Arch Toxicol DOI 10.1007/s00204-014-1314-7

Review Article

MicroRNAs as key regulators of xenobiotic biotransformation and drug response Jennifer Bolleyn · Joery De Kock · Robim Marcelino Rodrigues · Mathieu Vinken · Vera Rogiers · Tamara Vanhaecke 

Received: 9 May 2014 / Accepted: 8 July 2014 © Springer-Verlag Berlin Heidelberg 2014

Abstract  In the last decade, microRNAs have emerged as key factors that negatively regulate mRNA expression. It has been estimated that more than 50 % of protein-coding genes are under microRNA control and each microRNA is predicted to repress several mRNA targets. In this respect, it is recognized that microRNAs play a vital role in various cellular and molecular processes and that, depending on the biological pathways in which they intervene, distorted expression of microRNAs can have serious consequences. It has recently been shown that specific microRNA species are also correlated with toxic responses induced by xenobiotics. Since the latter are primarily linked to the extent of detoxification in the liver by phase I and phase II biotransformation enzymes and influx and efflux drug transporters, the regulation of the mRNA levels of this particular set of genes through microRNAs is of great importance for the overall toxicological outcome. Consequently, in this paper, an overview of the current knowledge with respect to the complex interplay between microRNAs and the expression of biotransformation enzymes and drug transporters in the liver is provided. Nuclear receptors and transcription factors, known to be involved in the transcriptional regulation of these genes, are also discussed. Keywords  MicroRNA · Transcription factors · Cytochrome P450 enzymes · Drug transporters · Drug metabolism

J. Bolleyn (*) · J. De Kock · R. M. Rodrigues · M. Vinken · V. Rogiers · T. Vanhaecke  Department of Toxicology, Center for Pharmaceutical Research, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium e-mail: [email protected]

Introduction Before a new pharmaceutical compound enters the market, rigorous preclinical testing has to be carried out to assure its safety and efficacy. Hereto, several features of the candidate drug need to be investigated, including its toxicological properties (Brandon et al. 2003). Biotransformation, predominantly occurring in hepatocytes, may be an initiating event of drug toxicity. Although the primary aim of biotransformation is detoxification by rendering compounds more hydrophilic, bioactivation may occur which could trigger toxicity. In general, the process of biotransformation is mediated by a network of biotransformation enzymes (BE) including (1) phase I reactions, implying oxidation, reduction or hydrolysis events and (2) phase II or conjugation reactions, in which chemicals and/or phase I metabolites are coupled to endogenous molecules to further facilitate excretion from the body (Brandon et al. 2003; Wei et al. 2012; Fasinu et al. 2012). Drug transporters (DT) also contribute to this process, since they mediate absorption, distribution and excretion by translocation of the compounds either via active or passive mechanisms (Ramboer et al. 2013). The interaction between BE and DT, together with variations in the expression and/or activity levels of these protein-encoding genes, has thus a number of vast consequences for the interindividual variability of drug disposition and toxicity (Yu 2009). Over the years, considerable research efforts have been done to elucidate the transcriptional regulation of BE and DT. As such, it is known that the expression of DT and BE genes is controlled by different nuclear receptors (NR) and transcription factors (TF) (Tirona and Kim 2005). A novel layer of complexity has recently been added to this regulatory network. Indeed, it has been shown that the protein expression levels of BE and DT often do not reflect their

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respective mRNA levels, supporting the existence of posttranscriptional regulation, such as microRNA (miR)-mediated mechanisms (Yu 2009; Zhang and Su 2009).

Arch Toxicol

triggered, resulting in endonucleolytic mRNA cleavage and degradation. In mammals, microRNA–target mRNA binding predominantly occurs with an imperfect complementarity, initiating translational inhibition (Filipowicz et al. 2008).

MicroRNAs Factors influencing microRNA expression MicroRNAs are a family of small (~22 nucleotides long), endogenous, noncoding RNAs that posttranscriptionally regulate gene expression through translational repression or mRNA degradation (Bartel 2004). It has been estimated that more than 50 % of protein-coding genes are under microRNA control and each microRNA is predicted to repress several mRNA targets (Krol et al. 2010). Currently, as much as 2,578 human mature microRNA sequences have been discovered (miRBase version 20, June 2013). Evidence is accumulating that these RNA molecules are involved in the regulation of different key biological functions, including organ development, cell differentiation and proliferation, cell death as well as biotransformation of endo- and exogenous substances (Zhang and Su 2009; Bartel 2004; Ambros 2004; To et al. 2008; Takagi et al. 2008; Pan et al. 2009b).

Previous research has shown that several BE and DT are posttranscriptionally regulated by microRNAs (To et al. 2008; Takagi et al. 2008; Pan et al. 2009a). As such, alterations in microRNA levels are anticipated to modify the expression of their target BE and DT genes. This may ultimately result in a deviated biotransformation and/or transport of the xenobiotic under investigation or a concurrent molecule and could lead to increased toxicity due to cellular accumulation (Yu 2009). Consequently, knowledge of potential changes in microRNA expression is promising while assessing drug safety and response. Variations in microRNA levels can occur as a result of (1) single nucleotide polymorphisms (SNPs) in microRNAs, (2) epigenetic modifications, (3) developmental changes, (4) progress of tissue from a healthy to a diseased state and (5) pharmacotherapy (Rukov et al. 2014) (Fig. 2).

Biogenesis of microRNA and mechanism of action Single nucleotide polymorphisms The biogenesis of microRNAs (Fig. 1) is initiated by the transcription of microRNA genes, located in intronic regions of protein-coding genes, or individual transcripts by RNA polymerase II or RNA polymerase III, yielding long primary transcripts (pri-miRNA). These transcripts, forming distinctive hairpin structures, are further processed by the Drosha–DiGeorge syndrome critical region (DGCR) 8 complex into a ~70 nucleotides long precursor hairpin (pre-miRNA) and are exported to the cytoplasm by Exportin 5 (Bartel 2004; Krol et al. 2010; Filipowicz et al. 2008). Besides this canonical pathway, pre-miRNA can also be formed by splicing and debranching of introns (miRtron) (Westholm and Lai 2011; Krol et al. 2010). Both pathways proceed by cleavage of the hairpin structures by means of the RNAse III enzyme Dicer, together with transactivationresponsive (TAR) RNA-binding protein (TRBP), to yield ~20bp microRNA duplexes. One strand, i.e., with the lowest thermodynamic stability at the 5′ untranslated region (UTR) end (guide strand), will act as a mature microRNA and will be incorporated into the RNA-induced silencing complex (RISC). The other strand, the so-called passenger strand, is normally degraded, but can also act as a mature microRNA (Krol et al. 2010; Filipowicz et al. 2008). When mature microRNAs pair through a nearly perfect complementarity with their “seed” region, represented by nucleotides 2–8, to the complementary target sites in the 3′ UTR of the target mRNA, a RNA interference-like mechanism is

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MicroRNA-mediated gene regulation can be affected by the occurrence of SNPs, also known as miRSNPs. These polymorphisms are present at or near microRNA-binding sites of functional genes as well as in genes involved in microRNA biogenesis and in primary, precursor or mature microRNA sequences (Zhang and Dolan 2010; Mishra et al. 2008). These miRSNPs can affect microRNA functions either by directly impairing mRNA expression or by influencing microRNA–target interaction. Hence, these miRSNPs may contribute to the interindividual variability in expression and activity of BE and DT and thus may underlie differences in drug toxicity and efficacy (Ramamoorthy et al. 2012; Zhang and Dolan 2010; Saunders et al. 2007). Epigenetics Many genes encoding for BE, DT and NR are known to be under epigenetic control (Baer-Dubowska et al. 2011). Epigenetics can be defined as all the heritable changes in gene expression that are not associated with alterations in DNA sequences (Iorio et al. 2010). Prototypical epigenetic events include DNA methylation and histone modifications, which both control the accessibility of gene promoters to the transcriptional machinery that determines whether a particular gene is transcriptionally active or repressed (Fraczek et al.

Arch Toxicol Canonical pathway

Mirtron pathway Exon 1

Exon 2

Transcription

5’ Drosha

3’

5’

Exon 1

Exon 2

3’

Pri-miRNA

Spliceosome

DGCR8

Processing

Splicing Pre -miRNA Ran GTP

Cytoplasm

Nucleus

Exportin

Exportin

Export

Dicer Dicer processing 5’ 3’

Translational repression

TRBP

3’ 5’

miRNA miRNA* duplex

miRISC RISC loading

mRNA cleavage

Fig. 1  Biogenesis of microRNA. After transcription of microRNA genes, microRNAs are processed through the canonical pathway or mirtron pathway into pre-miRNA. Both pathways proceed by cleavage of hairpin structures by means of RNAse III Dicer. MicroRNA– microRNA duplexes are formed, and from this duplex, only the guide strand is loaded into the RISC complex, forming miRISC. This com-

plex will cause translational repression or mRNA cleavage. Abbreviations: DGCR DiGeorge syndrome critical region, Pre-miRNA precursor microRNA, Pri-microRNA primary microRNA, RISC RNA-induced silencing complex, TRBP transactivation-responsive RNA-binding protein

2012). As microRNAs have been shown to play important roles in controlling DNA methylation and histone modifications (Iorio et al. 2010), microRNA-induced changes in the epigenetic machinery will ultimately affect xenobiotic biotransformation. Vice versa, epigenetic mechanisms can also influence microRNA expression. Indeed, studies show that histone deacetylase (HDAC) inhibitors change microRNA expression profiles in different cell types. In this context, our group demonstrated that trichostatin A (TSA), an acknowledged HDAC inhibitor, alters microRNA expression in primary rat hepatocyte cultures. In fact, 18

microRNAs were found to be differentially expressed upon exposure to TSA, thereby affecting different biological networks, including metabolism, genetic and environmental information processing, cellular processing and organismal system pathways (Bolleyn et al. 2011). Others reported that 32 microRNAs were differentially expressed in TSAtreated apoptosis-resistant human breast cancer cells (Rhodes et al. 2012). Collectively, these data clearly suggest that microRNAs are part of a multilevel regulatory mechanism that fine-tunes gene expression (Iorio et al. 2010; BaerDubowska et al. 2011).

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Arch Toxicol Development Transcription Promoter

RNA coding region for microRNA

Mature miRNA

Disease

Xenobiotics

TF

NR

Drug response

miRSNPs Epigenetic modulators

Promoter

RNA coding region for BE and DT

BE/DT mRNA

Transcription

BE/DT protein Translation

Fig. 2  Interplay between microRNA-mediated posttranscriptional regulation, and influencing factors, of biotransformation-related gene expression and drug response. Abbreviations: BE biotransformation

enzymes, DT drug transporters, NR nuclear receptors, SNP single nucleotide polymorphism, TF transcription factors

Development

of microRNAs has indeed been demonstrated in several liver pathologies, including nonalcoholic steatohepatitis (NASH), viral hepatitis, polycystic liver disease and hepatocellular carcinoma (HCC) (Chen 2009). Cheung and colleagues showed that NASH is associated with changes in the hepatic expression of microRNAs. The potential targets of these differentially expressed microRNAs are known to influence lipid metabolism, cell growth and differentiation, apoptosis and inflammation, which are all key processes involved in the development and progression of NASH (Cheung et al. 2008). miR-122 was the first microRNA species found to facilitate the replication of the hepatitis C virus (HCV) (Jopling et al. 2005). In this respect, HCV treatment based on miR-122 inhibition by antisense oligonucleotides has been recently tested in clinical trials. In addition, other microRNAs are currently being investigated as possible candidates for HCV treatment (Hoffmann et al. 2012). Similarly, miR-15a is actively involved in the progression of polycystic liver disease. Downregulation of miR-15a results in an increased expression of cell division cycle Cdc25A, which is accompanied in vitro by increased proliferation and cystogenesis (Lee et al. 2008; Chen 2009). Also, in the last few years, a number of studies showed differentially expressed microRNAs in HCC versus healthy liver tissue (Gramantieri et al. 2008; Gooderham

MicroRNA expression profiles vary during organismal development and are thought to be essential for tissuespecific mRNA expression (Rukov and Shomron 2011). In human adult liver, several microRNAs are abundantly expressed, including miR-1, miR-16, miR-27b, miR-30d, miR-122, miR-126, miR-133, miR-143 and the let-7 family. While miR-122 is the most highly expressed microRNA in adult liver, miR-92a and miR-483 seem to be preferentially expressed in fetal liver (Chen 2009). Further, miR-18a, miR-92a, miR-409-3p, miR-451 and miR-483-3p are more expressed at the embryonic stage (7–10 weeks from gestation) of the human liver development compared to the adult organ. During liver maturation, the let-7 family together with other microRNAs such as miR-22, miR-23b, miR-99a, miR-125b and miR-192 become more expressed, suggesting a regulatory role for the differentially expressed microRNAs (Tzur et al. 2009). Disease In humans, the microRNA profile of liver substantially changes during the transition from healthy to diseased state (Lu et al. 2005; Rukov and Shomron 2011). Involvement

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Arch Toxicol

and Koufaris 2014). Among these microRNAs, several ones were equally dysregulated in other types of liver cancer, highlighting the fact that microRNAs can potentially act as oncogenes or tumor suppressors (Gramantieri et al. 2008). Xenobiotics Acute or chronic exposure to pharmaceuticals or chemicals typically modifies microRNA expression profiles (Fukushima et al. 2007; Moffat et al. 2007; Pogribny et al. 2007; Shah et al. 2007; Lizarraga et al. 2012). For instance, Lizarraga et al. could show that benzo[a]pyrene, a genotoxic carcinogen, alters eight microRNAs in HepG2 cells, affecting apoptotic signaling, cell cycle arrest, DNA damage response and DNA damage repair (Lizarraga et al. 2012). When testing the influence of 19 xenobiotics on the expression of ten microRNAs in four different cell systems, Rodrigues and colleagues showed that changes in microRNA expression depend on both the drug and cell type investigated (Rodrigues et al. 2011). In addition, the appropriate exposure time and dose have to be determined as well. As such, it was shown in vivo that microRNA expression in the liver is initially relatively resistant to acute xenobiotic exposures, but the magnitude of the microRNA deregulations increasing progressively overtime (Gooderham and Koufaris 2014). Exposure of rats to tamoxifen, a potent rat hepatocarcinogen, leads to substantial changes in the expression of microRNA liver genes (Pogribny et al. 2007). Moreover, aberrant microRNA expression caused by drugs can induce drug resistance (Pogribny et al. 2010). In tumors, this process leads to the rise of a few cells with resistant phenotypes. Eventually, these resistant cells could overgrow the other ones and become the dominant cell type in the tumor. In this respect, Pogribny et al. (2010) showed that miR-7 and miR-345 were significantly downregulated in human cisplatin-resistant breast cancer MCF-7 cells. Hence, changes in microRNA expression profiles may have drastic implications for clinical cancer treatment (Rukov and Shomron 2011; Pogribny et al. 2007, 2010).

Direct posttranscriptional regulation of biotransformation enzymes and drug transporters by microRNAs Biotransformation enzymes During phase I biotransformation, 75 % of both exogenous and endogenous compounds are metabolized by cytochrome P450 enzymes (CYPs) via reduction, oxidation or hydrolysis, making the molecule more hydrophilic and thus better excretable (Fasinu et al. 2012). Interindividual CYP gene expression is primarily ascribed to genetic

polymorphism (Ingelman-Sundberg et al. 1999). However, growing evidence is emerging that the expression levels of CYPs are also under epigenetic and microRNA-mediated posttranscriptional control (Zanger and Schwab 2013). Phase II enzymes catalyze the conjugation of different cosubstrates, such as glutathione (GSH), uridine diphosphate (UDP)-glucuronic acid, sulfonates, acetyl Co-A, to xenobiotics or their phase I metabolite(s). As such, phase II BE are mainly transferases (Klaassen et al. 2011; Sheehan et al. 2001). An overview of the most important associations reported with respect to the expression of phase I and II BE and microRNAs is provided here. A more extensive summary can be found in Table 1. Cytochrome P450 enzymes CYP1A1  CYP1A1 catalyzes the hydroxylation of several polycyclic aromatic hydrocarbons (Zanger and Schwab 2013; Baer-Dubowska et al. 2011). Using lymphoblastoid cell lines, it was found that mRNA expression of CYP1A1 positively correlates with miR-18b and miR-20b levels. In addition, other members of the phase I BE family, including aldehyde dehydrogenase 2 and flavin-containing monooxygenase 4, also appear to be linked to the same microRNAs, indicating their important regulatory role in phase I biotransformation (Wang et al. 2009; Glubb and Innocenti 2011). Recently, an extensive study was performed by Rieger et al. comparing microRNA levels to protein and activity phenotypes for the ten most important drug metabolizing CYPs. A cohort of 92 human liver samples showed that the protein/ activity levels of CYP1A1 are negatively correlated with miR-130a, miR-132, miR-142-3p, miR-200a/b, miR-21, miR-27a, miR-31 and miR-34a. However, a possible link between miR-18b and miR-20b and CYP1A1 mRNA and protein levels in human liver samples was not found (Rieger et al. 2013). Yet, generalization of results obtained in different cell types must be done with caution since these discrepant results once more show that different microRNA profiles are present in different cell types. In addition, miR-20b was not among the selected microRNAs in the Rieger study. CYP1B1  CYP1B1, known to govern the extra-hepatic metabolism of several procarcinogens and promutagens, was the first phase I biotransformation enzyme discovered to be regulated by microRNAs (Yokoi and Nakajima 2013; Yu 2009; Klaassen et al. 2011). In particular, CYP1B1 is regulated by miR-27b, as evidenced by an increase of the protein level and enzymatic activity of endogenous CYP1B1 in cultured human breast cancer cells upon addition of antisense miR-27b (Tsuchiya et al. 2006). In relation to breast cancer, abnormal CYP1B1 expression caused by lower miR-27b levels leads to an increased conversion of estradiol to 4-hydroxy estradiol. The latter has a toxic

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13

Biotransformation enzymes

Phase I

Cytochrome P450 enzymes (CYP)

CYP1A2

CYP1A1

Direct

Indirect

− − +

miR-150 miR-18b



− −

miR-214 miR-27a miR-31



miR-34a

miR-24

miR-221

miR-214

miR-21

miR-204

miR-200b

miR-200a

miR-150

miR-148a

miR-142-3p

miR-132

miR-130a

miR-122

miR-101

miR-9

+

+

+







miR-29a

miR-539



miR-221

miR-21 −

+

miR-20b

miR-200c



+

miR-200a/b

miR-19a/b

miR-185

miR-148a



miR-146a

miR-143





miR-132 miR-142-3p



RNA level

miR-130a

miRNA













+







+



















+





+











Protein level





















ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND

Activity level

Table 1  Overview of direct and indirect regulation of biotransformation enzymes and their respective transcription factors or nuclear receptors by microRNA

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Wang et al. (2009)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Wang et al. (2009)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

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Table 1  continued





miR-130b miR-142-3p miR-150

CYP2C9

CYP2C8

CYP2B6



miR-106b







miR-223

let-7b

miR-539

miR-27a









miR-21

+

miR-204

+

miR-148a



miR-142-3p

miR-133a

miR-107

miR-103

+

+

miR-101

let-7c



miR-455-3p

miR-18b

+











miR-34a

miR-27a



miR-223

miR-221

miR-22







miR-21

miR-204



miR-200c

+



miR-185 miR-200b





miR-146a

miR-132





let-7g

CYP2A6 miR-130a







miR-34a

miR-31 −

Protein level −

RNA level

miR-27a

miRNA

miR-27b

Indirect

CYP1B1

Direct

+



ND

ND

+





































Activity level

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Zhang et al. (2012)

Zhang et al. (2012)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Tsuchiya et al. (2006)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

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Table 1  continued

13 CYP2C19

Direct

Indirect

− − −

miR-146a miR-16 miR-185



− − −

miR-146a miR-185

− −

miR-221

miR-22



− miR-214

miR-21









miR-200b

miR-200a

miR-19a/b

miR-150

miR-143

− miR-142-3p

miR-132







miR-130a/b

miR-106b

miR-539

miR-455-5p



miR-29a

miR-28-5p



miR-28-3p

miR-27a −



miR-24

miR-223



miR-221



miR-214



miR-19a/b





miR-18a/b

miR-17

miR-148a

miR-122 −



miR-106a/b

let-7f



RNA level

let-7d

let-7c

miRNA







+











+



+

+

+

Protein level







+











Activity level

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

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Table 1  continued

CYP2D6

Direct

Indirect



miR-34a



+



− miR-539

miR-455-5p



− miR-455-3p

miR-34a

miR-31

− −

miR-28-5p miR-29a



− −





















miR-28-3p

miR-27b

miR-27a

miR-26a

miR-24

miR-223

miR-221

miR-214





miR-21

miR-204



miR-200b

miR-200a

miR-18b



miR-185



miR-16



miR-152









miR-148b

miR-143

miR-142-3p

miR-10a









miR-130a −













Activity level













Protein level

miR-106b

let-7g

let-7f

let-7d

let-7c

let-7b

miR-539





miR-27a miR-31

RNA level

miRNA

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Arch Toxicol

13

Table 1  continued

13 CYP3A4

CYP2E1

Direct

Indirect



+



let-7f

+



miR-9 miR-101



miR-455-5p















miR-539





























miR-455-3p

miR-34a

miR-378

miR-323-3p

miR-31

miR-29a

miR-27a

miR-26a/b

miR-24

miR-221

miR-214

miR-21

miR-200b/c

miR-200a

miR-18b







miR-152

miR-150



miR-148b







miR-146a

miR-143









miR-142-3p

miR-133a

miR-132

miR-130a







miR-125b

miR-10a





let-7g miR-106b





let-7e

let-7c −



Activity level

let-7d



Protein level





RNA level

let-7b

let-7a

miRNA

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Mohri et al. (2010)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

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Transcription factors

Drug transporters

Table 1  continued

FMO4

ALDH2

ABC transporters

Solute carriers (SLC)



− + +

miR-192 miR-194

+

HNF4α

MRP1



miR-34a

miR-7

miR-345

miR-326



− miR-328

miR-519c



miR-520h

+

miR-363

ABCG2

+

miR-451

miR-27a

ABCB4

ABCB1







miR-213

miR-181b

miR-181a

SLC47A1





miR-20b

miR-18b

+

miR-20b

miR-18b

miR-125b +



mmu-miR-298

miR-9



miR-34a













+

+









miR-27b

miR-27a





miR-223

miR-221

miR-204 +



miR-200a

miR-148a +

Protein level −

RNA level

miR-142-3p

miRNA

miR-221

CYP7A1, CYP8B1, CYP27A1, PXR, TTR, ApoB, α1-AT

Indirect

SLC29A1

Glutathione S-transferase GSTP1 (GST)

Liver-enriched Hepatocyte nuclear factors transcription factors (LETF)

Phase III

Efflux

Phase 0

Uptake

Phase II

Others

CYP24A1

Direct

NA

ND

ND

ND

ND

ND

ND

ND

+

+

ND

ND

ND

ND

ND

ND

ND

ND

ND

ND



ND

+



ND





+



Activity level

Takagi et al. (2010), Ramamoorthy et al. (2012), Wang and Burke (2013)

Pogribny et al. (2010)

Pogribny et al. (2010)

Liang et al. (2010)

Pan et al. (2009b)

To et al. (2008)

Liao et al. (2008)

Wang et al. (2009)

Kovalchuk et al. (2008), Zhu et al. (2008)

Zhu et al. (2008)

Wang et al. (2009)

Wang et al. (2009)

Wang et al. (2009)

Wang et al. (2009)

Wang et al. (2009)

Wang et al. (2009)

Wang et al. (2009)

Wang et al. (2009)

Wang et al. (2009)

Wang et al. (2009)

Komagata et al. (2009)

Pan et al. (2009a)

Rieger et al. (2013)

Rieger et al. (2013)

Pan et al. (2009a)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

Rieger et al. (2013)

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13

13 PXR VDR

Erα

GR

Pregnane X receptor Vitamin D receptor

Estrogen receptors

Glucocorticoid receptor

 Contradictory results were reported by Rieger et al. (2013)







miR-27b

miR-124a

miR-18





miR-211 miR-222



miR-22





mmu-miR-298 miR-206

CYP3A4



miR-27bb

CYP3A4

miR-125b

miR-148a





miR-130 miR-27a



miR-27b

miR-27a





























miR-34c-5p

TTR, ApoB, α1-AT





miR-27b

miR-449a

PXR, TTR, ApoB, α1-AT



Protein level



miR-24

CYP7A1, CYP8B1, CYP27A1

RNA level

miR-21

miRNA

Indirect

 Contradictory results were reported by Wei et al. (2013) and Rieger et al. (2013)

b

a

−: negative correlation

+: positive correlation

Empty field: no or no significant correlation was found

RXRα

PPARγ

PPARα

Direct

Retinoid X receptor

Nuclear recep- Peroxisome proliferatoractivated receptors tors

ND not determined, NA not applicable

Table 1  continued

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Activity level

Vreugdenhil et al. (2008)

Vreugdenhil et al. (2008)

Zhao et al. (2008)

Zhao et al. (2008)

Xiong et al. (2010), Pandey and Picard (2009)

Adams et al. (2007)

Pan et al. (2009a)

Pan et al. (2009a)

Mohri et al. (2009)

Takagi et al. (2008)

Ji et al. (2009)

Ji et al. (2009)

Lee et al. (2010)

Karbiener et al. (2009), Jennewein et al. (2010)

Kim et al. (2010)

Kida et al. (2011)

Kida et al. (2011)

Wang and Burke (2013)

Ramamoorthy et al. (2012), Wang and Burke (2013)

Takagi et al. (2010)

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effect and appears to play a role in tumorigenesis, and has therefore been suggested to be involved in the development of estrogen-dependent carcinogenesis (Yokoi and Nakajima 2013; Tsuchiya et al. 2004; Baer-Dubowska et al. 2011). CYP2C8  CYP2C8 is responsible for the detoxification of an array of xenobiotic compounds in human liver, including taxol, cerivastatin, amiodarone, amodiaquine, troglitazone, rosiglitazone and verapamil (Zhang et al. 2012). In a panel of 31 human liver samples, miR-103 and miR-107 were inversely correlated with CYP2C8 protein expression, suggesting a negative regulation of CYP2C8 by miR-103/107 (Zhang et al. 2012). In addition, it was shown by Rieger et al. that let-7c, miR-101, miR133a, miR-142-3p, miR148a, miR-204, miR-21, miR-223, miR-27a and miR-539 are also involved in the regulation of CYP2C8 (Rieger et al. 2013). CYP2E1  CYP2E1 preferentially metabolizes low molecular weight molecules, including ethanol, drugs (paracetamol, chlorzoxazone), organic solvents (acetone) and narcotics (halothane) (Klaassen et al. 2011; Mohri et al. 2010; Zanger and Schwab 2013). In silico analysis revealed a possible binding region for miR-378 present in the 3′ UTR of CYP2E1 mRNA. In vitro follow-up studies using human embryonic kidney cells showed that both the CYP2E1 protein level and corresponding activity were decreased upon transfection with precursor miR-378. To verify this mode of action in vivo, a panel of human livers was investigated for their CYP2E1 protein and mRNA expression together with the miR-378 expression. In this study, miR-378 levels appeared to be inversely correlated with CYP2E1 protein and the protein/mRNA ratio (Mohri et al. 2010). Also, the expression of miR-130a, miR-132, miR-148b, miR-21, miR-214, miR-221 and miR-27a was negatively correlated with the mRNA/activity levels of CYP2E1 (Rieger et al. 2013). CYP3A4  CYP3A4 is indispensable for the biotransformation of the majority of therapeutic drugs (Zanger and Schwab 2013). Pan and colleagues revealed in two different cancer cell lines that the CYP3A4 protein expression is directly targeted by miR-27b and mmu-miR-298. Since a decrease in the CYP3A4 mRNA levels is accompanied by a downregulation of CYP3A4 protein production, involvement of mRNA degradation is proposed as the driving mechanism (Pan et al. 2009a). In contrast, to these in vitro studies, Rieger and colleagues were not able to detect a correlation between miR-27b and CYP3A4 in healthy human liver samples. This may demonstrate a difference in microRNA-mediated regulation of CYP expression in vitro versus in vivo, but also in cancerous versus normal tissues (Rieger et al. 2013).

CYP24A1  CYP24A1, also known as vitamin D3 hydroxylase, inactivates calcitrol, being a biological active metabolite of vitamin D3 with a determinating role in calcium homeostasis and displaying antitumor activity. CYP24A1 has been reported as an oncogene and may contribute to tumor aggressiveness by abrogating local anticancer effects of calcitrol (King et al. 2010). Calcitrol binds to the vitamin D receptor (VDR) to perform its function. Both CYP24A1 and VDR are believed to be posttranscriptionally regulated by miR-125b, as a recognition element for miR-125b has been identified in the 3′ UTR region of their mRNAs (Mohri et al. 2009; Komagata et al. 2009). Because CYP24A1 itself is a transcriptional target of VDR, miR-125b can regulate in a direct and/or indirect way the expression of CYP24A1 (Komagata et al. 2009). Glutathione S‑transferase GSTP1  GSTP1 is a cytosolic GST that catalyzes the conjugation of electrophilic substrates to GSH (Sheehan et al. 2001). The expression levels of GSTP1 parallel those of miR-192 and miR-194, pointing to a potential role of both microRNAs in the control of its production (Wang et al. 2009). Drug transporters DT are a family of proteins that facilitate the transport of chemical substances in and out the cells by means of passive and active mechanisms. Depending on the direction of this transport, uptake and efflux transporters can be distinguished (Klaassen and Aleksunes 2010). Uptake transporters are responsible for the uptake of both endogenous and exogenous molecules, prior to their biotransformation. They belong to the superfamily of solute carrier (SLC) proteins and act as facilitating transporters or secondary active transporters (Klaassen and Aleksunes 2010; Ramboer et al. 2013). Efflux transporters, convey xenobiotics or their metabolites, outside of the cell. They are primary active transporters and are called the adenosine triphosphate (ATP) binding cassette (ABC) transporters, because they contain ATP-binding domains with ATPase activity to provide energy for translocating substrates across membranes, most often against concentration gradients (Klaassen and Aleksunes 2010). Elevated expression levels of one or more ABC transporters have been associated with multidrug resistance, which is defined by the ability of tumor cells to resist several unrelated drugs after exposure to a single chemotherapeutic agent (Liang et al. 2010). Solute carrier superfamily SLC29A1  The SLC29A1 gene encodes for equilibrative nucleoside transporter 1 (ENT1), a cellular transporter

13



required for nucleoside transport together with the uptake of cytotoxic nucleosides used in chemotherapy. Using in silico techniques, it was found that SLC29A1 correlates with miR-221 expression (Wang et al. 2009; Glubb and Innocenti 2011). SLC47A1  The SLC47 family, also known as the multidrug and toxin extrusion (MATE) family, is a recently discovered group of SLC transporters. Unlike the other members of the SLC family, SLC47 possesses efflux transporter properties and takes part in the efflux of organic cations (Ramboer et al. 2013). Pairwise correlation coefficient analysis showed a correlation between miR-181a, miR-181b and miR-213 and SLC47A1 expression (Glubb and Innocenti 2011; Wang et al. 2009; Baer-Dubowska et al. 2011). ABC superfamily P‑glycoprotein  P-glycoprotein (Pgp/MDR1/ABCB1), a member of the multidrug resistance (MDR) proteins, is responsible for the efflux of a wide repertoire of xenobiotics including anticancer drugs, antibiotics and antiviral agents (Ramboer et al. 2013; Chan et al. 2004). Zhu et al. showed that the expression of Pgp is activated by miR-27a and miR451. The multidrug-resistant cancer cell lines A2780DX5 and KB-V1 showed a higher expression of both miR-27a and miR-451 compared to the parental lines. In the presence of miR-27a and miR-451 antagomirs, downregulation of both Pgp and MDR1 mRNA levels was noticed in A2780DX5 cells (Zhu et al. 2008). Kovalchuk et al. also unveiled the involvement of miR-451 in controlling the expression of the MDR1 gene in MCF-7 breast cancer cells. Moreover, upregulation of miR-451 in doxorubicin-resistant MCF-7 cells increased the sensitivity of the cells to doxorubicin, indicating that correction of an altered microRNA expression could serve as a possible therapeutic strategy to overcome multidrug resistance (Kovalchuk et al. 2008). Multidrug resistance protein 3  Multidrug resistance protein 3 (MDR3/ABCB4) is produced in the liver and guides the canalicular translocation of phospholipids and some cytotoxic drugs (Chan et al. 2004; Ramboer et al. 2013). Data mining performed by Wang et al. could associate miR-363 levels to the mRNA expression of ABCB4, which encodes MDR3 (Wang et al. 2009; Baer-Dubowska et al. 2011). Breast cancer resistance protein  Breast cancer resistance protein (Bcrp/ABCG2), a member of the ABCG subfamily, has an important function in the excretion of hydrophobic xenobiotics and conjugated, usually sulfated, metabolites (Ramboer et al. 2013). Overexpression of ABCG2 is also involved in the mechanism of multidrug resistance (To et al. 2008). Several

13

Arch Toxicol

studies indicated a role for microRNAs in the regulation of ABCG2 expression. Liao et al. were the first to prompt a possible correlation between microRNA and ABCG expression levels. In particular, it was found that co-transfection of pre-miR520h together with a pMIR-Luc-ABCG2 plasmid reduced luciferase activity significantly, indicating that ABCG2 is a target of hsa-miR-520h (Yu 2009; Liao et al. 2008). Subsequently, To and colleagues showed that ABCG2 is a target of hsa-miR-519c in S1 parental colon cancer. The repression of protein production was, however, linked to the length of the 3′ UTR region. As such, the microRNA could not bind to the 3′ UTR region of ABCG2 in resistant S1MI80 cells as a consequence of a shorter 3′ UTR region (To et al. 2008; Yu 2009). Furthermore, miR-328 and ABCG2 protein levels were shown to be inversely linked to drug-resistant and parental MCF-7 cells. Transfection of miR-328 resulted in a downregulation of the ABCG2 protein expression in MCF-7/MX100 cells, following a dramatic increase of the sensitivity of the cells to the anticancer drug mitoxantrone (Pan et al. 2009b; Yu 2009). Multidrug resistance‑associated protein 1 Multidrug resistance-associated protein 1 (MRP1) is responsible for the translocation of GSH-, UDP- and sulfate-conjugated and sulfate-unconjugated organic anions (Ramboer et al. 2013). Liang and colleagues compared the microRNA expression levels of the VP-16-resistant MDR cell line, i.e., MCF-7/VP, with its parent cell line MCF-7. They found that MCF-7/ VP overexpressed MRP1 mRNA and protein and showed a lower expression of miR-326 compared to the level found in MCF-7 cells. Furthermore, overexpression of miR-326 in MCF-7/VP downregulated MRP1 mRNA and protein expression and sensitized these cells to VP-16 and doxorubicin, indicating the involvement of microRNA in multidrug resistance via MRP1 expression (Liang et al. 2010). In addition, miR-345 and miR-7 were demonstrated to target MRP1 expression when comparing the breast cancer parent cell line MCF-7 with its cisplatin-resistant variant (MCF7/CDDP). In fact, transfection of MCF-7/CDDP cells with miR-345 and miR-7 resulted in a significant decrease of MRP1 cellular levels and an increased sensitivity of MCF7/CDDP to cisplatin (Pogribny et al. 2010).

Indirect influence of microRNAs on xenobiotic biotransformation via posttranscriptional regulation of nuclear transcription factors and receptors TF are trans-acting DNA-binding proteins, which can bind to a cis-acting DNA sequence in the regulatory elements of a gene (Schrem et al. 2002). TF enable selective gene expression and regulation. In combination with other proteins, coactivating or corepressing, they form a multiprotein complex that drives mRNA synthesis (Schrem et al. 2002).

Arch Toxicol

MicroRNAs also participate in this transcriptional control in at least two different motifs. First, microRNAs and TF can accomplish a coordinated repression, reinforcing each other’s activity. This process is called coherent feedforward. When microRNA and TF carry out an opposing activity (incoherent feedforward), a more uniform expression is ensured, which helps to maintain protein homeostasis (Tsang et al. 2007; Gurtan and Sharp 2013). Liver‑enriched transcription factors (LETF) The family of liver-enriched TF (LETF) consists of the hepatocyte nuclear factors (HNF) families HNF1, HNF3, HNF4, HNF6, the CCAAT/enhancer binding protein (c/EBP) and D-binding protein. These TF cooperate to maintain liver-specific gene transcription (Schrem et al. 2002; Duncan 1998). Hepatocyte nuclear factor 4 α HNF4α, a member of the nuclear hormone receptor superfamily, is considered to be a master regulator of the overall TF network in the liver (Wang and Burke 2013). HNF4α can transactivate the expression of several target genes, including CYPs, UDP-glucuronosyltransferases (UGTs), sulfotransferases and drug transporters either via direct binding or by regulation of nuclear receptors (NR), including the pregnane X receptor (PXR) and the constitutive androstane receptor (CaR). This intricate network of transcriptional regulation underscores the great influence of HNF4α on drug metabolism and disposition (Ramamoorthy et al. 2012). Takagi et al. (2010) found that the expression of HNF4α is controlled through translational repression and mRNA degradation, via miR-34a and miR-24, respectively. Overexpression of these microRNAs in HepG2 cells resulted in a downregulation of some bile acid synthesizing enzymes (CYP7A1 and CYP8B1) and the cholesterol-metabolizing enzyme CYP27A1, reflecting the downregulation of HNF4α (Takagi et al. 2010). Ramamoorthy et al. further showed that, next to miR-34a, miR-449a is also able to downregulate HNF4α protein and mRNA levels and of its target gene PXR in HepG2 cells (Ramamoorthy et al. 2012). In addition, miR-34a-, miR-34c-5p- and miR449a-induced repression of HNF4α protein expression also leads to a reduced HNF4α binding to its target genes transthyretin, apolipoprotein B and α1-antitrypsin (Wang and Burke 2013). Nuclear receptors NR are ligand-activated TF that regulate the expression of target genes by binding to cis-acting DNA sequences. NR

can either activate or repress target genes by directly binding to DNA response elements as homo- or heterodimers or through binding to other classes of DNA-bound TF. NR are classified based on their binding to different ligands. The estrogen receptor (ER), androgen receptor (AR), progesterone receptor (PR) and glucocorticoid receptor (GR) are regulated by endocrine ligands. “Adopted” orphan receptors, such as peroxisome proliferator-activated receptors (PPARs) and liver X receptor (LXR), however, have both natural and synthetic ligands (Yang and Wang 2011). Pregnane X receptor (PXR) PXR is an important regulator of drug metabolism in the liver and small intestine (Yang and Wang 2011). PXR is activated by several endogenous and exogenous chemicals, including antibiotics, antimycotics and steroids, to bind to DNA response elements in the regulatory regions of CYP3A genes and a number of other genes involved in the metabolism and elimination of xenobiotics (Kliewer et al. 2002). Takagi showed that miR-148 directly regulates PXR protein expression, but not the expression of CYP3A4, in human liver. Yet, the inducible and/or constitutive levels of CYP3A4 could be modulated via miR-148-mediated regulation of PXR (Takagi et al. 2008). However, in other studies, no correlation between hsa-miR-148 and the expression of PXR or CYP3A4 in human liver samples could be demonstrated at both the protein and mRNA level (Wei et al. 2013; Rieger et al. 2013). Vitamin D receptor Vitamin D receptor (VDR) is a nuclear steroid hormone receptor that exerts its function via binding to the vitamin D responsive element in the regulatory region of target genes, such as CYP3A4 (Mohri et al. 2009; Wang et al. 2008). Pan and colleagues showed that miR-27b and mmu-miR-298 are able to indirectly regulate CYP3A4 via VDR, in addition to the direct regulation of CYP3A4 through binding to its 3′ UTR region (Pan et al. 2009a). Furthermore, Mohri et al. (2009) identified another potential recognition element for miR-125b in the 3′ UTR region of human VDR mRNA. As VDR is regulated posttranscriptionally by miR-125b, the latter could indirectly influence the expression of genes under VDR control, including CYP3A4 (Mohri et al. 2009). Estrogen receptor α ERs are a group of ligand-activated NR which are triggered by estrogen (Xiong et al. 2010). CYP1B1 catalyzes the conversion of estradiol into 4-hydroxy estradiol and the metabolic activation of procarcinogens and promutagens (Yokoi and Nakajima 2011). CYP1B1 is regulated by ERα,

13



which is under microRNA control. miR-206 can repress endogenous ERα mRNA and protein expression in MCF-7 cells and T47D breast cancer cells (Adams et al. 2007). miR-22 (Xiong et al. 2010; Pandey and Picard 2009) and miR-211/222 (Zhao et al. 2008) have also been added to the list of possible ERα protein regulators. Glucocorticoid receptor Cortisol, as well as other glucocorticoids, binds to the GR, which plays a vital role in development, metabolism and immune responses (Klaassen et al. 2011). GR is involved in the regulation of several BE and NR genes, including CYP2B6, CYP2C9, CYP3A4, PXR and CaR (Nakajima and Yokoi 2011). Both miR-18 and miR-124a were reported to regulate GR protein levels in the brain (Vreugdenhil et al. 2008), but microRNA-dependent regulation of GR has not yet been linked to xenobiotic biotransformation. Peroxisome proliferator‑activated receptors PPARs are ligand-activated TF of the nuclear hormone receptor superfamily. PPARs are divided into three subtypes: α, β/δ and γ. They play a major role in fatty acid metabolism and energy homeostasis. In association with a coactivator complex, PPARs function as heterodimers that bind to a DNA sequence present in the promoter of target genes leading to their transactivation or transrepression (Tyagi et al. 2011). Research performed by Kida and colleagues indicates that microRNAs are involved in the regulation of lipidmetabolizing enzymes. miR-21 and miR-27b were found to regulate the protein expression of PPARα in Huh7 cells. However, no effect was seen on PPARα mRNA levels, suggesting translational repression (Kida et al. 2011). miR27a, miR-27b and miR-130 were also shown to be involved in PPARγ expression, another subtype of the PPAR family (Karbiener et al. 2009; Jennewein et al. 2010; Kim et al. 2010; Lee et al. 2011; Peng et al. 2014). Furthermore, retinoid x receptor α, the heterodimeric partner of PPARγ, was identified as a target of miR-27a and miR-27b in ratderived hepatic stellate cells, indicating a potential new role of both microRNAs in the regulation of fat metabolism and cell proliferation (Ji et al. 2009). Overall, it seems that miR-27 regulates a large variety of lipid-metabolizing enzymes such as CYP4A11, UGT1A9, UGT2B4 and acylCoA-synthase through PPAR (Yokoi and Nakajima 2013).

Concluding remarks There is accumulating evidence that microRNAs control the expression of several BE and DT genes at the

13

Arch Toxicol

posttranscriptional level and consequently play an important role in the process of drug metabolism and disposition (Table 1). Regulation can occur either through direct interaction with the BE and DT mRNAs, or in an indirect way by targeting NR and TF mRNAs that are necessary for the transcription of the BE and DT genes. Hence, modifications in microRNA profiles may cause an altered expression of BE and DT, which may result in a deviating xenobiotic metabolism and/or transport. In turn, this may affect the biological activity of the compound under consideration, including its potential toxicity. The discovery that microRNAs play a pivotal role in drug response and safety has given rise to the emerging new field of “microRNA pharmacogenomics,” in which microRNA and polymorphisms affecting microRNA function are studied in order to predict drug behavior. For example, a SNP located in the binding site of miR-24 in the 3′ UTR region of human dihydrofolate reductase gene leads to overexpression of this gene and methotrexate resistance (Mishra et al. 2007). Recently, it was shown that a microRNA-binding site polymorphism in SLC19A1 can influence methotrexate concentration in Chinese children with acute lymphoblastic leukemia (Wang et al. 2014). At present, we are only just beginning to understand the impact of microRNAs on the expression of genes involved in drug metabolism and disposition, and it is anticipated that much more data will become available in the coming years. It must, however, be emphasized that up till now, the majority of studies were performed on cancer cell lines that already display an altered microRNA profile. Because of chemoresistance, cancer cell lines frequently fail to identify the microRNA target genes involved which can distort the results. Therefore, further investigations should focus more on microRNA profiling in noncancerous cells. In addition, it should be noted that discrepant results can also be obtained when studying microRNA expression in vitro. Indeed, the in vivo situation is affected by numerous factors and potential effects caused by microRNA may be masked. However, it should also be noted that the in vivo situation has limitations due to the fact that administered drugs can have an effect on the microRNA profile. At last, whenever a possible link between microRNA and BE or DT is identified, its actual influence on drug metabolism should be further explored. Indeed, it is important to measure the enzyme activities involved using well-known reference drugs in the presence or absence of the identified microRNA(s). In actual studies, this is often neglected. In conclusion, unanswerable linking microRNA expression to particular genes involved in drug response and toxicity remains very difficult. Future research will undoubtedly provide more insights and shed further light on the intricate interplay between microRNAs and drug metabolism.

Arch Toxicol Acknowledgments  This work is financially supported by grants from the Fund for Scientific Research (FWO), Vlaanderen, Belgium, the Research Council (OZR) of the Vrije Universiteit Brussel, Belgium and The Johns Hopkins Centre for Alternatives to Animal Testing (CAAT), Baltimore, USA. Conflict of interest  The authors report no declarations of interest.

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MicroRNAs as key regulators of xenobiotic biotransformation and drug response.

In the last decade, microRNAs have emerged as key factors that negatively regulate mRNA expression. It has been estimated that more than 50% of protei...
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