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. Author manuscript; available in PMC: 2016 Aug 1.
Published in final edited form as: Clin Pharmacol Ther. 2015 Aug;98(2):205–215. doi: 10.1002/cpt.145

Age-related changes in microRNA expression and pharmacogenes in human liver

Kimberly S Burgess 1, Santosh Philips 1, Eric A Benson 1, Zeruesenay Desta 1, Andrea Gaedigk 1, Roger Gaedigk 1, Matthew W Segar 1, Yunlong Liu 1, Todd C Skaar 1
PMCID: PMC4512918  NIHMSID: NIHMS690102  PMID: 25968989

Abstract

Developmental changes in the liver can significantly impact drug disposition. Due to the emergence of microRNAs (miRNAs) as important regulators of drug disposition gene expression, we studied age-dependent changes in miRNA expression. Expression of 533 miRNAs was measured in 90 human liver tissues (fetal, pediatric (1-17 years), and adult (28-80 years); n=30 each). 114 miRNAs were upregulated and 72 were downregulated from fetal to pediatric, and 2 and 3, respectively, from pediatric to adult. Among the developmentally changing miRNAs, 99 miRNA-mRNA interactions were predicted or experimentally validated (e.g. hsamiR-125b-5p-CYP1A1; hsa-miR-34a-5p-HNF4A). In human liver samples (n=10 each), analyzed by RNA-sequencing, significant negative correlations were observed between the expression of >1000 miRNAs and mRNAs of drug disposition and regulatory genes. Our data suggest a mechanism for the marked changes in hepatic gene expression between the fetal and pediatric developmental periods, and support a role for these age-dependent miRNAs in regulating drug disposition.

Keywords: liver, fetus, pediatric, age, development, ontogeny

Introduction

Developmental changes in drug disposition create the need for age-appropriate pharmacotherapy.1-4 Although there have been many approaches for estimating pediatric drug dosing (e.g. extrapolation from adult data), these approaches are not adequate for many children, particularly for the very young patients (infants vs children).5 Developmental changes in drug response are difficult to predict at the individual patient level, in part due to our poor understanding of the mechanisms that regulate these developmental changes. Consequently, the high off-label use of drugs in this pediatric population is associated with an increased number of adverse events.6 Although more clinical studies are now being conducted in pediatric populations as a result of the Pediatric Exclusivity and the Best Pharmaceuticals for Children Acts, a fundamental understanding of the ontogenesis of drug disposition genes is still needed to develop more accurate dosing guidelines for both the clinical trials and clinical care in the young.

The majority of drug disposition genes are known to change during early and late developmental stages in humans; however, little is understood about the mechanisms that control these changes. The most well characterized age-associated drug disposition genes involve expression of drug metabolizing enzymes in the liver.7-10 These include phase I and II enzymes that are responsible for the modification and elimination of many drugs. The most notable of the phase I enzymes are the cytochrome P450s (CYPs) and many developmental changes have been observed in their expression patterns.7-10 For example, the predominant CYP gene expressed in fetal liver, CYP3A7, peaks shortly after birth and becomes undetectable in most children and adults. In contrast, CYP1A1, 2C9, 2C19, 2D6, and 2E1 are undetectable or expressed extremely low in the fetus, but are expressed at high levels after birth.11-15 A well-documented example of phase II enzyme ontogeny is the delayed onset of a member of the UGT2B family responsible for “gray baby” syndrome in neonates as an adverse event of chloramphenicol therapy.16 There is also data indicating that UGT1A6 and UGT1A9 function increases in older children and adults compared to younger children.17 Recently, developmental patterns of drug transporter expression indicates that hepatic MDR1 (ABCB1), MRP (ABCC2), OATP1B1 (SLCO1B1) and OATP1B1 (SLCO1B3) expression is increased in adults compared to fetal and neonatal periods.18

To elucidate the mechanisms underlying these developmental changes, this study focused on the developmentally-regulated expression of hepatic microRNAs (miRNAs). miRNAs are small noncoding RNAs approximately 17 to 22 nucleotides in length that bind to mRNAs and regulate translation and stability. The majority of miRNAs bind imperfect complimentary sequences in the 3’UTRs of target mRNAs.19-21 In addition, a minority of miRNAs target other regions of mRNA such as the 5’UTR and coding regions to repress translation, and in some cases, have even increased gene expression.22-24 The 3’UTR ‘seed sequence’ located at the 5’ end from bases 2-8 is a critical feature required for miRNA binding to its target mRNA.25 With over 2500 mature miRNAs (miRBase Registry version 20) and a seven base pair seed sequence, miRNAs have already been shown to regulate a large number of genes, including some involved in drug metabolism and disposition.26-30

Hepatic miRNA expression is altered under a variety of conditions. Hepatic diseases, such as liver cancer, cirrhosis, and hepatitis C infection, are associated with altered miRNA expression31-33 We have shown that rifampin, a known inducer of drug metabolism genes 34,35, alters miRNA expression patterns in primary human hepatocytes.36 Altered hepatic miRNA expression patterns may contribute to rifampin-mediated drug interactions. Furthermore, in rats, aging has been associated with alterations in the hepatic miRNA expression, and pulmonary expression of several miRNAs that are known to regulate susceptibility to carcinogens.37,38

In this study, we hypothesized that miRNA expression in human liver tissue changes with age and that this variable expression contribute to the developmental changes in drug metabolism and disposition genes. To test this hypothesis, we measured and analyzed miRNA and mRNA expression in human liver tissues from subjects in three different age groups (fetal, pediatric, and adult). Our results suggest that miRNAs are likely to regulate these genes either directly or indirectly through upstream regulatory genes.

Results

miRNA expression changes with developmental periods

To determine miRNA expression patterns across the fetal, pediatric, and adult developmental periods, we measured expression of 533 miRNAs in 90 liver samples using the TaqMan OpenArray platform. These are commercially available arrays that are pre-plated with 756 miRNAs that were known at the time of the array design (533 miRNAs were detected in our samples). This set of miRNAs includes those that have been shown to be important in the liver. We also measured four of these miRNAs, each in a unique set of 20 samples using single TaqMan assays to validate their expression patterns in the OpenArray platform (Supplemental Figure 1). miRNAs that did not generate threshold cycle values in at least 6 samples in a given developmental period were excluded from analysis unless that miRNA was not expressed in the group at all. Only miRNAs that were statistically significant after false discovery rate (FDR) correction are discussed.

As shown in the Principal Components Analysis, the expression profiles for the fetal group are distinctly separated from the pediatric and adult groups (Figure 1). Hierarchical clustering, using Spearman Rank Dissimilarity, was implemented to depict the grouping of both samples and miRNAs (Figure 2) and reveal 71% dissimilarity between the fetal group and the pediatric/adult groups. Although not as dramatic, there are notable dissimilarities among the pediatric and adult groups as well. There are also clusters of miRNAs that are significantly upregulated and downregulated together among the developmental groups. We show the top 45 developmentally-regulated miRNAs between the groups; the remaining statistically significant developmentally-regulated miRNAs are shown in the supplemental materials. During the transition from the fetal to the pediatric period, 114 miRNAs were upregulated and 72 miRNAs were downregulated (Table 1 & Supplemental Tables 1 & 2). Between the pediatric and adult periods, two miRNAs were upregulated and three were downregulated (Table 2). An example of miRNA expression changes in each category of Table 1 & 2 is provided in dot plots of individual miRNA threshold cycle (CT) values (Supplemental Figure 2). An increase in CT values indicate a decrease in miRNA expression. miRNAs that do not generate a CT value are assigned a value of 40 indicating that it is undetected. Supplemental Figure 2A shows an increase in hsa-let-7a-5p between the fetal and pediatric/adult group whereas Supplemental Figure 2B shows a decrease in hsa-miR-431-5p expression between these groups. Supplemental Figure 2C & 2D depicts the gradual increase and decrease in expression of hsa-miR-34a-5p and hsa-miR-18a-5p, respectively, across the developmental periods.

Figure 1. Principal components analysis of all miRNAs detected in human liver samples.

Figure 1

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Principal components analysis of all 533 miRNAs measured using TaqMan OpenArray. Axes are principal components 1, 2, and 3. Red: Fetal, Blue: Pediatric, Green: Adult

Figure 2. Heatmap of all developmentally-regulated miRNAs in human liver samples.

Figure 2

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Hierarchical clustering of miRNA expression levels using Spearman Rank Dissimilarity. X-axis: miRNAs. Y-axis: 90 samples across the 3 developmental periods (Red: Fetal, Blue: Pediatric, Green: Adult). Color scale below the figure shows increasing expression from blue to gray to red.

Table 1.

Changes in miRNA expression from fetal to pediatric

Upregulated miRNAs* p-value FDR** Fold Change
hsa-miR-139-5p 6.2E-26 2.0E-24 3.7
hsa-let-7a-5p 4.7E-16 5.5E-15 2.1
hsa-miR-10a-5p 4.9E-14 4.0E-13 3.4
hsa-miR-132-3p 2.7E-13 2.0E-12 3.7
hsa-let-7b-5p 5.0E-12 3.2E-11 > 500
hsa-miR-125b-5p 6.8E-11 3.8E-10 2.2
hsa-let-7g-5p 3.1E-08 1.3E-07 1.7
hsa-miR-1275 3.3E-08 1.3E-07 3.0
hsa-miR-122-3p 3.0E-07 1.1E-06 9.2
hsa-miR-1271-5p 2.3E-05 6.4E-05 4.2
hsa-miR-139-3p 2.4E-05 6.8E-05 19.5
hsa-miR-128a-3p 3.1E-05 8.7E-05 1.6
hsa-miR-10b-5p 5.6E-04 1.3E-03 9.1
hsa-miR-106a-5p 7.5E-04 1.8E-03 151.9
hsa-miR-126-5p 1.3E-03 3.0E-03 1.5
hsa-miR-10b-3p 2.8E-03 6.1E-03 17.3
hsa-let-7f-5p 3.5E-03 7.6E-03 1.5
hsa-miR-140-3p 4.5E-03 9.5E-03 2.2
hsa-let-7e-5p 8.8E-03 1.8E-02 1.3
hsa-miR-1244 1.8E-02 3.4E-02 1.9
Downregulated miRNAs p-value FDR** Fold Change
hsa-miR-431-5p 1.0E-58 5.5E-56 < −500
hsa-miR-668-3p 5.0E-42 1.3E-39 < −500
hsa-miR-539-5p 3.3E-36 4.3E-34 −101.6
hsa-miR-411-5p 8.3E-36 8.9E-34 −18.2
hsa-miR-409-3p 2.1E-34 1.6E-32 −30.8
hsa-miR-889-3p 4.7E-32 3.1E-30 −39.0
hsa-miR-433-3p 6.0E-32 3.6E-30 < −500
hsa-miR-483-3p 2.0E-30 1.1E-28 −52.6
hsa-miR-136-3p 3.2E-30 1.5E-28 −26.1
hsa-miR-512-3p 5.5E-30 2.5E-28 < −500
hsa-miR-432-5p 5.9E-28 2.2E-26 < −500
hsa-miR-485-3p 5.6E-28 2.2E-26 < −500
hsa-miR-382-5p 8.7E-28 3.1E-26 < −500
hsa-miR-487a-3p 3.8E-25 1.1E-23 < −500
hsa-miR-519e-5p 1.2E-24 3.4E-23 < −500
hsa-miR-130a-3p 3.1E-24 8.3E-23 −3.7
hsa-miR-515-3p 7.3E-23 1.8E-21 < −500
hsa-miR-154-3p 1.0E-22 2.3E-21 < −500
hsa-miR-144-5p 2.6E-21 5.8E-20 < −500
hsa-miR-519a-3p 2.2E-20 4.4E-19 < −500
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*

The alternative nomenclature for miRNAs and TaqMan assay IDs are provided in Supplemental Table 1, to allow relation of our data to that utilized in other reports using the # designation over the −3p or −5p designations.

**

False discovery rate comparing fetal vs. pediatric

Table 2.

Changes in miRNA expression from pediatric to adult

Downregulated miRNA p-value FDR* Fold Change
hsa-miR-18a-5p 1.4E-05 3.7E-03 −2.0
hsa-miR-20a-3p 7.8E-05 1.4E-02 −2.0
hsa-miR-219a-5p 1.7E-04 2.3E-02 −12.0
Upregulated miRNA p-value FDR* Fold Change
hsa-miR-34a-5p 1.6E-06 8.3E-04 2.1
hsa-miR-30a-3p 3.2E-04 3.4E-02 1.7
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*

False discovery rate comparing pediatric vs. adult

Inverse correlation of expression of miRNAs and ADME mRNAs

Over 14,000 human genes were analyzed using RNA-seq in 30 samples (10 per age group). Figure 3 displays the expression of 34 absorption, distribution, metabolism, elimination (ADME) and regulatory genes that are relevant to drug disposition between the populations (Figure 3 & Supplemental Table 3). Among those genes, expression of 28 mRNAs increased and expression of three decreased from fetal to pediatric, and two increased and five decreased from pediatric to adult. CYP2D6 and UGT2B17 gene expression levels were among those that increased from pediatric to adult while CYP1A1, CYP1A2, CYP2C19, GSTM1, and SLCO1B3 expression levels decreased from pediatric and adult. CYP3A7, GSTP1, and SLC15A2 expression decreased from fetal to pediatric/adult while most of the others increased. These findings are consistent with previous findings regarding the developmental expression profiles for these ADME genes.1,2,9,18

Figure 3. Heatmap of the top ADME mRNA expression across the developmental periods.

Figure 3

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Hierarchical clustering of the top ADME and regulatory gene mRNA using Spearman Rank Dissimilarity. X-axis: mRNAs. Y-axis: 90 samples across the 3 developmental periods (Red: Fetal, Blue: Pediatric, Green: Adult). Color scale below the figure shows increasing expression from blue to gray to red. *SLC22A2 and SLC22A6 are excluded due to insufficient reads in the majority of samples.

A linear regression model was used to determine negative correlations between the expression levels of the developmentally changing miRNAs and the ADME and regulatory mRNA expression levels. In order to eliminate bias due to the difference in the expression levels of these miRNAs and mRNAs, we considered developmental periods as a cofactor while searching for inverse correlations between the miRNA and target genes. The negative estimates shown in Table 3 indicate the changes in mRNA expression. We observed over 1000 negative correlations (data not shown), but only the top five for each mRNA are shown based on FDR. Some of these correlations may reflect direct regulation of the mRNAs by the miRNAs, but it is also conceivable that there may be indirect regulation through transcriptional regulators, such as HNF4A and PXR.39,40

Table 3.

Negative correlations between developmental miRNA and top ADME and regulatory mRNA

Gene miRNA p-value FDR Estimate*
Phase I CYP2A6 hsa-miR-10a-5p 1.1E-02 4.3E-02 −44.2
CYP2C8 hsa-miR-132-3p 1.3E-02 4.9E-02 −113.9
CYP2D6 hsa-miR-132-3p 1.3E-03 1.0E-02 −13.3
CYP2D6 hsa-miR-10b-3p 1.7E-03 1.2E-02 −2.3
CYP2D6 hsa-miR-34a-5p 2.3E-03 1.5E-02 −13.3
CYP2D6 hsa-miR-30a-3p 1.2E-02 4.7E-02 −8.5
CYP2D6 hsa-miR-125b-5p 1.3E-02 4.8E-02 −13.0
CYP2E1 hsa-miR-125b-5p 7.9E-05 2.0E-03 −795.4
CYP2E1 hsa-miR-219a-5p 1.0E-04 2.3E-03 −131.8
CYP2E1 hsa-miR-132-3p 4.6E-04 5.6E-03 −597.3
CYP2E1 hsa-miR-34a-5p 7.5E-04 7.3E-03 −604.7
CYP2E1 hsa-miR-30a-3p 1.0E-02 4.1E-02 −365.1
CYP3A5 hsa-miR-132-3p 3.7E-03 2.1E-02 −30.5
CYP3A5 hsa-miR-34a-5p 6.9E-03 3.1E-02 −30.0
CYP3A7 hsa-miR-431-5p 3.0E-11 6.9E-08 −34.4
CYP3A7 hsa-miR-668-3p 1.2E-10 2.2E-07 −42.0
CYP3A7 hsa-miR-889-3p 8.1E-08 3.3E-05 −94.7
CYP3A7 hsa-miR-539-5p 1.4E-07 4.7E-05 −93.8
CYP3A7 hsa-miR-485-3p 1.6E-07 4.8E-05 −24.3
Phase II GSTP1 hsa-miR-431-5p 3.6E-13 2.5E-09 −4.7
GSTP1 hsa-miR-668-3p 2.6E-11 6.9E-08 −5.7
GSTP1 hsa-miR-483-3p 5.2E-10 6.0E-07 −10.9
GSTP1 hsa-miR-382-5p 5.1E-10 6.0E-07 −4.0
GSTP1 hsa-miR-889-3p 5.0E-09 4.3E-06 −13.2
NAT2 hsa-miR-132-3p 1.2E-03 9.7E-03 −4.0
NAT2 hsa-miR-34a-5p 4.8E-03 2.5E-02 −3.7
TPMT hsa-miR-34a-5p 2.9E-04 4.2E-03 −2.5
TPMT hsa-miR-219a-5p 3.2E-04 4.5E-03 −0.5
TPMT hsa-miR-125b-5p 3.7E-03 2.0E-02 −2.4
TPMT hsa-miR-30a-3p 4.3E-03 2.3E-02 −1.6
TPMT hsa-miR-10a-5p 9.0E-03 3.8E-02 −1.4
UGT1A1 hsa-miR-10a-5p 3.9E-03 2.1E-02 −42.0
UGT1A1 hsa-miR-30a-3p 8.7E-03 3.7E-02 −39.3
UGT1A1 hsa-miR-219a-5p 1.2E-02 4.7E-02 −9.7
UGT2B15 hsa-miR-30a-3p 1.4E-03 1.1E-02 −10.6
UGT2B15 hsa-miR-219a-5p 1.9E-03 1.3E-02 −2.7
UGT2B15 hsa-miR-34a-5p 6.2E-03 2.9E-02 −12.3
UGT2B15 hsa-miR-125b-5p 8.8E-03 3.7E-02 −2.9
UGT2B15 hsa-miR-30a-3p 9.1E-03 3.8E-02 −1.9
UGT2B17 hsa-miR-125b-5p 8.2E-03 3.5E-02 −2.6
UGT2B17 hsa-miR-30a-3p 9.0E-03 3.8E-02 −1.7
UGT2B17 hsa-miR-219a-5p 1.3E-02 4.9E-02 −0.4
UGT2B7 hsa-miR-219a-5p 5.2E-03 2.6E-02 −3.3
UGT2B7 hsa-miR-125b-5p 8.0E-03 3.5E-02 −18.7
UGT2B7 hsa-miR-30a-3p 9.3E-03 3.8E-02 −12.0
UGT2B7 hsa-miR-132-3p 1.2E-02 4.6E-02 −14.6
Transporters ABCB1 hsa-miR-219a-5p 2.1E-06 1.9E-04 −0.9
ABCB1 hsa-miR-34a-5p 9.2E-05 2.1E-03 −3.9
ABCB1 hsa-miR-125b-5p 5.1E-04 5.9E-03 −4.1
ABCB1 hsa-miR-30a-3p 1.3E-03 1.0E-02 −2.5
ABCB1 hsa-miR-132-3p 1.5E-03 1.1E-02 −3.2
ABCC2 hsa-miR-10a-5p 8.8E-03 3.7E-02 −13.0
ABCG2 hsa-miR-668-3p 4.1E-03 2.2E-02 −0.2
ABCG2 hsa-miR-431-5p 9.0E-03 3.8E-02 −0.2
ABCG2 hsa-miR-512-3p 1.1E-02 4.3E-02 −0.1
SLC15A2 hsa-miR-889-3p 1.2E-04 2.4E-03 −0.1
SLC22A1 hsa-miR-30a-3p 1.7E-03 1.2E-02 −25.5
Regulatory HNF4A hsa-miR-132-3p 1.2E-04 2.4E-03 −4.3
HNF4A hsa-miR-30a-3p 3.8E-04 5.0E-03 −3.2
HNF4A hsa-miR-125b-5p 1.2E-03 9.9E-03 −4.5
HNF4A hsa-miR-219a-5p 3.6E-03 2.0E-02 −0.7
HNF4A hsa-miR-34a-5p 1.0E-02 4.0E-02 −3.2
NR1I2 hsa-miR-30a-3p 6.0E-03 2.8E-02 −2.0
NR1I2 hsa-miR-10b-3p 7.3E-03 3.3E-02 −0.4
NR1I2 hsa-miR-125b-5p 1.0E-02 4.1E-02 −3.0
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*

Estimates indicate the changes in mRNA expressed as RPKM (reads per kilobases per million) per one threshold cycle decrease in miRNA expression. Note: Decrease in threshold cycle is an increase in miRNA expression.

In silico identification of miRNAs predicted to target top ADME mRNA

Predicted miRNA target analysis was performed using Ingenuity Pathway Analysis. This program identifies predicted and experimentally validated targets from multiple databases. Among the 45 developmentally-regulated miRNAs and 34 ADME and upstream regulatory genes, 28 predictions and five experimentally validated targets were revealed involving 16 genes and 21 miRNAs (Table 4). Analysis using the remaining miRNAs that were upregulated or downregulated from fetal to pediatric to adult resulted in 65 predictions and one experimentally validated target involving 46 miRNAs and 23 of the top ADME and regulatory genes (Supplemental Table 4). Eighteen of these predictions overlapped with the negatively correlated miRNA-mRNA combinations (selected correlations are shown in Table 3). The predictions with experimentally validated results (indicated in Table 4 and Supplemental Table 4) mentioned here provide confirmation that at least some ADME mRNAs are regulated by miRNAs.

Table 4.

List of miRNA-mRNA predicted targets

miRNA Seed Sequence mRNA Source Confidence
hsa-let-7a-5p GAGGUAG ABCC2 TargetScan Human Moderate (predicted)
hsa-miR-154-3p AUCAUAC ABCG2 TargetScan Human Moderate (predicted)
hsa-miR-106a-5p AAAGUGC ABCG2 TargetScan Human Moderate (predicted)
hsa-miR-512-3p AGUGCUG ABCG2 TargetScan Human Moderate (predicted)
hsa-miR-519a-3p AAGUGCA ABCG2 miRecords Experimentally Observed
hsa-miR-125b-5p CCCUGAG CYP1A1 miRecords Experimentally Observed
hsa-miR-431-5p GUCUUGC CYP1A2 TargetScan Human Moderate (predicted)
hsa-miR-668-3p GUCACUC CYP1A2 TargetScan Human Moderate (predicted)
hsa-miR-1275 UGGGGGA CYP2B6 TargetScan Human Moderate (predicted)
hsa-miR-483-3p CACUCCU CYP2B6 TargetScan Human Moderate (predicted)
hsa-miR-539-5p GAGAAAU CYP3A7 TargetScan Human Moderate (predicted)
hsa-miR-106a-5p AAAGUGC DPYD TargetScan Human Moderate (predicted)
hsa-miR-34a-5p GGCAGUG DPYD TargetScan Human High (predicted)
hsa-miR-519e-5p UCUCCAA DPYD TargetScan Human Moderate (predicted)
hsa-miR-519a-3p AAGUGCA DPYD TargetScan Human Moderate (predicted)
hsa-miR-1271-5p UUGGCAC DPYD TargetScan Human Moderate (predicted)
hsa-miR-34a-5p GGCAGUG HNF4A TargetScan Human Experimentally Observed
hsa-miR-668-3p GUCACUC HNF4A TargetScan Human Moderate (predicted)
hsa-miR-18a-5p AAGGUGC NR1I2 TargetScan Human High (predicted)
hsa-miR-219a-5p GAUUGUC NR1I2 TargetScan Human Moderate (predicted)
hsa-miR-34a-5p GGCAGUG NR1I2 TargetScan Human Moderate (predicted)
hsa-miR-132-3p AACAGUC SLC15A2 TargetScan Human Moderate (predicted)
hsa-miR-519a-3p AAGUGCA SLC15A2 TargetScan Human Moderate (predicted)
hsa-miR-1275 UGGGGGA SLC22A1 TargetScan Human Moderate (predicted)
hsa-miR-382-5p AAGUUGU SLC22A2 TargetScan Human Moderate (predicted)
hsa-miR-431-5p GUCUUGC SLC22A2 TargetScan Human Moderate (predicted)
hsa-miR-139-5p CUACAGU SLCO1B1 TargetScan Human Moderate (predicted)
hsa-miR-10a-5p ACCCUGU TPMT TargetScan Human Moderate (predicted)
hsa-miR-125b-5p CCCUGAG UGT2B15 miRecords Experimentally Observed
hsa-miR-382-5p AAGUUGU UGT2B15 TargetScan Human Moderate (predicted)
hsa-miR-125b-5p CCCUGAG UGT2B17 miRecords Experimentally Observed
hsa-miR-382-5p AAGUUGU UGT2B17 TargetScan Human Moderate (predicted)
hsa-miR-409-3p AAUGUUG UGT2B17 TargetScan Human Moderate (predicted)
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Discussion

These data indicate that miRNA expression in the human liver is age-dependent which is consistent with observations of developmental changes of miRNA expression patterns in rat.37 Specifically, miRNAs 29c-3p, 195-5p, and 497-5p were significantly upregulated and 301a-3p, 106b-5p, 185-5p, and 539-5p downregulated between the human fetal and pediatric livers which is in agreement with miRNAs that changed with age in rat liver tissue; exceptions were miRNAs 148b-3p and let-7a-5p, which revealed opposite findings. Other miRNAs Mimura et al. found to change in aging rat liver were consistent with our data, but not significant after FDR-corrected.

We observed large variability between genes in the extent of induction or repression in mRNA expression between developmental stages, particularly between fetal and pediatric. Since drug metabolism and disposition is enzyme and transporter specific, these differences likely contribute to the differential developmental changes in drug metabolism and disposition. Furthermore, interindividual variability in the rate of change in the expression of these genes could likely also contribute to the interindividual variability in drug efficacy and toxicity. Thus, the developmental effect on drug metabolism must be determined for each specific drug. One limitation to our study is that we do not have serial samples within individuals or broad coverage of time points during fetal development, due to ethical reasons. Hence, the exact timing of some of the changes cannot be determined from our data. Also, due to limited sample materials, we were not able to measure protein expression by Western blot analysis or activity of the drug disposition genes. Since miRNAs can affect protein translation without substantial effects on mRNA levels, our miRNA-mRNA correlations may miss some gene specific effects and will need to be tested in individual experiments.

There is strong evidence that these changes in miRNA expression throughout the developmental periods are important in regulating mRNA expression levels of target genes including phase I and II enzymes, drug transporters, and regulatory genes. This regulation can be linked to the marked changes in mRNA expression levels between the fetal and pediatric/adult group as well as changes between the pediatric and adult group. Twenty-seven miRNAs, not expressed during fetal development, were expressed in the pediatric and adults samples; this can potentially be linked to highly expressed mRNA levels in the fetal liver, which are down-regulated in pediatrics and adults. Additionally, 43 miRNAs expressed during the fetal period were not expressed in pediatric and adult livers. The changes in hepatic miRNA expression from pediatric to adult periods may also contribute to changes in hepatic ADME gene expression. Several of these miRNA changes were associated with change in hepatic mRNA expression. There was substantial variability in the expression of many of the miRNAs during each of the developmental periods. This may be a result of the interindividual variability in the timing of the developmental changes. These developmental changes in miRNA expression are most likely not due to a discrete incremental change amongst individuals but more of a continual change over time; one that likely occurs in every individual, but at various rates and to variable extents. This may contribute to the variable rates of changes in hepatic drug disposition. A limitation of this study is that each of the liver samples was from a different subject, so we were not able to determine the variability in the rates of changes within individuals. Since liver biopsies within individuals would not be ethical, such a study is not feasible in human subjects. Previously, miR-34a-5p, miR-200a-3p, and miR-200b-3p in the liver were shown to increase with age in a Caucasian, mostly adult, population.41 In our data, miR-34a-5p was upregulated 1.7- and 2.1-fold from fetal to pediatric to adult and miR-200a-3p increased 2.7-fold from fetal to pediatric (Table 2, Supplemental Table 2 and 3). However, correlations of miRNAs and mRNAs between the two cohorts varied. For example, in both studies miR-28-3p negatively correlated with CYP2C9 mRNA, whereas miR-148b-3p was negatively correlated with CYP2C9 mRNA in our study (data not shown), but was described to be positively correlated with CYP2C9 by Rieger et al.41 This could be due to a number of reasons as liver sample demographics and ages varied between studies.

SNPs in the 3’UTR of drug metabolism and disposition genes have been predicted to interfere with miRNA-mRNA interactions.42,43 The cytochrome P450s analyzed in our current data set were shown to contain SNPs predicted to alter miRNA targeting in these two studies. miR-34a-5p has been previously validated to target hepatic nuclear factor 4α (HNF4A).39,40 Furthermore, a SNP in the 3’UTR of HNF4A (rs11574744) abolished the miRNA binding sites of both miR-34a-5p and another miRNA validated as a target, miR-449a. This SNP prevented the ability of miR-34a-5p to downregulate HNF4A and appeared to be associated with altered CYP2D6 activity in a cohort previously phenotyped with the CYP2D6 probe drug, dextromethorphan.39 This SNP is present in only African Americans, revealing the degree of complexity to miRNA regulation as we know that these SNPs occur at differing frequencies among populations. This knowledge reveals the importance in miRNA regulation of drug disposition and regulatory genes as there is a need to determine the extent of age-dependent miRNA regulation on these genes.

In this study, we have shown that miRNA expression in the human liver changes with age. These changes are most marked between fetal and pediatric ages; however, there were also notable changes between the pediatric and adult periods. Several of these miRNAs have either been validated to target ADME genes and upstream regulatory genes, as shown in Table 4, or are predicted to target these genes. These data strongly suggest that hepatic miRNA expression contributes to the clinical variability in hepatic drug disposition. The marked change in miRNA expression between the fetal and pediatric/adult groups may contribute to the high susceptibility of fetuses to adverse drug events. These developmental changes in miRNA expression may also contribute to changes in hepatic functions other than drug metabolism, such as cholesterol metabolism and xenobiotic detoxification. Collectively, this information suggests that age-dependent miRNAs significantly impact a variety of human liver functions. Additional studies are warranted to: validate the hepatic miRNA-mRNA interactions; and determine indirect effects of miRNAs on ADME genes as well as the effects of SNPs on creating or abolishing miRNA binding sites that control ADME genes.

Methods

Tissue samples and RNA preparation

Human liver tissue specimens were obtained from the NIH-supported tissue programs: the Liver Tissue Cell Distribution System (LTCDS) (n=22), the Minnesota and Pittsburg collection centers (n=35) and the Laboratory of Developmental Biology at the University of Washington (Seattle, WA; n=30). Three liver tissue samples were provided by XenoTech, LLC (Lenexa, KS). Liver tissues were stored at -80°C. This study was approved by the Indiana University Institutional Review Board.

Total RNA was extracted from 90 human liver samples (fetal, pediatric, and adult; n=30 each). Estimated fetal ages ranged from 15-24 weeks (average=18.8 weeks), pediatric ages from 1-17 years (average=8.6 years), and adult ages 28-80 years (average=54.5 years). This cohort consisted of tissue specimen from 43 male and 35 female donors (the sex of the remaining samples was unknown). Of the 90 tissue samples, 16, 10, 1 and 63 samples were from Caucasian, African American, Hispanic or donors of unknown ethnicity, respectively.

Total RNA, including small RNAs, was extracted with the Qiagen miRNAeasy Mini Kit (Valencia, CA). Briefly, approximately 30 mg frozen liver tissue was excised and homogenized for 30 sec with a PRO 200 rotary homogenizer (PRO Scientific, Oxford, CT) in QIAzol Lysis Reagent. Subsequently, chloroform was added, mixed, centrifuged, and the aqueous phase transferred to a new tube and mixed with 100% ethanol. The sample was applied onto an RNeasy Mini spin column and washed as recommended by the protocol. To avoid any DNA contamination, an on-column DNase digest was performed. After 2 additional washes of the column, RNA was eluted twice with 30 μL of RNase-free water. RNA quality (RQI; RNA quality index) was determined on an Experion StdSens RNA chip (Bio-Rad, Hercules, CA) and concentration measured with a NanoDrop 1000 instrument (Thermo Scientific, Wilmington, DE). The RQI values of the RNA preparations ranged between 7.0 and 9.9. Seventy-six RNA samples had RQI values above 8.1. RNA preparations were stored at -80°C until analysis.

miRNA expression profiling

Expression of 533 microRNAs (miRNA) was measured using TaqMan OpenArray Human miRNA Panel using the NT Cycler (Applied Biosystems, Foster City, CA). The threshold cycles were set manually based on visual inspection of real-time amplification curves of each miRNA.

Bioinformatics analysis of the miRNA expression data

The miRNA expression data was quantile normalized using the threshold cycle values of miRNA data obtained from the TaqMan OpenArray. Differential expression of the miRNAs was determined by p value comparisons between the 60 fetal and pediatric samples and between the 59 pediatric and adult samples (one outlying adult sample was removed from statistical analyses). They were corrected for by false discovery rate (FDR) using the Benjamini Hochberg equation.44The top absorption, distribution, metabolism, elimination (ADME) genes (http://www.pharmaadme.org/joomla/) that were predicted to be targeted by the differentially expressed miRNAs in this study were determined using Ingenuity Pathway Analysis. The IPA program extracts predicted and experimentally validated miRNA targets from TargetScan, miRecords, TarBase, and text mining through Ingenuity Expert Findings and Ingenuity Expert Assisted Findings.

Measurement of mRNA expression

RNA-seq was performed as previously described.39 Standard methods were used for RNA-sequencing library construction, EXBead preparation, and Next-Generation sequencing, based on the protocol provided for the Life Technologies SOLiD4 system. Briefly, 2μg of total RNA per sample of only 30 of the 90 total samples (fetal, pediatric, and adult; n=10) were used for library preparation. The rRNA was depleted using RiboMinus Eukaryote Kit for RNA-Seq.

Bioinformatics analysis of RNA-seq data

RNA-seq data analysis included the following steps: quality assessment, sequence alignment, and gene expression analysis.

Data Processing and Quality Assessment

We used SOLiD Instrument Control Software and SOLiD Experiment Tracking System software for the read quality recalibration. Each sequence read was scanned for low-quality regions, and if a 5-base sliding window had an average quality score less than 20, the read was truncated at that position. Any reads less than 35 bases were discarded. Our experience suggested that this strategy effectively eliminated low-quality reads while high-quality regions were retained.45-47

Sequence Alignment

The BFAST (http://bfast.sourceforge.net) 48 was used as the primary alignment algorithm because it has high sensitivity for aligning reads on loci containing small insertions and deletions compared to the reference genome (hg19). NGSUtils (http://ngsutils.org/) 45, a TopHat-like strategy 49, was employed to align the sequencing reads that crossed splicing junctions. After aligning the sequence reads to a filtering index including repeats, ribosomal RNAs, and other sequences that were not of interest, a sequence alignment was conducted for three levels: genome, known junctions (University of California Santa Cruz Genome Browser), and novel junctions (based on the enriched regions identified in the genomic alignment). We restricted our analysis to the uniquely aligned sequences with no more than two mismatches.

RNA-Seq Differential Expression Analysis

Differentially expressed genes were identified using edgeR 50, a Bioconductor package for differential expression analysis of digital gene expression data, based on a negative binomial distribution. To ensure the reliable gene expression measurements, genes with less than 1 read per million mappable reads were removed. A p value was calculated both for differential expression of the drug disposition genes between the 20 fetal and pediatric samples and between the 20 pediatric and adult samples analyzed for RNA-seq. Benjamini and Hockberg's algorithm was used to control the FDR.44

Inverse Correlations

A linear regression model considering developmental periods as a cofactor was used to identify inverse correlations between the expression levels of the developmentally changing miRNAs and the drug disposition mRNA expression levels. The resultant p values were FDR corrected using Benjamini Hockberg.44

Supplementary Material

Supp FigureS1-S2
Supp TableS1-S4

Study Highlights.

What is the current knowledge on the topic? The expression of hepatic drug disposition genes undergo large changes during liver development; however, the mechanisms behind these changes remain unknown. Emerging evidence implicate microRNAs in regulation of drug disposition genes.

What question did this study address? We hypothesized that developmental changes in human liver microRNAs and their variability in the rate of these changes contribute to developmental changes in drug disposition genes. We measured global miRNA and mRNA expression in fetal, pediatric, and adult human liver.

What this study adds to our knowledge? Hepatic miRNAs undergo marked age-dependent changes in expression. Significant negative correlations were observed between the developmental change in miRNA expression and the developmental change in expression of several drug disposition and regulatory genes.

How this might change clinical pharmacology and therapeutics? This study represents the first comprehensive analysis demonstrating a link between age-dependent microRNA expression in human liver and the age-dependent changes in important drug disposition genes. These findings help to further understand mechanisms of age-dependent changes in drug disposition.

Acknowledgements

This project was funded by an NIH grant (R01GM088076; TCS). The RNA-seq assays were carried out in the Center for Medical Genomics at Indiana University School of Medicine. The Liver Tissue Cell Distribution System is funded by NIH Contract #N01-DK-7-0004/HHSN267200700004C. The project entitled “Laboratory of Developmental Biology” was supported by NIH Award Number 5R24HD0008836 from the Eunice Kennedy Shriver National Institute of Child Health & Human Development. The content does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health. We are also grateful to XenoTech, LLC for their generous gift of tissue fractions.

Footnotes

Author Contributions

K.S.B., Z.D., A.G., and T.C.S wrote the manuscript; K.S.B., S.P., Z.D., A.G., Y.L., and T.C.S. designed the research; K.S.B., S.P., and R.G. performed the research; K.S.B., E.A.B., M.W.S., and Y.L. analyzed the data.

Conflict of Interest/Disclosure

None.

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Associated Data

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Supplementary Materials

Supp FigureS1-S2
NIHMS690102-supplement-Supp_FigureS1-S2.pdf (495KB, pdf)
Supp TableS1-S4
NIHMS690102-supplement-Supp_TableS1-S4.pdf (166.9KB, pdf)

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