Biomedical Microdevices

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Study of melatonin-mediated effects on various hepatic inflammatory responses stimulated by IL-6 in a new HepG2-on-a-chip platform

  • Mi Jang
  • Andreas Manz
  • Thomas Volk
  • Astrid KleberEmail author


Hepatocytes exhibit diverse reactions upon stimulation with the interleukin IL-6, mainly in the context of inflammation and energy metabolism. Melatonin has been shown to exert pleiotropic protective actions, such as anti-inflammation and anti-oxidative stress on many cell- and organ-types. The key role of the liver to maintain homeostasis and metabolic regulation prompted us to evaluate the direct modification of IL-6-induced alterations in HepG2 cells in a chip by melatonin. IL-6 administration was followed by the reduced expression and activity of MRP2, a loss of CYP1A activity, and the decline of PXR expression. Other effects were the induction of acute phase responses (reduced albumin production as well as increased CRP and hepcidin expression) and lowered expression of CREB3L3. IL-6 affected also the mitochondrial membrane potential together with elevated mitochondrial superoxide generation, and glycogen deposition was reduced. Melatonin counteracted all observed IL-6-induced alterations except the rise in CRP release and CYP1A activity. Altogether, this new in vitro model can be applied to investigate hepatic inflammatory responses stimulated by IL-6, and these results indicate that hepatocellular inflammatory responses to IL-6 are mitigated by melatonin.


Melatonin IL-6 Microfluidic cell culture HepG2 

1 Introduction

The liver has diverse functions including the control of the blood composition, detoxification processes, nutrient procession and storage, and it is the major organ to produce factors of the acute phase response (APR). Liver diseases are generally associated with increased inflammation accompanied by the rise of various pro- and anti-inflammatory cytokines in the blood stream but also locally secreted by liver macrophages. Hepatocytes react on inflammatory stimuli with diverse stress response mechanisms. One mechanism encompasses reactions denoted as endoplasmic reticulum (ER) stress response, unfolded protein response (UPR), or heat-shock response aiming to directly protect the respective cell from protein damage and dysfunction of cellular organelles. Another mechanism pertains the APR as being part of the innate immune response, therefore acting globally to protect all affected cells and organs.

Interleukin 6 (IL-6) is a cytokine and secreted by activated immune cells, leading to elevated plasma levels found in multiple diseases, and also secreted by liver macrophages, thereby stimulating hepatocytes locally (Norris et al. 2014). This cytokine stands out for very pleiotropic actions which are regulated by the composition of the complex that IL-6 forms with its receptors IL-6R and gp130 either in their soluble or in their membrane-bound forms (Wolf et al. 2014). IL-6 is involved in inflammation and acts as a major regulator for the APR, whereas other pro-inflammatory cytokines such as IL-1 and tumor necrosis factor (TNF)-α could play a minor role due to their limited stimulation of major APR proteins (Ramadori et al. 1988) (Heinrich et al. 1990). Some studies also indicate a link between IL-6 signaling, the UPR, and the induction of APR genes (Chung et al. 2011; Liu et al. 2015; Zhang et al. 2006). Other effects of IL-6 on hepatocytes are related to altered expression levels of cytochrome P450 monooxygenases and drug transporters, thereby modifying hepatic detoxification mechanisms (Fardel and Le Vée 2009; Rubin et al. 2015). IL-6 also affects glucose metabolism in the liver, accompanying glycogen depletion (Lienenlüke and Christ 2007). A recent study discovered the translocation of signal transducer and activator of transcription 3 (STAT3) to the mitochondria of immune cells, thereby modifying the mitochondrial membrane potential and highlighting a new pathway of IL-6 signaling (Yang et al. 2015).

The hormone melatonin is particularly known for its main function in regulating the circadian clock. Moreover, melatonin has been shown to possess strong anti-inflammation and anti-oxidative stress organ-protective properties (Carrillo-Vico et al. 2005; Mathes 2010; Wu et al. 2008). In previous studies, we found evidence for the modification of cellular stress mechanisms by melatonin in the context of inflammation. For example, transcription factors of the UPR can be altered by this hormone. This was demonstrated by increased gene expression levels of the ER stress proteins PERK (Protein Kinase RNA-like Endoplasmic Reticulum Kinase) and DDIT3 (DNA damage-inducible transcript 3 protein) in melatonin-treated septic animals (Kleber et al. 2014). Microarray analysis on rats with hemorrhagic shock revealed that melatonin suppressed the shock-induced upregulation of UPR modifying proteins (Kleber et al. 2015). These studies also revealed that melatonin modifies the expression of the transcription factor CREB3L3 (cAMP responsive element binding protein 3) that seems to be an important player for mediating the hepatic signaling of IL-6 via the ER stress response to activate the APR (Shin et al. 2012). Furthermore, regulatory effects on glucose metabolism by CREB3L3 were described (Chanda et al. 2011). Therefore, it is very interesting to check whether melatonin involves the acute phase response regulation via CREB3L3. Although the anti-inflammatory function of melatonin is well known, to our knowledge there is no study relating APR and melatonin.

Mitochondria provide energy to the cell but are also responsible for free radical production during ATP (adenosine triphosphate) production, resulting in oxidative stress. In addition, UPR induced overload of reactive oxygen species (ROS), calcium production in the ER, accompanying altered mitochondrial membrane potential (Chaudhari et al. 2014). Interestingly, melatonin is accumulated in mitochondria and exerts protective effects via scavenging reactive oxygen species (ROS) and inhibiting the mitochondrial permeability transition pore (MPTP) (Tan et al. 2016). Therefore, mitochondria are considered as main target organelles for melatonin.

Still, most inflammation studies rely on animal experiments, even though the poor correlation with human conditions often misleads the physiological and genetic results on humans (Akhtar 2015; Seok et al. 2013). Since one-decade, various new in vitro liver platforms have been developed to reduce this gap. However, most applications focused on drug screening and drug-induced hepatotoxicity studies. Nearby this detoxification function, the human liver is also involved in inflammation processes. Therefore, the need of developing precise and predictable in vitro human liver models to study inflammation is urgent. We developed a new culture system as a liver-on-a-chip platform which preserves a considerable range of human hepatic functions on HepG2 cells (Jang et al. 2015). The compatibility of HepG2 cells, which are considered as a model for liver epithelial cells, for the study of inflammatory responses induced by lipopolysaccharide (Kheder et al. 2016), IL-6 (Karlsson et al. 1998), IL-1, and TNF-α (Ma et al. 2008), rifampin antibacterial drug (Yuhas et al. 2011), and vitamin D metabolites (Wehmeier et al. 2016) in vitro was shown in the past. Moreover, HepG2 cells have been proposed as a model for studying liver acute phase response during chronic inflammation (Kasza et al. 1994; Scheers et al. 2014). Therefore, it is very interesting to evaluate how this new HepG2-on-a chip system works for the study of liver cells in response to inflammation stimulated by IL-6 and the interplay of melatonin. The aim of this study is to evaluate the various inflammatory hepatocellular responses by IL-6/melatonin, including detoxification, APR, glycogen storage, and mitochondria functions in the HepG2-on-a-chip system.

2 Materials and methods

2.1 Cell culture conditions

The HepG2 (human hepatocellular carcinoma) cells were purchased from the German collection of microorganisms and cell cultures (DSMZ, Braunschweig, Germany). The cells were cultivated in William’s E medium (Pan-Biotech GmbH, Aidenbach, Germany) supplemented with 10% FBS, penicillin (100 U ml−1), and streptomycin (100 μg ml−1) (Sigma-Aldrich, Munich, Germany) in a 25 cm2 flask. They were incubated at 37 °C and 5% CO2 in a cell incubator (Binder, Tuttlingen, Germany). The number of cells was counted by using a hemocytometer and the cell viability was assessed by trypan blue exclusion. 5 × 107 cells/ml were transferred to each chamber of the OrganoPlate™ (Mimetas company, The Netherlands) as described previously (Jang et al. 2015). The chambers and a schematic depiction of the culture system is presented in Fig. 1. Briefly, 50 μl PBS were added to the observation window to prevent evaporation. The number of HepG2 cells was counted and the appropriate amount was suspended with Matrigel™ (8.2 mg/ml) on ice. This mixture was injected and soaked by capillary forces into the inner channel along the phaseguide. The cells were incubated to gelatinise at 37 °C for 20 min, and 25 μl of the medium was added to the medium outlet. Further incubation for 5–6 h allowed entire gelling. The perfusion was started by adding 100 μl of medium to the inlet well. The medium was renewed every 2 days. The average size of cell aggregates was 100 ± 50 μm.
Fig. 1

OrganoPlate™ microfluidic device for HepG2 cells 3D cultivation. a The top is based on a 384 wells microplate, and the bottom of the plate possesses 40 cell-culture chambers combined with 3-lanes of microfluidic structures. b The microfluidic cell culture chamber consists of 3-lanes, each lane with an inlet and outlet, respectively. c Left: HepG2 cells are embedded in Matrigel™ in the side lane. Perfusion flow is generated by gravitational leveling between the inlet and outlet well in the middle lane. 3-lanes are separated by two phaseguides (yellow color) to allow 3D culture formation by gelation patterning. Right: A view of the vertical cross section of the cell culture chamber. Red arrow indicates the perfusion flow in the middle lane

2.2 IL-6 and melatonin treatment conditions

An IL-6 (Humanzyme, Chicago, USA) stock solution (10 μg/ml) was prepared with 0.1% BSA in sterile 18 MΩ·cm water (Sigma-Aldrich, Munich, Germany). Melatonin (Sigma-Aldrich, Munich, Germany) was solved in DMSO (1 M) and stored at −20 °C. Working solutions were prepared freshly with similar amounts of BSA and DMSO. At the 10th culture day, cells were treated with IL-6 (100 ng/ml), physiological melatonin concentration (1 nM) (Shieh et al. 2009), IL-6 plus melatonin, or control (0.001 mg/ml BSA, 0.0000001% of DMSO) solutions for 72 h. The cytokine concentration was selected according to preliminary experiments (Supplementary Figures, Fig. S1) and literature reports (Diao et al. 2010). The dose of 100 ng/ml of IL-6 was chosen because production levels of mitochondrial superoxide, albumin, and CRP differed significantly from the control.

2.3 Efflux transport assays

The efflux transport assay was performed by using fluorescent substrates. 5-CFDA (Sigma-Aldrich, Germany) was used as a substrate for the multidrug resistance associated protein (MRP2). HepG2 cells in the chip were washed with uptake buffer (Anthérieu et al. 2010) and then incubated for 30 min with 5 μM of 5-CFDA. Cells were washed three times with PBS buffer and immediately monitored under a fluorescence microscope (FITC filter set). Two independent experiments for each group were performed. At least 5 spheroids in each group from each experiment were monitored, and representative spheroids were shown in the results.

2.4 Expression of CYP1A (phase I metabolism cytochromes)

The CYP1A assay was performed with resorufin ethyl ether (Sigma-Aldrich, Munich, Germany) as a substrate which is converted by the cytochrome P450 monooxygenases CYP1A1 and CYP1A2 to a fluorescent resorufin product. After the treatment of IL-6 and melatonin for 72 h, the cells were incubated with 10 μM of 7-ethoxyresorufin in a serum-free medium for 4 h at 37 °C and 5% of CO2 in a cell incubator. Fluorescence intensity was measured at 525/580–640 nm by using a fluorescence microplate reader.

2.5 Immunofluorescence staining

The protein expression levels of MRP2, the pregnane X receptor (PXR), CREB3L3 and hepcidin were determined by immunostaining. Cells in the microfluidic device were fixed in 4% paraformaldehyde for 30 min at room temperature and permeabilized with 0.2% solution of Triton X-100 in PBS for 30 min. After blocking with 1% BSA for 30 min, the cells were incubated with a primary antibody for hepcidin (1:100, ab30760, Abcam, Cambridge, UK), MRP2 (1:50, ab3373, Abcam), PXR (10 μg/ml, ab118336, Abcam), and CREB3L3 (1 μg/ml, ab150865, Abcam, Cambridge, UK) at 4 °C overnight. Subsequently, the cells were stained with the secondary antibody DyLight 488 goat anti rabbit (1:100, ab96899, Abcam, Cambridge, UK) and with Hoechst 33,345 (Sigma-Aldrich, Germany) for nucleic acids staining for 1 h at room temperature. After washing with PBS three times, images were acquired using a Zeiss fluorescent microscope (485 nm LED and FITC filter sets). For the quantification of image analysis, the upper lane and bottom lane were analyzed separately for the accurate calculation and selection of the cell cluster area. All experiments were performed at least 3 to 5 times as independent biological replicates. The number of independent biological replicates (n) for each experiment is indicated in each figure. Finally, 6–18 images from each 4 different condition groups were analyzed following setting of constant threshold values to the four groups and normalization to the total cell area using image J.

2.6 Albumin and CRP measurement

Commercially available ELISA kits were used to determine the amount of albumin (Human Albumin ELISA Kit, E88–129, Bethyl Laboratories, Montgomery, Texas, USA) and CRP (Human C-Reactive Protein ELISA Kit, KHA0031, Life technology, Frankfurt, Germany). The culture medium was collected at indicated time points and stored at −80 °C until usage. All procedures were followed by the manufacturer’s instructions. Optical density at 450 nm was measured with a microplate reader.

2.7 Assays on mitochondrial integrity

The JC-1 mitochondrial membrane potential kit (No. 10009172, Cayman chemical, Tallinn, Estonia) was used according to the manufacturer’s instructions. The cells were washed with PBS and incubated for 20 min with the freshly prepared JC-1 working solution (3 μg/ml) in a CO2 incubator at 37 °C. The cells were washed with PBS, the fluorescence of JC-1 aggregates (red) and monomers (green) were measured microscopically (Texas Red, 530/590 nm; FITC, 485/520 nm), and the red to green ratio was calculated.

MitoSOX™ Red (M36008, Molecular probes, Karlsruhe, Germany) was used to determine the mitochondrial superoxide production. According to the manufacturer’s instructions, a 5 mM stock solution in DMSO was further diluted to 2.5 μM in PBS as a working solution. Cells were incubated 10 min and gently washed three times with warm PBS buffer. After washing, cells were examined under a Zeiss fluorescent microscope (Texas Red filter set). All fluorescent images were quantified as mentioned above using Image J and normalized to the total cell area.

2.8 Alteration in glucose metabolism (glycogen content)

For the determination of the amount of stored glycogen in HepG2 cells clusters, the Periodic Acid-Schiff (PAS) Staining Kit (No. 395B, Sigma-Aldrich, Munich, Germany) was used. Staining was performed according to the manufacturer’s instructions. Briefly, cells were fixed with FAA (formalin-acetic-alcohol) for 3 min and washed three times with distilled water, incubated with the PA solution for 5 min, washed four times, and placed in Schiff’s reagent for 15 min. After continuous washing with distilled water, cells were stained with hematoxylin for 1 min. Images were obtained microscopically. 20–25 cell clusters in each group were selected from 4 independent replicates (n = 4). The PAS-stained area was quantified and normalized to the total area of cell clusters using Image J.

2.9 Statistics

All data are presented as fold change to the respective control group. Statistical analysis was performed using SPSS (IBM, Ehningen, Germany) by using One Way ANOVA for normally distributed data, otherwise, Kruskal-Wallis was performed. Tuckey’s range and Dunnett test were applied to the homogeneity and non-homogeneity of variance samples respectively for multiple comparisons. P < 0.05 was considered significant. For reasons of clarity, all data are expressed as means ± standard deviation (SD).

3 Results

3.1 IL-6 reduced the detoxification capacity of HepG2 cells

3.1.1 MRP2 expression level and efflux activity

To investigate whether the expression level and functional activity of MRP2 are maintained under IL-6 and/or melatonin treatment, immune staining and a specific substrate (5-CFDA) for MRP2 were used. Representative fluorescent images for the staining of MRP2 expression and the respective quantified results are shown in Fig. 2a+b. IL-6 treatment markedly decreased MRP2 expression (43% of the control). The treatment with melatonin alone did not change MRP2 expression levels. In the case of co-treatment of melatonin and IL-6, MRP2 expression levels were comparable to basal levels and significantly higher than in the group that received IL-6 alone. Furthermore, we determined the functional activity of MRP2 transporters by measuring the accumulation of 5-CF intracellularly and at the bile canaliculi after excretion through MRP2. Clusters of HepG2 cells in the control and melatonin groups showed accumulation of exported 5-CF green fluorescence in the middle of the cell spheroids and only little intracellular fluorescence, indicating high transporter activity. In contrast to this, fluorescence was observed exclusively intracellularly in the presence of IL-6. The MRP2 inhibitor MK571 served as a control. Cells treated with MK571 showed only diffused and blurred intracellular fluorescence (Fig. 2c).
Fig. 2

MRP2 expression levels and its transporter efflux assay on HepG2 cells after IL-6 and/or melatonin treatment. a + b MRP2 expression presented by representative light and fluorescent images and quantified by the calculated fluorescent intensity per total cell area. Scale bar indicates 100 μm. 10–18 images in each group were analyzed and data are shown as mean ± SD (n = 4 or 5). Significant differences (p < 0.05) are indicated as follows: * vs. Control, Melatonin, IL-6 + Melatonin. c Representative images of the MRP2 efflux activity assay of each group. The HepG2 spheroids were observed by phase contrast and fluorescence microscopy. The light images (first horizontal line in c), excreted 5-CF (middle horizontal line in c), nucleic acids staining (last horizontal line) are shown of each group. Scale bar indicates 50 μm

3.1.2 Effect of IL-6 and melatonin on PXR expression

Furthermore, we investigated whether PXR expression levels are influenced by IL-6 and melatonin, since PXR is known to be a major regulator for MRP2 expression (Arana et al. 2016). Representative fluorescent images for PXR protein expression and the respective quantified results are shown in Fig. 3. After the treatment with melatonin alone, PXR expression levels did not change (92% of the control). However, IL-6 administration induced significantly decreased PXR expression levels, while melatonin co-treatment reversed this IL-6-induced reduction (78% of the control).
Fig. 3

PXR expression level on HepG2 cells after IL-6/and or melatonin treatment. a PXR expression presented by representative light and fluorescent images and b quantified by the calculated fluorescent intensity per total cell area. Scale bar indicates 100 μm. 8–16 images in each group were analyzed and data are shown as mean ± SD (n = 3 or 4). Significant differences (p < 0.05) are indicated as follows: * vs. Control, Melatonin, IL-6 + Melatonin

3.1.3 Effects of IL-6 and melatonin on the activity of CYP1A

The addition of IL-6 to HepG2 cell clusters in the Organoplate™ for 72 h resulted in a significant 10–15% reduction of CYP1A activity in comparison to the control group, as indicated by 7-ethoxy resorufin-O-deethylase (EROD) activity (Fig. 4). When cells were treated simultaneously with melatonin, CYP1A activity levels were comparable to basal levels. A slight tendency of lowered activity was seen by melatonin administration alone (95% of the control).
Fig. 4

CYP1A activity in HepG2 cells after IL-6 and/or melatonin treatment estimated by EROD activity. Data are shown as mean ± SD (n = 3). Significant differences (p < 0.05) are indicated as follows: $ vs. Control

3.2 Melatonin modified CREB3L3 expression levels and the IL-6-induced acute phase response

Albumin production was strongly reduced after administration of IL-6 (15% of control, Fig. 5a). This fact became apparent after 24 h (data not shown) and persisted up to 72 h. Simultaneous melatonin administration tended to reduce the albumin repression with the same variability than melatonin treatment alone, showing a significant difference in comparison to the control group. Melatonin alone did not increase albumin production over basal levels of the control group.
Fig. 5

Acute phase response of HepG2 cells 72 h after IL-6 and/or melatonin treatment. a + b Albumin and CRP levels determined by ELISA and normalized to fold change to control. Data are shown as mean ± SD (n = 4). c + d Hepcidin expression presented by representative light microscopical and fluorescent images and quantified by the calculated fluorescent intensity per total cell area. 6–10 images in each group were analyzed and data are shown as mean ± SD (n = 3). Significant differences (p < 0.05) are indicated as follows: * vs. Control, Melatonin, IL-6 + Melatonin; $ vs. Control; & vs. Control, Melatonin

C-reactive protein (CRP) production by HepG2 cells was increased 19-fold in cultures treated with IL-6 (Fig. 5b). Melatonin did not alter CRP levels markedly, neither when given alone nor in combination with IL-6. Hepcidin expression levels were also elevated by IL-6 administration (2.2-fold over control, Fig. 5c+d). Opposed to CRP, melatonin prevented this hepcidin upregulation entirely.

In order to determine the impact of IL-6 and melatonin on CREB3L3, the ER-bound transcription factor representing the possible link between IL-6 signaling, the ER stress response and the APR, its expression levels were determined in the different treatment groups. CREB3L3 protein expression was significantly reduced by IL-6 treatment to 40% in comparison to the control (Fig. 6). Melatonin alone did not alter the protein amount of this transcription factor, but clearly counteracted the lowered expression observed in the IL-6 group.
Fig. 6

CREB3L3 expression in HepG2 cells after IL-6 and/or melatonin treatment. a Representative images via transmitted light microscopy and immune fluorescent imaging of HepG2 cells cultured in the OrganoPlate™. Scale bar indicates 100 μm. b Quantification of the fluorescent intensity per total cell area. 8–12 Images in each group were analyzed and data are shown as mean ± SD (n = 3). Significant differences (p < 0.05) are indicated as follows: * vs. Control, Melatonin, IL-6 + Melatonin

3.3 Alterations of mitochondrial functions in HepG2 cells

Mitochondrial functions were determined on the basis of alterations in the mitochondrial membrane potential (MMP) and the production of mitochondrial superoxide, by employing fluorescent probes. The MMP of HepG2 cells was constant within repeated control experiments. IL-6 treatment consistently reduced the red/green fluorescence ratio of JC-1 by half in comparison to the control (Fig. 7a). Simultaneous melatonin treatment mitigated the IL-6-induced reduction of the MMP but did not elevate the MMP to basal levels (70% of control). Melatonin alone did not considerably change the MMP although the values scattered stronger than in the control group.
Fig. 7

Mitochondrial functions of HepG2 cells assessed by measuring the MMP and superoxide production after IL-6 and/or melatonin treatment. a Fold change of red/green ratio of JC-1 for the assessment of the MMP. 6 to 8 images were analyzed in each group and data are shown as mean ± SD (n = 3). b Mitochondrial superoxide production assessed by MitoSOX™ staining. 8–10 images were analyzed in each group and data are shown as mean ± SD (n = 3). Significant differences (p < 0.05) are indicated as follows: * vs. Control, Melatonin, IL-6 + Melatonin; # vs. control and IL-6 + Melatonin; § vs. control and IL-6

Mitochondrial superoxide production was also affected. IL-6 treatment increased the levels 2.6-fold while cells co-treated with melatonin had almost basal superoxide levels (Fig. 7b). Moderately increased values (< 1.7-fold) were obtained when cells were treated with melatonin alone, although this was not significant.

3.4 Reduced glycogen storage of IL-6 treated HepG2 cells

In order to assess the impact of IL-6 and melatonin on glucose metabolism, glycogen storage was determined in the cultures with different treatments. While most vehicle and melatonin-treated cells exhibited strong glycogen staining visible by the dark purple color, only 57% were darkly stained in the IL-6 treatment group while the majority of other cells remained clear (Fig. 8).
Fig. 8

Glycogen staining of HepG2 cells after IL-6 and/or melatonin treatment. a Representative images via transmitted light microscopy of HepG2 cells cultured in the OrganoPlate™ after PAS staining. Scale bar indicates 100 μm. b Quantification of the glycogen staining intensity per cluster. 20 to 25 spheroids at each condition were analyzed and data are shown as mean ± SD (n = 4). Significant differences (p < 0.05) are indicated as follows: * vs. Control, Melatonin, IL-6 + Melatonin

4 Discussion

The cells cultivated in this new chip platform show alterations in a wide range of hepatic functions in response to inflammation stimulation by IL-6. While some of these changes might impair proper liver function (downregulation of detoxification tools), others may aim at protecting the organ and ameliorate the situation (increasing stress responses, increasing energy supply). Interestingly, simultaneous melatonin administration counteracted most of the detected alterations, although not always to basal levels, suggesting that melatonin attenuates IL-6-induced cellular responses without completely inhibiting them.

Multidrug resistance-associated protein 2 (MRP2) is localized on the canalicular membrane of hepatocytes and transports a wide range of compounds as part of the hepatic detoxification process. MRP2 expression in HepG2 spheroids was shown in previous literature (Ramaiahgari et al. 2014) Reduced expression and activity of this transporter was observed, for example, during inflammation, drug-induced liver injury, or after ischemia with reperfusion, and this may further contribute to aggravated liver damage (Donner et al. 2013; Geier et al. 2007; Zollner et al. 2014). We determined significantly reduced MRP2 expression levels and efflux activity in response to IL-6 treatment. These results are mainly comparable with previous studies on primary human hepatocytes cultivated in sandwich cultures where IL-6 treatment led to lower MRP2 mRNA and protein expression levels (Diao et al. 2010; Yang et al. 2012). The expression of MRP2 is mainly regulated by the pregnane X receptor (PXR) (Arana et al. 2016). Down-regulated expression of PXR by IL-6 stimulation has already been demonstrated in human hepatocyte cultures (Pascussi et al. 2000; Yang et al. 2010). Interestingly, melatonin was shown to attenuate lipopolysaccharides (LPS)-induced down-regulated PXR expression in mouse liver in previous literature (Xu et al. 2005). Our results also demonstrate reduced expression of MRP2 as well as PXR after IL-6-stimulation. Both effects were counteracted by melatonin administration, suggesting that melatonin’s effects might act via this PXR-axis. Previous literature has shown reduced EROD activity in extracted human liver microsomes and human recombinant CYP1A1 and CYP1A2 enzyme activities in response to melatonin concentrations in a micromolar range (Chang et al. 2010). The authors suggested that only micromolar melatonin concentrations lead to the inhibition of CYP1A enzymes and their catalytic activity, whereas nanomolar melatonin concentrations (considered as physiological) are not likely to have inhibitory effects on this enzyme. Our result also corroborated the fact that nanomolar melatonin concentrations did not alter the catalytic activity of CYP1A.

The induction of the acute phase response by IL-6 was well documented in previous literature. IL-6 is a main mediator for alterations in the expression of positive and negative APR proteins such as the reduction of albumin, the strong increase of CRP, and the elevated hepcidin levels (Huang et al. 2015; Karlsson et al. 1998; Shin et al. 2012; Zhang et al. 2015). Melatonin counteracted reduced albumin production along with elevated hepcidin levels, therefore, the administration of this hormone might ameliorate the detrimental consequences of an overwhelming APR. Previous studies demonstrated that melatonin treatment increased serum albumin levels reduced by doxorubicin and gamma-irradiation in in vivo rat models (El-Missiry et al. 2007; Öz and Ilhan 2006). Those in vivo results are in accordance with our study, as well. CRP expression levels displayed a high variability after IL-6 treatment (with or without melatonin). Similar findings were presented by others treating human primary hepatocytes with different doses of Il-6 where the variation of mRNA expression levels increased in a dose-dependent manner (Le Vee et al., 2009). Despite the high variability, the protein production levels of CRP in our study differed significantly from the control groups without IL-6, therefore, we consider the results reliable.

CREB3L3, a mediator of the ER stress response, is known to increase the CRP transcription, and these signaling pathways seem to interact with IL-6-signaling in hepatocytes (Chung et al. 2011). Surprisingly, in our study, the protein expression of CREB3L3 was lowered by IL-6. This might negate the hypothesis of a positive regulation of CRP transcription by this transcription factor. Nevertheless, we did not analyze the activation of CREB3L3 which typically occurs upon ER stress by proteolytic cleavage, subsequent detachment from the ER membrane, and translocation of the N-terminal fragment to the nucleus (Zhang et al. 2006). CREB3L3 cleavage arises in response to IL-6 probably synergistically with other elements of the UPR (Chung et al. 2011). Shin et al. reported the activation of CREB3L3 upon IL-6 treatment of HepG2 cells (Shin et al. 2012). Additionally, they found direct evidence for the transcriptional regulation of CRP and hepcidin by CREB3L3. This is especially interesting when considering that in our study the co-administration of melatonin prevented the IL-6-induced upregulation of hepcidin but did not alter the increased CRP levels. The main mediators of hepcidin transcriptional activation are SMAD (Suppressor of Mothers Against Decapentaplegic) via JAK (Janus Activated Kinase) and BMP (Bone morphogenetic proteins) signaling pathways, respectively (Wang et al. 2005; Wrighting and Andrews 2006). According to our knowledge, there is no evidence for transcriptional regulation of CRP by BMP/SMAD signaling. In addition, melatonin did not counteract the strong increase of IL-6-induced STAT3-phosphorylation (Supplementary Figures, Fig. S2). Therefore, the different mechanisms that lead to hepcidin but not CRP regulation by melatonin remain unclear.

In the context of glycogen storage, Ritchie et al. found direct evidence that the hepatic glycogen metabolism is altered by IL-6 stimulation, resulting in the release of glucose from glycogen in rat hepatocytes (Ritchie 1990). Others also observed reduced glycogen content upon IL-6 treatment and they correlated it with decreased activation of the Akt/GSK (Protein kinase B/Glycogen synthase kinase) pathway in mouse and human hepatocytes (Dou et al. 2015; Dou et al. 2013). Forkhead box protein O1 (FOXO1) is a transcription factor that regulates G6Pase (Glucose 6-phosphatase), which mediates the last step of glycogenolysis (Rui 2014). It is well known that insulin-mediated activation of the Akt signaling pathway induces the degradation of FOXO1 by its phosphorylation, this leads to an elevated glycogen synthesis. Interestingly, melatonin was shown to increase the activity of Akt signaling in a rat model (Hadj Ayed Tka et al. 2015) and in HepG2 cells, where it elevated glycogen synthesis (Shieh et al. 2009). PXR may act as a repressor of FOXO1 (Kodama et al. 2004), thereby decreasing glycogenolysis. In addition, inhibition of the PI3K (Phosphatidylinositide 3-kinases)/Akt pathway directly downregulated MRP2 mRNA (Jakubíková et al. 2005). This might explain the proposed mechanism that melatonin alleviates reduced MRP2 and glycogen storage by the modulation of PXR expression and Akt signaling via FOXO1 and G6Pase and strengthens the hypothesis of a possible PXR-mediated link between detoxification mechanisms and glucose metabolism. However, it would be desirable to analyze IL-6 signaling, Akt signaling, FOXO1, and PXR within the interplay of IL-6 and melatonin in further studies.

The particular role of mitochondria as an energy supplier but also as a controller of intracellular signaling pathways such as apoptosis prevention or initiation and its close interaction with the ER, prompted us to evaluate mitochondrial functions in the context of IL-6 and/or melatonin. Similar to the results from Berthiaume et al. on primary rat hepatocytes in a sandwich culture system, MMP of the HepG2 cells in our culturing systems was lowered by IL-6 administration (Berthiaume et al. 2003). Based on their comprehensive studies the authors postulated that in IL-6 treated hepatocytes processes to stabilize the MMP are favored over processes generating ATP which might lead to limited availability of energy for hepatocellular functions. They determined increased flux via the electron transport chain but reduced flux via the ATP synthase complex. Mitochondrial superoxide is predominantly generated at complex I and III of the electron transport chain. Therefore, this is in accordance with the elevated superoxide levels we determined in our setup. Melatonin administration counteracted both the reduced MMP and the increased mitochondrial superoxide, as well, suggesting that cellular energy supply might be improved and that oxidative stress could be alleviated by this hormone. Evidence for this was described previously. For example, Reyes-Tosco et al. concluded from their experiments that melatonin is able to attenuate excessive oxygen consumption of liver mitochondria and consequently protects them from oxidative damage (Reyes-Toso et al. 2006; Reyes-Toso et al. 2003). Also, melatonin was described to modulate the mitochondrial respiratory activity by increasing the action of complex I and III, thereby altering the generation of reactive oxygen species (Martín et al. 2000). Additionally, Lopez et al. demonstrated that melatonin can directly decrease mitochondrial superoxide production and increase the activity of respiratory complex I and III (López et al. 2009). Apart from its action as a ligand to membrane-bound melatonin receptors, its localization within mitochondria and nuclei was shown (Martín et al. 2000; Mathes 2010). Taken together, melatonin seems to be able to exert protective activities at different sites within the cell, allowing the modification of a wide range of cellular stress responses.

This study shows the interplay of melatonin actions and inflammation stimulated by IL-6 in a newly developed HepG2-on-a-chip platform model, which allows quantitative measurements of various hepatic functions including detoxification mechanisms, the acute phase response, and glucose metabolism. Previously, Esch et al. developed a microfluidic device which is capable for multiple cell cultures including human primary hepatocytes and non-parenchymal cells (NPCs). They assessed the production level of one type of cytokine (IL-8) in response to LPS (Esch et al. 2015). So far, only the work group of Mosig reported the establishment of a biochip-based human liver platform with co-culture of NPCs (non-parenchymal cells) for the study of hepatic inflammation responses (Gröger et al. 2016). They examined the interaction between NPCs and hepatocytes by assessing few LPS-induced hepatocyte dysfunctions such as declined MRP expression and activity, and reduced albumin production rate. To our knowledge, our study reports for the first time not only direct responses of hepatocytes exposed to inflammatory cytokines but also provides solid evidence that this new in vitro biochip-based platform can be used for the investigation of effects mediated by the hormone melatonin on diverse hepatic inflammation responses. In addition, results from this new in vitro platform where well comparable with previous literature on primary hepatocyte and in vivo models, thus, this culture method overcomes the limitations of typical HepG2 culture models. Interestingly, our chip culture model showed a higher increase of CRP production in the IL-6 group in comparison to the control group than did cells of static 2D and 3D cultures which showed a 2–4 fold change (data are not shown). The range of CRP increase in the chip culture is in a range similar to human in vivo clinical data (10–100 fold change) (Heinrich et al. 1990). In addition, we did not observe significant changes between each group for CYP1A activity and mitochondrial dysfunction in the 2D culture platform. Therefore, it might be intriguing to scrutinize various hepatic inflammation responses by comparing various culture platforms (i.e. static 2D, 3D, chip-based culture) for the future study, but it goes beyond the scope of the present study.

The culture in this device in its present form is impracticable for a large quantity of cells, making it hard to conduct several general molecular biological experiments but relying on quantitative microscopic-based image analysis with various staining methods as shown in previous studies (Moreno et al. 2015; Trietsch et al. 2017). Technical improvements of the current platform would be helpful for high-throughput culture or automated systems to extend the scope of experiments. Taken together, the present research provides the first step to build a bridge to study complex hepatic inflammatory events in response to IL-6 and melatonin in organ-on-chip devices, which paves the way for exploring the genetic and molecular mechanisms that involve the interplay of hepatic inflammation and melatonin for further studies.

5 Conclusions

The results of this study clearly demonstrate that IL-6 modifies inflammatory responses including detoxification, APR proteins, glucose metabolism, and mitochondrial functions of HepG2 cells in a new in vitro platform Melatonin alleviated IL-6-induced reduction of MRP2 expression, the lowered production of albumin, increased expression of hepcidin, reduced glycogen storage, and the diminished mitochondrial functions. This study provides further evidence of the positive properties of this hormone and proposes new candidate pathways.



The authors would like to thank Karen Schneider for revising this work linguistically. None of the authors have disclosed any potential conflict of interest.

Supplementary material

10544_2018_300_MOESM1_ESM.docx (694 kb)
ESM 1 (DOCX 694 kb)


  1. A. Akhtar, Camb. Q. Healthc. Ethics 24, 407 (2015)CrossRefGoogle Scholar
  2. S. Anthérieu, C. Chesné, R. Li, S. Camus, A. Lahoz, L. Picazo, M. Turpeinen, A. Tolonen, J. Uusitalo, C. Guguen-Guillouzo, A. Guillouzo, Drug Metab. Dispos. 38, 516 (2010)CrossRefGoogle Scholar
  3. M.R. Arana, G.N. Tocchetti, J.P. Rigalli, A.D. Mottino, S.S.M. Villanueva, Pharmacol. Res. 109, 32 (2016)CrossRefGoogle Scholar
  4. F. Berthiaume, A.D. MacDonald, Y.H. Kang, M.L. Yarmush, Metab. Eng. 5, 108 (2003)CrossRefGoogle Scholar
  5. A. Carrillo-Vico, P.J. Lardone, L. Naji, J.M. Fernández-Santos, I. Martín-Lacave, J.M. Guerrero, J.R. Calvo, J. Pineal Res. 39, 400 (2005)CrossRefGoogle Scholar
  6. D. Chanda, D.K. Kim, T. Li, Y.H. Kim, S.H. Koo, C.H. Lee, J.Y.L. Chiang, H.S. Choi, J. Biol. Chem. 286, 27971 (2011)CrossRefGoogle Scholar
  7. T.K.H. Chang, J. Chen, G. Yang, E.Y.H. Yeung, J. Pineal Res. 48, 55 (2010)CrossRefGoogle Scholar
  8. N. Chaudhari, P. Talwar, A. Parimisetty, C. Lefebvre d’Hellencourt, P. Ravanan, Front. Cell. Neurosci. 8, 213 (2014)CrossRefGoogle Scholar
  9. J. Chung, D.Y. Shin, M. Zheng, Y. Joe, H.O. Pae, S.W. Ryter, H.T. Chung, Mol. Immunol. 48, 1793 (2011)CrossRefGoogle Scholar
  10. L. Diao, N. Li, T.G. Brayman, K.J. Hotz, Y. Lai, J. Biol. Chem. 285, 31185 (2010)CrossRefGoogle Scholar
  11. M.G. Donner, S.A. Topp, P. Cebula, A. Krienen, T. Gehrmann, A. Sommerfeld, R. Reinehr, A. Macher, D. Herebian, E. Mayatepek, B.H. Pannen, W.T. Knoefel, D. Häussinger, Biol. Chem. 394, 97 (2013)CrossRefGoogle Scholar
  12. L. Dou, T. Zhao, L. Wang, X. Huang, J. Jiao, D. Gao, H. Zhang, T. Shen, Y. Man, S. Wang, J. Li, J. Biol. Chem. 288, 22596 (2013)CrossRefGoogle Scholar
  13. L. Dou, S. Wang, X. Sui, X. Meng, T. Shen, X. Huang, J. Guo, W. Fang, Y. Man, J. Xi, J. Li, Cell. Physiol. Biochem. 35, 1413 (2015)CrossRefGoogle Scholar
  14. M.A. El-Missiry, T.A. Fayed, M.R. El-Sawy, A.A. El-Sayed, Ecotoxicol. Environ. Saf. 66, 278 (2007)CrossRefGoogle Scholar
  15. M.B. Esch, J.-M. Prot, Y.I. Wang, P. Miller, J.R. Llamas-Vidales, B.A. Naughton, D.R. Applegate, M.L. Shuler, Lab Chip 15, 2269 (2015)CrossRefGoogle Scholar
  16. O. Fardel, M. Le Vée, Expert Opin. Drug Metab. Toxicol. 5, 1469 (2009)CrossRefGoogle Scholar
  17. A. Geier, M. Wagner, C.G. Dietrich, M. Trauner, Biochim. Biophys. Acta. 1773, 283 (2007)CrossRefGoogle Scholar
  18. M. Gröger, K. Rennert, B. Giszas, E. Weiß, J. Dinger, H. Funke, M. Kiehntopf, F.T. Peters, A. Lupp, M. Bauer, R.A. Claus, O. Huber, A.S. Mosig, Sci. Rep. 6, 21868 (2016)CrossRefGoogle Scholar
  19. K. Hadj Ayed Tka, A. Mahfoudh Boussaid, M.A. Zaouali, R. Kammoun, M. Bejaoui, S. Ghoul Mazgar, J. Rosello Catafau, H. Ben Abdennebi, Anal. Cell. Pathol (Amst). 2015, 635172 (2015)Google Scholar
  20. P.C. Heinrich, J.V. Castell, T. Andus, Biochem. J. 265, 621 (1990)CrossRefGoogle Scholar
  21. C.-Y. Huang, W.-M. Chen, Y.-G. Tsay, S.-C. Hsieh, Y. Lin, W.-J. Lee, W.H.-H. Sheu, A.-N. Chiang, J. Biomed. Sci. 22, 12 (2015)CrossRefGoogle Scholar
  22. J. Jakubíková, J. Sedlák, R. Mithen, Y. Bao, Biochem. Pharmacol. 69, 1543 (2005)CrossRefGoogle Scholar
  23. M. Jang, P. Neuzil, T. Volk, A. Manz, A. Kleber, Biomicrofluidics 9, 34113 (2015)CrossRefGoogle Scholar
  24. J.O. Karlsson, M.L. Yarmush, M. Toner, Hepatology 28, 994 (1998)CrossRefGoogle Scholar
  25. A. Kasza, M. Bugno, A. Koj, Biol. Chem. Hoppe Seyler 375, 779 (1994)CrossRefGoogle Scholar
  26. R.K. Kheder, J. Hobkirk, C.M. Stover, Front. Cell. Dev. Biol. 4, 61 (2016)CrossRefGoogle Scholar
  27. A. Kleber, D. Kubulus, D. Rössler, B. Wolf, T. Volk, T. Speer, T. Fink, Exp. Mol. Pathol. 97, 565 (2014)CrossRefGoogle Scholar
  28. A. Kleber, C.G. Ruf, A. Wolf, T. Fink, M. Glas, B. Wolf, T. Volk, M. Abend, A.M. Mathes, Exp. Mol. Pathol. 99, 189 (2015)CrossRefGoogle Scholar
  29. S. Kodama, C. Koike, M. Negishi, Y. Yamamoto, Mol. Cell. Biol. 24, 7931 (2004)CrossRefGoogle Scholar
  30. M. Le Vee, V. Lecureur, B. Stieger, and O. Fardel, Drug Metab Dispos. 37, 685 (2009)Google Scholar
  31. B. Lienenlüke, B. Christ, Histochem. Cell Biol. 128, 371 (2007)CrossRefGoogle Scholar
  32. Y. Liu, M. Shao, Y. Wu, C. Yan, S. Jiang, J. Liu, J. Dai, L. Yang, J. Li, W. Jia, L. Rui, Y. Liu, J. Hepatol. 62, 590 (2015)CrossRefGoogle Scholar
  33. A. López, J.A. García, G. Escames, C. Venegas, F. Ortiz, L.C. López, D. Acuña-Castroviejo, J. Pineal Res. 46, 188 (2009)CrossRefGoogle Scholar
  34. K.L. Ma, X.Z. Ruan, S.H. Powis, Y. Chen, J.F. Moorhead, Z. Varghese, Hepatology 48, 770 (2008)CrossRefGoogle Scholar
  35. M. Martín, M. Macías, G. Escames, R.J. Reiter, M.T. Agapito, G.G. Ortiz, D. Acuña-Castroviejo, J. Pineal Res. 28, 242 (2000)CrossRefGoogle Scholar
  36. A.M. Mathes, World J. Gastroenterol. 16, 6087 (2010)CrossRefGoogle Scholar
  37. E.L. Moreno, S. Hachi, K. Hemmer, S.J. Trietsch, A.S. Baumuratov, T. Hankemeier, P. Vulto, J.C. Schwamborn, R.M.T. Fleming, F. Ali, S.R.W. Stott, R.A. Barker, J. Yu, M.A. Vodyanik, K. Smuga-Otto, J. Antosiewicz-Bourget, J.L. Frane, S. Tian, J. Nie, G.A. Jonsdottir, V. Ruotti, R. Stewart, I.I. Slukvin, J.A. Thomson, K. Takahashi, K. Tanabe, M. Ohnuki, M. Narita, T. Ichisaka, K. Tomoda, S. Yamanaka, M. Bellin, M.C. Marchetto, F.H. Gage, C.L. Mummery, R. Gonzalez, I. Garitaonandia, T. Abramihina, G.K. Wambua, A. Ostrowska, M. Brock, A. Noskov, F.S. Boscolo, J.S. Craw, L.C. Laurent, E.Y. Snyder, R.A. Semechkin, J.N.L. Grand, L. Gonzalez-Cano, M.A. Pavlou, J.C. Schwamborn, A. Swistowski, J. Peng, Q. Liu, P. Mali, M.S. Rao, L. Cheng, X. Zeng, S.M. Chambers, C.A. Fasano, E.P. Papapetrou, M. Tomishima, M. Sadelain, L. Studer, H. Braak, K. Del Tredici, H.N. Nguyen, B. Byers, B. Cord, A. Shcheglovitov, J. Byrne, P. Gujar, K. Kee, B. Schüle, R.E. Dolmetsch, W. Langston, T.D. Palmer, R.R. Pera, A. Sanchez-Danes, Y. Richaud-Patin, I. Carballo-Carbajal, S. Jimenez-Delgado, C. Caig, S. Mora, C. Di Guglielmo, M. Ezquerra, B. Patel, A. Giralt, J.M. Canals, M. Memo, J. Alberch, J. Lopez-Barneo, M. Vila, A.M. Cuervo, E. Tolosa, A. Consiglio, A. Raya, O. Cooper, H. Seo, S. Andrabi, C. Guardia-Laguarta, J. Graziotto, M. Sundberg, J.R. McLean, L. Carrillo-Reid, Z. Xie, T. Osborn, G. Hargus, M. Deleidi, T. Lawson, H. Bogetofte, E. Perez-Torres, L. Clark, C. Moskowitz, J. Mazzulli, L. Chen, L. Volpicelli-Daley, N. Romero, H. Jiang, R.J. Uitti, Z. Huang, G. Opala, L.A. Scarffe, V.L. Dawson, C. Klein, J. Feng, O.A. Ross, J.Q. Trojanowski, V.M.-Y. Lee, K. Marder, D.J. Surmeier, Z.K. Wszolek, S. Przedborski, D. Krainc, T.M. Dawson, O. Isacson, P. Reinhardt, M. Glatza, K. Hemmer, Y. Tsytsyura, C.S. Thiel, S. Honing, S. Moritz, J.A. Parga, L. Wagner, J.M. Bruder, A.L. Paguirigan, D.J. Beebe, S. Halldorsson, E. Lucumi, R. Gómez-Sjöberg, R.M.T. Fleming, G. Hargus, O. Cooper, M. Deleidi, A. Levy, K. Lee, E. Marlow, A. Yow, F. Soldner, D. Hockemeyer, P.J. Hallett, T. Osborn, R. Jaenisch, O. Isacson, K. Hemmer, M. Zhang, T. van Wüllen, M. Sakalem, N. Tapia, A. Baumuratov, C. Kaltschmidt, B. Kaltschmidt, H.R. Schöler, W. Zhang, J.C. Schwamborn, G. Hargus, C. Brito, D. Simão, I. Costa, R. Malpique, C.I. Pereira, P. Fernandes, M. Serra, S.C. Schwarz, J. Schwarz, E.J. Kremer, P.M. Alves, E.J. Gualda, D. Simao, C. Pinto, P.M. Alves, C. Brito, D. van Noort, S.M. Ong, C. Zhang, S. Zhang, T. Arooz, H. Yu, D. Huh, H.J. Kim, J.P. Fraser, D.E. Shea, M. Khan, A. Bahinski, G.A. Hamilton, D.E. Ingber, J.H. Sung, M.B. Esch, J.-M. Prot, C.J. Long, A. Smith, J.J. Hickman, M.L. Shuler, S.N. Bhatia, D.E. Ingber, S.J. Trietsch, G.D. Israëls, J. Joore, T. Hankemeier, P. Vulto, P. Vulto, S. Podszun, P. Meyer, C. Hermann, A. Manz, G.A. Urban, E. Yildirim, S.J. Trietsch, J. Joore, A.v.d. Berg, T. Hankemeier, P. Vulto, J. Lee, M.J. Cuddihy, N.A. Kotov, M.W. Tibbitt, K.S. Anseth, H.K. Kleinman, G.R. Martin, N. Stuurman, A.D. Edelstein, N. Amodaj, K.H. Hoover, R.D. Vale, J.T. Vogelstein, A.M. Packer, T.A. Machado, T. Sippy, B. Babadi, R. Yuste, L. Paninski, M.M. Daadi, B.A. Grueter, R.C. Malenka, D.E. Redmond, G.K. Steinberg, Y. Yan, D. Yang, E.D. Zarnowska, Z. Du, B. Werbel, C. Valliere, R.A. Pearce, J.A. Thomson, S.-C. Zhang, P.K. Mattila, P. Lappalainen, S. Iden, J.G. Collard, A.J. Koleske, C. Stosiek, O. Garaschuk, K. Holthoff, A. Konnerth, H.C. Johannssen, F. Helmchen, M.K. Sanghera, M.E. Trulson, D.C. German, W.H. Yung, M.A. Häusser, J.J. Jack, P.M.A. Antony, N.J. Diederich, R. Krüger, R. Balling, A.A. Grace, B.S. Bunney, P.M. van Midwoud, A. Janse, M.T. Merema, G.M.M. Groothuis, E. Verpoorte, A.L. Paguirigan, D.J. Beebe, M.F. Underhill, C.M. Smales, Lab Chip 15, 2419 (2015)CrossRefGoogle Scholar
  38. C.A. Norris, M. He, L.I. Kang, M.Q. Ding, J.E. Radder, M.M. Haynes, Y. Yang, S. Paranjpe, W.C. Bowen, A. Orr, G.K. Michalopoulos, D.B. Stolz, W.M. Mars, PLoS One 9, 1 (2014)Google Scholar
  39. E. Öz, M.N. Ilhan, Mol. Cell. Biochem. 286, 11 (2006)CrossRefGoogle Scholar
  40. J.M. Pascussi, S. Gerbal-Chaloin, L. Pichard-Garcia, M. Daujat, J.M. Fabre, P. Maurel, M.J. Vilarem, Biochem. Biophys. Res. Commun. 274, 707 (2000)CrossRefGoogle Scholar
  41. G. Ramadori, J. Van Damme, H. Rieder, K.H. Meyer zum Büschenfelde, Eur. J. Immunol. 18, 1259 (1988)CrossRefGoogle Scholar
  42. S.C. Ramaiahgari, M.W. den Braver, B. Herpers, V. Terpstra, J.N.M. Commandeur, B. van de Water, L.S. Price, Arch. Toxicol. (2014)Google Scholar
  43. C.F. Reyes-Toso, C.R. Ricci, I.R. de Mignone, P. Reyes, L.M. Linares, L.E. Albornoz, D.P. Cardinali, A. Zaninovich, Neuro. Endocrinol. Lett. 24, 341 (2003)Google Scholar
  44. C.F. Reyes-Toso, I.R. Rebagliati, C.R. Ricci, L.M. Linares, L.E. Albornoz, D.P. Cardinali, A. Zaninovich, Amino Acids 31, 299 (2006)CrossRefGoogle Scholar
  45. D.G. Ritchie, Am. J. Physiol. Endocrinol. Metab. 258, E57 (1990)CrossRefGoogle Scholar
  46. K. Rubin, A. Janefeldt, L. Andersson, Z. Berke, K. Grime, T.B. Andersson, Drug Metab. Dispos. 43, 119 (2015)CrossRefGoogle Scholar
  47. L. Rui, Compr. Physiol. 4, 177 (2014)CrossRefGoogle Scholar
  48. N.M. Scheers, A.B. Almgren, A.-S. Sandberg, J. Nutr. Biochem. 25, 710 (2014)CrossRefGoogle Scholar
  49. J. Seok, H.S. Warren, A.G. Cuenca, M.N. Mindrinos, H.V. Baker, W. Xu, D.R. Richards, G.P. McDonald-Smith, H. Gao, L. Hennessy, C.C. Finnerty, C.M. López, S. Honari, E.E. Moore, J.P. Minei, J. Cuschieri, P.E. Bankey, J.L. Johnson, J. Sperry, A.B. Nathens, T.R. Billiar, M.a. West, M.G. Jeschke, M.B. Klein, R.L. Gamelli, N.S. Gibran, B.H. Brownstein, C. Miller-Graziano, S.E. Calvano, P.H. Mason, J.P. Cobb, L.G. Rahme, S.F. Lowry, R.V. Maier, L.L. Moldawer, D.N. Herndon, R.W. Davis, W. Xiao, R.G. Tompkins, Proc. Natl. Acad. Sci. U. S. A. 110, 3507 (2013)CrossRefGoogle Scholar
  50. J.M. Shieh, H.T. Wu, K.C. Cheng, J.T. Cheng, J. Pineal Res. 47, 339 (2009)CrossRefGoogle Scholar
  51. D.Y. Shin, J. Chung, Y. Joe, H.O. Pae, K.C. Chang, G.J. Cho, S.W. Ryter, H.T. Chung, Blood 119, 2523 (2012)CrossRefGoogle Scholar
  52. D.-X. Tan, L. Manchester, L. Qin, R. Reiter, Int. J. Mol. Sci. 17, 2124 (2016)CrossRefGoogle Scholar
  53. S.J. Trietsch, E. Naumovska, D. Kurek, M.C. Setyawati, M.K. Vormann, K.J. Wilschut, H.L. Lanz, A. Nicolas, C.P. Ng, J. Joore, S. Kustermann, A. Roth, T. Hankemeier, A. Moisan, P. Vulto, Nat. Commun. 8, 262 (2017)CrossRefGoogle Scholar
  54. R.H. Wang, C. Li, X. Xu, Y. Zheng, C. Xiao, P. Zerfas, S. Cooperman, M. Eckhaus, T. Rouault, L. Mishra, C.X. Deng, Cell Metab. 2, 399 (2005)CrossRefGoogle Scholar
  55. K. Wehmeier, L.M. Onstead-Haas, N.C.W. Wong, A.D. Mooradian, M.J. Haas, J. Mol. Endocrinol. 57, 87 (2016)CrossRefGoogle Scholar
  56. J. Wolf, S. Rose-John, C. Garbers, Cytokine 70, 11 (2014)CrossRefGoogle Scholar
  57. D.M. Wrighting, N.C. Andrews, Blood 108, 3204 (2006)CrossRefGoogle Scholar
  58. J.-Y. Wu, M.-Y. Tsou, T.-H. Chen, S.-J. Chen, C.-M. Tsao, C.-C. Wu, J. Pineal Res. 45, 106 (2008)CrossRefGoogle Scholar
  59. D.X. Xu, W. Wei, M.F. Sun, L.Z. Wei, J.P. Wang, J. Pineal Res. 38, 27 (2005)CrossRefGoogle Scholar
  60. J. Yang, C. Hao, D. Yang, D. Shi, X. Song, X. Luan, G. Hu, B. Yan, Toxicol. Lett. 197, 219 (2010)CrossRefGoogle Scholar
  61. Q. Yang, U. Doshi, N. Li, A.P. Li, Curr. Drug Metab. 13, 938 (2012)CrossRefGoogle Scholar
  62. R. Yang, D. Lirussi, T.M. Thornton, D.M. Jelley-Gibbs, S. a Diehl, L.K. Case, M. Madesh, D.J. Taatjes, C. Teuscher, L. Haynes, M. Rincón, Elife 4, 1 (2015)Google Scholar
  63. Y. Yuhas, E. Berent, S. Ashkenazi, Antimicrob. Agents Chemother. 55, 5541 (2011)CrossRefGoogle Scholar
  64. K. Zhang, X. Shen, J. Wu, K. Sakaki, T. Saunders, D.T. Rutkowski, S.H. Back, R.J. Kaufman, Cell 124, 587 (2006)CrossRefGoogle Scholar
  65. H. Zhang, S. Kuang, Y. Wang, X. Sun, Y. Gu, L. Hu, Q. Yu, Acta Pharmacol. Sin. 36, 507 (2015)CrossRefGoogle Scholar
  66. G. Zollner, A. Thueringer, C. Lackner, P. Fickert, M. Trauner, Digestion 90, 81 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Mi Jang
    • 1
    • 2
  • Andreas Manz
    • 1
    • 2
  • Thomas Volk
    • 3
  • Astrid Kleber
    • 3
    Email author
  1. 1.Department of MechatronicsSaarland UniversitySaarbrückenGermany
  2. 2.KIST EuropeSaarbrückenGermany
  3. 3.Department of Anaesthesiology, Intensive Care and Pain TherapySaarland University Medical CenterHomburg/SaarGermany

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