Molecular Mechanisms behind Safranal's Toxicity to HepG2 Cells from Dual Omics

Abstract

The spice saffron (Crocus sativus) has anticancer activity in several human tissues, but the molecular mechanisms underlying its potential therapeutic effects are poorly understood. We investigated the impact of safranal, a small molecule secondary metabolite from saffron, on the HCC cell line HepG2 using untargeted metabolomics (HPLC-MS) and transcriptomics (RNAseq). Increases in glutathione disulfide and other biomarkers for oxidative damage contrasted with lower levels of the antioxidants biliverdin IX (139-fold decrease, p = 5.3 × 10⁵), the ubiquinol precursor 3-4-dihydroxy-5-all-trans-decaprenylbenzoate (3-fold decrease, p = 1.9 × 10^-5), and resolvin E1 (-3282-fold decrease, p = 4⁵), which indicates sensitization to reactive oxygen species. We observed a significant increase in intracellular hypoxanthine (538-fold increase, p = 7.7 × 10^-6) that may be primarily responsible for oxidative damage in HCC after safranal treatment. The accumulation of free fatty acids and other biomarkers, such as S-methyl-5'-thioadenosine, are consistent with safranal-induced mitochondrial de-uncoupling and explains the sharp increase in hypoxanthine we observed. Overall, the dual omics datasets describe routes to widespread protein destabilization and DNA damage from safranal-induced oxidative stress in HCC cells.

Keywords: DNA damage; cancer; hepatocellular carcinoma; hypoxanthine; natural products; saffron; safranal.

Figure 1

Overview of experiment and omics datasets. (A) Safranal is an active small molecule compound in saffron derived from the pistils of Crocus sativus. We performed RNAseq and HPLC–MS on extracts from safranal-treated and non-treated HCC cells. (B) Volcano plot showing transcript expression changes (x-axis) and negative log p-values (y-axis). (C,D) Mass-to-charge ratios (m/z; y-axis), retention time (x-axis), and fold-change (bubble size) of metabolites in response to safranal treatment (p < 0.001 for all shown features) in (C) cell pellet extracts or (D) conditioned media extracts. Yellow bubbles indicate increased, and blue indicates decreased compounds in HCC pellet extracts after safranal treatment.

Figure 2

Up (top) and down (bottom)-regulated metabolic pathways in pellet fractions from safranal-treated HCC cells as predicted from the HPLC–MS results in the Systems Biology module of XCMS [17]. These pathways were based on metabolic pathways annotated in the BioCyc4 (biocyc.org) and Uniprot6 (uniprot.org) databases. Their respective metabolites, degree fold-change (x-axis), and mass-to-charge ratios (m/z, heatmap) are shown. Dysregulated metabolites are indicated inside or adjacent to bars for increased (top) or decreased (below) metabolites in safranal-treated cells. Multiple adducts for the same compound are shown as stacked bars.

Figure 3

Overlap of metabolomic- and transcriptomic-predicted pathway dysregulation (as observed from cell pellets) in safranal-treated HCC cells. Maps show metabolites (gray dots), edges with no evidence (gray edges), transcriptomic reaction dysregulation evidence (green edges), metabolomic reaction dysregulation evidence (blue edges), and reaction dysregulation evidence from both omics experiments (bold black edges) after safranal treatment. Metabolites and their respective metabolic pathways were identified using LC/MS–QToF and Mummichog (http://mummichog.org/, accessed on 20 March 2021) in XCMS and their overlaps with those identified from RNAseq analysis (black edges, panels A–C). (A) Overview of metabolic dysregulation evidence from the dual omics experiments visualized in the Interactive Pathways Explorer v3 (https://pathways.embl.de/, accessed 29 May 2021). (B) Lipid biosynthesis dysregulation after safranal treatment (see also Table S2). (C) Steroid biosynthesis dysregulation after safranal treatment (see also Table S2). (D) Overlap of metabolomics and transcriptomics dysregulated metabolic reactions from CAS and EC numbers. Metabolite accessions were retrieved from the Chemical Abstract Service (CAS) using the METLIN (https://metlin.scripps.edu, accessed 4 May 2021) batch search tool, ECs were retrieved from transcripts using EC Domain Miner [22]. The CAS and EC accession numbers were compared in iPATH3 (https://pathways.embl.de/, accessed 29 May 2021) to detect dysregulated.