis that the Saccharomyces cerevisiae transcriptome might be used as an indicator of phytochemical variation of closely-related yet distinctly different extracts prepared from a single species of a phytogeographically widely distributed medicinal plant. We combined phytochemical profiling using chromatographic methods (HPTLC, HPLC-PDA-MS/MS) and gene expression studies using Affymetrix Yeast 2.0 gene chip with principal component analysis and k-nearest neighbor clustering analysis to test this hypothesis using extracts prepared from the phytogeographically widely distributed medicinal plant Equisetum arvense as a test case.
Results: We found that the Equisetum arvense extracts exhibited qualitative and quantitative differences in their phytochemical composition grouped along their phytogeographical origin. Exposure of yeast to the extracts led to changes in gene expression that reflected both the similarities and differences in the phytochemical composition of the extracts. The Equisetum arvense extracts elicited changes in the expression of genes involved in mRNA translation, drug transport, metabolism of energy reserves, phospholipid metabolism, and the cellular stress response.
Conclusions: Our data show that functional genomics in S. cerevisiae may be developed as a sensitive bioassay for the scientific investigation of the interplay between phytochemical composition and transcriptional effects of complex mixtures of chemical compounds. S. cerevisiae transcriptomics may also be developed for testing of mixtures of conventional drugs ("polypills") to discover novel antagonistic or synergistic effects of those drug combinations.
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| Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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| Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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| Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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| Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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| Site | Modification | Modifier | Source | Reference |
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| Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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| Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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| Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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| Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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| Evidence ID | Analyze ID | File | Description |
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