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Non-targeted metabolomic profiling of Cremastra appendiculata providing insights for phytochemical analyses

ABSTRACT:

Abstract

Background. Cremastra appendiculata (D. Don) Makino, known as 'Shan Cigu' in China, is a valuable medicinal plant historically employed for its antibacterial and anti-inflammatory properties. However, its comprehensive metabolome remains underexplored, hindering the establishment of standardized quality control.

Methods. In this study, a non-targeted metabolomics approach based on the Thermo Fisher Orbitrap Exploris 120 LC-MS (Liquid Chromatography-Mass Spectrometry) platform was employed to systematically profile the metabolites of C. appendiculata.

Results. A total of 174 compounds were annotated through a dual-validation workflow integrating Compound Discoverer 3.3 and manual tandem mass spectrometry spectral verification. Orthogonal partial least squares discriminant analysis prioritized 30 candidate quality markers, of which, six were further validated through network pharmacology-based bioactivity screening. Hierarchical clustering analysis revealed distinct metabolic patterns across the different tissues (roots, pseudobulbs, and leaves), establishing a tissue-specific chemical atlas. The integration of chemometric, network pharmacological, and chemotaxonomic analyses resulted in a robust, molecularly guided quality control framework, providing novel insights for phytochemical research and medical applications of C. appendiculata.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase

PROVIDER: MTBLS13252 | MetaboLights | 2025-11-03

REPOSITORIES: MetaboLights

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			Action	DRS
	a_MTBLS13252_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS13252_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS13252_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular
	m_MTBLS13252_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular

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Project description:With thousands of chemicals in commerce and the environment, rapid identification of potential hazards is a critical need. Combining broad molecular profiling with targeted in vitro assays, such as high-throughput transcriptomics (HTTr) and receptor screening assays, could improve identification of chemicals that perturb key molecular targets associated with adverse outcomes. We aimed to link transcriptomic readouts to individual molecular targets and integrate transcriptomic predictions with orthogonal receptor-level assays in a proof-of-concept framework for chemical hazard prioritization. Transcriptomic profiles generated via TempO-Seq in U-2 OS and HepaRG cell lines were used to develop signatures comprised of genes uniquely responsive to reference chemicals for distinct molecular targets. These signatures were applied to 75 reference and 1,126 non-reference chemicals screened via HTTr in both cell lines. Selective bioactivity towards each signature was determined by comparing potency estimates against the bulk of transcriptomic bioactivity for each chemical. Chemicals predicted by transcriptomics were confirmed for target bioactivity and selectivity using available orthogonal assay data from US EPA’s ToxCast program. A subset of 37 selectively acting chemicals from HTTr that did not have sufficient orthogonal data were prospectively tested using one of five receptor-level assays. Of the 1,126 non-reference chemicals screened, 201 demonstrated selective bioactivity in at least one transcriptomic signature, and 57 were confirmed as selective nuclear receptor agonists. Chemicals bioactive for each signature were significantly associated with orthogonal assay bioactivity, and signature-based points-of-departure were equally or more sensitive than biological pathway altering concentrations in 81.2% of signature-prioritized chemicals. Prospective profiling found that 18 of 37 (49%) chemicals without prior orthogonal assay data were bioactive against the predicted receptor. Our work demonstrates that integrating transcriptomics with targeted orthogonal assays in a tiered framework can support Next Generation Risk Assessment by informing putative molecular targets and prioritizing chemicals for further testing. NOTE: this GEO entry only includes the HepaRG cell line data; the U-2 OS cell line data can be located via GEO accession number GSE274318.

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Non-targeted metabolomic profiling of Cremastra appendiculata providing insights for phytochemical analyses

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