August 2015
J Nat Prod.
Abstract
Silymarin, a characterized extract of the seeds of milk thistle (Silybum marianum), suppresses cellular inflammation. To define how this occurs, transcriptional profiling, metabolomics, and signaling studies were performed in human liver and T cell lines. Cellular stress and metabolic pathways were modulated within 4 h of silymarin treatment: activation of Activating Transcription Factor 4 (ATF-4) and adenosine monophosphate protein kinase (AMPK) and inhibition of mammalian target of rapamycin (mTOR) signaling, the latter being associated with induction of DNA-damage-inducible transcript 4 (DDIT4).
Metabolomics analyses revealed silymarin suppression of glycolytic, tricarboxylic acid (TCA) cycle, and amino acid metabolism. Anti-inflammatory effects arose with prolonged (i.e. 24 h) silymarin exposure, with suppression of multiple pro-inflammatory mRNAs and signaling pathways including nuclear factor kappa B (NF-κB) and forkhead box O (FOXO).
Studies with murine knock out cells revealed that silymarin inhibition of both mTOR and NF-κB was partially AMPK dependent, while silymarin inhibition of mTOR required DDIT4. Other natural products induced similar stress responses, which correlated with their ability to suppress inflammation. Thus, natural products activate stress and repair responses that culminate in an anti-inflammatory cellular phenotype. Natural products like silymarin may be useful as tools to define how metabolic, stress, and repair pathways regulate cellular inflammation.
Natural products are used to prevent and treat a plethora of chronic, debilitating, and inflammatory diseases. Over one-third of adults in the US reported self-medicating with complementary and alternative medicines (CAM).1 Defining precise mechanisms of action is a critical barrier to the optimal application of botanicals as CAMs and as pharmaceuticals.
Natural products, like the compounds contained in silymarin (a.k.a. milk thistle extract; from the plant Silybum marianum [L.] Gaertn. [Asteraceae]), protect cells by various antioxidant, anti-inflammatory, antiviral, immunomodulatory, proliferative, and metabolic effects,2,3 resulting in diverse protective phenotypes, both in vitro and in vivo. For example, silymarin and silymarin-derived flavonolignans inhibit in vitro hepatitis C virus (HCV) infection of human liver cell cultures, HCV-induced oxidative stress, NF-κB pro-inflammatory signaling, and T cell activation and inflammatory cytokine production.4
Although there is a clear precedence for silymarin and silbinin, the major component of silymarin, having anti-cancer,5 and cytoprotective effects,6 an integrated view of the cellular responses and pathways behind these phenotypes has not been established. While the anti-cancer activities of silymarin arise via induction of cell death, it is not clear how non-toxic doses of silymarin confer cellular protection in the form of reduced cellular inflammation.
Environmental cues such as energy restriction, oxygen depletion, and viral infection activate stress responses in a cell. By triggering stress pathways, the cell initiates responses that result in either adaptation (through reparative responses) or cell death (via apoptosis, necrosis, or pyroptosis). Regardless of the final cellular fate, there exists common cellular responses to stress such as early metabolic and cell cycle changes,7 damage repair processes,8 and the initiation of protective mechanisms, such as induction of antioxidant response genes.9
Cell responses to stress are initiated often with cross talk between key metabolic signaling kinases and transcription factors. For example, the mammalian target of rapamycin (mTOR), involved in approximately 80% of all cancers,10 is a major hub for several metabolic inputs and cues. By sensing energy status, mTOR affords switching between anabolic (in a nutrient-rich environment) and catabolic (during stress) cellular processes. Thus, when nutrients are abundant, mTOR kinase activity leads to the phosphorylation of several downstream targets including eukaryotic translation initiation factor 4E-binding protein (4EBP-1) and S6 kinase (S6K), which promotes cellular anabolism. Conversely, mTOR signaling is inhibited during amino acid depletion.11,12
mTOR receives input from multiple stress-sensing pathways. For example, when the 5′ adenosine monophosphate-activated protein kinase (AMPK) pathway is activated and phosphorylated by energy stress (i.e. decreased cellular ATP/ADP ratios), it inhibits the mTOR pathway.13 Moreover, the expression of DNA damage-inducible transcript 4 (DDIT; also known as REDD1), a major negative regulator of mTOR activity, is induced during stress.14,15 During endoplasmic reticulum (ER) stress, the translational control factor eukaryotic initiation factor 2-alpha (eIF2α) becomes phosphorylated on a conserved serine residue, leading to translational repression of most cellular mRNAs with the exception of a few key stress-response proteins, such as ATF4. Moreover, the mTOR pathway also converges on the transcription factor FOXO3a, which plays pivotal roles in determining cellular fates (adaptation vs. death) to environmental stress by altering metabolism, initiating immune responses, and initiating apoptosis.16
This study presents the first transcriptional and metabolomics profiling study of silymarin-treated human liver-derived cells with protein and signaling validation in both human liver and T cell cultures. It is shown that non-toxic doses of silymarin initially induce ER and energy stress responses, and that instead of dying, the cell responds to these stresses with adaptive and reparative responses. In this environment, cells adapt to the non-toxic stress by establishing a cellular milieu that is anti-inflammatory. Moreover, metabolic pathways, like AMPK, that are modulated by silymarin, are involved in suppression of mTOR and inflammatory (i.e. NF-κB) signaling. Finally, other natural products induced similar stress responses, which correlate with their ability to suppress inflammation. Using natural products as tools to define how cellular stress links to inflammatory status may reveal opportunities for selective reprogramming of cellular metabolism in order to alter immune and inflammatory responses in both health and disease.
Results and discussion
This workflow of this study started with a whole genome microarray study of silymarin versus DMSO solvent control treated human hepatoma Huh7.5.1 cells for four, eight, and 24 h. These time points were chosen in order to capture the earliest transcriptional changes in a cell following exposure to silymarin.
Preliminary whole genome microarray studies were also performed following one h and four h of silymarin exposure. Unsupervised hierarchical clustering of differentially expressed transcripts revealed that silymarin–specific clustering only occurred with the 4h treatment (data not shown). Thus, four h was chosen as the earliest time point. Gene expression data were then analyzed by various bioinformatics software to identify key genes and pathways modulated by silymarin.
Microarray results were then validated by independent gene expression assays (quantitative reverse transcriptase polymerase chain reaction (qRT-PCR)) and protein expression by Western blot. Gene expression analysis suggested silymarin was altering cellular metabolism. As such, a metabolomics study was also performed. Pathways modulated by silymarin were validated by signal transduction studies in Huh7.5.1 cells and Jurkat T cells. Huh7.5.1 human hepatoma cells and Jurkat T cells were chosen for this study because we have previously shown that silymarin inhibits inflammatory signaling via NF-κB in these cell types4,17, and they are also relevant model systems to study HCV and HIV infection-associated inflammation.
In human hepatoma Huh7.5.1 cells, a non-toxic dose of 80 μM silymarin or DMSO for four h resulted in the significant differential expression of 82 mRNAs, with 67 mRNAs significantly induced and 15 mRNAs significantly inhibited. The number of genes modulated by silymarin treatment grew over time, with 98 and 58 genes significantly induced and 165 and 188 genes significantly repressed at the eight and 24 h time points, respectively.
There were two major patterns of transcriptional regulation induced by silymarin treatment. As shown in the heat map in Figure S1, Supporting Information, 443 genes were differentially expressed between the DMSO and silymarin treatments at four, eight, and 24 h. The first pattern (highlighted in the right third of the heat map) was a significant induction of mRNAs by silymarin treatment at four h that remained elevated at eight and 24 h. The second pattern of mRNA expression (highlighted in the left-most section of the heat map) was unchanged at four h, decreased slightly by eight h and was strongly suppressed by the 24-h time point. Thus, the cellular response to silymarin treatment was biphasic: an initial and sustained induction of gene expression, followed by progressive suppression of mRNAs with prolonged silymarin treatment.
Eighteen significantly up-regulated and down-regulated genes were further validated by qRT-PCR (Figure S2A, Supporting Information). Genes were selected primarily if they showed strong regulation by silymarin. Some genes were secondarily selected based on biological relevance to the anti-inflammatory actions of silymarin. There was strong correlation between array and qRT-PCR results of the gene expression patterns for all 18 genes compared across all time points (Figure S2B, Supporting Information). As shown in Figure S3A, Supporting Information, DDIT4 (REDD1) mRNA was one of the most highly induced transcripts observed with silymarin treatment. By qRT-PCR, DDIT4 mRNA was significantly induced at four, eight, and 24-h post-silymarin treatment. Silymarin treatment also induced DDIT4 protein in Huh7.5.1 cells (Figure S3B, Supporting Information).
In contrast, CXCL10, a highly pro-inflammatory gene, was the most down-regulated mRNA post-silymarin treatment. CXCL10 mRNA was most suppressed at the 24-h time point, exemplary of the progressive temporal decline in gene expression due to silymarin treatment (Figure S3C, Supporting Information). Furthermore, silymarin dose-dependently reduced CXCL10 mRNA and CXCL10 protein expression in Jurkat cells co-stimulated by IFNγ and TNFα (Figure S3D, Supporting Information), demonstrating that silymarin treatment inhibited induction of the chemokine CXCL10 in two distinct cell types.
