Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2013 Mar 2.
Published in final edited form as: Circ Res. 2012 Mar 2;110(5):755–763. doi: 10.1161/CIRCRESAHA.111.256768

Decoding the Cardiac Message

Gerald W Dorn II 1
PMCID: PMC3294415  NIHMSID: NIHMS356546  PMID: 22383710

Abstract

This review reflects and expands upon the contents of the author’s presentation at The Thomas W. Smith Memorial Lecture at AHA Scientific Sessions, 2011. “Decoding the cardiac message” refers to accumulating results from ongoing microRNA research that is altering longstanding concepts of the mechanisms for, and consequences of, messenger RNA (mRNA) regulation in the heart. First, I provide a brief historical perspective of the field of molecular genetics, touching upon seminal research that paved the way for modern molecular cardiovascular research and helped establish the foundation for current concepts of mRNA regulation in the heart. I follow with some interesting details about the specific research that led to the discovery and appreciation of microRNAs as highly conserved pivotal regulators of RNA expression and translation. Finally, I provide a personal viewpoint as to how agnostic genome-wide techniques for measuring microRNAs, their mRNA targets, and their protein products can be applied in an integrated multi-systems approach to uncover direct and indirect effects of microRNAs. Experimental designs integrating next-generation sequencing and global proteomics have the potential to address unanswered questions regarding microRNA-mRNA interactions in cardiac disease, how disease alters mRNA targeting by specific microRNAs, and how mutational and polymorphic nucleotide variation in microRNAs can affect end-organ function and stress-response.

Keywords: RNA silencing, transcriptional regulation, genetic mutation, microRNA

Introduction

We are members of a biomedical research community whose world-view has been completely altered over the past 150 years, first by the discovery of the principles of genetics, then by the development of the techniques of molecular biology, and most recently by the application of computerized/roboticized “omics” approaches to broad biological questions. The scientific topic of this session, microRNAs in the cardiovascular system, is an example of an area of research that did not exist even a decade ago. Yet, the ongoing explosion of information on microRNAs has provided insight into integration and regulation of biological systems. We must therefore react by adjusting our concepts and experimental approaches.

Because “a dwarf on a giant’s shoulders sees farther of the two” 1, here I offer some perspective on the scientific road that has brought molecular genetics to its current state. I then propose a conceptual paradigm for investigating microRNA-mRNA-protein regulation that extends the typical reductionist experimental approach in favor of global analyses that lend themselves to integrative interpretation of biological pathways in terms of functional modules. Finally, I discuss future developments that have the potential to integrate basic molecular, proteomic, and physiological research with clinical genetics of microRNAs.

The genesis of molecular biology

The underlying principle of molecular biology can be summarized as follows: Genes are vertically transmitted packets of biological information that determine phenotype, and DNA is the stuff of which genes are made. The experimental basis for this paradigm, now accepted as natural law, was developed by two nineteenth century Europeans who were contemporaries, and yet apparently unaware of each other’s work. Fr. Gregor Mendel is familiar to every middle school student. During the period immediately before and during the American Civil War, his meticulous observation and quantitative documentation of patterns for vertical phenotype transmission of specific traits in Pisum sativum (peas) identified dominant and recessive genetic characteristics and enabled him to derive the “laws of inheritance” 2. Mendel’s original work was misunderstood and largely ignored until it was independently reproduced and acknowledged over thirty years later by Hugo de Vries and Carl Correns 3, 4.

Contemporaneous with Mendel’s botanical studies, the young physician Friedrich Miescher was embarking on what would now be considered a post-doctoral research fellowship studying the chemistry of blood cells. Although his project in Felix Hoppe-Seyler’s program was to have focused on lymphocytes, they were difficult to obtain. By contrast, leukocytes were abundant in the purulent suppurations of bandages obtained from the local surgical clinic. From leukocyte nuclei, Miescher isolated an acid-insoluble, sodium hydroxide- and disodium phosphate-soluble non-proteinaceous substance he designated “nuclein” 5, later termed nucleic acid or DNA. Publication of Miescher’s results in his mentor’s journal was delayed for two years while Hoppe-Seyler reproduced them, extended them to yeast, and published his own data 6.

The work of Mendel and Miescher and their successors established the foundations of molecular biology by defining genetic principles and discovering DNA. However, the revelation that genes are made of DNA required 70 additional years, until World War II. As Professor Emeritus at Rockefeller University, 66 year old Oswald Avery, MD and his colleagues reported that DNA was the substance that induced virulent transformation of non-pathological pneumococcus.7 The identity of DNA as the “transforming principle” of genes was confirmed by tracing bacteriophage 32P-DNA in studies performed by Alfred Hershey and Martha Chase 8. Together, the Avery-MacLeod-McCarty and Hershey-Chase experiments established the molecular basis for heredity and helped to focus attention on the mechanism by which DNA encodes information, and on defining its molecular structure. Accordingly, X-ray crystallographic studies performed by Rosalind Franklin and Maurice Wilkins 9, 10 helped James Watson and Francis Crick divine the double helical nature of DNA 11, which in turn permitted the physicist George Gamow to propose triplet nucleotide codons for amino acids 12.

From DNA to RNA

Understanding DNA was the key that unlocked the door of molecular genetics. A time-line of Nobel Prizes given for DNA research since World War II reveals a burst of DNA research that characterized the second half of the twentieth century (Figure 1; top). Discovery of the enzymes responsible for DNA replication and RNA transcription, elucidation of the mechanism of bacterial transduction, and development of the technique for saturation mutagenesis were acknowledged in the late 1950s, followed by recognition of Watson and Crick’s elucidation of the three dimensional structure for DNA in 1962. Over the next 15 years the attention of the Nobel committee was elsewhere, but in the 15 years that followed (i.e. 1978-1993) four Nobels were awarded for DNA research, three related to development of DNA manipulation or diagnostic techniques (restriction endonucleases in 1978, recombinant DNA expression and DNA sequencing in 1980, and polymerase chain reaction and site-directed mutagenesis in 1993). Just five years ago the Nobel Prize in Physiology or Medicine was awarded for targeted gene manipulation in mice. Thus, by the time I graduated from Medical School just over thirty years ago, we had a detailed understanding of the fundamental mechanisms of DNA replication and transcription. An appreciation of the impact of DNA mutations provided a mechanistic basis for “Mendelian” human disease, including the first recognized example, alkaptonuria, the heritable features of which had originally been described in 1902 13.

Figure 1. Time-line of Nobel prizes for DNA- and RNA-based research.

Figure 1

Open in a new tab

The year, classification of Nobel Prize (P o M = Physiology or Medicine; Chem = Chemistry), and surnames of awardees are listed for DNA-related (top) and RNA-related (bottom) research. In italics immediately below each is a brief description of the prize. Note that the 1959 Nobel awardee Arthur Kornberg who isolated and characterized DNA polymerase I, is the father of Roger Kornberg, who received a Nobel prize in 2006 for elucidating the role of RNA polymerase II and other factors in transcription. Detailed information on all the Nobel laureates and their work is available at www.nobelprize.org/nobel_prizes/.

Our understanding of the roles for genetic mutation and regulated gene transcription in heart disease is built upon these fundamental observations of the mid and late 20th century. Using a combination of genetic techniques and functional genomics, Christine and Jon Seidman first described in 1990 a molecular basis for the most common Mendelian cardiac disease, familial hypertrophic cardiomyopathy 14, 15. This seminal pair of publications inaugurated an era of molecular genetics in cardiology that has, to date, uncovered over 1,000 mutations of more than thirty different genes causing “monogenic” or Mendelian heritable hypertrophic and dilated cardiomyopathies. (For a fascinating personal perspective of the Seidman’s body of work and the overall field, the interested reader is referred to reference 16.) Likewise, the pathways by which molecular regulation of muscle gene expression determines cardiac development, and the contribution of dysregulated cardiac gene expression to heart disease, were initially uncovered by Eric Olson and his colleagues (reviewed in 17-20). The general paradigm established by these and other researchers engendered detailed investigations of genetic reprogramming in heart disease, provided a foundation for molecular diagnostics in human and experimental heart failure, and suggested novel therapeutic avenues aimed at normalizing the pathological effects of disease-regulated cardiac genes 21-23.

Observations that there were distinctive gene expression patterns within disease was one of many factors prompting mechanistic investigations that moved beyond DNA to examine its messenger RNA gene products. The evolutionary advantage of DNA as the archive for biological design lies in its chemical stability, ability to undergo repair, and high fidelity of replication permitting faithful vertical transmission of encoded phenotypes. In other words, DNA is comparatively static and immutable. For these reasons results from the Human Genome Project have provided not only a blueprint of our species’ molecular architecture in comparison with that of other species 24, 25, but also revealed to what extent past Homo sapiens genetic admixture with other ancient hominids impacts modern humans 26. Metaphorically however, when purchasing a car one doesn’t just compare parts lists and schematic diagrams of various models, you take the car for a test drive. By the same logic, in moving beyond the genetic blueprint it was necessary to determine how mRNA and protein gene products are regulated and interact with direct biological processes.

Compared to DNA, mRNA is evanescent and susceptible to enzymatic and physical degradation. Thus, the major insights into RNA biology followed and built upon those of DNA. Returning to the Nobel prize time-line (Figure 1; bottom), the first RNA Nobel prize was awarded in 1968 for deciphering the mechanisms of translation, followed by a 20 year hiatus, which was brought to an end by the recognition of the discoveries that RNAs can have catalytic activity similar to proteins (1989) and how RNA splicing produces protein diversity (1993). By 2006 RNA science had clearly arrived when the Nobel prizes for both Chemistry and Physiology or Medicine were awarded for RNA biology, the former for deciphering the mechanisms of transcription and the latter for the discovery of modulated gene expression by double-stranded RNAs, i.e. RNA interference. This brings us to the modern era and the overall topic of this scientific session, microRNAs.

RNA interference and microRNAs

The first microRNA, lin-4, was described in Caenorhabditis elegans in 1993 by Victor Ambros and colleagues 27. At the time, lin-4 was largely considered to be a biological curiosity limited to nematodes: it was widely accepted that an antisense RNA complementary to a given mRNA could suppress that mRNA, but it was not appreciated how endogenous (or exogenous) double stranded small RNAs could function as modulators of biological function. This apparent contradiction was addressed by Andrew Fire and Craig Mello’s subsequent discovery of RNA interference by double stranded RNA was more efficient than that by single-stranded anti-sense RNA 28 (for which they received the 2006 Nobel Prize in Physiology or Medicine). This observation revealed the presence of previously unknown mechanisms for processing double-stranded small RNAs and presenting the active single strand to its mRNA target. These revelations were rapidly followed by identification and characterization of the second microRNA, let-7, by Gary Ruvkun 29, 30, setting the stage for three concurrent 2001 reports in Science that demonstrated unexpected abundance and evolutionary conservation of microRNAs, and suggested their broad impact on biological function 31-33. These three papers inaugurated the burst of microRNA research that continues to the present; they also received the AAAS Newcomb Cleveland Prize for the most significant papers published in Science during that year.

Conceptually, what do microRNAs do? To quote a past student from my laboratory, “microRNAs make mRNAs not make protein”. Although there may be some exceptions, the general function of microRNAs is to prevent mRNA translation into protein. Two possible mechanisms have been identified, mRNA destabilization and translational suppression (although, again, this is an area of research that continues to evolve). As schematically depicted in Figure 2a, a fully processed single-stranded microRNA is incorporated into an Argonaute2-containing macromolecular complex, the RNA-induced silencing complex or RISC 34, to which target mRNAs are recruited via complementary Watson-Crick binding to the incorporated microRNA. mRNA destabilization takes place through nucleolytic cleavage and degradation of the complementary mRNA. Accordingly, steady state expression levels of mRNAs targeted by microRNAs may be decreased due to degradation. Alternately, mRNAs incorporated into RISCs may undergo translational suppression via deadenylation, i.e. removal of the polyA+ tail, which may or may not require participation in the RISC of poly(A) binding protein (PABP) and associated deadenylases 35, 36 in association with GW182 family proteins 37 (Figure 2b). It is likely that translational suppression and mRNA degradation both occur, or that they take place sequentially.

Figure 2. Schematic representations of microRNA-mRNA interactions in the RNA-Induced Silencing Complex (RISC).

Figure 2

Open in a new tab

A. mRNA destabilization: a red microRNA is incorporated into a green Argonaute 2 complex, the RISC, and recruits its target mRNA (black) through sequence complementarity to binding sites in the 3’ region; nucleolytic cleavage degrades the mRNA. B. mRNA translational suppression: Here, additional RISC co-factors poly(A) binding protein (PABP, blue) and GW182 proteins (red), de-adenylate the target mRNA and suppress ribosomal translation. C. (left) Equation adapted from steady-state receptor-ligand interactions showing the relationship of microRNA (miR) and target mRNA concentrations and microRNA-mRNA binding energy on RNA duplex formation at the RISC. (right) Nucleotide sequence of miR-499 (top) and five bioinformatically-predicted binding sites of its validated target, Sox6 (bottom). Note that the computer algorithm selects binding sites based on perfect seed-sequence (underlined) complementarity. Nucleotides in red are mismatches between miR-499 and target site. There are no available data as to which of these sites (or others not bioinformatically predicted) are responsible for miR-499-mediated Sox6 suppression.

My goal here is not to provide an encyclopedic review of cardiac microRNAs encompassing the explosion of data from the past few years. Others have contributed more to the field, and any such compendium would soon be made obsolete by ongoing progress and developments. Rather, I would like to offer a perspective on investigative and analytical approaches that may be useful for interrogating cardiac microRNAs in health and disease.

An unbiased and genome-wide approach to microRNA target analysis

Association of an mRNA with a microRNA-RISC complex is required for gene silencing. This association is reversible and therefore governed by the same general principles as non-covalent receptor-ligand binding 38: the microRNA/RISC is analogous to an unoccupied receptor, the mRNA is like its ligand, and the microRNA-mRNA duplex is like the active receptor-ligand complex. Thus, the steady-state probability that a given microRNA-mRNA pair will form in the RISC is determined by the concentration of microRNA, the concentration of its mRNA target, and the binding affinity of the microRNA-mRNA duplex (Figure 2c). For RNA duplexes, binding affinity is more properly described by binding energy, which is a function of Watson-Crick complementarity and can be calculated using relatively straightforward methods 39. The formula for calculating the steady-state interaction between a microRNA and a given mRNA target is simple, but each of the formulaic variables exhibits tremendous biological variability. The concentration (expression level) of ~two hundred cardiac microRNAs varies widely in human and experimental heart disease 21, 40, 41. Likewise, the concentration (expression level) of cardiac mRNAs varies independently due to transcriptional regulation 21, 42, 43, and dependently due to destabilization by cardiac-expressed microRNAs. Finally, the binding energy for a given microRNA-mRNA complex will vary with the primary sequence complementarity and can be influenced by secondary mRNA structures that affect binding site accessibility to RISC-associated microRNAs. Figure 2c (right panel) provides an example of sequence variability within five bioinformatically predicted miR-499 binding sites in one of its thoroughly validated cardiac mRNA targets, Sox6 44.

One way to account for confounding biological variability is to quantify each of the variables simultaneously and in its proper clinical or experimental context. Multiple microRNAs and mRNAs are simultaneously regulated in cardiac disease (vide supra). Furthermore, a given microRNA has the potential to independently target multiple mRNAs, and a given mRNA is likely to be targeted by more than one microRNA 45. Thus, agnostic whole genome approaches are essential to de-convoluting and understanding the pathophysiology of microRNA-mRNA interactions in heart disease. Toward this end we have employed next-generation sequencing of cardiac mRNA (RNA-Seq; 46) to digitally measure absolute mRNA expression levels (e.g. the transcriptome) in normal and diseased hearts, and have adapted a similar approach to quantifying the mRNAs within cardiac RISC complexes (RISC-Seq; 47). The latter utilizes Argonaute 2 immunoprecipitation combined with micro-extraction and next-generation sequencing of associated mRNAs to identify RISC-targeted mRNAs (the RISCome), i.e. those mRNAs that are enriched in the RISC compared to the transcriptome due to the actions of endogenous microRNAs. It is therefore also essential to quantify all the endogenously expressed microRNAs, for which next-generation sequencing protocols are available 48, 49. By comparing the results of microRNA sequencing, RNA-Seq, and RISC-Seq performed under different sets of pathophysiological conditions (Figure 3a), it is possible to obtain an overview of how different diseases alter the cardiac microRNA-mRNA interactome. To date, this type of comprehensive and unbiased genome-wide approach to cataloguing disease-induced miR-mRNA interactions has not been performed for the heart.

Figure 3. Schematic depiction of in vivo whole-genome approaches for measuring microRNA-mRNA interactions.

Figure 3

Open in a new tab

A. Next generation sequencing techniques are used to digitally quantify microRNA (red), mRNA (blue), and RISC-associated mRNA (i.e. microRNA-mRNA duplexes; green). The result is a catalog of microRNA-mRNA expression and interaction that can be compared between pathophysiological conditions. B. Schematic depiction of cardiac RISC programming by transgenesis. Transgenic expression of a microRNA of interest (red) will specifically increase its mRNA targets in the RISCome, which can be identified by comparison to nontransgenic (or other microRNA-programmed) RISComes.

The combination of RISC-Seq and RNA-Seq will identify microRNA targets, but does not indicate which of the expressed microRNAs is directly targeting which of the RISC-associated mRNAs. Available bioinformatics platforms can help refine the universe of potential microRNA-mRNA pairs, but their results can be inconsistent and are heavily biased toward near-perfect seed-sequence (microRNA nucleotides 2-8) complementarity 50. We therefore adapted the procedure for “RISC programming” with a microRNA of interest 51 to our in vivo analyses of cardiac microRNA-mRNA interactions 47. Conceptually, we compare the RISComes of unprogrammed tissue (in which RISC-Seq will identify direct mRNA targets of all endogenous microRNAs) to those from tissue expressing higher levels of the microRNA of interest (in which the mRNA targets for that microRNA will be further enriched, compared to the unprogrammed RISCome). In vivo cardiac RISC programming can be achieved by conventional or inducible cardiomyocyte-specific transgenic overexpression, producing cell-autonomous data. We take care to express the programming microRNA(s) at levels that are observed in either healthy or diseased tissue, to prevent recruitment of non-physiological mRNA targets to the RISC. Our analyses also evaluate RISC-enrichment of mRNAs as a function of mRNA levels in the respective transcriptomes, thereby accounting for secondary effects of the programming microRNA (or any induced phenotype) on transcriptionally-regulated mRNA expression. For the most part, comparing programmed (transgenic) to un-programmed (non-transgenic) RISComes identifies direct mRNA targets of the programming microRNA (Figure 3b), although mRNAs indirectly enriched in the RISCome through the effects of indirectly upregulated microRNAs will also be detected. When we used this approach to compare cardiac mRNA targets of two muscle-specific micro-RNAs that are from different families, but expressed in the heart at similar levels (miR-133a and miR-499), RISC-Seq identified mRNA targets for each that were largely non-overlapping and were consistent with the previously reported effects of these microRNAs on cardiac development and myofibrillar gene expression, respectively 47.

The approach of assaying Argonaute 2-associated mRNAs (typically by microarray) to define mRNA targets has been used in several technical variations, including HITS-CLIP 52 and PAR-CLIP 53. Thus, this general approach is increasingly accepted as the gold standard for biologically-defining mRNA targets of micro-RNAs. The decision of whether or not to employ some form of chemical RNA cross-linking (CLIP: Cross-Linking ImmunoPrecipitation) depends upon what type of information is desired from the specific study. CLIP indicates direct microRNA-mRNA binding and provides spatial information on microRNA binding and flanking sites at the mRNAs at a cost of biasing the results towards microRNA-mRNA pairs exhibiting the strongest RNA-binding protein interactions; CLIP may also be less sensitive to dynamic regulation of microRNAs than techniques that do not employ cross-linking 54.

There are some important caveats for microRNA target identification by RISC-Seq: First, to account for inter-individual variablility (as after a physiological manipulation such as aortic banding or myocardial infarction), results of RISC-Seq should be analyzed in comparison to RNA-seq from the same sample. In this manner RISC enrichment is assessed per individual tissue sample, and not grouped by experimental treatment. Second, levels of statistical significance should be appropriate for the intent of the study. In our initial RISC-Seq validation studies we set particularly rigorous statistical filters for RISC-enrichment (two-fold change, P<0.0001, false discovery rate<0.01) to avoid false positive values 47. In so doing we knowingly failed to identify many weak mRNA targets; this will not always be the preferred approach. Finally, assessment of RISC-enrichment (i.e. level of RISC-associated mRNA/level of that mRNA in the transcriptome) might theoretically overlook the strongest mRNA targets of highly expressed microRNAs, which may already be highly represented in the non-programmed RISC. Thus, RISC-programming data need to be interpreted in the context of parallel miR-Seq, RNA-Seq, and RISC-Seq data from non-programmed tissues.

The multiple degrees of separation between a microRNA and its induced phenotype

The above discussion suggests how cardiac-directed forced expression of a microRNA can be useful for identifying its mRNA targets. By extension, this approach can also help define the impact of differential mRNA targeting after genetic reprogramming, as in cardiac hypertrophy or heart failure. The conventional experimental transgenic approach overexpresses a factor, defines the resulting phenotype, and then fills in the intervening biological pathway to establish a plausible mechanism. When a microRNA is overexpressed, the underlying assumption is often that it will suppress a specific messenger RNA that encodes a particular protein, and that suppression of the protein produces the phenotype. This linear view, and its relatively modest variations (i.e. some proteins regulate gene expression, etc. providing for feed-forward and feed-back regulation) has conceptual appeal because its simplicity and structure lend it to interrogation by molecular perturbation (Figure 4a). The cardiac literature is replete with manuscripts that follow this general experimental and analytical pattern, and much has been learned over the course of these investigations. Using miR-133a as a well-studied example, six relatively early manuscripts each reported single or small groups of mRNA targets implicated as the causal factor for various observed phenotypes. Validated miR-133a targets include Whsc2 55, Ccnd2 56, Ctgf 57, Casp9 58, HCN4 59, and Hand2 60. Indeed, these mRNAs were also revealed as miR-133a targets in heart by RISC-Seq performed in miR-133a cardiac transgenic mice 47. The point is that conclusions based on an underlying assumption that one miR primarily affects one mRNA whose protein product is largely responsible for an observed phenotype are likely to be significant oversimplifications of the true situation.

Figure 4. Differing conceptual approaches linking a microRNA with its induced phenotype.

Figure 4

Open in a new tab

A. The classic linear approach as applied to microRNAs (miR). The miR of interest suppresses a particular mRNA that encodes a specific protein, silencing of which is responsible for the observed phenotype. Operating under this concept, gain- and loss-of-function manipulations have predictable and discreet effects. B. Alternate conceptual paradigm incorporating parallelism and redundancy at the level of miRs and their many mRNA targets. Direct effects on multiple proteins are shown with potential feedback regulation of microRNA expression, mRNA target expression, and secondary and tertiary post-translational modifications, all of which may integrate to induce the observed phenotype. This complexity produces unpredictable affects that can be detected using global analytical approaches.

The following considerations suggest a more complex interpretation of microRNA actions and support a global/genome-wide approach to their delineation: First, many microRNAs are members of families having several other members with similar sequences; these different family members will have overlapping mRNA targets. For this reason, genetic ablation of one member of a microRNA family will not necessarily have a major effect when other family members remain. miR-133, for which there are three family members, provides an example of functional compensation with single gene ablation that was revealed with multi-gene ablation 56. Second, as noted above, a single microRNA typically targets dozens or more different mRNAs, and in many instances the effects on individual mRNAs and proteins are modest 61. Thus, the aggregate phenotype induced by a given microRNA results from the cumulative effects on all of its targets. Because multiple mRNA targets of a given microRNA or microRNA family tend to cluster within specific functional pathways (such as cell growth, differentiation, motility, or programmed death), this action of microRNAs can be considered as modular with respect to biological processes, rather than specific to one or a few mRNAs. Finally, the direct targets of microRNAs may have multiple degrees of separation from the end-organ phenotype, being a culmination of first, second, and third generation effects. Figure 4b schematically depicts a microRNA signaling pathway that takes into account multiple microRNAs acting simultaneously upon their mRNA targets, multiple mRNA targets being suppressed by a given microRNA, and reflecting likely avenues of regulatory feedback that can produce indirect secondary and tertiary effects. The best characterized cardiac example of indirect microRNA effects relates to myomiR- (miRs-208a, -208b, and -499) directed cardiac myosin isoform switching in cardiac hypertrophy 44, 62. These effects are so striking that antimiR 208a therapy has been show to prevent hypertensive cardiac remodeling 63, and yet myosin heavy chain mRNAs are not direct targets of any myomiR. Thus, the major myomiR action on the heart, regulation of myosin isoforms, is indirect.

An unbiased approach to protein regulation by microRNAs would seem to complement the agnostic techniques of microRNA target identification and transcriptional profiling described above. Indeed, global proteomics have been used to connect molecular mechanism with microRNA-conferred phenotype, with some unexpected results. An important finding of the first in vitro global proteomics analyses of non-cardiac cells was that the vast majority of proteins regulated by microRNAs are not direct micrRNA targets 61. The likely explanations for the disconnect between microRNA targeted mRNA and protein include secondary and tertiary effects noted above, and the inherent bias of conventional proteomics toward the most highly expressed genes, which may not be the direct microRNA targets. Nevertheless, proteomics may be a useful addition to assays of bioinformatically-predicted candidate mRNA targets and steady quantification of their specific protein products when determining the mechanistic basis for microRNA-induced phenotypes.

The pathological potential for genetic microRNA variation

The unique ability of microRNAs to impact multiple effectors within a biological pathway optimally positions them as therapeutic targets. MicroRNA-based therapeutics offer an avenue to deal with the inherent plasticity of biological systems, in which critical responses are characteristically mediated through multiple parallel and redundant pathways. Biological redundancy provides an obvious evolutionary advantage: When the primary biological pathway is under attack by a pathological organism or genetic mutation, the essential response can be retained through induction of alternate mechanisms. For the same reasons, the reaction of an organism to experimental manipulation of a single molecular effector does not represent the normal role of that effector in the response. The stimulus-response is more like that of a water balloon than a linear pathway. When one side is pushed in, redundancy in the rest of the structure/pathway compensates and maintains overall systematic integrity. The ability of a single microRNA to coordinately regulate multiple effectors within a given biological pathway avoids this compensation by functionally overlapping factors. Extending the water balloon metaphor, by simultaneously targeting multiple effectors in a given pathway, microRNAs can be considered to adjust the overall volume of water in the balloon.

A largely overlooked aspect of microRNA biology is the potential impact of naturally-occurring nucleotide sequence variation on microRNA function, i.e. microRNA mutations. It is striking that the nucleotide sequence of mature microRNAs tends to be very highly conserved across species, whereas sequence variability within putative microRNA binding sites in mRNA 3’ UTRs is common 64, 65. This makes intuitive sense: Nucleotide sequence is the primary determinant of microRNA function because it determines the binding energy of a microRNA-mRNA duplex, and therefore the efficiency of mRNA targeting to RISC and resulting mRNA suppression. With rare exceptions 66, and even though mRNA binding sites far outnumber microRNAs, sequence variation within a microRNA binding site will most likely have modest impact because it will affect the binding of only one family of microRNAs to only one binding site in only one target mRNA. By contrast, sequence variation in a mature microRNA has the potential to alter the mRNA targeting profile of that microRNA for any mRNA with a binding site that either loses or gains sequence complementarity as a consequence of the microRNA mutation. Consistent with this paradigm, a human seed-sequence mutation in miR-96 produces heritable hearing loss 67. Recently, we uncovered a non-seed sequence mutation of human miR-499 that alters its mRNA targeting profile and modified the functional and proteomic phenotype induced by miR-499 overexpression in mouse hearts 68. A u to c mutation at nucleotide position 17 altered the pattern of cardiac RISC mRNA enrichment for a sub-set of mRNA targets whose binding sites had imperfect seed sequence complementarity and tended to have appropriately complementary nucleotides corresponding to miR-499 position 17. These findings demonstrate for the first time that a non-seed sequence mutation can alter microRNA function and the consequent organ/organism phenotype. These results support genetic screening for, and functional analysis of, microRNA sequence variants in human disease.

Final thoughts

The ongoing explosion of microRNA research reflects a propitious convergence of biology and technology in an area where nucleotide sequence is the key determinant of function. Next generation sequencing is being used to globally profile microRNA and mRNA expression, to examine microRNA-mRNA interactions in different pathophysiological contexts, and to discover and evaluate the function of microRNA and binding site mutations. Sophisticated proteomics are being applied to assays of protein expression and post-translational modification. Computerized algorithms can be used for in silico modeling of microRNA-mRNA interactions and to understand the structural determinants of biological function. Pathway analysis of micoRNA-mRNA-protein interactions can be applied to results of genome-wide profiling studies to provide unbiased insight into integrated biological function of specific microRNAs. Future efforts will therefore likely have greatest success when they take an agnostic approach, when they integrate multiple techniques, and when they bridge scientific disciplines.

Acknowledgments

Sources of Funding Supported by National Institutes of Health R01 HL59888, HL087871, HL1072726, and HL108943.

Non-standard Abbreviations and Acronyms

CLIP

cross-linking immunoprecipitation

PABP

poly(A) binding protein

RISC

RNA-induced silencing complex, i.e. a microRNA bound to Argonaute 2 protein

RISCome

the population of mRNAs in a RISC, i.e. those mRNAs targeted by present microRNAs

RISC-Seq

identification of microRNA-targeted mRNAs by next-generation sequencing of mRNAs from Argonaute 2 immunoprecipitates

RNA-Seq

quantitative transcriptional profiling by next-generation sequencing of mRNA

UTR

untranslated region

Footnotes

Disclosures The author declares that he has no conflicts of interest relating to this manuscript

References

  • 1.Bartlett J comp. Familiar Quotations 10th ed, rev and enl by Nathan Haskell Dole. Boston: Little, Brown 1919; 2000. Bartleby.com. [Google Scholar]
  • 2.Mendel G. Versuche über Pflanzenhybriden. Verhandlungen des naturforschenden Vereins Brünn. 1866;4:3–47. [Google Scholar]
  • 3.De Vries H. Sur la loi de disjonction des hybrides. CR Acad Sci, Paris. 1900;130:845–848. [Google Scholar]
  • 4.Correns CG. Mendel’s Regel über das Verhalten der Nachkommenschaft der Rassenbastarde. Ber Deut Botan Gessel. 1900;18:158–168. [Google Scholar]
  • 5.Miescher F. Ueber die chemische Zusammensetzung der Eiterzellen. Medizinisch-chemische Untersuchungen. 1871;4:441–460. [Google Scholar]
  • 6.Hoppe-Seyler F. Ueber die chemische Zusammensetzung des Eiters. Medizinisch-chemische Untersuchungen. 1871;4:486–501. [Google Scholar]
  • 7.Avery OT, Macleod CM, McCarty M. Studies on the Chemical Nature of the Substance Inducing Transformation of Pneumococcal Types : Induction of Transformation by a Desoxyribonucleic Acid Fraction Isolated from Pneumococcus Type III. J Exp Med. 1944;79:137–158. doi: 10.1084/jem.79.2.137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Hershey AD, Chase M. Independent functions of viral protein and nucleic acid in growth of bacteriophage. J Gen Physiol. 1952;36:39–56. doi: 10.1085/jgp.36.1.39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Franklin RE, Gosling RG. Molecular configuration in sodium thymonucleate. Nature. 1953;171:740–741. doi: 10.1038/171740a0. [DOI] [PubMed] [Google Scholar]
  • 10.Wilkins MH, Stokes AR, Wilson HR. Molecular structure of deoxypentose nucleic acids. Nature. 1953;171:738–740. doi: 10.1038/171738a0. [DOI] [PubMed] [Google Scholar]
  • 11.Watson JD, Crick FH. Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature. 1953;171:737–738. doi: 10.1038/171737a0. [DOI] [PubMed] [Google Scholar]
  • 12.Hayes B. The Invention of the Genetic Code. Am Sci. 1998;86:8–14. [Google Scholar]
  • 13.Garrod AE. The Incidence of Alkaptonuria: A Study in Chemical Individuality. Lancet. 1902;ii:1616–1602. [Google Scholar]
  • 14.Tanigawa G, Jarcho JA, Kass S, Solomon SD, Vosberg HP, Seidman JG, Seidman CE. A molecular basis for familial hypertrophic cardiomyopathy: an alpha/beta cardiac myosin heavy chain hybrid gene. Cell. 1990;62:991–998. doi: 10.1016/0092-8674(90)90273-h. [DOI] [PubMed] [Google Scholar]
  • 15.Geisterfer-Lowrance AA, Kass S, Tanigawa G, Vosberg HP, McKenna W, Seidman CE, Seidman JG. A molecular basis for familial hypertrophic cardiomyopathy: a beta cardiac myosin heavy chain gene missense mutation. Cell. 1990;62:999–1006. doi: 10.1016/0092-8674(90)90274-i. [DOI] [PubMed] [Google Scholar]
  • 16.Seidman CE, Seidman JG. Identifying sarcomere gene mutations in hypertrophic cardiomyopathy: a personal history. Circ Res. 2011;108:743–750. doi: 10.1161/CIRCRESAHA.110.223834. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Olson EN. Gene regulatory networks in the evolution and development of the heart. Science. 2006;313:1922–1927. doi: 10.1126/science.1132292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Potthoff MJ, Olson EN. MEF2: a central regulator of diverse developmental programs. Development. 2007;134:4131–4140. doi: 10.1242/dev.008367. [DOI] [PubMed] [Google Scholar]
  • 19.Hill JA, Olson EN. Cardiac plasticity. N Engl J Med. 2008;358:1370–1380. doi: 10.1056/NEJMra072139. [DOI] [PubMed] [Google Scholar]
  • 20.Haberland M, Montgomery RL, Olson EN. The many roles of histone deacetylases in development and physiology: implications for disease and therapy. Nat Rev Genet. 2009;10:32–42. doi: 10.1038/nrg2485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Matkovich SJ, Van Booven DJ, Youker KA, Torre-Amione G, Diwan A, Eschenbacher WH, Dorn LE, Watson MA, Margulies KB, Dorn GW., 2nd Reciprocal Regulation of Myocardial microRNAs and Messenger RNA in Human Cardiomyopathy and Reversal of the microRNA Signature by Biomechanical Support. Circulation. 2009;119:1263–1271. doi: 10.1161/CIRCULATIONAHA.108.813576. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Heidecker B, Kittleson MM, Kasper EK, Wittstein IS, Champion HC, Russell SD, Hruban RH, Rodriguez ER, Baughman KL, Hare JM. Transcriptomic biomarkers for the accurate diagnosis of myocarditis. Circulation. 2011;123:1174–1184. doi: 10.1161/CIRCULATIONAHA.110.002857. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Frey N, Katus HA, Olson EN, Hill JA. Hypertrophy of the heart: a new therapeutic target? Circulation. 2004;109:1580–1589. doi: 10.1161/01.CIR.0000120390.68287.BB. [DOI] [PubMed] [Google Scholar]
  • 24.Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, Stange-Thomann N, Stojanovic N, Subramanian A, Wyman D, Rogers J, Sulston J, Ainscough R, Beck S, Bentley D, Burton J, Clee C, Carter N, Coulson A, Deadman R, Deloukas P, Dunham A, Dunham I, Durbin R, French L, Grafham D, Gregory S, Hubbard T, Humphray S, Hunt A, Jones M, Lloyd C, McMurray A, Matthews L, Mercer S, Milne S, Mullikin JC, Mungall A, Plumb R, Ross M, Shownkeen R, Sims S, Waterston RH, Wilson RK, Hillier LW, McPherson JD, Marra MA, Mardis ER, Fulton LA, Chinwalla AT, Pepin KH, Gish WR, Chissoe SL, Wendl MC, Delehaunty KD, Miner TL, Delehaunty A, Kramer JB, Cook LL, Fulton RS, Johnson DL, Minx PJ, Clifton SW, Hawkins T, Branscomb E, Predki P, Richardson P, Wenning S, Slezak T, Doggett N, Cheng JF, Olsen A, Lucas S, Elkin C, Uberbacher E, Frazier M, Gibbs RA, Muzny DM, Scherer SE, Bouck JB, Sodergren EJ, Worley KC, Rives CM, Gorrell JH, Metzker ML, Naylor SL, Kucherlapati RS, Nelson DL, Weinstock GM, Sakaki Y, Fujiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Kawagoe C, Watanabe H, Totoki Y, Taylor T, Weissenbach J, Heilig R, Saurin W, Artiguenave F, Brottier P, Bruls T, Pelletier E, Robert C, Wincker P, Smith DR, Doucette-Stamm L, Rubenfield M, Weinstock K, Lee HM, Dubois J, Rosenthal A, Platzer M, Nyakatura G, Taudien S, Rump A, Yang H, Yu J, Wang J, Huang G, Gu J, Hood L, Rowen L, Madan A, Qin S, Davis RW, Federspiel NA, Abola AP, Proctor MJ, Myers RM, Schmutz J, Dickson M, Grimwood J, Cox DR, Olson MV, Kaul R, Shimizu N, Kawasaki K, Minoshima S, Evans GA, Athanasiou M, Schultz R, Roe BA, Chen F, Pan H, Ramser J, Lehrach H, Reinhardt R, McCombie WR, de la Bastide M, Dedhia N, Blocker H, Hornischer K, Nordsiek G, Agarwala R, Aravind L, Bailey JA, Bateman A, Batzoglou S, Birney E, Bork P, Brown DG, Burge CB, Cerutti L, Chen HC, Church D, Clamp M, Copley RR, Doerks T, Eddy SR, Eichler EE, Furey TS, Galagan J, Gilbert JG, Harmon C, Hayashizaki Y, Haussler D, Hermjakob H, Hokamp K, Jang W, Johnson LS, Jones TA, Kasif S, Kaspryzk A, Kennedy S, Kent WJ, Kitts P, Koonin EV, Korf I, Kulp D, Lancet D, Lowe TM, McLysaght A, Mikkelsen T, Moran JV, Mulder N, Pollara VJ, Ponting CP, Schuler G, Schultz J, Slater G, Smit AF, Stupka E, Szustakowski J, Thierry-Mieg D, Thierry-Mieg J, Wagner L, Wallis J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Yang SP, Yeh RF, Collins F, Guyer MS, Peterson J, Felsenfeld A, Wetterstrand KA, Patrinos A, Morgan MJ, de Jong P, Catanese JJ, Osoegawa K, Shizuya H, Choi S, Chen YJ. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921. doi: 10.1038/35057062. [DOI] [PubMed] [Google Scholar]
  • 25.Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N, Levine AJ, Roberts RJ, Simon M, Slayman C, Hunkapiller M, Bolanos R, Delcher A, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, Hannenhalli S, Kravitz S, Levy S, Mobarry C, Reinert K, Remington K, Abu-Threideh J, Beasley E, Biddick K, Bonazzi V, Brandon R, Cargill M, Chandramouliswaran I, Charlab R, Chaturvedi K, Deng Z, Di FV, Dunn P, Eilbeck K, Evangelista C, Gabrielian AE, Gan W, Ge W, Gong F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke Z, Ketchum KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin X, Lu F, Merkulov GV, Milshina N, Moore HM, Naik AK, Narayan VA, Neelam B, Nusskern D, Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Wang Z, Wang A, Wang X, Wang J, Wei M, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M, Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Zhong W, Zhu S, Zhao S, Gilbert D, Baumhueter S, Spier G, Carter C, Cravchik A, Woodage T, Ali F, An H, Awe A, Baldwin D, Baden H, Barnstead M, Barrow I, Beeson K, Busam D, Carver A, Center A, Cheng ML, Curry L, Danaher S, Davenport L, Desilets R, Dietz S, Dodson K, Doup L, Ferriera S, Garg N, Gluecksmann A, Hart B, Haynes J, Haynes C, Heiner C, Hladun S, Hostin D, Houck J, Howland T, Ibegwam C, Johnson J, Kalush F, Kline L, Koduru S, Love A, Mann F, May D, McCawley S, McIntosh T, McMullen I, Moy M, Moy L, Murphy B, Nelson K, Pfannkoch C, Pratts E, Puri V, Qureshi H, Reardon M, Rodriguez R, Rogers YH, Romblad D, Ruhfel B, Scott R, Sitter C, Smallwood M, Stewart E, Strong R, Suh E, Thomas R, Tint NN, Tse S, Vech C, Wang G, Wetter J, Williams S, Williams M, Windsor S, Winn-Deen E, Wolfe K, Zaveri J, Zaveri K, Abril JF, Guigo R, Campbell MJ, Sjolander KV, Karlak B, Kejariwal A, Mi H, Lazareva B, Hatton T, Narechania A, Diemer K, Muruganujan A, Guo N, Sato S, Bafna V, Istrail S, Lippert R, Schwartz R, Walenz B, Yooseph S, Allen D, Basu A, Baxendale J, Blick L, Caminha M, Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Dahlke C, Mays A, Dombroski M, Donnelly M, Ely D, Esparham S, Fosler C, Gire H, Glanowski S, Glasser K, Glodek A, Gorokhov M, Graham K, Gropman B, Harris M, Heil J, Henderson S, Hoover J, Jennings D, Jordan C, Jordan J, Kasha J, Kagan L, Kraft C, Levitsky A, Lewis M, Liu X, Lopez J, Ma D, Majoros W, McDaniel J, Murphy S, Newman M, Nguyen T, Nguyen N, Nodell M. The sequence of the human genome. Science. 2001;291:1304–1351. doi: 10.1126/science.1058040. [DOI] [PubMed] [Google Scholar]
  • 26.Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, Patterson N, Li H, Zhai W, Fritz MH, Hansen NF, Durand EY, Malaspinas AS, Jensen JD, Marques-Bonet T, Alkan C, Prufer K, Meyer M, Burbano HA, Good JM, Schultz R, Aximu-Petri A, Butthof A, Hober B, Hoffner B, Siegemund M, Weihmann A, Nusbaum C, Lander ES, Russ C, Novod N, Affourtit J, Egholm M, Verna C, Rudan P, Brajkovic D, Kucan Z, Gusic I, Doronichev VB, Golovanova LV, Lalueza-Fox C, de la Rasilla M, Fortea J, Rosas A, Schmitz RW, Johnson PL, Eichler EE, Falush D, Birney E, Mullikin JC, Slatkin M, Nielsen R, Kelso J, Lachmann M, Reich D, Paabo S. A draft sequence of the Neandertal genome. Science. 2010;328:710–722. doi: 10.1126/science.1188021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.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. doi: 10.1016/0092-8674(93)90529-y. [DOI] [PubMed] [Google Scholar]
  • 28.Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE, Mello CC. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature. 1998;391:806–811. doi: 10.1038/35888. [DOI] [PubMed] [Google Scholar]
  • 29.Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000;403:901–906. doi: 10.1038/35002607. [DOI] [PubMed] [Google Scholar]
  • 30.Pasquinelli AE, Reinhart BJ, Slack F, Martindale MQ, Kuroda MI, Maller B, Hayward DC, Ball EE, Degnan B, Muller P, Spring J, Srinivasan A, Fishman M, Finnerty J, Corbo J, Levine M, Leahy P, Davidson E, Ruvkun G. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature. 2000;408:86–89. doi: 10.1038/35040556. [DOI] [PubMed] [Google Scholar]
  • 31.Lee RC, Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Science. 2001;294:862–864. doi: 10.1126/science.1065329. [DOI] [PubMed] [Google Scholar]
  • 32.Lau NC, Lim LP, Weinstein EG, Bartel DP. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science. 2001;294:858–862. doi: 10.1126/science.1065062. [DOI] [PubMed] [Google Scholar]
  • 33.Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T. Identification of novel genes coding for small expressed RNAs. Science. 2001;294:853–858. doi: 10.1126/science.1064921. [DOI] [PubMed] [Google Scholar]
  • 34.Rivas FV, Tolia NH, Song JJ, Aragon JP, Liu J, Hannon GJ, Joshua-Tor L. Purified Argonaute2 and an siRNA form recombinant human RISC. Nat Struct Mol Biol. 2005;12:340–349. doi: 10.1038/nsmb918. [DOI] [PubMed] [Google Scholar]
  • 35.Fabian MR, Mathonnet G, Sundermeier T, Mathys H, Zipprich JT, Svitkin YV, Rivas F, Jinek M, Wohlschlegel J, Doudna JA, Chen CY, Shyu AB, Yates JR, 3rd, Hannon GJ, Filipowicz W, Duchaine TF, Sonenberg N. Mammalian miRNA RISC recruits CAF1 and PABP to affect PABP-dependent deadenylation. Mol Cell. 2009;35:868–880. doi: 10.1016/j.molcel.2009.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Fukaya T, Tomari Y. PABP is not essential for microRNA-mediated translational repression and deadenylation in vitro. EMBO J. 2011 doi: 10.1038/emboj.2011.426. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Liu J, Rivas FV, Wohlschlegel J, Yates JR, 3rd, Parker R, Hannon GJ. A role for the P-body component GW182 in microRNA function. Nat Cell Biol. 2005;7:1261–1266. doi: 10.1038/ncb1333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Limbird LE. Cell Surface Receptors: A short course on theory and methods. 3. Springer; 2004. [Google Scholar]
  • 39.Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA. 2004;10:1507–1517. doi: 10.1261/rna.5248604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.van Rooij E, Sutherland LB, Liu N, Williams AH, McAnally J, Gerard RD, Richardson JA, Olson EN. A signature pattern of stress-responsive microRNAs that can evoke cardiac hypertrophy and heart failure. Proc Natl Acad Sci USA. 2006;103:18255–18260. doi: 10.1073/pnas.0608791103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Thum T, Galuppo P, Wolf C, Fiedler J, Kneitz S, vanLaake LW, Doevendans PA, Mummery CL, Borlak J, Haverich A, Gross C, Engelhardt S, Ertl G, Bauersachs J. MicroRNAs in the human heart: A clue to fetal gene reprogramming in heart failure. Circulation. 2007;116:258–267. doi: 10.1161/CIRCULATIONAHA.107.687947. [DOI] [PubMed] [Google Scholar]
  • 42.Margulies KB, Matiwala S, Cornejo C, Olsen H, Craven WA, Bednarik D. Mixed messages: Transcription patterns in failing and recovering human myocardium. Circ Res. 2005;96:592–599. doi: 10.1161/01.RES.0000159390.03503.c3. [DOI] [PubMed] [Google Scholar]
  • 43.Barth AS, Kumordzie A, Frangakis C, Margulies KB, Cappola TP, Tomaselli GF. Reciprocal transcriptional regulation of metabolic and signaling pathways correlates with disease severity in heart failure. Circ: Cardiovasc Genet. 2011;4:475–483. doi: 10.1161/CIRCGENETICS.110.957571. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.van Rooij E, Quiat D, Johnson BA, Sutherland LB, Qi X, Richardson JA, Kelm RJ, Jr, Olson EN. A family of microRNAs encoded by myosin genes governs myosin expression and muscle performance. Dev Cell. 2009;17:662–673. doi: 10.1016/j.devcel.2009.10.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120:15–20. doi: 10.1016/j.cell.2004.12.035. [DOI] [PubMed] [Google Scholar]
  • 46.Matkovich SJ, Zhang Y, Van Booven DJ, Dorn GW., 2nd Deep mRNA sequencing for in vivo functional analysis of cardiac transcriptional regulators: application to Galphaq. Circ Res. 2010;106:1459–1467. doi: 10.1161/CIRCRESAHA.110.217513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Matkovich SJ, Van Booven DJ, Eschenbacher WH, Dorn GW., 2nd RISC RNA sequencing for context-specific identification of in vivo microRNA targets. Circ Res. 2011;108:18–26. doi: 10.1161/CIRCRESAHA.110.233528. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Morin RD, O’Connor MD, Griffith M, Kuchenbauer F, Delaney A, Prabhu AL, Zhao Y, McDonald H, Zeng T, Hirst M, Eaves CJ, Marra MA. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 2008;18:610–621. doi: 10.1101/gr.7179508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Buermans HP, Ariyurek Y, van Ommen G, den Dunnen JT, t Hoen PA. New methods for next generation sequencing based microRNA expression profiling. BMC genomics. 2010;11:716. doi: 10.1186/1471-2164-11-716. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Dorn GW., 2nd MicroRNAs: Redefining Mechanisms in Cardiac Disease. J Cardiovasc Pharmacol. 2010;56:589–595. doi: 10.1097/FJC.0b013e3181f8d173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Karginov FV, Conaco C, Xuan Z, Schmidt BH, Parker JS, Mandel G, Hannon GJ. A biochemical approach to identifying microRNA targets. Proc Natl Acad Sci USA. 2007;104:19291–19296. doi: 10.1073/pnas.0709971104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Chi SW, Zang JB, Mele A, Darnell RB. Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. 2009;460:479–486. doi: 10.1038/nature08170. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M, Jr, Jungkamp AC, Munschauer M, Ulrich A, Wardle GS, Dewell S, Zavolan M, Tuschl T. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell. 2010;141:129–141. doi: 10.1016/j.cell.2010.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Wen J, Parker BJ, Jacobsen A, Krogh A. MicroRNA transfection and AGO-bound CLIP-seq data sets reveal distinct determinants of miRNA action. RNA. 2011;17:820–834. doi: 10.1261/rna.2387911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Care A, Catalucci D, Felicetti F, Bonci D, Addario A, Gallo P, Bang ML, Segnalini P, Gu Y, Dalton ND, Elia L, Latronico MV, Hoydal M, Autore C, Russo MA, Dorn GW, 2nd, Ellingsen O, Ruiz-Lozano P, Peterson KL, Croce CM, Peschle C, Condorelli G. MicroRNA-133 controls cardiac hypertrophy. Nat Med. 2007;13:613–618. doi: 10.1038/nm1582. [DOI] [PubMed] [Google Scholar]
  • 56.Liu N, Bezprozvannaya S, Williams AH, Qi X, Richardson JA, Bassel-Duby R, Olson EN. microRNA-133a regulates cardiomyocyte proliferation and suppresses smooth muscle gene expression in the heart. Genes Dev. 2008;22:3242–3254. doi: 10.1101/gad.1738708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Duisters RF, Tijsen AJ, Schroen B, Leenders JJ, Lentink V, van dMI, Herias V, van Leeuwen RE, Schellings MW, Barenbrug P, Maessen JG, Heymans S, Pinto YM, Creemers EE. miR-133 and miR-30 regulate connective tissue growth factor: implications for a role of microRNAs in myocardial matrix remodeling. Circ Res. 2009;104:170–178. doi: 10.1161/CIRCRESAHA.108.182535. [DOI] [PubMed] [Google Scholar]
  • 58.Xu C, Lu Y, Pan Z, Chu W, Luo X, Lin H, Xiao J, Shan H, Wang Z, Yang B. The muscle-specific microRNAs miR-1 and miR-133 produce opposing effects on apoptosis by targeting HSP60, HSP70 and caspase-9 in cardiomyocytes. J Cell Sci. 2007;120:3045–3052. doi: 10.1242/jcs.010728. [DOI] [PubMed] [Google Scholar]
  • 59.Luo X, Lin H, Pan Z, Xiao J, Zhang Y, Lu Y, Yang B, Wang Z. Down-regulation of miR-1/miR-133 contributes to re-expression of pacemaker channel genes HCN2 and HCN4 in hypertrophic heart. J Biol Chem. 2008;283:20045–20052. doi: 10.1074/jbc.M801035200. [DOI] [PubMed] [Google Scholar]
  • 60.Vo NK, Dalton RP, Liu N, Olson EN, Goodman RH. Affinity purification of microRNA-133a with the cardiac transcription factor, Hand2. Proc Natl Acad Sci USA. 2010;107:19231–19236. doi: 10.1073/pnas.1013162107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP. The impact of microRNAs on protein output. Nature. 2008;455:64–71. doi: 10.1038/nature07242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.van Rooij E, Sutherland LB, Qi X, Richardson JA, Hill J, Olson EN. Control of stress-dependent cardiac growth and gene expression by a microRNA. Science. 2007;316:575–579. doi: 10.1126/science.1139089. [DOI] [PubMed] [Google Scholar]
  • 63.Montgomery RL, Hullinger TG, Semus HM, Dickinson BA, Seto AG, Lynch JM, Stack C, Latimer PA, Olson EN, van Rooij E. Therapeutic inhibition of miR-208a improves cardiac function and survival during heart failure. Circulation. 2011;124:1537–1547. doi: 10.1161/CIRCULATIONAHA.111.030932. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Chen K, Rajewsky N. Natural selection on human microRNA binding sites inferred from SNP data. Nat Genet. 2006;38:1452–1456. doi: 10.1038/ng1910. [DOI] [PubMed] [Google Scholar]
  • 65.Saunders MA, Liang H, Li WH. Human polymorphism at microRNAs and microRNA target sites. Proc Natl Acad Sci USA. 2007;104:3300–3305. doi: 10.1073/pnas.0611347104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Brest P, Lapaquette P, Souidi M, Lebrigand K, Cesaro A, Vouret-Craviari V, Mari B, Barbry P, Mosnier JF, Hebuterne X, Harel-Bellan A, Mograbi B, Darfeuille-Michaud A, Hofman P. A synonymous variant in IRGM alters a binding site for miR-196 and causes deregulation of IRGM-dependent xenophagy in Crohn’s disease. Nat Genet. 2011;43:242–245. doi: 10.1038/ng.762. [DOI] [PubMed] [Google Scholar]
  • 67.Mencia A, Modamio-Hoybjor S, Redshaw N, Morin M, Mayo-Merino F, Olavarrieta L, Aguirre LA, del Castillo I, Steel KP, Dalmay T, Moreno F, Moreno-Pelayo MA. Mutations in the seed region of human miR-96 are responsible for nonsyndromic progressive hearing loss. Nat Genet. 2009;41:609–613. doi: 10.1038/ng.355. [DOI] [PubMed] [Google Scholar]
  • 68.Dorn GW, 2nd, Matkovich SJ, Eschenbacher WH, Zhang Y, Hu Y, Dorn LE. A human 3’ miR-499 mutation alters cardiac mRNA targeting and function. Circ Res. 2011 doi: 10.1161/CIRCRESAHA.111.260752. Submitted. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES