Articles in PresS. Physiol Genomics (October 29, 2013). doi:10.1152/physiolgenomics.00106.2013

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MicroRNA Associated with Dyslipidemia and Coronary Disease in Humans Elena Flowers,1 Bradley E. Aouizerat1, 2 1 2

Department of Physiological Nursing, School of Nursing, University of California, San Francisco Institute for Human Genetics, University of California, San Francisco

Running Head: MicroRNA and Lipids in Humans Contact Information: Elena Flowers Department of Physiological Nursing 2 Koret Way, #605L San Francisco, CA 94143-0610 415-476-0983 [email protected] Author Contributions: Disclosures/conflicts of interest: none

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Copyright © 2013 by the American Physiological Society.

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ABSTRACT

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messenger RNA translation. Recently, there is significant interest in the application of microRNA as a

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blood-based biomarker of underlying physiologic conditions. Dyslipidemia is a complex, heterogeneous

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condition conferring substantially increased risk for cardiovascular disease. The purpose of this review is

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to describe the current body of knowledge on the role of microRNA regulation of lipoprotein metabolism

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in humans, and to discuss relevant methodologic and study design considerations. We highlight the

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potential roles for microRNA in gene-environment interactions.

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KEY WORDS

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microRNA

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lipids

MicroRNAs are structural components of an epigenetic mechanism of post-transcriptional regulation of

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Figure 1 Legend

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CAD: coronary artery disease TC: total cholesterol LDL: low-density lipoprotein cholesterol HDL: high-density lipoprotein cholesterol PBMCs: peripheral blood mononuclear cells

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Introduction Dyslipidemia is a common risk factor for cardiovascular disease, and reductions in the levels of

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atherogenic lipoproteins are associated with substantially decreased risk. (39) The two primary

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approaches to treatment for dyslipidemia are pharmacologic agents and lifestyle change. (39) However,

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even in combination these interventions are not completely effective at achieving adequate risk reduction

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in all individuals. One important reason that risk reduction efforts fail to achieve full success is

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incomplete understanding of inter-individual differences in the underlying pathophysiology. While single

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genes and discrete environmental exposures that increase risk have been identified, for most individuals

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the etiology is complex (i.e., multifactorial). (9)

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A recent paradigm shift in biomarker research has resulted from the development of methods to

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measure adaptive changes of the genome and its expression, termed epigenetics. Epigenetic events are

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particularly attractive for clinical research because they represent an aggregate response to many

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upstream events. In other words, epigenetic events are the sum of genetic risk factors and sustained

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environmental exposures that drive an organism towards adaptive responses to maintain homeostasis. To

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date, three primary epigenetic mechanisms have been identified and described in detail elsewhere: histone

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modification (16), DNA methylation (22), and microRNA (54).

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MicroRNAs (miRs) are an epigenetic mechanism of post-transcriptional regulation of messenger

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RNA (mRNA). MiRs function by binding to a complementary 18-24 nucleotide precursor region on

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mRNA known as the “seed” sequence, thereby preventing initiation of translation of mRNA to amino

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acids. (28) MiR regulation is dynamic; the function can be temporary, when the microRNA release the

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mRNA, or permanent, causing degradation of the mRNA strand. (46) Currently, there are 2,042 discrete

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miR species identified in humans, and approximately 300 are detectable in blood. (27) MiR are the

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subject of numerous research studies because they currently have three potential clinical implications: (1)

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biomarkers of underlying disease; (2) therapeutic treatments (i.e., synthetic miRs or miR antagonists); and

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(3) as markers for response to therapeutic interventions to prevent and treat disease. The subset of miR

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detectable in blood are of particular interest because their measurement is minimally invasive and blood3

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based diagnostic testing is common in the clinical setting. Additional studies are needed in order to

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determine the origins of miRs found in blood to fully understand the pathophysiological underpinnings of

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dyslipidemia.

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Non-Human Studies

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Numerous in vitro and animal model studies have examined the regulatory role of microRNA on

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lipid metabolism (Table 1). (7, 8, 12, 36, 37, 42, 45) MiR-122 is the most prevalent miR in animal model

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hepatocytes, targeting numerous genes involved in lipid metabolism in the liver (i.e., diacylglycerol O-

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acyltransferase 2, glycogen synthase 1, solute carrier family 7, acetyl-coA carboxylase α, nuclear receptor

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subfamily 1, group H, member 3/liver X receptor α, fatty acid synthase, sterol regulatory element binding

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transcription factor-1c , cholesterol 7α-hydroxylase), and adipocytes (i.e., adenosine triphosphate binding

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cassette transporter-1, diacylglycerol O-acyltransferase 2) with associated changes in lipid parameters.

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Greater detail on the effects of miR-122 on lipid metabolism has previously been published. (12) The

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aggregate effects of miR-122 in non-human studies are decreased total cholesterol and triglycerides and

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decreased production of the LDL receptor. Further research on miR-122 in humans is needed to determine

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whether this miR has the same effects as those seen in in vitro and animal model studies, as well as

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whether this miR might be useful as a therapeutic agent for modification of lipoprotein levels.

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Recently, miR-33 has become a potential therapeutic target for dyslipidemia in animal models. In

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African Green Monkeys, inhibition of miR-33 results in decreased serum triglycerides and increased

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HDL. (41) Additional studies in the same primate model and also in mouse models demonstrated miR-33

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is located in an intron of the sterol-regulatory binding element transcription factor gene and regulates this

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mRNA as well as adenosine triphosphate binding cassette transporter and others. (5, 12, 18, 38, 43)

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Although the findings from in vitro and animal model studies established the role of miR-33 in

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modulating biological pathways underlying dyslipidemia, the findings have limited generalizability to

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humans. For example, the miR-33a isoform, which is readily detectable in mice, is not expressed in

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humans; thus initial findings from studies of mouse models had limited implications for potential translation to clinical practice. Although there is growing evidence to support miR regulation of lipoprotein metabolism, the

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translational implications of studies from in vitro and animal model studies are limited. Currently

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published reviews in this area have not concentrated on findings from studies in humans with more

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imminent translational potential (i.e., miRs as surrogate biomarkers for underlying pathophysiology). The

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primary focus of this review is the synthesis of findings from studies of miR regulation of lipid

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metabolism in humans.

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Human Studies

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Cardiovascular Risk Factors

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There is a small but growing body of literature about miR expression and dyslipidemia in

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humans, and particularly blood-based expression of miR associated with underlying perturbations in lipid

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metabolism and/or sequelae (e.g., coronary artery disease (CAD)) (Table 1). Karolina, et. al.,

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characterized miRs in whole blood and exosomes in 265 Singaporean patients with and without the

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metabolic syndrome and its component risk factors (i.e., dyslipidemia, hypertension, and type-2 diabetes).

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(26) Using microarray-based screening of known human miRs followed by validation of a subset of

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targets by quantitative polymerase chain reaction (qPCR), unique miR profiles characterizing three risk

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factor profiles associated with metabolic syndrome were identified. (26) Eight miR species (miR-103,

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miR-17, miR-183, miR-197, miR-23a, miR-509-5p, miR-584, miR-652) were unique to the metabolic

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syndrome and dyslipidemia cluster (n = 41) compared to the controls (n = 17) by microarray. (26) Three

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of these (miR-197, miR-23a, miR-509-5p) were also significantly correlated with body mass index. (26)

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Only a subset (miR-17, miR-197, miR-509-5p) were detectable by qPCR in exosomes. (26) Flowers,

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et.al., (in review) similarly used microarray methods to screen for the presence of 85 known miR species

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in South Asian men with (n=22) and without (n=22) atherogenic dyslipidemia. Fifteen miRs (miR-100,

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miR-106b, miR-125b, miR-143, miR-148a, miR-17, miR-17*, miR-18a, miR-20a, miR-21, miR-221, 5

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miR-374a, miR-7, miR-93, miR-96) were found to be differentially expressed at a magnitude of at least

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two-fold. Of note, miR-17 was observed to be differentially expressed by microarray-based measurement

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in both studies, and has previously been observed to target and suppress angiogenic pathways in in vitro

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studies. (6, 24) Karolina, et.al., observed increased expression in dyslipidemic individuals compared to

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healthy controls (fold change 2.77), whereas Flowers, et.al., observed decreased expression (fold change -

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2.27). One possible explanation for these apparently discrepant findings is the specific dyslipidemic

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phenotype selected for case status was primarily focused on hypercholesterolemia (i.e., LDL > 3.4

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mmol/L or total cholesterol > 5.0 mmol/L (26) as compared to hypertrigclyeridemia (i.e., HDL < 1.03

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mmol/L and triglycerides >1.7 mmol/L (Flowers, et.al.)) and the differing racial background of each

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sample.

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Coronary Artery Disease

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Two studies have reported blood-based differential expression of miRs in individuals with and

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without CAD, with associated alterations in lipoprotein profiles. Gao, et.al. studied 355 Chinese patients

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who underwent coronary angioagraphy, 255 of whom had dyslipidemia (total cholesterol ≥ 5.72mmol/L

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and/or triglycerides ≥ 1.7mmol/L). (15) QPCR was used to measure blood plasma expression of miR-122,

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miR-370, miR-33a, and miR-33b, miRs frequently examined in in vitro and animal model studies of lipid

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metabolism. (15) Hyperlipidemic patients displayed statistically significantly greater expression of both

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miR-122 and miR-370, and this effect was attenuated in those treated with statins. (15) Both miRs were

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positively associated with total cholesterol, triglycerides, and LDL cholesterol, but no relationship was

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observed for HDL cholesterol. (15) Patients with CAD also displayed greater expression of both miRs,

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and this relationship remained significant after adjusting for covariates, including lipoprotein levels. (15)

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Of note, the two miR-33 isoforms were undetectable in plasma. (15) In a similar study, Tabuchi, et.al.

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studied miR-34 in 70 Japanese patients undergoing coronary angiography and 48 controls with no

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symptoms of CAD. (49) The patients with CAD were randomized to receive one of two statins following

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angiography. (49) This study also used qPCR but quantified miR expression from peripheral blood

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mononuclear cells, which are a subset of the cell types found in whole blood. (49) MiR-34 was chosen 6

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based on prior data from in vitro studies indicating that this miR regulates silent information regulator 1

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(SIRT1), which has anti-atherosclerotic effects by influencing vascular endothelial cell homeostasis and is

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a proposed therapeutic target of statins. MiR-34 showed statistically significantly greater expression in

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patients with CAD compared to the controls, and an inverse association with SIRT1 protein levels. (49)

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Post-treatment, no differences between the two statin groups for miR-34 or SIRT1 levels were observed.

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(49) Comparisons of pre-post statin treatment were not reported, thus no determination of the effects of

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statins on miR-34 or SIRT1 levels can be made. Both of these studies provide evidence that individual

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circulating miRs are associated with underlying pathophysiology (i.e., dyslipidemia and CAD). Although

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the samples, covariates (i.e., statin therapy), and miR measurement methods were similar between these

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two studies, there was no overlap in the miR targets tested. Further, the studies evaluated different tissue

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sources (i.e., plasma versus peripheral blood mononuclear cells), and used different reference genes to

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calculate normalized miR expression. Although comparison of the miR results is not possible, this

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aggregate set of miR species with a plausible role in lipid metabolism warrant further investigation.

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Methodological Considerations

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expression associated with dyslipidemia in humans. (11) The first is the tissue source used for detection

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of miRs. Similar to mRNA, miR expression varies among tissues. In most cases, peripheral blood is the

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most readily accessible tissue source in routine clinical care, which is a fundamental driver of widespread

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interest in circulating miRs as a biomarker(s) for underlying disease processes and risk. MiRs are readily

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detectable in the circulation, and are differentially expressed in numerous disease states and/or

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pathophysiological processes compared to healthy controls. (3, 35) MiRs are detectable in several blood

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compartments: plasma, serum, exosomes, microparticles, platelets, erythrocytes, and leukocytes. (14, 19,

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51, 56) Prior studies of miR and lipid metabolism examined several discrete blood compartments (Figure

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1). Comparison of blood-based expression of miRs must consider the specific source from which miR

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were isolated. For example, in the studies of miR expression and CAD, Gao, et.al. quantified miRs in

There are several important methodological considerations for interpretation of studies of miR

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blood plasma, whereas Tabuchi, et.al. studied peripheral blood mononuclear cells. Even plasma and

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serum show some differences in the specific species and quantity of miRs detectable. (56) To date, it is

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unclear whether detection of blood-based miRs detects causal pathways to diseaseor whether miRs

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originate from apoptotic cells in ischemic or damaged solid organs that are sloughed into the circulation.

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Depending on the origin of the miRs and underlying pathophysiology (i.e., blood-based versus solid

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organ-based), some miRs may need compartment-specific measures and others will not. There are

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examples of both types of biomarkers in routine clinical use at present. Cardiac troponins, which are

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released into the circulation by ischemic cardiomyocytes, reflect a causal relationship and are widely used

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in clinical care to diagnose myocardial infarction. By contrast, elevated LDL cholesterol is used to

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identify individuals who are at high risk for atherosclerosis, but the biomarker itself is not diagnostic for

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CAD. Additional studies are needed to determine the origins of circulating miR in order to understand the

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primary physiology. Regardless of the underlying causal relationship, blood-based miR expression has

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the potential to be a useful marker for disease.

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The second consideration is how miRs are measured. Until recently, the gold standard for

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measurement was qPCR, and for practical reasons (i.e., cost, access to resources, data analysis), it remains

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the primary method reported in the literature. Whole transcriptome sequencing is the next generation of

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measurement for RNA, including miR, improving measurement of miR considerably through more

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accurate quantitation of the miR species and number. Another option, microarray, uses hybridization to

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detect the type and number of miRs present, and offers higher throughput and greater sensitivity and

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specificity compared to qPCR. (11) There are several commercially available platforms for both

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microarray and qPCR-based quantitation of miRs. Comparison studies show a high level of variability in

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the discrete miR species detected and correlation in the magnitude of expression between commercial

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miR measurement products (2, 56), suggesting that some caution should be taken in comparing results of

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studies using differing measurement platforms and commercial products.

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Commonly, studies of gene expression report normalized expression, in which expression of each individual sample is standardized to the expression of a reference gene or combination of genes that is 8

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ubiquitously expressed at constant levels in most tissue types across individuals. (1, 50, 52) Such

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normalization controls for variation in the starting quantity of biologic material that could otherwise skew

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comparisons of relative expression between groups (i.e., diseased versus non-diseased). However for

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miR, a universally accepted normalizer control has not been identified. A commonly used normalizer is

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the small nuclear RNU6b. However, recent studies have reported considerable variation of expression of

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this RNA. (40, 57) Normalization to the average expression of all measured miRs in all samples in a

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single experiment, normalization to the least variable miR in each experiment, and alternative approaches

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have been proposed. (4, 33, 34, 58) To date, there is no universally accepted method for normalization of

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expression, and different strategies used between studies are an important consideration for interpreting

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conclusions.

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Gene-Environment Interactions A potential implication of miR activity is modulation of the effects of environmental exposures.

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Gene-environment interactions are widely known to effect risk factors for cardiovascular disease,

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including dyslipidemia. (10) MiRs may be the structural cause of gene-environment interactions, as is the

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case when a DNA polymorphism lies in the miR coding site or within the seed sequence in the promoter

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region of the mRNA target and secondary exposure to an environmental insult triggers deleterious

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consequences. For example, a single nucleotide polymorphism in the lipoprotein lipase (LPL) gene

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(rs13702) appears to lie within the miR-410 seed sequence. (44) This polymorphism is also linked to a

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gene-environment interaction, as the rare C allele is associated with an additive inverse relationship

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between polyunsaturated fat intake and triglycerides. (44) Genome-wide association data indicate that the

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rare C allele is associated with lower triglycerides and higher HDL, and in vitro transfection of the

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genomic region containing the rare C allele along with a miR-410 mimic results in gain of function of

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LPL compared to transfection with the common T allele. (44) Further, there is a dose-dependent response

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for the miR-410 mimic in the major allele transfected cells. (44)

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Alternatively, miR may be a mechanism for gene-environment interactions through its inherent

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regulatory function on gene expression, resulting in increased or decreased expression of an mRNA target

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as an adaptive response to environmental exposure. For example, workers exposed to perflouroaklyl

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chemicals (PFCs) in their workplace show an inverse correlation between blood levels of PFCs and HDL,

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and array-based differential expression of 9 miRs (miR-127, miR-601, miR-106b, miR-24, miR-199a-3p,

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miR-92a, miR-26b, miR-30c, miR-30b) compared to nearby residents of in the same community who

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were not exposed to PFCs. (55) These differences were validated by qPCR for miR-26b and miR-199a-

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3p, and a linear association was seen between fold-change miR expression and blood levels of PFCs. (55)

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Another example of how miR may function to allow an organism to adapt to environmental changes is

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through diet-induced miR activity. We were unable to find any published studies describing the effect of

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dietary factors on miR activity and dyslipidemia performed in humans, but several in vitro and animal

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model studies of other conditions indicate that this relationship is plausible.(23, 25, 29, 31, 47, 48, 53, 60)

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Increasing interest in the implications of miRs in the area of gene-environment interactions is evidenced

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by the recent creation of the miREnvironment Database, which cross-references miR species with

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environmental risk factors and phenotypes. (59)

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Racial Ancestry and MiR Expression Although the currently published studies reporting miR expression associated with dyslipidemia

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do not lend themselves to direct comparison because of differences in study design, sample, and methods,

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we can anticipate that future studies will incorporate comprehensive screening of miR targets detectable

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in blood in samples with well-characterized phenotypes, creating the possibility of direct comparison. The

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trajectory of miR-based research, although relatively new, closely follows the arc of both genetic (i.e.,

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DNA polymorphisms) and gene-expression (i.e., mRNA) studies. Particularly in genetic studies, there

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exists variability among studies in the observed associations between gene polymorphisms and risk

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phenotypes. (21) This is particularly true for multi-factorial phenotypes like dyslipidemia (17) and CAD

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(30). Proposed alternative explanations for discrepant findings in genetic studies include unknown and 10

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unmeasured gene-gene and gene-environment interactions. (10, 30) Differences in the underlying

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prevalence of gene polymorphisms among racial groups may add another layer of complexity to the

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aforementioned interactions. (13, 32) While the myriad gene-gene and gene-environment interactions for

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dyslipidemia have yet to be fully characterized, methods to quantify population substructure (i.e., racial

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ancestry), have been developed – namely measurement of ancestry informative markers (AIMs) that

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distinguish five racial groups on the basis of genotype prevalence. (20) To date, no studies investigating

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the corollary for miRs have been reported. We posit that differential expression of miRs might be

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influenced in part by differences in underlying genetic makeup between ancestry groups. For example,

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four of the studies on miRs and lipid metabolism cited above had samples of Asian ancestry (i.e.,

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Singaporean, South Asian, Chinese, Japanese) – racial groups which, although categorized as Asian, may

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not share equal genetic commonalities. Thus, confounding may occur in studies of miRs in humans when

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genetic background is not properly accounted for in data analyses. Further investigation of possible miR

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AIMs will bring to light the prospect of controlling for differences in ancestry that may be confounding

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observed associations or lack of associations between miR expression and disease states.

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Conclusion

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Dyslipidemia is a complex, multi-factorial risk factor for cardiovascular disease that arises in the

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setting of both genetic and environmental exposures. MiRs are an epigenetic mechanism of regulation of

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gene expression that facilitate dynamic adaptation of organisms to their environment. As such, miRs are

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an appealing possible biomarker with two important implications: (1) earlier detection of impending

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cardiovascular risk than is currently available through routine screening of lipoprotein levels; and (2)

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elucidation of the underlying genetic mechanisms at play in a given individual. As blood is readily

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accessible in clinical care, there is particular interest in detection of both causal and non-causal blood-

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based miR expression associated with underlying pathophysiology. In addition to the biomarker

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implications, the possibility exists that miRs may be a therapeutic agent, through either administration of

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synthetic miR or miR antagonists. To date, a number of in vitro and animal model studies describing miR 11

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regulation of lipoprotein metabolism have been published, but less is known about these mechanisms in

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humans, with virtually no data to describe the molecular functions of miR in humans. The few human

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studies consistently show differential expression of miRs in blood compartments of individuals with

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dyslipidemia and/or CAD. Methodologic differences in the design of these studies (i.e., miR tissue

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source, specific miR targets evaluated, measurement method) limit the possibility for making meaningful

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conclusions about which miR are of potential clinical relevance for detection and treatment of

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dyslipidemia as well as comparison of results between studies. Additional possible methodologic and

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study design issues relevant to studies of miRs and dyslipidemia include gene-environment interactions

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and racial ancestry. Further work is needed to evaluate the full cadre of blood-based miRs associated with

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dyslipidemia in humans, and to delve into possible racial differences in the prevalence and variability of

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individual miR species and the presence of gene-environment interactions exerting pressure on miR

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regulation of gene expression.

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16

485

Table 1. Prior studies reporting microRNA expression associated with dyslipidemia. NON-HUMAN STUDIES – PRIOR REVIEWS MicroRNA

Cell/tissue source

Citation

miR-33a/b

Human (hsa) cell lines, mouse (mmu) models

Fernandez-Hernando, et.al. (7)

miR-27, miR-33a/b, miR-3a, miR-103/107, miR-122, miR-124, miR-143, miR-148, miR-182, miR-370, miR378/378*

Human (hsa) cell lines

Fernandez-Hernando, et.al. (8)

miR-let7, miR-9, miR-24, miR-26, miR-29, miR-33a/b, miR-3a, miR-103/107, miR-122, miR-208, miR-375

Mouse (mmu) models

miR-9, miR-96

Rat (rno) cell lines

miR-122 miR-370 miR-758 miR-106b

Human (hsa) cell lines, mouse (mmu) models Human (hsa) cell lines Human (hsa) cell lines, mouse (mmu) models Human (hsa) cell lines

Flowers et.al. (12)

miR-122

Human (hsa) cell lines, mouse (mmu) models

Najafi-Shoushtari, et.al. (33)

miR-370

Human (hsa) cell lines

miR-33

Human (hsa) cell lines, mouse (mmu) models

miR-33a/b

Human (hsa) cell l lines, mouse (mmu) models, primate (sae) models

Rayner, et.al. (38)

miR-33a/b

Human (hsa) cell lines, mouse (mmu) models, primate (sae) models

Rottiers et.al. (41)

miR-122

Mouse (mmu) models, primate (sae, ptr) models

Moore, et.al. (32)

miR-370

Human (hsa) cell lines

miR-378/378*

Human (hsa) cell lines

miR-33

Human (hsa) cell l lines, mouse (mmu) models HUMAN STUDIES Whole Blood 17

MicroRNA identified

Tissue source

Racial characteristics

Phenotype

Citation

Dyslipidemia: miR-103, miR-17, miR183, miR-197, miR-23a, miR-509-5p, miR-584, miR-652 BMI: miR-197, miR-23a, miR-509-5p

Whole blood

miR-17, miR-197, miR-509-5p, miR92a, miR-320a

Exosomes

Singaporean

Metabolic syndrome components

miR-100, miR-106b, miR-125b, miR143, miR-148a, miR-17/17*, miR-18a, miR-20a, miR-21, miR-221, miR374a, miR-7, miR-93, miR-96

Whole blood

South Asian

High triglycerides/low HDL

Flowers et.al.

Singaporean

Metabolic syndrome components Karolina et.al. (24)

Blood Components

486 487 488 489 490 491

Dyslipidemia: miR-103, miR-17, miR183, miR-197, miR-23a, miR-509-5p, miR-584, miR-652 BMI: miR-197, miR-23a, miR-509-5p

Exosomes

Singaporean

Metabolic syndrome components

Karolina et.al. (24)

miR-17, miR-197, miR-509-5p, miR92a, miR-320a

Blood plasma

Chinese

Coronary artery disease

Gao et.al. (15)

miR-34

Peripheral blood mononuclear cells

Japanese

Coronary artery disease

Tabuchi et.al. (43)

qPCR: quantitative polymerase chain reaction BMI: body mass index HDL: high-density lipoprotein cholesterol

18

Figure 1. Perturbations in microRNA expression associated with dyslipidemia and coronary artery disease on whole blood and blood components.

Legend CAD: coronary artery disease TC: total cholesterol LDL: low-density lipoprotein cholesterol HDL: high-density lipoprotein cholesterol PBMCs: peripheral blood mononuclear cells