Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The impact of microRNAs and alternative splicing in pharmacogenomics

Pharmacogenomic studies emphasize the use of genomic information to enhance success in finding new medicines and also to improve those that are already used in clinics. Therefore, this field has a special interest in knowing how patients metabolize drugs depending on their genetic background. Most of the studies so far have focused on the impact of single genetic differences on drug metabolism. However, this may be only the tip of the iceberg in terms of how interpatient variability can influence the response to drugs. For example, control of gene expression by microRNAs (miRNAs) and alternative splicing are cellular mechanisms that have an effect on proteome diversity and have already been implicated in complex diseases such as cancer, arthritis and others. Changes in the sequence of a miRNA and/or variations in the miRNA target region of a transcript can have a major impact on post-transcriptional regulation. Events of alternative splicing can occur in more than half of the human genes, thereby changing the sequence of key proteins related to drug resistance, activation and metabolism. Furthermore, alternative splicing and miRNAs can work together to differentially control genes. This perspective article will highlight recent exciting discoveries in pharmacogenomics and also discuss how players such as miRNAs and alternative splicing may affect the way we design and apply future therapies.


The improvement of patient treatment is always a concern for modern medicine. One aspect that can influence the effectiveness of therapies in patients during drug-based treatment is the genetic background. One of the major challenges for proper drug development by companies is the difference in response between individuals and between populations. The idea that genetic variability between patients might influence response to drugs was described and termed ‘pharmacogenetics’ by Vogel.1 Pharmacogenetics became a field of importance for scientists and physicians in the early 1950s, when available technologies were able to measure enzyme activity, drug metabolites and drug response (for a timeline review of the pharmacogenetic history see2). Since then, the number of cases described in the literature has increased exponentially and they were mainly focused on single-nucleotide polymorphism (SNP), insertions and deletions (indels) of nucleotides, copy number variation (CNV) and missense mutations (for review see Roses3 and Giacomini et al.4).

The most studied gene family so far is the cytochrome P450 enzyme5, 6, 7 (for more details see Table 2). The genetic variability observed in this gene family has been associated with differences in drug metabolism in several pathologies, such as cancer, cardiovascular diseases and rheumatoid arthritis.5, 6, 7, 8 There is one SNP in CYP2C9 that causes an amino acid change that modify the ability of the enzyme to metabolize the anticoagulant warfarin.9 This genetic variation has important implications in the dose a patient will receive of warfarin depending on the genetic background of the individual. Another gene that metabolizes warfarin is the vitamin K epoxide reductase complex 1 (VKORC1). The allele variations in VKORC1 strongly alter warfarin dosage when compared with CYP2C9 genetic variant: almost three times.9, 10

The main goal of the emerging discipline of pharmacogenomics is to use personalized therapy based on an individual's genotype. This term has evolved from the older pharmacogenetics, which was focused on studying inherited differences in a single gene responsible for drug metabolism and response. The emergence of the term pharmacogenomics was possible after the availability of the human haplotype map (HapMap)11 and of high-throughput genotyping platforms that have been facilitating more systematic genetic screens for new and clinically important drug targets.12 As already discussed by Bertino et al.,13 our understanding of an individual's genetic background will provide knowledge to predict the host response to specific drugs.

New players in pharmacogenomics

The elucidation of the sequence of the human genome in 200114, 15 and the subsequent release of other versions are allowing a better understanding of structural variations and how they can affect diseases.16 Recently, the identification and analysis of functional elements in 1% of the human genome by the ENCODE Project represented a major step towards a more comprehensive characterization of all functional elements in the human genome. The ENCODE project — standing for ENCyclopedia Of DNA Elements — has set out to identify all the functional elements in the human genome.17 It is becoming clear that the definition of ‘gene’ is changing and noncoding transcripts (also referred as to noncoding RNAs) are an important component of the information that is being transcribed by eukaryotic cells (for review see18, 19, 20). Noncoding RNAs (ncRNAs) are transcripts that do not have protein-coding potential but might be still functional.18 ncRNAs are a large group of transcripts that can differ in size which is indicative of their mechanism of action.18 These ncRNAs can vary in size range from 18 to 25 nucleotides for the families of microRNAs (miRNAs) and small interfering RNAs (siRNAs), 20 to 300 nucleotides for small RNAs commonly found as transcriptional and translational regulators or up to and beyond 10 000 nucleotides in length for RNAs involved in various other processes (for review see18, 19, 20). miRNAs can block mRNA translation and affect both the expression of protein-coding genes21 and long ncRNAs.22 A growing number of reports have been showing that miRNAs are master regulators of important gene networks in eukaryotic cells.23 Associations of deregulated expression of miRNAs in complex diseases have been also described (for review see Gartel and Kandel24). miRNAs have been considered as attractive drug targets in complex diseases such as cancer as they may be differentially expressed in malignant cells compared to normal cells altering the regulation of expression of many important genes.

Another molecular mechanism that produces gene expression diversity is alternative splicing of pre-mRNAs. During the maturation of an mRNA, exons can be spliced out, intronic sequences can be retained and cryptic splice sites can be used to form more than one mRNA from a single gene. The rate of alternative splicing of mRNAs has been the focus of different studies and it seems that more than 60% of the human genes produce at least one alternative mRNA.25 The functional implication of alternative splicing in normal and pathological states has been studied by several groups,26, 27 but there are still many questions to be answered. Future studies will be focused on the functional relevance of splice variants in the context of whole genome studies, instead of a single gene to understand how they will affect cellular networks and pathways.

Recently, reports have shown that changes in the sequence of miRNAs and/or variations of the miRNA target region within the transcripts can have major effects on post-transcriptional regulation.28, 29 More importantly, genetic variations such as SNPs can affect the way miRNAs regulate their targets, indicating that this could be important in drug metabolism and in phenotypic variation.30 In the same way that miRNA can regulate protein translation, alternative splicing can have several implications which can affect the biological activity of proteins (for example enzymes, antagonist proteins, and so on). For example, splice variants of the human BCL2L1 gene (also known as BCL-X) switches an antiapoptotic protein to a proapoptotic one.31 Thus, alternative splicing differences can have a major impact on drug metabolism and therefore resistance.

This article is aimed at describing new discoveries in the field of pharmacogenomics as well as discuss how players such as miRNAs and alternative splicing may affect the way we design and apply future therapies into the clinic.


Biology of microRNAs

miRNAs are part of a group of ncRNAs that can block mRNA translation and affect mRNA stability but there are several questions still to be answered in the biology of miRNAs (for review see Eulalio et al.32). miRNAs are generally 18–25 nt long and were first described in the early 1990s in the worm Caenorhabditis elegans as regulators of development and differentiation.33 It is estimated that the human genome has thousands of miRNA genes, but only 700 have been described so far. This class of non-coding genes is predicted to regulate at least 30% of all the human protein-coding genes by targeting their 3′-UTR sequences.34 There is also evidence that miRNAs can regulate the expression of large ncRNAs, indicating that these small genes have a big impact in transcriptome networks in eukaryotic cells.22

Several groups studying the biology of miRNAs have been able to describe each step in the processing and mechanism of action of these regulators. It is already known that they are initially transcribed as pri-miRNAs which can be processed into pre-miRNAs and subsequently into mature miRNAs. Mature miRNAs have the ability to affect the translational efficiency of various protein-coding genes at the same time. In the past 5 years, there have been several reports implicating miRNAs in posttranscriptional regulation of proteins with diverse roles (for review see Filipowicz et al.35). Evidence based on computational studies has already revealed that there is a ‘seed’ region of 8 nt at the 5′-end of miRNAs that is essential to miRNA function.36 This region is important for binding to the mRNA targets and for mRNA target degradation. One of the concepts in the biology of miRNAs that is of particular therapeutic relevance is that one miRNA can downregulate multiple target proteins by interacting with different target mRNAs (‘one hit multiple targets’ concept).37 The hypothesis of using the ‘one-hit-multiple targets’ concept to treat diseases was previously discussed by Wurdinger and Costa.37

microRNAs in complex diseases

Recently, miRNA-deregulated expression has been extensively described in a variety of diseases, including cancer. Deregulation of miRNAs in other diseases such as obesity,38 diabetes39 and schizophrenia40 has also been described in the literature and the list of examples is growing fast. Some lines of evidence have already shown that up or downregulation of miRNAs correlate with numerous human cancers indicating that miRNAs can function as oncogenes and/or tumor suppressors (for review see Garzonet et al.41). In cancer, miRNAs are associated with the tumorigenesis process42 and with important cancer gene networks such as the p53 pathway.43 In addition, they have also been implicated in the metastatic process in breast cancer.44, 45 miRNA expression profiles were also evaluated by several groups46, 47 and they have been used for prognosis and early diagnosis showing how important these players are in different aspects of complex diseases. More recently, some reports have been showing that miRNA deregulated expression in diseases can be due to epigenetic changes such as DNA methylation and histone modifications.48, 49

microRNA variations and the impact in pharmacogenomics

SNPs are frequent variations in the human genome and estimates suggest that they can have a frequency of one in every thousand base pairs.11 SNPs have been extensively studied in order to understand the susceptibility to specific diseases in the general population. Initially, SNP association studies were focused in protein-coding genes. Several polymorphisms in coding regions, or the so-called nonsynonymous SNPs of genes associated to complex diseases such as cancer, were previously identified.50 Recently, there has been an explosion of whole-genome association studies aiming at identifying association markers that can predict diseases based on the HapMap.51, 52, 53 One of the aims of the HapMap Project is to generate a haplotype map of the human genome, which describes the common patterns of human genetic variation. HapMap is expected to be a key resource for researchers to find genetic variants affecting health, disease and responses to drugs and environmental factors. It is becoming clear that understanding multigenic diseases will require complex association studies to evaluate patient risk to specific pathologies. As drug metabolism can involve a group of genes, we believe that the determination of how patients react to and metabolize drugs might be focused on master regulators of gene expression, such as miRNAs. In that regard, reports have recently shown that changes in the sequence of a miRNA and/or variations in the target region of a transcript that is regulated by a miRNA can have major effects in posttranscriptional regulation of proteins.28 More importantly, variations of sequence such as SNPs can affect the way miRNAs regulate their targets, pointing to a function in drug metabolism and in phenotypic variation.30 For example, two groups have evaluated the presence of SNPs located in miRNA-binding sites of the 3′UTR of several genes and SNPs in the microRNA seed regions by genome-wide analyses.54, 55 Yu et al.54 were able to identify twelve miRNA binding SNPs that display an aberrant allele frequency in human cancers. Moreover, Saunders et al.55 were able to identify approximately 250 SNPs that create novel target sites for miRNAs in humans and may result in phenotypic differences. Similar studies have evaluated genetic variants in miRNAs and the risk of cancer. Wu et al.56 studied 100 human tumor tissues and 20 cancer cell lines and have identified a mutation in miRNA let-7e that causes a significant reduction of its expression in vivo contributing to tumorigenesis. A similar study by Yang et al.57 has genotyped several SNPs from miRNA genes in 746 matched normal and bladder cancer tissues and discovered that SNPs in GEMIN3 gene can affect miRNA binding, thereby significantly increasing bladder cancer risk.

Polymorphisms in miRNA target sites of protein-coding genes that are associated to cancer,58, 59, 60 hypertension,61 asthma,62 cardiovascular disease63 and polymorphisms in microRNAs that are associated with schizophrenia40 were also described. Mishra et al.64 were able to show that a SNP located near the miR-24 binding site in the dihydrofolate reductase (DHFR) gene 3′UTR results in a loss-of-function mutation. Furthermore, the SNP affects DHFR expression by interfering with miR-24 post-translational regulation, resulting in DHFR over-expression and methotrexate resistance in cancer cells.64 MicroRNA miR-24 was already implicated in the regulation of important proteins such as the p16 tumor suppressor gene,65 ALK4 gene that is involved in erytropoiesis66 and TGFbeta gene that has a function in skeletal muscle differentiation.67

Sethupathy et al.61 have shown that SNPs in miR-155 target sites located in the 3′UTR of the human angiotensin type 1 receptor (AGTR1) gene downregulates the expression of the allele that has been associated with hypertension. In another study, Tan et al.62 were able to identify a SNP in the 3′UTR of HLA-G that influences the targeting of three miRNAs (miR-148a, miR-148b and miR-152) to this gene. The authors suggest that allele-specific targeting of these miRNAs can account at least in part for the observations that HLA-G is associated with asthma.62 In cardiovascular disease, Martin et al.63 reported an association of the human angiotensin II type 1 receptor polymorphism and miR-155. Finally, Hansen et al.40 identified important associations between brain-expressed miRNAs and schizophrenia for two SNPs located in mir-206 and mir-198 sequences. Noteworthy is the fact that these miRNAs have a surprisingly large number of targets in common, eight of which are connected by the same transcription factors.40 There is also a study showing that variations within the mir-433 target site of the gene FGF20 increases the risk of Parkinson's disease by increasing the levels of the protein α-synuclein.28 Finally, polymorphisms in the precursor microRNA can affect the biogenesis and processing which will then result in altered control of gene expression.68 All the examples described here are listed in Table 1.

Table 1 Examples of polymorphisms that can attenuate the binding of miRNAs to their targets in several diseases

The identification of all the genetic and epigenetic differences that are the cause of phenotypic variations in patients is a major objective in pharmacogenomics. A database was recently created to catalogue all the SNPs in miRNA target sites and link these changes to complex traits and diseases.69 This database was termed polymorphism in miRNA target site (PolymiRTS) and it integrates sequence of polymorphisms, phenotypes, gene expression profiles in several microarray data sets and characterizes the PolymiRTSs that are the potential candidates responsible for quantitative trait locus (QTL) effects.69 The impact of this database and of other upcoming studies are of great importance for a better understanding on how SNPs and other genetic variations will affect the expression of protein-coding and noncoding genes in the genome by changing the miRNA regulatory network (Figure 1). Furthermore, the knowledge from these studies will be of relevance to evaluate drug metabolism and tolerance in patients suffering from complex diseases.

Figure 1

microRNAs and other noncoding RNAs as new players in pharmacogenomics. (a) Our current view of how genome variations such as single-nucleotide polymorphisms (SNPs) can affect drug metabolism. In this simplistic view, polymorphisms in one or a few protein-coding genes will affect drug resistance, efficacy and metabolism. (b) A proposed new view with microRNA variations, protein variations and noncoding RNA variations affecting the way drugs are metabolized is depicted. In this new integrated concept, all variations in the genome will have additive effects in pharmacokinetics and pharmacodynamics of drugs.

Alternative splicing and proteome diversity

Alternative splicing of pre-mRNAs was proposed 30 years ago by the Nobel Prize winner Walter Gilbert as a way of generating different mRNAs from a single gene.70 The chemical reaction of intron splicing and removal is regulated by the spliceosome, a heterogeneous complex comprised of RNAs and proteins.71, 72 The biology of the splicing mechanism has been recently elucidated, and some important cofactors in the selection of cryptic splice sites have been described in normal and pathological states.73 One example is the splicing factor SPF45 that was described with limited expression in normal tissue, but with a high expression in a many carcinomas associated with drug resistance.74, 75

Alternative splicing was postulated as one of the main cellular mechanisms for proteome diversity generation.76 Alternative splicing has also emerged as a key mechanism responsible for the expansion of the transcriptome and proteome complexity in humans and in other organisms.77 Recently, several studies have shown how the alternative splicing process is controlled and how the expression of some splice variants might be associated with diseases.78, 79 Many of these studies were performed using a bioinformatics approach, as the amount of available transcriptome and proteome data have been increasing exponentially.80, 81 The proteome diversity generated by alternative splicing of pre-mRNAs varies from protein to protein. To date, the gene that is able to produce the greatest number of splice variants is the Down syndrome cell adhesion molecule (DSCAM) gene in Drosophila.82 DSCAM can produce 38,016 putative splice forms using the combination of 4 clusters of mutually exclusive exons and the usage of 20 constitutive exons.82 DSCAM splice variant proteins are responsible for axon guidance in brain development and are also expressed in specific olfactory receptor neuron cells in Drosophila.83, 84

Databases designed to store and provide access to reliable annotations of the alternative splicing pattern of human genes and to the functional annotation of predicted splicing isoforms have been described by several groups.85, 86, 87 Splice-site detection in full-length transcripts have been carried out in genome-wide analyses using specific bioinformatic algorithms, based on multiple alignments of gene-related transcripts to the genomic sequence. Alternative splicing databases will be able to provide resources for functional interpretation of splicing variants for the human and mouse genomes and also for detection of splicing isoforms associated to diseases.

Alternative splicing has become one of the most elegant and important mechanisms for proteome diversity generation. Growing evidence is also indicating that defects in the alternative splicing pathways and generation of wrong alternative variants is a common feature of complex diseases.88 However, the implications of splice variants in gene networks and how they can affect the metabolism of drugs in patients remains to be explored.

Alternative splicing in drug resistance, activation and metabolism

Several lines of evidence have already suggested that protein diversity produced by alternative splicing might affect important genes in pathways related to prodrug activation and drug metabolism. Our understanding of how alternative splicing can act in drug resistance and on how it can affect the way patients metabolize drugs is still unexplored. Some examples of alternative splicing events with pharmacological relevance will be described in this section and listed in Table 2. Our proposed model is depicted in Figure 2.

Table 2 Examples of alternative splicing events in protein-coding genes affecting drug response in several diseases
Figure 2

Alternative splicing of protein-coding genes and the impact on pharmacogenomics. In our current and simplistic view, genetic variations (such as single nucleotide polymorphisms, SNPs) can change the sequence of proteins and affect the expression of protein-coding genes that will be involved in drug metabolism. The proposed view suggests that alternative splicing is an important player in drug resistance, activation and metabolism. In this model, different isoforms produced from a specific protein can be responsible for different rates of drug metabolism and clearance

One of the most important examples is the Philadelphia chromosome which is generated by a translocation between the human chromosomes 9 and 22, leading to the fusion of two genes: BCR and ABL. The BCR–ABL fused gene is constitutively active and there are important consequences to the cell: increase in cell proliferation and high rates of genomic instability. This chromosomal rearrangement is highly associated with chronic myelogenous leukemia (CML).93 In acute lymphoblastic leukemia (ALL), around 15–30% of patients have malignant transformation related to the BCR–ABL fusion protein.94 Patients with CML are commonly treated with Imatinib mesylate that, in turn, inhibits the activity of the BCR–ABL fusion protein.95 Alternative splicing has been described as one of the possible reasons for drug resistance in some patients treated with Imatinib mesylate;96 however, the majority of the cases of drug resistance have been described through the observation of SNPs in the kinase domain of BCR–ABL. The first observation of alternative splicing variants in the BCR–ABL fused gene and correlation with drug resistance was in 2006, and a mutation (L248V) in two CML patients with Imatinib mesylate resistance was described.96 Sequence analysis was performed and it was found that this mutation produces two distinct mRNAs: one identical to the wild-type, differing only in one amino acid change (L248V), and another with the usage of a cryptic splice site within the exon 4, shortening in 81 nucleotides its 3′ portion (variant Δ248–274).96 The leucine to valine change is located in the ATP binding site and may abrogate the kinase activity of the Δ248–274 splice variant protein.96 Although the frequency of the alternative splicing variant Δ248–274 mRNA was low, the authors suggested a dominant-negative role for this splice variant.96 Recently, another splice variant in the BCR–ABL fused gene was described in a cohort of 175 patients in which 3 presented imatinib-resistance, and a new splice variant was also detected.108 In this case, the BCR–ABL isoform is produced by the usage of an alternative 35-nucleotide-long extra exon between exons 8 and 9.108 This new exon interrupts the open reading frame of the BCR–ABL transcript and produces a truncated protein. In a similar way that was described before,96 this splice variant has very low expression in 2 out of 3 patients. In both cases, protein production and/or activity will have to be better studied in order to understand the role of these splice variants and how they can affect the mechanism of imatinib resistance. Recently, the microRNA miR-203 was described as a regulator of the BCR–ABL gene in cancer but the connection with splicing isoforms and drug resistance remains to be determined.109

Another example of a gene that can have splicing variants is the glucocorticoid receptor (GR, NR3C1) (Figure 3a). NR3C1 (also known as GR) is a transcription factor that binds to cortisol and other glucocorticoids and has two main alternative splice products: GRα and GRβ.110, 97 GRα is located in the cytoplasm and is translocated to the nucleus after binding to glucocorticoids, whereas GRβ cannot bind to its ligands.97 The GRβ isoform has been associated with glucocorticoid resistance.97 The expression rate of both GR isoforms has been studied and GRα is expressed up to 3000 times more than GRβ.111, 112 The role of GR in many diseases is still unknown, but some associations of the splice variant GRβ have been suggested in asthma,113 systemic lupus erythematosus,98 Crohn's disease100 and nasal polyposis99 (for more details see Table 2).

Figure 3

Schematic representation of genes with alternative splicing described here according to the Human Genome Browser (UCSC, Human genome release of March 2006, (a) Gene structure of the glucocorticoid receptor (GR). Red arrow indicates the alternative splicing event of exon 9 that produces the isoform GRβ as described by Oakley et al.112 (b) Gene structure of the EGFR gene. The alternative splicing of EGFR encoding sEGFR p110 is indicated with a red arrow as described by Reiter et al.115 (c) Gene structure of the COX-1 gene. Red arrow indicates the splicing event of partial usage of exon 9 as previously described by Diaz et al.120 (d) Gene structure of the COX-2 gene. Red arrow indicates the partial usage of exon 5 generating the COX-2a isoform as described by Censarek et al.105 Boxes represent the alignment of exons with the human genome sequence. RefSeq mRNAs are represented in blue and known mRNAs are represented in black.

A gene that also has splice variants and has been associated with many types of cancer is the epidermal growth factor receptor (EGFR).114 Some studies have already shown that inhibiting the binding of EGFR to its ligands was associated with decreased cellular proliferation.101, 102 The EGFR gene has several splice variants,115 and one of the variants can produce a protein of 110 kDa that was termed soluble EGFR (sEGFR) or p110 sEGFR.116 In metastatic breast cancer patients under letrozole adjuvant therapy, sEGFR showed decreased concentrations in 76% of the individuals and was also used as a biological marker.116 However, the authors suggest that additional studies are needed to understand the role of p110 sEGFR in response to letrozole and the significance of EFGR as a cancer biomarker for metastatic breast cancer. The p110 sEGFR splice isoform is depicted in Figure 3b. In another study, a cohort of 57 women with metastatic breast cancer was analyzed for the EGFR splice variant and reduced sEGFR concentration was observed when compared to healthy individuals.117 On the other hand, two studies trying to understand the role of sEGFR in metastatic breast cancer patients under trastuzumab therapy did not find any statistical significant correlation between the splice variant and drug metabolism.118, 119 More studies are needed to understand the function and importance of EGFR receptor variants in drug metabolism and activation.

The cyclooxygenase gene family is represented in the human genome by two genes: PTGS1 (also known as COX-1) and PTGS2 (also known as COX-2). Cyclooxygenases (COXs) are key enzymes in prostaglandin biosynthesis. Both protein products are activated by acetylsalicylic acid (aspirin), acetaminophen (paracetamol) and celecoxib, among others drugs. Hence, the knowledge of splicing variants of the COX-1 and COX-2 transcripts is important in order to better understand how drugs are metabolized. The COX-1 gene has three splice variants already described.120, 121, 104 The first splice variant identified was one that does not contain exons 1 and 2, and also has an alternative 5′ end with which uses part of intron 2 and it was termed COX1-SV. As there is a protein frame shift after the translation of the mature mRNA, this spliced variant was thought to produce a truncated protein. The action of acetaminophen (paracetamol) in inhibiting the action of this isoform of the protein in humans has already been shown in some studies (for review see Hersh et al.122). The splice variants have also been associated with the coronary artery bypass grafting (CABG), which is the most common medical intervention to treat heart failure. Aspirin (acetylsalicylic acid) is extensively used to inhibit platelet formation in this type of intervention, but up to 60% of the patients show some degree of resistance to aspirin.123, 124 In addition, it has been described that after CABG surgery there is an overexpression of the COX-2 gene,125 and this could be the explanation for platelet formation in CABG patients.126 In a study to elucidate the function of COX-2, an alternative spliced product was discovered, named COX-2a, and it was associated with CABG and platelet formation.105 Splice variants for COX-1 and COX-2 are shown in Figures 3c and d.

Genetic variability in nuclear receptors can contribute to human variation at the magnitude of clinically significant drug–drug interactions. This is the case for the orphan nuclear receptors: pregnane × receptor (PXR) and constitutive androstane receptor (CAR).127 These receptors are sensors that mediate drug-induced changes by increasing transcription of genes that are involved in drug metabolism and clearance.127 It was already described that genetic variants such as splicing isoforms of PXR and CAR can affect the pharmacokinetics and pharmacodynamics of docetaxel and doxorubicin in Asian patients.106

A recent study also showed that the NOVA2 gene interacts with a cis-acting polymorphism to influence the proportions of drug-responsive splice variants of SCN1A.106 The authors emphasize that genetic polymorphisms are important factors for modulation of the drug effect, illustrating alternative splicing as a potential therapeutic target and the importance of considering the activity of compounds at alternative splice isoforms in screening programs.128 In that regard, variations in the production of an HMGCR splice isoform were connected to reduced statin sensitivity and associated with interindividual differences in the metabolism of this drug.107

There are several examples in literature showing that splice variants of genes can change the way cells become resistant to drugs, as well as the way drugs are metabolized but this is probably just the tip of the iceberg. We are just starting to understand the impact of proteome diversity on the pharmacogenomics field. We propose a model in which alternative splicing is a player with big impact on pharmacogenomics.

microRNAs and alternative splicing: a new emerging field?

The relationship between alternative splicing, the 3′UTR of protein-coding genes and how alternative splicing can affect the binding of microRNAs and change gene regulation in the cells is an unexplored field. It is already accepted that alternative splicing can generate transcripts with different 3′UTR and 5′UTR,129 but the impact on gene regulation by microRNAs and how this will affect drug metabolism is unknown. One group was already able to show that retained introns in 3′UTR of genes can increase putative miRNA targets in human mRNAs.130 More recently, a study has shown that shorter 3′-UTR isoforms can affect the regulation by microRNAs suggesting that alternative splicing can have a major effect in gene regulatory networks.131 It was also shown that microRNAs such as miR-124 are able to change neuron fate by affecting brain-specific pre-mRNA alternative splicing.132 In addition, a study has recently shown that miR-148 is able to regulate specific DNA methyltransferase (DNMT) protein isoforms, providing evidence that this type of mechanism might be involved in determining the relative abundance of different splice variants.133 Thus, as microRNAs are able to regulate hundreds of effector genes in a multilevel regulatory mechanism that allow individual miRNAs to profoundly affect the gene expression program in the cells,134 we propose that alternative splicing might affect microRNA regulation (Figure 4). We also propose that changes in proteome diversity by both microRNA regulation and alternative splicing can affect the way drugs are metabolized by patients, and this will have major implications for both drug design and personalized medicine in the future.

Figure 4

The impact of alternative splicing events in microRNA regulation. As most microRNAs target the 3′UTR of mRNAs, if a gene has different isoforms or splice variants it can affect regulation by microRNAs. In this proposed model, several differences in drug metabolism could be explained, at least in part, by loss of microRNA control in shorter isoforms of mRNA targets. Other variations are also exemplified as potential players in pharmacogenomic differences.

Conclusions and future prospects

miRNAs are noncoding RNAs that can regulate gene expression by Watson–Crick base pairing to target several mRNAs in a gene regulatory network. They are involved in several important biological and pathological processes. The binding of miRNAs to their target mRNAs is critical for regulating mRNA levels and therefore protein expression. It has also been shown that they can regulate the expression of longer non-coding RNAs. It is becoming clear that the binding of miRNAs to their targets can be affected by polymorphisms such as SNPs and genomic variations. Hence, polymorphisms in miRNAs represent a newly identified type of genetic variability that can influence the risk of certain human diseases and also influence how drugs can be activated and metabolized by patients. Depending on phenotypic differences in sequences coding for miRNAs, levels of cellular effectors such as enzymes and other proteins will be different and patients can have different pharmacokinetics and pharmacodynamics. Another layer of gene regulation that is still unexplored is alternative splicing in protein-coding genes. Depending on the splice variant of a specific enzyme related to drug metabolism, patients will respond better or worse to therapies. Several examples described here support the hypothesis that alternative splicing can modify drug resistance and metabolism suggesting that these events could be one of the factors impacting in interpatient variability in drug response. The final model that we propose here is that genetic variations in the sequence of miRNAs, target sites of miRNAs and alternative splicing will result in pharmacogenomic differences. As miRNAs and isoforms of the same protein will affect gene networks in the cells, changes in these mechanisms will be important in clinics. Personalized medicine must take all of these variations into account so we can administer the correct dosage of drugs and evaluate drug efficacy. We believe that a map of all variations in miRNAs and alternative variants of protein-coding genes should be generated. A lot of work is still needed to completely understand the consequences of variations in gene regulatory networks and pathways responsible for drug metabolism and resistance, but we believe that these new paradigms are clearly redefining the pharmacogenomics field.


  1. 1

    Vogel F . Moderne probleme der humangenetik. Ergebnisse Inneren Medizin und Keinderheilkunde 1959; 12: 52–125.

    Google Scholar 

  2. 2

    Meyer UA . Pharmacogenetics—five decades of therapeutic lessons from genetic diversity. Nat Rev Genet 2004; 5: 669–676.

    CAS  Article  PubMed  Google Scholar 

  3. 3

    Roses AD . Genome-based pharmacogenetics and the pharmaceutical industry. Nat Rev Drug Discov 2002; 1: 541–549.

    CAS  Article  PubMed  Google Scholar 

  4. 4

    Giacomini KM, Brett CM, Altman RB, Benowitz NL, Dolan ME, Flockhart DA et al. Pharmacogenetics Research Network. The pharmacogenetics research network: from SNP discovery to clinical drug response. Clin Pharmacol Ther 2007; 81: 328–345.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5

    Ingelman-Sundberg M, Sim SC, Gomez A, Rodriguez-Antona C . Influence of cytochrome P450 polymorphisms on drug therapies: pharmacogenetic, pharmacoepigenetic and clinical aspects. Pharmacol Ther 2007; 116: 496–526.

    CAS  Article  PubMed  Google Scholar 

  6. 6

    Gardiner SJ, Begg EJ . Pharmacogenetics, drug-metabolizing enzymes, and clinical practice. Pharmacol Rev 2006; 58: 521–590.

    CAS  Article  PubMed  Google Scholar 

  7. 7

    Chauhan N, Padh SR . Pharmacogenetics: genetic basis for rational drug therapy. Indian J Pharm Sci 2007; 69: 180–189.

    CAS  Article  Google Scholar 

  8. 8

    Rodriguez-Antona C, Ingelman-Sundberg M . Cytochrome P450 pharmacogenetics and cancer. Oncogene 2006; 25: 1679–1691.

    CAS  Article  PubMed  Google Scholar 

  9. 9

    Oner Ozgon G, Langaee TY, Feng H, Buyru N, Ulutin T, Hatemi AC . VKORC and CYP2C9 polymorphisms are associated with warfarin dose requirements in Turkish patients. Eur J Clin Pharmacol 2008; 64: 889–894.

    CAS  Article  PubMed  Google Scholar 

  10. 10

    Wadelius M, Chen LY, Downes K, Ghori J, Hunt S, Eriksson N et al. Common VKORC1 and GGCX polymorphisms associated with warfarin dose. Pharmacogenomics J 2005; 5: 262–270.

    CAS  Article  PubMed  Google Scholar 

  11. 11

    International HapMap consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 2007; 449: 851–861.

    Article  CAS  Google Scholar 

  12. 12

    Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ et al. Variations in DNA elucidate molecular networks that cause disease. Nature 2008; 452: 429–435.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13

    Bertino JR, Banerjee D, Mishra PJ . Pharmacogenomics of microRNA: a miRSNP towards individualized therapy. Pharmacogenomics 2007; 12: 1625–1627.

    Article  Google Scholar 

  14. 14

    Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J et al. Initial sequencing and analysis of the human genome. Nature 2001; 409: 860–921.

    CAS  Article  PubMed  Google Scholar 

  15. 15

    Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG et al. The sequence of the human genome. Science 2001; 291: 1304–1351.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16

    Kidd JM, Cooper GM, Donahue WF, Hayden HS, Sampas N, Graves T et al. Mapping and sequencing of structural variation from eight human genomes. Nature 2008; 453: 56–64.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17

    ENCODE Project Consortium et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 2007; 447: 799–816.

    Article  CAS  Google Scholar 

  18. 18

    Costa FF . Non-coding RNAs: new players in eukaryotic biology. Gene 2005; 2: 83–94.

    Article  CAS  Google Scholar 

  19. 19

    Costa FF . Non-coding RNAs: lost in translation? Gene 2007; 1-2: 1–10.

    Google Scholar 

  20. 20

    Costa FF . Non-coding RNAs, epigenetics and complexity. Gene 2008; 1: 9–17.

    Article  CAS  Google Scholar 

  21. 21

    Bartel D . MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 2: 281–297.

    Article  Google Scholar 

  22. 22

    Calin GA, Liu CG, Ferracin M, Hyslop T, Spizzo R, Sevignani C et al. Ultraconserved regions encoding ncRNAs are altered in human leukemias and carcinomas. Cancer Cell 2007; 3: 215–229.

    Article  CAS  Google Scholar 

  23. 23

    Hobert O . Gene regulation by transcription factors and microRNAs. Science 2008; 319: 1785–1786.

    CAS  Article  Google Scholar 

  24. 24

    Gartel AL, Kandel ES . miRNAs: little known mediators of oncogenesis. Semin Cancer Biol 2008; 18: 103–110.

    CAS  Article  PubMed  Google Scholar 

  25. 25

    Clark TA, Schweitzer AC, Chen TX, Staples MK, Lu G, Wang H et al. Discovery of tissue-specific exons using comprehensive human exon microarrays. Genome Biol 2007; 8: R64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Tress ML, Martelli PL, Frankish A, Reeves GA, Wesselink JJ, Yeats C . The implications of alternative splicing in the ENCODE protein complement. Proc Natl Acad Sci USA 2007; 104: 5495–5500.

    CAS  Article  PubMed  Google Scholar 

  27. 27

    Romero PR, Zaidi S, Fang YY, Uversky VN, Radivojac P, Oldfield CJ et al. Alternative splicing in concert with protein intrinsic disorder enables increased functional diversity in multicellular organisms. Proc Natl Acad Sci USA 2006; 103: 8390–8395.

    CAS  Article  PubMed  Google Scholar 

  28. 28

    Wang G, van der Walt JM, Mayhew G, Li YJ, Züchner S, Scott WK et al. Variation in the miRNA-433 binding site of FGF20 confers risk for Parkinson disease by overexpression of alpha-synuclein. Am J Hum Genet 2008; 82: 283–289.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29

    Jazdzewski K, Murray EL, Franssila K, Jarzab B, Schoenberg DR, de la Chapelle A . Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci USA 2008; 105: 7269–7274.

    CAS  Article  PubMed  Google Scholar 

  30. 30

    Georges M, Clop A, Marcq F, Takeda H, Pirottin D, Hiard S et al. SNPs can affect the way miRNAs regulate their targets pointing to a function in drug metabolism and in phenotypic variation. Cold Spring Harb Symp Quant Biol 2006; 71: 343–350.

    CAS  Article  PubMed  Google Scholar 

  31. 31

    Akgul C, Moulding DA, Edwards SW . Alternative splicing of Bcl-2-related genes: functional consequences and potential therapeutic applications. Cell Mol Life Sci 2004; 61: 2189–2199.

    CAS  Article  Google Scholar 

  32. 32

    Eulalio A, Huntzinger E, Izaurralde E . Getting to the root of miRNA-mediated gene silencing. Cell 2008; 132: 9–14.

    CAS  Article  PubMed  Google Scholar 

  33. 33

    Lee RC, Feinbaum RL, Ambros V . The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993; 75: 843–854.

    CAS  Article  Google Scholar 

  34. 34

    Rajewsky N . microRNA target predictions in animals. Nat Genet 2006; 38 (Suppl): S8–13.

    CAS  Article  Google Scholar 

  35. 35

    Filipowicz W, Bhattacharyya SN, Sonenberg N . Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 2008; 9: 102–114.

    CAS  Article  PubMed  Google Scholar 

  36. 36

    Bartel D . MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116: 281–297.

    CAS  Article  PubMed  Google Scholar 

  37. 37

    Wurdinger T, Costa FF . Molecular therapy in the microRNA era. Pharmacogenomics J 2007; 7: 297–304.

    CAS  Article  PubMed  Google Scholar 

  38. 38

    Esau C, Davis S, Murray SF, Yu XX, Pandey SK, Pear M et al. miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting. Cell Metab 2006; 3: 87–98.

    CAS  Article  Google Scholar 

  39. 39

    Poy MN, Eliasson L, Krutzfeldt J, Kuwajima S, Ma X, Macdonald PE et al. A pancreatic islet-specific microRNA regulates insulin secretion. Nature 2004; 432: 226–230.

    CAS  Article  PubMed  Google Scholar 

  40. 40

    Hansen T, Olsen L, Lindow M, Jakobsen KD, Ullum H, Jonsson E et al. Brain expressed microRNAs implicated in schizophrenia etiology. PLoS ONE 2007; 2: e873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Garzon R, Fabbri M, Cimmino A, Calin GA, Croce CM . MicroRNA expression and function in cancer. Trends Mol Med 2006; 12: 580–587.

    CAS  Article  PubMed  Google Scholar 

  42. 42

    Mayr C, Hemann MT, Bartel DP . Disrupting the pairing between let-7 and Hmga2 enhances oncogenic transformation. Science 2007; 315: 1576–1579.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43

    He L, He X, Lim LP, de Stanchina E, Xuan Z, Liang Y et al. A microRNA component of the p53 tumour suppressor network. Nature 2007; 447: 1130–1134.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44

    Ma L, Teruya-Feldstein J, Weinberg RA . Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 2007; 449 (7163): 682–688.

    CAS  Article  PubMed  Google Scholar 

  45. 45

    Tavazoie SF, Alarcón C, Oskarsson T, Padua D, Wang Q, Bos PD et al. Endogenous human microRNAs that suppress breast cancer metastasis. Nature 2008; 451: 147–152.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46

    Schetter AJ, Leung SY, Sohn JJ, Zanetti KA, Bowman ED, Yanaihara N et al. MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. JAMA 2008; 299: 425–436.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Yu SL, Chen HY, Chang GC, Chen CY, Chen HW, Singh S et al. MicroRNA signature predicts survival and relapse in lung cancer. Cancer Cell 2008; 13: 48–57.

    CAS  Article  PubMed  Google Scholar 

  48. 48

    Saito Y, Liang G, Egger G, Friedman JM, Chuang JC, Coetzee GA et al. Specific activation of microRNA-127 with downregulation of the proto-oncogene BCL6 by chromatin-modifying drugs in human cancer cells. Cancer Cell 2006; 9: 435–443.

    CAS  Article  PubMed  Google Scholar 

  49. 49

    Brueckner B, Stresemann C, Kuner R, Mund C, Musch T, Meister M et al. The human let-7a-3 locus contains an epigenetically regulated microRNA gene with oncogenic function. Cancer Res 2007; 67: 1419–1423.

    CAS  Article  PubMed  Google Scholar 

  50. 50

    Hung RJ, McKay JD, Gaborieau V, Boffetta P, Hashibe M, Zaridze D et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature 2008; 452: 633–637.

    CAS  Article  PubMed  Google Scholar 

  51. 51

    Carlson CS, Eberle MA, Kruglyak L, Nickerson DA . Mapping complex disease loci in whole-genome association studies. Nature 2004; 429: 446–452.

    CAS  Article  PubMed  Google Scholar 

  52. 52

    Dunckley T, Huentelman MJ, Craig DW, Pearson JV, Szelinger S, Joshipura K et al. Whole-genome analysis of sporadic amyotrophic lateral sclerosis. N Engl J Med 2007; 357: 775–788.

    CAS  Article  PubMed  Google Scholar 

  53. 53

    Fellay J, Shianna KV, Ge D, Colombo S, Ledergerber B, Weale M et al. A whole-genome association study of major determinants for host control of HIV-1. Science 2007; 317: 944–947.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. 54

    Yu Z, Li Z, Jolicoeur N, Zhang L, Fortin Y, Wang E et al. Aberrant allele frequencies of the SNPs located in microRNA target sites are potentially associated with human cancers. Nucleic Acids Res 2007; 35: 4535–4541.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55

    Saunders MA, Liang H, Li WH . Human polymorphism at microRNAs and microRNA target sites. Proc Natl Acad Sci USA 2007; 104: 3300–3305.

    CAS  Article  PubMed  Google Scholar 

  56. 56

    Wu M, Jolicoeur N, Li Z, Zhang L, Fortin Y, Denis L et al. Genetic variations of microRNAs in human cancer and their effects on the expression of miRNAs. Carcinogenesis 2008; 29: 1710–1716.

    CAS  Article  PubMed  Google Scholar 

  57. 57

    Yang H, Dinney CP, Ye Y, Zhu Y, Grossman HB, Wu X . Evaluation of genetic variants in microRNA-related genes and risk of bladder cancer. Cancer Res 2008; 68: 2530–2537.

    CAS  Article  PubMed  Google Scholar 

  58. 58

    Brendle A, Lei H, Brandt A, Johansson R, Enquist K, Henriksson R . Polymorphisms in predicted microRNA binding sites in integrin genes and breast cancer: ITGB4 as prognostic marker. Carcinogenesis 2008; 29: 1394–1399.

    CAS  Article  PubMed  Google Scholar 

  59. 59

    Landi D, Gemignani F, Naccarati A, Pardini B, Vodicka P, Vodickova L . Polymorphisms within micro-RNA-binding sites and risk of sporadic colorectal cancer. Carcinogenesis 2008; 29: 579–584.

    CAS  Article  PubMed  Google Scholar 

  60. 60

    Hu Z, Chen J, Tian T, Zhou X, Gu H, Xu L . Genetic variants of miRNA sequences and non-small cell lung cancer survival. J Clin Invest 2008; 118: 2600–2608.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61

    Sethupathy P, Borel C, Gagnebin M, Grant GR, Deutsch S, Elton TS et al. Human microRNA-155 on chromosome 21 differentially interacts with its polymorphic target in the AGTR1 3′ untranslated region: a mechanism for functional single-nucleotide polymorphisms related to phenotypes. Am J Hum Genet 2007; 81: 405–413.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. 62

    Tan Z, Randall G, Fan J, Camoretti-Mercado B, Brockman-Schneider R, Pan L et al. Allele-specific targeting of microRNAs to HLA-G and risk of asthma. Am J Hum Genet 2007; 81: 829–834.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. 63

    Martin MM, Buckenberger JA, Jiang J, Malana GE, Nuovo GJ, Chotani M et al. The human angiotensin II type 1 receptor +1166 A/C polymorphism attenuates microrna-155 binding. J Biol Chem 2007; 282: 24262–24269.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64

    Mishra PJ, Humeniuk R, Mishra PJ, Longo-Sorbello GS, Banerjee D, Bertino JR . A miR-24 microRNA binding-site polymorphism in dihydrofolate reductase gene leads to methotrexate resistance. Proc Natl Acad Sci USA 2007; 104: 13513–13518.

    CAS  Article  PubMed  Google Scholar 

  65. 65

    Lal A, Kim HH, Abdelmohsen K, Kuwano Y, Pullmann Jr R, Srikantan S et al. p16(INK4a) translation suppressed by miR-24.PLoS ONE. 2008; 3: e1864.

  66. 66

    Wang Q, Huang Z, Xue H, Jin C, Ju XL, Han JD et al. MicroRNA miR-24 inhibits erythropoiesis by targeting activin type I receptor ALK4. Blood 2008; 111: 588–595.

    CAS  Article  PubMed  Google Scholar 

  67. 67

    Sun Q, Zhang Y, Yang G, Chen X, Zhang Y, Cao G et al. Transforming growth factor-beta-regulated miR-24 promotes skeletal muscle differentiation. Nucleic Acids Res 2008; 36: 2690–2699.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. 68

    Duan R, Pak C, Jin P . Single nucleotide polymorphism associated with mature miR-125a alters the processing of pri-miRNA. Hum Mol Genet 2007; 16: 1124–1131.

    CAS  Article  PubMed  Google Scholar 

  69. 69

    Bao L, Zhou M, Wu L, Lu L, Goldowitz D, Williams RW et al. PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits. Nucleic Acids Res 2007; 35 (Database issue): D51–D54.

    CAS  Article  PubMed  Google Scholar 

  70. 70

    Gilbert W . Why genes in pieces? Nature 1978; 271: 501.

    CAS  Article  PubMed  Google Scholar 

  71. 71

    Smith CW, Valcárcel J . Alternative pre-mRNA splicing: the logic of combinatorial control. Trends Biochem Sci 2000; 25: 381–388.

    CAS  Article  PubMed  Google Scholar 

  72. 72

    Valadkhan S . The spliceosome: caught in a web of shifting interactions. Curr Opin Struct Biol 2007; 17: 310–315.

    CAS  Article  PubMed  Google Scholar 

  73. 73

    House AE, Lynch KW . Regulation of alternative splicing: more than just the ABCs. J Biol Chem 2008; 283: 1217–1221.

    CAS  Article  PubMed  Google Scholar 

  74. 74

    Sampath J, Long PR, Shepard RL, Xia X, Devanarayan V, Sandusky GE et al. Human SPF45, a splicing factor, has limited expression in normal tissues, is overexpressed in many tumors, and can confer a multidrug-resistant phenotype to cells. Am J Pathol 2003; 163: 1781–1790.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. 75

    Perry III WL, Shepard RL, Sampath J, Yaden B, Chin WW, Iversen PW et al. Human splicing factor SPF45 (RBM17) confers broad multidrug resistance to anticancer drugs when overexpressed—a phenotype partially reversed by selective estrogen receptor modulators. Cancer Res 2005; 65: 6593–6600.

    CAS  Article  PubMed  Google Scholar 

  76. 76

    Black DL . Protein diversity from alternative splicing: a challenge for bioinformatics and post-genome biology. Cell 2000; 103: 367–370.

    CAS  Article  PubMed  Google Scholar 

  77. 77

    Kim E, Magen A, Ast G . Different levels of alternative splicing among eukaryotes. Nucleic Acids Res 2007; 35: 125–131.

    CAS  Article  PubMed  Google Scholar 

  78. 78

    Wang GS, Cooper TA . Splicing in disease: disruption of the splicing code and the decoding machinery. Nat Rev Genet 2007; 8: 749–761.

    CAS  Article  PubMed  Google Scholar 

  79. 79

    Skotheim RI, Nees M . Alternative splicing in cancer: noise, functional, or systematic? Int J Biochem Cell Biol 2007; 39: 1432–1449.

    CAS  Article  PubMed  Google Scholar 

  80. 80

    Thorsen K, Sørensen KD, Brems-Eskildsen AS, Modin C, Gaustadnes M, Hein AM et al. Alternative splicing in colon, bladder, and prostate cancer identified by exon-array analysis. Mol Cell Proteomics 2008; 7: 1214–1224.

    CAS  Article  PubMed  Google Scholar 

  81. 81

    Ben-Dov C, Hartmann B, Lundgren J, Valcárcel J . Genome-wide analysis of alternative pre-mRNA splicing. J Biol Chem 2008; 283: 1229–1233.

    CAS  Article  PubMed  Google Scholar 

  82. 82

    Schmucker D, Clemens JC, Shu H, Worby CA, Xiao J, Muda M et al. Drosophila Dscam is an axon guidance receptor exhibiting extraordinary molecular diversity. Cell 2000; 101: 671–684.

    CAS  Article  PubMed  Google Scholar 

  83. 83

    Schmucker D . Molecular diversity of Dscam: recognition of molecular identity in neuronal wiring. Nat Rev Neurosci 2007; 8: 915–920.

    CAS  Article  PubMed  Google Scholar 

  84. 84

    Hummel T, Vasconcelos ML, Clemens JC, Fishilevich Y, Vosshall LB, Zipursky SL . Axonal targeting of olfactory receptor neurons in Drosophila is controlled by Dscam. Neuron 2003; 37: 221–231.

    CAS  Article  PubMed  Google Scholar 

  85. 85

    Kim N, Alekseyenko AV, Roy M, Lee C . The ASAP II database: analysis and comparative genomics of alternative splicing in 15 animal species. Nucleic Acids Res 2007; 35 (Database issue): D93–D98.

    CAS  Article  PubMed  Google Scholar 

  86. 86

    Lee Y, Lee Y, Kim B, Shin Y, Nam S, Kim P et al. ECgene: an alternative splicing database update. Nucleic Acids Res 2007; 35 (Database issue): D99–D103.

    CAS  Article  PubMed  Google Scholar 

  87. 87

    Holste D, Huo G, Tung V, Burge CB . HOLLYWOOD: a comparative relational database of alternative splicing. Nucleic Acids Res 2006; 34 (Database issue): D56–D62.

    CAS  Article  PubMed  Google Scholar 

  88. 88

    Dahmcke CM, Büchmann-Møller S, Jensen NA, Mitchelmore C . Altered splicing in exon 8 of the DNA replication factor CIZ1 affects subnuclear distribution and is associated with Alzheimer's disease. Mol Cell Neurosci 2008; 38: 589–594.

    CAS  Article  PubMed  Google Scholar 

  89. 89

    Ariyoshi N, Shimizu Y, Kobayashi Y, Nakamura H, Nakasa H, Nakazawa K . Identification and partial characterization of a novel CYP2C9 splicing variant encoding a protein lacking eight amino acid residues. Drug Metab Pharmacokinet 2007; 22: 187–194.

    CAS  Article  PubMed  Google Scholar 

  90. 90

    Zhu-Ge J, Yu YN . Enzyme activity analysis of CYP2C18 with exon 5 skipped. Acta Pharmacol Sin 2004; 25: 1065–1069.

    PubMed  Google Scholar 

  91. 91

    Ibeanu GC, Blaisdell J, Ferguson RJ, Ghanayem BI, Brosen K, Benhamou S et al. A novel transversion in the intron 5 donor splice junction of CYP2C19 and a sequence polymorphism in exon 3 contribute to the poor metabolizer phenotype for the anticonvulsant drug S-mephenytoin. J Pharmacol Exp Ther 1999; 290: 635–640.

    CAS  PubMed  Google Scholar 

  92. 92

    Roh HK, Dahl ML, Tybring G, Yamada H, Cha YN, Bertilsson L . CYP2C19 genotype and phenotype determined by omeprazole in a Korean population. Pharmacogenetics 1996; 6: 547–551.

    CAS  Article  PubMed  Google Scholar 

  93. 93

    Daley GQ, Van Etten RA, Baltimore D . Induction of chronic myelogenous leukaemia in mice by the P210 bcr/abl gene of the Philadelphia chromosome. Science 1990; 247: 824–830.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  94. 94

    Faderl S, Kantarjian HM, Thomas DA, Cortes J, Giles F, Pierce S et al. Outcome of Philadelphia chromosome-positive adult acute lymphoblastic leukemia. Leuk Lymphoma 2000; 36: 263–273.

    CAS  Article  PubMed  Google Scholar 

  95. 95

    Druker BJ, Talpaz M, Resta DJ, Peng B, Buchdunger E, Ford JM et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N Engl JMed 2001; 344: 1031–1037.

    CAS  Article  Google Scholar 

  96. 96

    Gruber FX, Hjorth-Hansen H, Mikkola I, Stenke L, Johansen T . A novel Bcr-Abl splice isoform is associated with the L248V mutation in CML patients with acquired resistance to imatinib. Leukemia 2006; 20: 2057–2060.

    CAS  Article  PubMed  Google Scholar 

  97. 97

    Lewis-Tuffin LJ, Cidlowski JA . The physiology of human glucocorticoid receptor beta (hGRbeta) and glucocorticoid resistance. Ann NY Acad Sci 2006; 1069: 1–9.

    CAS  Article  PubMed  Google Scholar 

  98. 98

    Piotrowski P, Burzynski M, Lianeri M, Mostowska M, Wudarski M, Chwalinska-Sadowska H . Glucocorticoid receptor beta splice variant expression in patients with high and low activity of systemic lupus erythematosus. Folia Histochem Cytobiol 2007; 45: 339–342.

    CAS  PubMed  Google Scholar 

  99. 99

    Hamilos DL, Leung DY, Muro S, Kahn AM, Hamilos SS, Thawley SE et al. GRbeta expression in nasal polyp inflammatory cells and its relationship to the anti-inflammatory effects of intranasal fluticasone. J Allergy Clin Immunol 2001; 108: 59–68.

    CAS  Article  PubMed  Google Scholar 

  100. 100

    Towers R, Naftali T, Gabay G, Carlebach M, Klein A, Novis B . High levels of glucocorticoid receptors in patients with active Crohn's disease may predict steroid resistance. Clin Exp Immunol 2005; 141: 357–362.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  101. 101

    Masui H, Kawamoto T, Sato JD, Wolf B, Sato G, Mendelsohn J . Growth inhibition of human tumor cells in athymic mice by anti-epidermal growth factor receptor monoclonal antibodies. Cancer Res 1984; 44: 1002–1007.

    CAS  PubMed  Google Scholar 

  102. 102

    Mendelsohn J . Anti-EGF receptor monoclonal antibodies: biological studies and potential clinical applications. Trans Am Clin Climatol Assoc 1989; 100: 31–38.

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103

    Kowalski ML, Borowiec M, Kurowski M, Pawliczak R . Alternative splicing of cyclooxygenase-1 gene: altered expression in leucocytes from patients with bronchial asthma and association with aspirin-induced 15-HETE release. Allergy 2007; 62: 628–634.

    CAS  Article  PubMed  Google Scholar 

  104. 104

    Censarek P, Freidel K, Hohlfeld T, Schrör K, Weber AA . Human cyclooxygenase-1b is not the elusive target of acetaminophen. Eur J Pharmacol 2006; 551: 50–53.

    CAS  Article  PubMed  Google Scholar 

  105. 105

    Censarek P, Freidel K, Udelhoven M, Ku SJ, Hohlfeld T, Meyer-Kirchrath J et al. Cyclooxygenase COX-2a, a novel COX-2 mRNA variant, in platelets from patients after coronary artery bypass grafting. Thromb Haemost 2004; 92: 925–928.

    CAS  Article  PubMed  Google Scholar 

  106. 106

    Hor SY, Lee SC, Wong CI, Lim YW, Lim RC, Wang LZ . PXR, CAR and HNF4alpha genotypes and their association with pharmacokinetics and pharmacodynamics of docetaxel and doxorubicin in Asian patients. Pharmacogenomics J 2008; 2: 139–146.

    Article  CAS  Google Scholar 

  107. 107

    Medina MW, Gao F, Ruan W, Rotter JI, Krauss RM . Alternative Splicing of 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase Is Associated With Plasma Low-Density Lipoprotein Cholesterol Response to Simvastatin. Circulation 2008; 118: 355–362.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  108. 108

    Laudadio J, Deininger MW, Mauro MJ, Druker BJ, Press RD . An intron-derived insertion/truncation mutation in the BCR-ABL kinase domain in chronic myeloid leukemia patients undergoing kinase inhibitor therapy. J Mol Diagn 2008; 10: 177–180.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  109. 109

    Bueno MJ, Pérez de Castro I, Gómez de Cedrón M, Santos J, Calin GA, Cigudosa JC et al. Genetic and epigenetic silencing of microRNA-203 enhances ABL1 and BCR-ABL1 oncogene expression. Cancer Cell 2008; 6: 496–506.

    Article  CAS  Google Scholar 

  110. 110

    Lu NZ, Cidlowski JA . Glucocorticoid receptor isoforms generate transcription specificity. Trends Cell Biol 2006; 16: 301–307.

    CAS  Article  PubMed  Google Scholar 

  111. 111

    Pujols L, Mullol J, Picado C . Alpha and beta glucocorticoid receptors: relevance in airway diseases. Curr Allergy Asthma Rep 2007; 7: 93–99.

    CAS  Article  PubMed  Google Scholar 

  112. 112

    Oakley RH, Sar M, Cidlowski JA . The human glucocorticoid receptor beta isoform. Expression, biochemical properties, and putative function. J Biol Chem 1996; 271: 9550–9559.

    CAS  Article  PubMed  Google Scholar 

  113. 113

    Goleva E, Li LB, Eves PT, Strand MJ, Martin RJ, Leung DY . Increased glucocorticoid receptor beta alters steroid response in glucocorticoid-insensitive asthma. Am J Respir Crit Care Med 2006; 173: 607–616.

    CAS  Article  PubMed  Google Scholar 

  114. 114

    Yarden Y, Sliwkowski MX . Untangling the ErbB signalling network. Nat Rev Mol Cell Biol 2001; 2: 127–137.

    CAS  Article  Google Scholar 

  115. 115

    Reiter JL, Threadgill DW, Eley GD, Strunk KE, Danielsen AJ, Sinclair CS et al. Comparative genomic sequence analysis and isolation of human and mouse alternative EGFR transcripts encoding truncated receptor isoforms. Genomics 2001; 71: 1–20.

    CAS  Article  PubMed  Google Scholar 

  116. 116

    Lafky JM, Baron AT, Cora EM, Hillman DW, Suman VJ, Perez EA et al. Serum soluble epidermal growth factor receptor concentrations decrease in postmenopausal metastatic breast cancer patients treated with letrozole. Cancer Res 2005; 65: 3059–3062.

    CAS  Article  PubMed  Google Scholar 

  117. 117

    Asgeirsson KS, Agrawal A, Allen C, Hitch A, Ellis IO, Chapman C et al. Serum epidermal growth factor receptor and HER2 expression in primary and metastatic breast cancer patients. Breast Cancer Res 2007; 9: R75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. 118

    Hudelist G, Köstler WJ, Gschwantler-Kaulich D, Czerwenka K, Kubista E, Müller R et al. Serum EGFR levels and efficacy of trastuzumab-based therapy in patients with metastatic breast cancer. Eur J Cancer 2006; 42: 186–192.

    CAS  Article  PubMed  Google Scholar 

  119. 119

    Cameron D, Casey M, Press M, Lindquist D, Pienkowski T, Romieu CG et al. A phase III randomized comparison of lapatinib plus capecitabine versus capecitabine alone in women with advanced breast cancer that has progressed on trastuzumab: updated efficacy and biomarker analyses. Breast Cancer Res Treat 2008, e-pub ahead of print.

  120. 120

    Diaz A, Reginato AM, Jimenez SA . Alternative splicing of human prostaglandin G/H synthase mRNA and evidence of differential regulation of the resulting transcripts by transforming growth factor beta 1, interleukin 1 beta, and tumor necrosis factor alpha. J Biol Chem 1992; 267: 10816–10822.

    CAS  PubMed  Google Scholar 

  121. 121

    Kitzler J, Hill E, Hardman R, Reddy N, Philpot R, Eling TE . Analysis and quantitation of splicing variants of the TPA-inducible PGHS-1 mRNA in rat tracheal epithelial cells. Arch Biochem Biophys 1995; 316: 856–863.

    CAS  Article  PubMed  Google Scholar 

  122. 122

    Hersh EV, Lally ET, Moore PA . Update on cyclooxygenase inhibitors: has a third COX isoform entered the fray? Curr Med Res Opin 2005; 8: 1217–1226.

    Article  CAS  Google Scholar 

  123. 123

    Zimmermann N, Kienzle P, Weber AA, Winter J, Gams E, Schrör K et al. Aspirin resistance after coronary artery bypass grafting. J Thorac Cardiovasc Surg 2001; 121: 982–984.

    CAS  Article  PubMed  Google Scholar 

  124. 124

    Gasparyan AY, Watson T, Lip GY . The role of aspirin in cardiovascular prevention: implications of aspirin resistance. J Am Coll Cardiol 2008; 51: 1829–1843.

    CAS  Article  PubMed  Google Scholar 

  125. 125

    Weber AA, Zimmermann KC, Meyer-Kirchrath J, Schrör K . Cyclooxygenase-2 in human platelets as a possible factor in aspirin resistance. Lancet 1999; 353: 900.

    CAS  Article  PubMed  Google Scholar 

  126. 126

    Zimmermann N, Wenk A, Kim U, Kienzle P, Weber AA, Gams E et al. Functional and biochemical evaluation of platelet aspirin resistance after coronary artery bypass surgery. Circulation 2003; 108: 542–547.

    CAS  Article  PubMed  Google Scholar 

  127. 127

    Lamba J, Lamba V, Schuetz E . Genetics variants of PXR (NR1I2) and CAR (NR1I3) and their implications in drug metabolism and pharmacogenetics. Curr Drug Metab 2005; 4: 369–383.

    Article  Google Scholar 

  128. 128

    Heinzen EL, Yoon W, Tate SK, Sen A, Wood NW, Sisodiya SM . Nova2 interacts with a cis-acting polymorphism to influence the proportions of drug-responsive splice variants of SCN1A. Am J Hum Genet 2007; 5: 876–883.

    Article  CAS  Google Scholar 

  129. 129

    Kwan T, Benovoy D, Dias C, Gurd S, Provencher C, Beaulieu P et al. Genome-wide analysis of transcript isoform variation in humans. Nat Genet 2008; 40: 225–231.

    CAS  Article  PubMed  Google Scholar 

  130. 130

    Tan S, Guo J, Huang Q, Chen X, Li-Ling J, Li Q et al. Retained introns increase putative microRNA targets within 3′ UTRs of human mRNA. FEBS Lett 2007; 6: 1081–1086.

    Article  CAS  Google Scholar 

  131. 131

    Sandberg R, Neilson JR, Sarma A, Sharp PA, Burge CB . Proliferating cells express mRNAs with shortened 3′ untranslated regions and fewer microRNA target sites. Science 2008; 5883: 1643–1647.

    Article  CAS  Google Scholar 

  132. 132

    Makeyev EV, Zhang J, Carrasco MA, Maniatis T . The MicroRNA miR-124 promotes neuronal differentiation by triggering brain-specific alternative pre-mRNA splicing. Mol Cell 2007; 3: 435–448.

    Article  CAS  Google Scholar 

  133. 133

    Duursma AM, Kedde M, Schrier M, le Sage C, Agami R . miR-148 targets human DNMT3b protein coding region. RNA 2008; 5: 872–877.

    Article  CAS  Google Scholar 

  134. 134

    Makeyev EV, Maniatis T . Multilevel regulation of gene expression by microRNAs. Science 2008; 5871: 1789–1790.

    Article  CAS  Google Scholar 

Download references


We thank the insights and suggestions from Dr Tom Wurdinger, Dr Patricia Savio de Araujo Souza, Dr Alexandre da Costa Pereira, Dr Elio Vanin and Kelly Arndt. FP and CGF are supported by the Swiss Bridge Foundation and Fundação Ary Frauzino para Pesquisa e Controle do Câncer. FFC is supported by the Maeve McNicholas Memorial Foundation and Children's Memorial Research Center.

Author information



Corresponding author

Correspondence to F F Costa.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Passetti, F., Ferreira, C. & Costa, F. The impact of microRNAs and alternative splicing in pharmacogenomics. Pharmacogenomics J 9, 1–13 (2009).

Download citation


  • polymorphisms
  • miRNAs
  • alternative splicing
  • drug metabolism
  • drug resistance
  • complex diseases and therapy

Further reading


Quick links