*3.1. Sc Induces miRNA Expression in HepaRG Cells*

First, the miRNA expression profile in HepaRG cells was analyzed with a customized liver-specific miCURY LNA Array covering 84 miRNAs. Compared to the control group, the number of deregulated miRNAs above 1 log2 Fold Change (FC) or below 1 log2 FC tended to be highest at 250 μM after 8 h and at 35 μM after 24 h, respectively (see Supplementary Figure S1). Among the top deregulated miRNAs, a selection of five downregulated and four upregulated miRNAs were chosen to be investigated within senecionine (Sc) treated HepaRG cells. Sc was used for qPCR in order to have greater certainty for general PA effects and to exclude very specific effects of one single PA. To this end, HepaRG cells were treated with 35 μM of Sc and miRNA expression was analyzed after five different time points. Seven out of nine miRNAs proved to be differentially expressed after Sc treatment. The results are summarized in a heat map in Figure 1. Interestingly, all but one miRNA showed an increased expression compared to the solvent control in a time-dependent manner and for three miRNAs, the upregulation was highest after 8 h. *miRNA-122-3p* (alias miRNA-122\*) was the only one which showed significant downregulation after 24 h.

**Figure 1.** miRNA expression in differentiated HepaRG cells after exposure to 35 μM Sc at 5 different time-points. The results were evaluated using the 2−ΔΔCt method [40]. Results are shown as log2 fold changes (log2 FC) and as mean of three independent biological replicates. Gene expression values were referred to the solvent control (ctrl) of the respective time point but, for clarity, only the ctrl values of the 2 h time point are depicted here. Up- or downregulation of gene expression is indicated in green or red, respectively. The higher the values, the stronger the coloration. Mean values and standard deviations (SD) are summarized in the Supplementary Materials Table S2. Statistical analysis was performed using Student's *t*-test as this is a case of single comparison analysis. That is, each miRNA expression value of one time point was compared to the ctrl of this respective time point. Subsequently, *p*-values were subjected to FDR correction. Significant differences are depicted as follows: \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p*< 0.001.

#### *3.2. Prediction of Biological Consequences of PA-Mediated miRNA Level Alterations*

Subsequently, the target genes of seven miRNAs that were identified to have significantly altered expression log2 FC values in HepaRG cells (see Figure 1) were predicted with IPA software (see Figure 2 for workflow).

The predicted target genes (773 in total) were compared to the gene expression dataset from a whole genome microarray conducted with primary human hepatocytes incubated with 100 μM Sc for 24 h [44]. Matched and prioritized targets (143 target genes) were submitted to a so-called IPA expression analysis to predict affected diseases and functions. In Figure 3 the top "diseases and bio functions" of the deregulated target genes of seven selected miRNAs predicted in Sc-treated primary human hepatocytes are shown. "Cancer" and "organismal injury and abnormalities" are the two main diseases and disorders that are regulated by the highest number of predicted targets. Among molecular and cellular functions, "cell death and survival" has the highest number of target genes with the most pronounced statistical significance. "Liver hyperproliferation" is the top hepatotoxicity function with 57 target genes involved, followed by "hepatocellular carcinoma" with 16 target genes.

Based on the expression analysis, 21 target genes involved in cellular growth and development, apoptosis and inflammation were chosen to be further investigated by qPCR in HepaRG cells incubated with 35 μM Sc for five different time points. The results are depicted in Figure 4 as a heat map. A total of 11 out of 21 genes showed an opposing expression pairing (indicated by superscript number 1); that is, significant downregulation, while their attributed miRNAs were upregulated. For the miRNA-4434 in particular, interaction with 5 out of 11 targets was predicted. These five targets comprised Growth Arrest-Specific protein 2 (*GAS2*), P21-Activated Kinase-1 (*PAK1*), LEPtin Receptor Overlapping Transcript (*LEPROT*), POZ/BTB and AT hook containing Zinc finger 1 (*PATZ1*) and

**Figure 2.** Workflow of miRNA target gene prediction with IPA. The target genes of 7 differentially and significantly expressed miRNAs in HepaRG cells were predicted with IPA (773 predicted targets) and compared to the dataset of a whole genome microarray conducted with primary human hepatocytes [44]. Among the 8623 Differentially Expressed Genes (DEGs) of the primary human hepatocytes after Sc-treatment (100 μM, 24 h), 143 targets were found to overlap with the predicted miRNA target genes. Out of these, 21 targets that simultaneously showed both a significant deregulation in the microarray as well as a high (predicted) confidence of miRNA-target interaction, were selected to be further investigated in HepaRG cells exposed to 35 μM of Sc for 5 different time points. A total of 11 out of 21 target genes showed an opposing expression pairing to their annotated miRNAs (indicated by superscript number 1), as verified by qPCR in HepaRG cells. IPA analysis revealed that 5 out of 11 targets were regulated by one miRNA (miRNA-4434).

**Figure 3.** Top 3 pathways in 3 "diseases and bio functions" of deregulated target genes in primary human hepatocytes listed in IPA after expression analysis with 7 miRNAs. IPA target gene prediction of 7 significantly deregulated miRNAs in HepaRG cells treated with 35 μM of Sc for 24 h was compared to the gene expression data from Sc-treated primary human hepatocytes (100 μM, 24 h) [44]. The numbers of deregulated target genes (number of molecules) predicted to be involved in diseases and bio functions are depicted as bar charts and their lower range of the *p*-value is shown in −log2 FC (orange graph).

ST6 beta-GALactoside alpha-2,6-sialyltransferase 1 (*ST6GAL1*). Therefore, this miRNA was chosen for further experiments.


**Figure 4.** Target gene expression in differentiated HepaRG cells after exposure to 35 μM Sc at 5 different time points. The results were evaluated using the 2−ΔΔCt method [40]. Results are shown as log2 FC and as mean of three biological replicates. Gene expression values were referred to the solvent control (ctrl) of the respective time point but, for clarity, only the ctrl values of the 2 h time point are depicted here. Up- or downregulation of gene expression is indicated in green or red, respectively. The higher the values, the stronger the coloration. Mean values and standard deviations are summarized in the Supplementary Materials Table S3. Superscript number 1 indicates the target genes that show an opposite regulation to their annotated miRNAs and the black arrows indicate the predicted regulatory miRNAs, respectively. Statistical analysis was performed using Student's *t*-test as this is a case of single-comparison analysis. That is, each target gene expression value of one time point was compared to the solvent control of this respective time point. Subsequently, *p*-values were subjected to FDR correction. Significant differences are depicted as follows: \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p*< 0.001.

#### *3.3. MiR-4434 Negatively Regulates PAK1 Gene Expression in HepaRG Cells*

In order to investigate whether it is indeed the PA-mediated induction of miR-4434 levels that leads to the downregulation of the pre-selected target genes, differentiated HepaRG cells were treated with 35 μM of the PA Sc and, subsequently, transiently transfected either with the synthetic miRNA antagonist antagomiR-4434 or with an antagomiR-NC (both at 50 and 75 nM concentrations) or with transfection reagent and water only, as a negative treatment control. The miRNA inhibitor does not degrade its target, but forms a stable complex with it, resulting in an inhibition of miRNA-4434 function. The antagomiR-NC is non-homologous to any mammalian gene and was used to examine if the results of the antagomiR-4434-mediated inhibition were specific. That is, results achieved with the antagomiR-NC should be similar to results from negative treatment control (transfection reagent and water only). It was ensured that the combined incubation with antagomiR and Sc was non-cytotoxic (see Supplementary Materials Figure S2). The gene expression of all 21 target genes was assessed after 48 h and the values of the Sc-treated cells were always referred to their respective solvent control (antagomiR-4434, antagomiR-NC, or negative treatment control) (see Supplementary Materials Figure S3). When comparing antagomiR-4434-transfected cells with the negative control (antagomiR-NC) within the Sc-treatment group, the antisense effect of the antagomiR and its subsequent effect on miR-4434 target gene expression becomes evident. Here, the inhibition of miR-4434 led to significantly attenuated downregulation of *PAK*1 gene expression, with increased significance at the higher antagomiR concentration (Figure 5).

**Figure 5.** *PAK1* gene expression in differentiated HepaRG cells after exposure to 35 μM Sc and antagomiR-mediated inhibition of miR-4434 after 48 h. The results were evaluated according to the 2−ΔΔCt method [40]. (**a**) After housekeeper normalization, the values of the Sc-treated cells were referred to the respective solvent control (0.35% ACN). Noteworthy, the different treatments (antagomiR-4434, antagomiR-NC and negative treatment control) were referred to their respective solvent controls. AntagomiR/antagomiR-NC treatment was applied in the two concentrations 50 and 75 nM. Results are shown as log2 FC and as mean of three replicates. Mean values and standard deviations are summarized in the Supplementary Materials Table S4. (**b**) For normalized *PAK1* expression analysis within the Sc-treated cells, antagomiR-4434 treatment was referred to antagomiR-NC treatment. Again, antagomiR/antagomiR-NC treatment was applied in the two concentrations 50 and 75 nM. Additionally, the negative treatment control was referred to antagomiR-NC to ensure specific antagomiR-mediated inhibition. Statistical analysis was performed using Student's *t*-test as this is a case of single-comparison analysis. That is, the value of *PAK1* gene expression with antagomiR-4434 treatment (50 and 75 nM, respectively) was compared to the value of *PAK*1 gene expression with antagomiR-NC treatment (50 and 75 nM, respectively). Significant differences are depicted as follows: \* *p* < 0.05, \*\*\* *p* < 0.001.

#### **4. Discussion**

This study aimed to investigate the PA-induced effects on miRNA expression. Furthermore, miRNA-mediated effects on potential target genes were considered as an additional, however indirect, molecular regulator in PA-mediated hepatotoxicity. In the last years, miRNAs have emerged as very important players in the regulation of a variety of biological processes in many cell types, including those in the liver [32]. miRNA function ensures fine tuning of target gene expression, and subsequently protein abundance and protein distribution under constantly changing cellular conditions [47]. For example, in the liver intracellular miRNA levels regulate lipid and glucose metabolism [48], inflammation [49–51], apoptosis and necrosis [52–54], cell cycle and proliferation [55], as well as epithelial–mesenchymal transition during liver regeneration [56] and liver fibrosis [57,58]. Alterations in physiological miRNA levels correlate with various liver diseases such as viral hepatitis, alcoholic and nonalcoholic steatohepatitis, drug-induced liver injury, and autoimmune liver disease [32]. In metazoan cells, it is generally assumed that translational repression, presumably occurring during translation initiation, is the predominant mechanism by which miRNAs negatively regulate their target genes [29]. Therefore, it could be concluded that protein analysis is the investigative method of choice. However, transcript degradation of the target mRNA is an unfailing secondary effect triggered by the initial event of translational repression [59–61]. Therefore, qPCR analysis of target mRNA expression is a reliable and widely used method to assess the effects of miRNA-mediated gene silencing and was also applied here.

To elucidate the regulatory effects of miRNAs on their target genes is rather challenging because of its sheer complexity. For example, one miRNA can regulate many targets, and one target can be regulated by many different miRNAs in turn. Furthermore, miRNAs can directly bind their target gene, or they can indirectly regulate a target gene via binding to regulatory molecules like transcription factors, which have their own mode of action [62]. Thus, miRNAs can actually decrease, increase or not change the expression levels of their target genes [63]. This study aimed to achieve the first insights into the mechanistic of miRNA expression in HepaRG cells to understand its implication in PA-mediated hepatotoxicity. Therefore, for simplicity we only considered target genes that showed reverse correlation in gene expression in relation to their assigned miRNAs. Thus, target genes potentially showing a positive correlation or no correlation at all could have been overlooked. In general, miRNA upregulation upon PA-treatment has been described before. For example, *miRNA-34a*, which is considered to be a biomarker for exposure to genotoxic compounds, showed significant upregulation after chronic PA ingestion in a feeding study with rats [35]. Furthermore, blood samples from patients with HSOS showed elevated levels of miRNA-148a-3p, miRNA-362-5p, and miRNA-194-5p, which could be correlated to the severity of the PA-induced liver injury [37].

Upon Sc-incubation, seven liver-specific miRNAs could be verified to be significantly deregulated in HepaRG cells. All but one showed increased gene expression in a timedependent manner. For *miRNA-4301, miRNA-5100* and *miRNA-4454*, upregulation was highest after 8 h, showing the early responsiveness of miRNA levels. miRNA-4301 and miRNA-5100 have been observed to regulate proliferation and apoptosis in lung and breast cancer cells [64–67], but according to our assessment there are no reports on their implication in hepatic diseases. For *miRNA-4454*, it was reported that upregulation positively enhances hepatic carcinoma progression [68], miR-223 is a common regulator in various liver diseases [69], and *miRNA-3663-3p* was shown to be downregulated in hepatocellular carcinoma cells, thus positively regulating cell proliferation of cancer cells [70]. The involvement of these three miRNAs in liver diseases might pose an interface between their PA-induced upregulation and possible carcinogenic properties that have been described for PAs [71–73]. *miRNA-4434* had the highest upregulation of all miRNAs investigated. This miRNA plays a role in different tumors, either as promoter or inhibitor of proliferation [74–76]. However, reports on its relevance in liver function and disease are rare. One study observed miRNA-4434 to be inhibited by a long non-coding RNA

(lncRNA) called Long Stress Induced Non-Coding Transcripts 5 (LSINCT5) in hepatocellular carcinoma progression, potentially resulting in inhibited miRNA-4434 induced apoptosis [77]. *miRNA-122-*3p (alias miRNA-122\*, derived from the antisense strand of the precursor (pre)-miRNA; [78]) was the only miRNA that showed a significant downregulation only after 24 h. Noteworthy, *miRNA-122-*3p downregulation has been observed during early and advanced liver fibrosis [79]. As fibrosis is a disease which is also observed after chronic PA-intoxication, this could point towards a connection between downregulated *miRNA-122-3p* levels and PA-mediated toxicity resulting in fibrosis. The most abundant miRNA in the liver, *miRNA-122-5p* (alias miRNA-122a, derived from the sense strand of the pre-miRNA), did not show a statistically relevant deregulation in gene expression after Sc-treatment at any time-point and was therefore excluded from further analysis.

The comparison analysis between IPA-predicted mRNA targets and differentially expressed genes obtained from a whole genome microarray [44] revealed a target match of 143 genes that are involved in many diseases and disorders such as cancer in general and hepatocellular carcinoma in particular, with cellular growth and proliferation as underlying molecular and cellular functions. Of course, this analysis is a rather generalized evaluation. Therefore, out of these targets, a set of 21 genes regulating processes in cellular growth, development, apoptosis and inflammation was chosen to be verified in Sc-treated HepaRG cells and subjected to further IPA analysis. For cAMP-dependent PRotein Kinase type I-Alpha Regulatory subunit (*PRKAR1A*), a miRNA-dependent regulation was also observed in Mc-treated mice for type II-alpha subunit (*Prkar2a*). Moreover, in the same study, target gene V-type proton ATPase subunit e 2 (*Atp6v0e2*) also showed a significant upregulation in a miRNA-dependent way [36]. Here, another subunit type (*ATP6V1H*) was selected. Both *PRKAR1A* and *ATP6V1H* showed upregulation of gene expression, as was observed in the study mentioned above. A small subgroup of 11 genes showed reverse correlation in gene expression in comparison to their assigned miRNAs. These five miRNAs out of the initial set of seven were miRNA-223, miRNA-3663-3p, miRNA-4301, miRNA-4434, and miRNA-5100. According to IPA analysis, the 11 dysregulated genes generally promote apoptosis and necrosis and decrease cellular survival. Interestingly, miRNA-4434 was predicted to be an upstream regulator of 5 out of the 11 genes, which was the highest number of annotated targets for one miRNA. Surprisingly, it was not a well-established hepatic miRNA such as miRNA-223 or miRNA-122-5p, but the rather less-known miRNA-4434. The five target genes are involved in processes such as cell cycle progression (*GAS2* and *PAK1*), growth hormone signaling (*LEPROT*), apoptosis (*PATZ1*) and immunity (*ST6GAL1*), suggesting a correlation between PA-induced upregulation of miRNAs and subsequent downregulation of these five target genes, presumably resulting in disturbed cellular function. Ultimately, antagomiR-mediated inhibition of miRNA-4434 resulted in significantly altered gene expression pattern of the target gene *PAK*1, strongly indicating a biological connection. The *PAK1* gene encodes one family member of the serine/threonine-specific intracellular protein kinases that are involved in a number of cellular functions including cell cycle regulation, apoptosis, and cytoskeletal motility, usually through substrate phosphorylation [80]. PAK1 is a regulator in key signaling pathways which are relevant for cell cycle progression and proliferation. On the one hand, *PAK1* is overexpressed in many cancers and positively correlates with promotion of cell survival, invasion and metastasis, and drug resistance [81]. In the context of tumor therapy, for example, PAK1 triggers DNA repair caused by genotoxic therapeutic agents [82]. On the other hand, *PAK1* hinders cell cycle progression upon inhibition [83]. Additionally, miRNA interaction with *PAK1* expression has been described previously: in hepatocellular carcinoma development, miRNA-485-5p was observed to suppress *PAK1* levels, and lncRNA-mediated binding of miRNA-485-5p resulted in the upregulation of *PAK1* during hepatocellular carcinoma progression [84]. Here, a PA-induced upregulation of miRNA-4434 can be assumed to negatively regulate *PAK1* expression, presumably resulting in cell-cycle arrest. Notably, an implication of PAs in cell cycle regulation, which is closely linked to DNA damage, has been observed before [85]. This effect was observed to be higher for the more toxic PAs in

comparison to the less toxic PAs. In conclusion, we suggest that the identification of the regulatory mechanism of miRNA-4434-initiated and *PAK1*-induced dysregulation of cell cycle signaling may help to understand the molecular mode of action of some hepatotoxic and carcinogenic effects of Sc.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/foods11040532/s1: Table S1: miRNAs investigated with miR-CURY LNA miRNA custom PCR array, including 84 liver-specific miRNAs, 7 housekeeping genes, 1 interplate calibrator, 3 RNA isolation efficiency spike-in controls and 1 reverse transcription efficiency spike-in control; Figure S1: miRNA expression profile in differentiated HepaRG cells after exposure to Lc and determined with miRCURY LNA miRNA Array; Table S2: miRNA expression in differentiated HepaRG cells after exposure to 35 μM Sc at 5 different time-points; Table S3: Target gene expression in differentiated HepaRG cells after exposure to 35 μM of Sc for 5 time points; Figure S2: Cytotoxicity of differentiated HepaRG cells after antagomiR/antagomiR-NC- and Sc-incubation; Figure S3: Target gene expression in differentiated HepaRG cells after exposure to 35 μM Sc and antagomiR-mediated inhibition of miR-4434 after 48 h; Table S4: Target gene expression in differentiated HepaRG cells after exposure to 35 μM Sc and antagomiR-mediated inhibition of miR-4434 after 48 h.

**Author Contributions:** A.-M.E. conceptualization, investigation, visualization, writing. H.S. conceptualization, evaluation, writing—review and editing. A.B. conceptualization, supervision, writing review and editing. S.H.-P. conceptualization, supervision, funding acquisition, project administration, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the GERMAN FEDERAL INSTITUTE FOR RISK ASSESS-MENT (grant number 1322-624).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is contained within the article (or Supplementary Material).

**Acknowledgments:** We thank Claudia Luckert for graciously providing additional documents and Beatrice Rosskopp for her excellent technical work.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**


