Enhancing non-coding RNA information content with ADAR editing

Neuroscience Letters 466 (2009) 89–98

Contents lists available at ScienceDirect

Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet

Mini-Review

Enhancing non-coding RNA information content with ADAR editing Georges St. Laurent III a,b , Yiannis A. Savva a , Robert Reenan a,∗ a b

Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI 02912, USA Immunovirology – Biogenisis Group, University of Antioquia, A.A. 1226 Medellin, Colombia

a r t i c l e

i n f o

Article history: Received 1 July 2009 Received in revised form 2 September 2009 Accepted 5 September 2009 Keywords: RNA editing ADAR Nervous system transcriptome Non-coding RNA (ncRNA) ADAR activity during stress and inflammation

a b s t r a c t The depth and complexity of the non-coding transcriptome in nervous system tissues provides a rich substrate for adenosine de-amination acting on RNA (ADAR). Non-coding RNAs (ncRNAs) serve diverse regulatory and computational functions, coupling signal flow from the environment to evolutionarily coded analog and digital information elements within the transcriptome. We present a perspective of ADARs interaction with the non-coding transcriptome as a computational matrix, enhancing the information processing power of the cell, adding flexibility, rapid response, and fine tuning to critical pathways. Dramatic increases in ADAR activity during stress response and inflammation result in powerful information processing events that change the functional state of the cell. This review examines the pathways and mechanisms of ADAR interaction with the non-coding transcriptome, and their functional consequences for information processing in nervous system tissues. © 2009 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Cells of the mammalian nervous system receive, process, and distribute vast amounts of information, with high fidelity, sensitivity, and resolution. Synaptic regions, in particular, appear to challenge the classic thermodynamic limits of electrophysiological information flow with the sheer density of their information content. Yet such nearly infinite computing power is orchestrated through the actions of very finite numbers of proteins deployed into the molecular machineries of cognition. To reach beyond these limits while respecting the laws of physics and information theory, the nervous system takes advantage of evolutionary innovation in the transcriptome. Traditional views for articulating the form and functionality of the nervous system adhere to the primacy of protein, as defined by the Central Dogma of Molecular Biology [17]. However, recent discoveries revealing tens of thousands of non-coding (nc) RNAs and transcripts of unknown function in metazoan genomes [14,23,37], together with their extensive and specific expression in defined brain regions [59,69], articulate a new substrate for nervous system information coding and processing. This emerging ncRNA landscape provides a computational matrix able to increase information densities within the spatially and thermodynamically limited real estate of the brain [82].

∗ Corresponding author. Tel.: +1 401 863 6353. E-mail addresses: robert [email protected], [email protected] (R. Reenan). 0304-3940/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.neulet.2009.09.009

The diverse portfolio of non-coding transcripts expressed throughout the brain could hardly avoid the adenosine deamination acting on RNA (ADAR) editing system, considering that most ncRNAs contain secondary structure as mature products, or proceed through double-stranded (ds) RNA intermediates. Mammalian genomes code for three ADAR enzymes, ADAR1, ADAR2, and ADAR3, whose catalytic activity de-aminates adenosine residues located in certain double-stranded regions of RNA to produce an inosine, which is recognized by protein machineries as a guanosine. Recent reviews present the many aspects of ADAR biochemistry, genetics, and biology that now serve as the basis to study the impact of this intriguing activity on cellular and organismal behavior [34,54,58]. Nervous system tissues prominently express ADARs in all metazoans, resulting in a repertoire of alterations in mRNAs coding for important proteins in neuronal transmission and synaptic plasticity [57,29]. In order to achieve optimal levels of function, key components of electrical and chemical signaling machineries may depend on the precise modulation of ADAR editing activity in response to cell identity, fluctuations in the micro-region of synapses, or changes in the environment of the neuron. For example, the Drosophila Shaker potassium channel exhibits striking tissue specific expression of differentially edited isoforms, epistatic differences in biophysical channel phenotypes, and coupling between editing and the expression distributions of a number of the isoforms [33]. ADARs recognize minute differences in secondary structures among similar RNAs [73], permitting the elaboration of a codable language of signaling and recognition between the ADARs and the transcriptome. A partial understanding of how ADAR chooses its targets from millions of RNA structures in the transcriptome has emerged, using

90

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

comparative genomics [29], bioinformatics [16], and sequencing of ADAR targets in the transcriptome [50]. An examination of the wider non-coding transcriptome’s secondary structure landscape reveals a profile similar to that of the coding transcriptome, with an abundance of the same RNA structural motifs that promote binding and de-amination by ADAR enzymes. One class of RNAs with a particularly favorable structural motif for ADAR, ALU containing RNAs, has already been shown to contain thousands of editing sites, distributed fairly equally among coding and non-coding members [4,39,47]. Thus, potential ADAR targets abound by the thousands in the non-coding transcriptomes of nervous system cell types, leading to a map expansion for the significance of ADAR action in the brain. These developments place ADAR in the path of interaction with thousands of previously unknown members of the neuronal transcriptome, potentially opening new dimensions of nervous system information flow. We argue that this map expansion will include a widespread role for the ADAR–ncRNA interface in the acquisition, computational processing, and distribution of information to macromolecular networks throughout the nervous system. This review explores these concepts, including the mechanisms of interactions between brain enriched ADARs and the ncRNA transcriptome, and how these interactions could serve as a computational matrix to enhance information processing by the organism.

2. The shapes and contours of an ADAR interactive transcriptome The results of the Human Genome project [43,88] disappointed the neuroscience community because the surprisingly low estimates of the number of human coding genes could not explain the robustly expanding complexity of mammalian nervous systems compared to so-called “lower” organisms with similar gene numbers, but far simpler brains. Sequencing data indicated that less than 2% of the human genome coded for mRNAs, yielding a total number of proteins uncomfortably close to that of flies or even worms. Yet, even as these large sequencing datasets were published, work from C. elegans had begun to uncover what lay beneath the tip of the iceberg [24,64]. Micro-RNAs (miRNAs) became the first of many families of ncRNAs whose functions center on information processing, regulation of gene expression, and modification of protein interaction pathways. Later work has shown extensive transcription across large majorities of metazoan genomes [26,37]: a transcriptomic “dark matter” [35] composed of complex patterns of nested, overlapping, sense, and antisense transcripts from large numbers of transcriptional start sites. Transcriptional machineries produce a plethora of different types of RNAs, which often proceed through extensive processing before acquiring their final form. For example, at least 40% of coding genes express small RNAs from their 5 regions, called promoter associated (PASRs) and their 3 regions, called termini associated RNAs (TASRs) [26]. These fascinating RNAs appear to derive from much longer transcripts from the same loci, which are cleaved and then capped, yielding a novel short RNA with a differentiating cap structure. The nervous system expresses many families of ncRNAs that participate in spatio-temporal regulation of complex functions, including miRNAs, natural antisense transcripts (NATs), small nucleolar RNAs (snoRNAs), and endogenous small interfering RNAs (esiRNAs), all of which have been extensively reviewed [57,82]. Other ncRNAs, such as human accelerated RNA (HAR-1) [69], still have little or no annotated function, yet exhibit complex temporal or spatial expression patterns suggestive of regulatory or computational roles [59].

2.1. The structure of transcriptome information content One useful approach for understanding the information content of the non-coding transcriptome focuses on the shape space of RNA structures in the cellular transcriptome, and its variation throughout the different micro-regions of the cell. A given cellular transcriptome contains millions of these RNA secondary structure elements, which cluster into thousands of groups, and localize into different regions of RNA shape space. They first form during transcription, as a result of enthalpy driven folding of the elongating RNA strand. This process enhances the acquisition of certain RNA binding proteins along the nascent transcript, while at the same time inhibiting others. For a variety of reasons, RNAs tend to fold with greater amounts of heterogeneous local secondary structures than do proteins [45], resulting in a molecule with many relatively firm hairpins and other local secondary structures, and yet a somewhat more flexible tertiary structure. As each secondary structure forms along the length of the nascent transcript, it presents an affinity profile for some subset of the RNA binding proteome, essentially information content coded into the recognizable shape of secondary structure. The final result of the co-transcriptional folding process can contain a large amount of information, usually augmented by the coordinated presence of dozens or hundreds of RNA binding proteins that associate with the transcript. After termination of transcription, RNAs advance into post-transcriptional processing pathways, subcellular localization, and finally functional activity. These events depend on the population of RNA binding proteins in each cellular compartment, as well as those that have already bound to the RNA [74]. Several recent publications have explored the compound language of information content in the non-coding transcriptome, focusing on the usefulness of combining secondary structure (analog information) and primary sequence elements (digital information) in transcripts destined to regulate multiple layers of gene expression [51,77]. These features bestow an unusually high level of flexibility on ncRNAs, allowing them to function as environmental sensors, as signaling partners in protein interaction networks, and at the same time as elements that precisely address other nucleic acids with high fidelity through sequence complementarity interactions. When functionally coupled, analog and digital information bestow increased throughput and enhanced regulatory capabilities to molecular machineries, sometimes in unusual and unexpected ways that would be unavailable to either coding modality alone [51,77]. For example, the formation of secondary structures and their subsequent analog interaction with proteins can sequester important digital information elements such as miRNA target sites in a temporally or spatially coded manner [52]. As a result, ncRNAs can enhance the shapes and contours of information processing pathways, generating a high level of codable semantic complexity at the interface between proteome and transcriptome [53], in a thermodynamically parsimonious fashion. For ncRNAs to function in an organism, they must interact with at least some node of the proteome network. Proteomic databases currently recognize approximately 350 RNA binding proteins in the human genome [8], but this number could be far higher. Considering the preponderance of functional ncRNAs in the genome, many cellular proteins from diverse pathways likely exhibit RNA interaction affinity, a more widespread phenomenon than currently recognized. Given RNAs versatility as both a structure and a function, the potential of thousands of RNAs evolved to bind proteins (whose own functions may be far removed from RNA binding) has not been examined. The unique language of transcriptome information content may provide a rich source for an ongoing flow of regulatory signals to many nodes within cellular protein networks. In concert with an RNAs secondary structure (analog) information content, its neighboring digital sequence motifs can further

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

increase the resolution of its encounters with the protein landscape, or promote hybridization with other nucleic acids [53]. For example, the Lin28 protein plays a previously known role in stem cell differentiation by binding to and preventing further maturation of the let-7 pri-miRNA [75,89]. Studies showed that lin-28 associates with the stem loop region of the pri-miRNA, but not the stem loops of other pri-miRNAs [62]. The data suggests that the protein first recognizes the analog information contained in the stem loop structure of the pri-miRNA, and then once associated with the RNA at lower affinity, relies on the close physical approximation to query the digital information in several nucleotides within the 3 region of the stem. Since these nucleotides do not base pair in the native RNA, they are free to rotate and conform to binding pockets available within the critical region of lin-28. As this example illustrates, a ncRNA can contain both analog and digital information, with a high level of evolutionary coding plasticity, which it can use in a combinatorial and coordinated fashion to achieve physiologically significant interactions with proteins and nucleic acids [57,82]. Primary sequence elements can contribute to their information content, either to enhance the distinctions between associating RNA binding proteins, to produce affinity with other nucleic acids, or to produce memory effects. 3. ADAR’s machinery of transcriptome recognition drives information processing Within a cell’s proteome, hundreds or even thousands of distinct RNA binding regions compete for RNA epitopes as they form during transcription, processing, or transport. The behavior of each family of RNA binding domains depends on the modulation of surrounding conditions, including a constant influx of changes from the environment, signaling events such as protein phosphorylation, and statistical distributions of interactions at the RNA–protein interface [20]. Overlapping affinity profiles of RNA binding proteins create competition in vivo for their preferred RNA elements. The

91

RNA–protein interactions that occur in vivo result from the intersection of this competition with the population of RNA shapes in the given subcellular compartment. In this regard, recent evidence from Ingleby et al. [33] implies that certain cellular RNA binding proteins differentially expressed across tissues can change ADAR editing profiles. In this complex environment, ADAR proteins work to recognize the shape modulation of the transcriptome and channel that modulation into information flows destined to regulate cellular systems and behavior (see Fig. 1). The initiation of the editing process occurs with the recognition of suitable target structures in the transcriptome by a variable number of double-stranded RNA binding motifs (dsRBM), the core RNA affinity component of the ADAR molecular machinery [86]. These polypeptide modules of approximately 70 amino acids include the characteristic ␣–␤–␤–␤–␣ folding pattern and some 22 or so conserved amino acid positions that define an affinity for three successive grooves along the length of a dsRNA double helix [12,84]. Yet substantial variation exists within these structural boundaries that define the motif: between species, between protein families, and even within individual proteins. These variations bestow additional qualities of discernment for RNA structures. Recent evidence indicates that these motifs comprise a finely tuned and versatile family, members of which have received evolutionary programming to sense small differences among similar RNA secondary structures, and even perhaps recognize primary structure nuances within or near the target secondary structure [12]. ADARs ability to make subtle distinctions between populations of similar RNA structures permits it to acquire and process vital physiological information at high resolution. Once ADAR has located a high affinity binding site among the many potential RNA secondary structures in the transcriptome, a further set of information containing transactions occurs to establish the precise level of editing at that site. Evidence suggests that these two events are largely independent [18,36]. Within an individual ADAR isoform, differences between its multiple dsRBMs bestow affinity refinements

Fig. 1. ADAR editing modulates the phase state of the non-coding transcriptome. External signals such as stress and sensory information produce diverse changes in transcriptome secondary structure (analog information content). Here these changes are reflected as a perturbed landscape. ADAR, itself induced by some types of stress, detects these changes and produces changes in digital and analog information, decisively changing the transcriptome landscape. The re-patterned transcriptome landscape can effect major downstream reprogramming of molecular machineries involved in multiple levels of cellular regulation, from epigenetic signals and chromatin state, to stress response, and pathways of small RNAs.

92

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

that determine which RNA structures will interest the protein. For example, Stefl et al. recently compared the structures of the two dsRBMs in the ADAR2 enzyme to find subtle but critical variations in their affinities for RNA [83]. These features contribute to the ability of the ADAR system to make distinctions between candidate RNAs with extraordinary accuracy, perhaps pushing against the limits normally associated with thermodynamic noise [41]. In fact, the ADAR system occasionally demonstrates this extraordinary accuracy and precision. In the Drosophila clade for example, sites exist with editing levels of precisely 5%, conserved across various species, spanning 85 million years of evolutionary time [29]. Additional refinements to ADAR site selectivity involve the wide variety of available isoforms combined with the combinatorial potential of dimerization. Extensive alternative splicing of ADAR transcripts naturally will contribute to spatial and temporal heterogeneity of editing profiles. Furthermore, ADAR dimerization [87] opens the possibility of dynamic modulation of substrate affinity through the combinatorial heterodimerization of alternatively spliced isoforms of the enzyme. Changes in intracellular metabolic conditions provide an additional source of information to feed into the ADAR editing computational matrix. For example, ADAR2 requires a molecule of inositol hexaphosphate (IP6) bound at its active site for editing activity [55]. In the nervous system, IP6 increases in response to serotonin binding to its receptor 5HT2C, whose transcript, in turn, is an important editing target of ADAR2 (see below). In other microenvironments, cellular stress increases IP6 levels, potentially stimulating increased levels of ADAR2 activity, and new choices of transcripts edited in response to stress. In addition to its highly selective editing mode described above, ADAR appears to function in another more promiscuous mode [70]. In this mode ADAR creates many edits along a target RNA, compared to the sparse and selective edits found in traditional ADAR targets. While it appears that longer, more complete RNA duplexes catalyze the more promiscuous mode of ADAR editing, other determinants may influence this activity. Promiscuously edited target RNAs open intriguing additional downstream information processing pathways (see below). The distinctive features of the ADAR family form a system of signal transduction and biological computation, implemented through its affinity profiles for RNA information coding elements. The emerging picture departs somewhat from the classic biochemistry of macromolecular interactions. The ADAR protein–ncRNA interactome in many ways resembles the boundary value problems of wave mechanics. ADAR recognition of RNA appears to depend on widely occurring but highly heterogeneous secondary structure elements in the transcriptome. The affinity of ADAR for these secondary structures likely varies over many orders of magnitude, establishing an in vivo gradient of ADAR binding to RNAs. At the same time, a variety of properties in cellular micro-regions affect RNA secondary structures. Changes in these properties occur both as waves passing through the aqueous environment, such as the highly articulated Ca2+ waves [27,42] or mitochondrial signaling waves [3], and as events occurring in local scaffolding structures, such as MAPK phosphorylation and microtubule assembly/disassembly. The net result may achieve an integration of wave processing with digital processing, transduced by the ADAR proteome as its members sense the changing transcriptome. 4. ADAR’s impact on the nervous system non-coding transcriptome Perhaps no other tissue matches the diversity, heterogeneity, and complexity of the non-coding transcriptome expressed in the nervous system. Adjacent cells in local micro-regions, with otherwise identical morphological features can express highly

divergent levels of ncRNAs. While precise characterization awaits deep sequencing at the single cell level, Nelson et al. have proposed redefining nervous system cell types using cell specific transcriptome signatures [61]. Non-coding transcriptomes likely contribute to the functional heterogeneity observed in morphologically similar cells, and to the multilayer regulation of complex processes such as neuronal differentiation [75], and synaptic plasticity [10]. Perhaps not coincidentally, nervous systems tissues also consistently demonstrate the highest levels of ADAR expression. Considering the biochemical reaction kinetics associated with the high concentrations of enzyme and substrate in these tissues, interesting patterns of ncRNA editing likely result. Important candidate families of ncRNAs provide exciting potential targets for ADAR editing. The Mattick laboratory recently analyzed the high throughput in situ hybridization (ISH) dataset created by the Allen Brain Institute [44] by using a Bioinformatics pipeline to identify long ncRNAs [59]. This family of ncRNAs features well defined promoter structures, splicing, 5 cap structures, and polyadenylation, making them structurally similar to coding mRNAs, and attractive candidates for ADAR editing. Likewise, it appears that the genomic loci of these ncRNAs contain similar concentrations of ALU elements to coding loci, increasing yet further their likelihood as targets of ADAR [46]. The Mattick group serendipitously identified a total of 1328 of the approximately 20,000 probes used by the Allan Brain Atlas as corresponding to long ncRNAs. 849 of these RNAs demonstrated expression levels above background in mouse brain. 349 of them contained conserved secondary structures, suggesting widespread evolutionarily coded analog information content in these ncRNAs. Striking visualizations included many examples of tissue and cell specific expression, as well as intriguing patterns of subcellular localization. Both the dynamic range as well as the regional variation of expression levels for the ncRNAs significantly exceeded those of the coding RNAs, suggesting highly articulated specific expression as a consequence of their functional biological roles. Using the dataset to make genome-wide calculations, the Mattick group concluded that mammalian brain expresses at least 20,000 long ncRNAs, thus providing a large set of previously uncharacterized potential targets for ADAR editing [57]. Insights for understanding the biological consequences of editing in long ncRNAs come from a number of sources. In the serotonin receptor pre-mRNA (5HT2CR), ADAR editing favorably influences the choice of splice sites required to produce a complete mRNA, and consequently a functional version of the serotonin receptor. The small nuclear RNA (snoRNA) HBII-52 independently promotes the correct exon inclusion pattern, by a mechanism that requires a sense–antisense interaction between a conserved region in HBII-52 and the region containing the ADAR target sites in the Serotonin receptor pre-mRNA [40]. In another case, the long natural antisense (NAT) RNA to the BACE1 mRNA, called BACE1-AS, harbors a conserved region of sequence homology to its coding counterpart. Normally sequestered in the nucleus, BACE1-AS traffics to the cytoplasm within minutes of neuronal stress. Once in the cytoplasm it hybridizes with and stabilizes the translation of the BACE1 mRNA, thereby rapidly upregulating BACE1 expression [21]. ADAR editing also drives RNA subcellular localization. For example, the CTN RNA, transcribed from an alternate promoter of the cationic amino acid transporter (CAT) gene, associates with the p54nrb RNA binding protein through multiple edited sites in its 3 UTR, promoting its nuclear retention until released by stress such as interferon (IFN) treatment [71]. Several proteins such as Vigilin [90], P54nrb [68,92], Matrin 3 [19], and PSF [81], exhibit high affinity for edited RNAs such as CTN. Furthermore, vigilin appears to mediate ADAR triggered changes not only in RNA subcellular localization, but also in heterochromatin regulation. The Drosophila homologue of Vigilin, DDP1, localizes to heterochromatin, and is

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

essential for heterochromatic gene silencing in flies [32]. Vigilin associates with RNA Helicase A, Ku70, Ku86, and DNA–PKcs. The latter phosphorylates a number of proteins, in an RNA-dependant fashion, including the heterochromatin forming protein, HP1. These relationships indicate that the resulting vigilin–DNA–PKcs protein complex plays a role in the formation or maintenance of heterochromatin, and likely depends on edited RNAs for information flow critical to the correct computations required for this process [90] (see Fig. 2). Evidence from the other side of the RNA–protein interface provides additional support for a broad interpretation of ADAR mediated signaling. A rapidly expanding body of evidence now demonstrates editing in a variety of ncRNAs, and suggests that they are not isolated events [33]. For example, Nishikura and colleagues recently surveyed the human brain transcriptome for editing events in miRNAs, and found that editing occurs in approximately 16% of miRNAs expressed in the brain [38]. Work by several groups found widespread editing concentrated in the ALU containing RNAs of the human transcriptome [4,39,47,48]. The evolutionary expansion of ALU elements to over a million copies resulted in a major shift in genome architecture that has facilitated the formation of new exons, the inclusion of exons, and the alternative splicing of existing exons, compared with lower mammals [51]. The ALU connection to alternative splicing likely means a role for ADAR in the splicing choices of many transcripts both coding and non-coding [46]. Furthermore, since ALU elements more often locate to the non-coding 3 UTRs of human coding transcripts, edit-

93

ing in these regions may affect trafficking and translation of ALU containing mRNAs by including, eliminating, or modifying miRNA target sites in the 3 UTR. In addition, recent observations of large increases in repeat RNA expression (including ALUs) in many tissues in response to stress [22,28,78] provides a further intriguing example of how ADAR edited changes in the transcriptome may impact cellular information processing. Since analog information behaves as a continuous variable, RNA processing proteins can compete for RNA secondary structures based on their affinity profiles and the relative populations of RNA secondary structures. ADAR appears to compete with the pri-miRNA processing protein Drosha over hairpin structures in pri-miRNA transcripts, resulting in a different pathway for those RNAs more readily recognized by ADAR. Nishikura and colleagues have shown that ADAR1 p150, itself an IFN [67] and inflammationinduced [91] alternatively spliced isoform of ADAR1, edits miR-134 at relatively high levels which could increase during inflammation or increased temperature [7,63]. In some cases edited pri-miRNAs flow into degradation pathways mediated by Tudor-SN [49,77], while in other cases they may produce an altered miRNA with affinity for a different set of targets [63], potentially useful in a stress response. Thus, from both sides of the RNA–protein interaction machinery, coinciding evidence points to a systemic involvement of RNA editing of non-coding transcripts in cellular information processing and regulation of gene expression. While the systems level logic of this involvement remains obscure, the ability of ADAR to modify the

Fig. 2. Pathways of edited ncRNAs and their downstream regulation of cellular pathways. ADAR editing occurs in many different families of ncRNAs, with heterogeneous downstream consequences. ADAR promiscuously edits a region of the CTN RNA, resulting in its association with p54nrb and nuclear localization. Stress triggers cleavage of the edited region, and rapid transport to the cytoplasm for translation of the cationic amino acid transporter protein (CAT). Long ds RNAs derived from transcription of repeat sequences result in promiscuous editing, association with Vigilin, and the formation of a complex with DNA-PKcs. This complex likely drives downstream formation of heterochromatin, with broad regulatory consequences. esiRNAs and miRNAs also exhibit evidence of editing, which can result in downstream changes in target recognition, changes in subcellular localization, or increased rates of degradation. Changes in computational processing depend on whether analog or digital information elements change after the editing event.

94

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

analog and digital information content of ncRNAs will play a significant role in the overall system [5,90]. These examples and others increasingly support a functional theme for the coupling and interleaving of analog and digital information content within ncRNAs, both of which can be modified, erased or rewritten by ADAR editing, in a stress dependent, signal dependent, or temporo-spatially dependent fashion, to effect changes in downstream regulatory networks. With so many mechanisms of action emerging for ncRNAs, ADAR editing of these molecules likely impacts downstream information processing networks at multiple nodes. In effect, wherever digital or analog information elements along a ncRNA transcript overlaps an ADAR editing site, however transient, ADAR can change the course of information processing downstream of that network node. Editing of these information containing elements can alter the binding affinity of the RNA for critical RNA binding proteins, or change the recognition sequence for hybridization with other nucleic acids, as in RNAs driving chromatin and epigenetic modifications [9,60]. Even subtle changes in the protein affinity profiles for a given ncRNA can modify its trafficking patterns and subcellular localization. The presence or absence of a ncRNA in any of the large RNA–protein complexes in the nervous system, such as P bodies [66], stress granules [2], inter-chromatin granules (speckles) [30], or paraspeckles [76], can change the behavior of other members of the complex, or of downstream interacting partners of the ncRNA. These effects may have more focused impact during transient responses to environmental signals such as stress or inflammation, which require reprogramming and radical departures from unstressed cellular information processing. 5. The ADAR information processing cascade: coupling environmental stimuli and stress to ncRNA editing levels and downstream information signaling Interesting recent work by Barkai and colleagues provides evidence that stress response in yeast responds primarily to evolutionarily coded predictions made from information contained in extra-cellular signaling cascades, rather than direct measurement of conditions within the cell. In wild type yeast, modest increases in temperature result in a more permissive environment, with reduced stress signaling and curtailed stress responsive gene expression. On the other hand, conditional lethal temperature mutants develop severe malfunctions followed by death as temperature increases. Yet, even as they are dying, these mutants still behave as if the increased temperature had lowered their stress levels [85]. The situation may be far more complex in multicellular organisms, where cellular differentiation, and isolation from the environment likely drive a more sophisticated sensory network. The advent of mechanisms such as ADAR editing and the information rich non-coding transcriptome represented an evolutionary innovation that went well beyond the resources and systems available to unicellular organisms [56]. Coupling ADAR editing to the dynamic non-coding transcriptome creates a machinery ideally suited to acquire rapidly changing information from the intracellular environment, and introduce that information into the cell’s computational processing machinery. Such a system would compare and contrast extra-cellular signals with measurements made of transcriptome information content at potentially many intracellular locations. Consistent with the macro-program of a differentiated cell in a multicellular organism, these systems would dramatically increase the surveillance of molecular conditions inside the cell, working to preserve the cell and its differentiated state, rather than the typical open loop focus on cell growth and division of a unicellular organism. ADARs ability to survey the entire transcriptome landscape, introducing carefully orchestrated

changes that flow to downstream regulation in potentially hundreds of pathways, suggests that it is an ideal component of the cellular stress response system. In effect, mechanisms such as ADAR allow a sensory duality, comparing extra-cellular signaling directly with intracellular molecular landscapes and information content, and allowing for the evaluation of coherence between these two sources of information. Comparing internal cellular micro-states to ongoing extra-cellular signaling may represent a fundamental advance in fitness for multicellular organisms. A number of important variables provide a stream of modulation to shape the secondary structure ‘phase state’ of the local transcriptome. Environmental and sensory inputs from the surrounding micro-region induce dynamic changes in non-structural ncRNAs, in part because their thermodynamic properties result in relatively flexible folding profiles. Changes in temperature [15,65], small molecule concentrations [13], and perhaps pH or ATP levels, can effect changes in RNA secondary structures. Using this feature, evolution has recruited ncRNAs to serve as cellular sensors. On a transcriptome-wide basis, perhaps hundreds or thousands of RNAs undergo structural transitions as a result of sensory signals and changes in microenvironmental parameters at any given moment, in any given cellular compartment. In this way, ncRNAs can absorb information from their microenvironment, effect computations on this information, and transduce the resulting signals to downstream effector pathways through their association with other macromolecules. RNA binding proteins such as ADAR operate through their dynamic interactions with this multitude of changing RNA shapes. They identify, distinguish, and interpret shape modulation as information input for their downstream computation and regulation of cellular control systems. In effect, the high level of plasticity of analog information within the non-coding transcriptome drives the sensitive acquisition and filtered retention of environmental inputs, which in turn present as a modified landscape to the ADAR enzymes. The resulting changes in editing levels depend not only on ADARs affinity for a specific editing site, but also on the surrounding macrostate of all other RNA analog elements in the correct region of shape space, competing for ADAR localization and cleavage [6]. Temperature is well suited for coding and processing by the ADAR–ncRNA system. Since molecular machines depend on a delicate balance between enthalpic and entropic influences for their ability to process information and execute biological functions, physiological networks place a high priority on the acquisition of temperature information. RNA secondary structures respond rapidly to temperature changes in both directions. At reduced temperatures for example, RNA chaperones called “cold shock proteins” function to catalyze the orderly folding of RNAs [72]. During increased temperature transients, Heat Shock RNA (HSR1), a 600 nt constitutively expressed ncRNA, associates with Heat Shock Factor (HSF) to initiate HSFs migration to the nucleus and induction of the heat shock transcriptional response [80]. siRNA mediated inhibition of HSR expression completely abrogates the heatshock response in treated cells, showing that HSF requires HSR in order to properly sense and transduce increased temperature into the heat shock transcriptional program. HSR’s expression levels remain constant during temperature changes, suggesting that a shape change in HSR represents the key event in this sensory information processing cascade [80]. Temperature-induced changes represent an important source of dynamic secondary structure variation in the transcriptome. Considering ADARs ability to make sensitive and finely tuned distinctions between secondary structure information elements in the transcriptome, we propose that temperature mediates substantial transcriptome-wide changes in ADAR editing landscapes. In the hippocampus for example, normal temperature variation ranges over 2 ◦ C, which may lead to changes in ADAR editing levels [1].

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

The confluence of temperature modulation and stress response establishes mechanisms for ADARs likely involvement in a yet broader and more critical system. In higher organisms, spatiotemporal temperature gradients provide key information signals for the inflammation system’s orchestration of coordinated behavior among multiple cell types. As the primary driver of tissue regeneration, the inflammation system seeks to re-establish specific biological endpoints as a culmination of many interleaved signaling cascades and information flows from stress sensing and response pathways [25,79,93]. Not surprisingly from the above discussion of the association between temperature and editing, ADAR plays a major role. Lymphocytes and macrophages stimulated with pro-inflammatory cytokines such as IFN␣, IFN␥, or TNF␣, respond within 4 h with a 3× fold induction of ADAR1 mRNA, and up to a 12× increase in inosine mRNA content (the product of editing). At the zenith of the inflammation, ADAR converts as much as 5% of the adenosines in total nuclear RNA to inosine [91]. Temperature changes during inflammation also induce increased expression of repeat RNAs in complex patterns in different mammalian tissues [22]. With the known enrichment of editing events in ALU repeat containing transcripts, we predict increased editing of these stress induced RNA populations during inflammation. Inflammation-induced expression of the ADAR1 locus produces a large number of alternatively spliced isoforms, each with a different profile of dsRBMs and editing levels, resulting in a much wider distribution of affinities for RNA secondary structures to edit. This suddenly greater heterogeneity of expressed isoforms widens considerably ADARs range of action across RNA shape space, supporting the pan-transcriptome vision of ADAR function during inflammation. Increased levels of ADAR, distributed among many alternate isoforms encounter a transcriptome whose temperature modulation has produced new ADAR accessible features, resulting in large amounts of adenosines converted to inosine. Within the context of the many information processing feedback loops involved in regulating the inflammation process, such a dramatic shift in ADAR activity likely affects a large number of functional ncRNAs.

95

creating functional landscapes of information processing, cellular signaling, and regulation of gene (transcriptome) expression. If ADAR functions as a negative feedback regulator of the endogenous siRNA response [63], it could in turn couple environmental signaling into this sophisticated pathway, permitting small RNA regulatory circuits to modulate the cellular state in response to environmental signals (see Fig. 3). In fact, in the broad context of cellular functional networks, feedback loops may represent a key engineering design element to achieve high levels of robustness as well as the diverse complex functions of differentiated cells [41]. The striking evidence reviewed here implies that the flexibility, sensitivity, and diversity of the ADAR system functions together with the vast information content of the non-coding transcriptome to elaborate many such cascades of feedback and feed-forward loops within cellular networks. In stress responses that require large and urgent changes in cellular behavior, ADAR intervention in the non-coding transcriptome likely achieves a rapid transition between phase states quite distant in cellular phase space [11,31]. ADARs unique ability to intervene with decisive changes to RNA information content provides a useful mechanism to code for and program rapid changes in the architecture of pre-established feedback loops. Recursive evolutionary processes likely maintain pressure on a large part of the transcriptome to drive secondary structures

6. Conclusion Even though the non-coding transcriptome remains sparsely surveyed, especially in the nervous system, existing examples provide a preview of the broad scope of ncRNA function. Discovery of ADAR editing events outside the well established mRNA targets associated with the synaptic proteome suggests that ADAR edits many ncRNAs involved in nervous system information processing pathways. Together with the high levels of ADAR expression localized to brain regions, these facts suggest that ADAR editing modulates the non-coding landscape in the nervous system, with far reaching functional consequences. ADAR editing of the noncoding transcriptome may increase the information coding degrees of freedom, relative to the thermal degrees of freedom in the matrix. This would imply an increased coupling of energy to information flow in the brain, facilitating greater information density at less thermal cost. A fascinating question in contemporary biology asks how cells effect the complex multilayer information processing required to maintain functional order and robustness in the face of the myriad of stresses that chaotically bombard the cell throughout its life. How does the cell compute and generate such widely divergent phase states by modulating the cellular machinery of gene expression? As high throughput genomic and proteomic datasets proliferate, the tools and resources of Systems Biology begin to address these questions by orchestrating the integration of molecular data into networks and holistic biological systems. Not surprisingly, feedback and feed-forward circuits abound within these networks,

Fig. 3. Servo loops determine the systems architecture of the edited transcriptome. Information flows in transcriptome pathways organize themselves into multiple nested layers of servo loops. Stress induces inflammation, which in turn induces editing. ADAR editing targets drive stress response, resulting in a negative feedback loop. ADAR2 autoediting drives a tight local feedback loop. esiRNAs attenuate repeat RNAs, which are themselves induced by stress and inflammation. ADAR likely attenuates silencing and translational inhibition by small RNAs, and drives the formation of heterochromatin.

96

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

either towards or away from recognition by ADAR. Entire regions of RNA shape space may serve as zones of recruitment for ADAR regulated transformations of cellular phase state, maintaining low level editing sites available for future use. The natural conclusion is that ADAR participates in an evolutionary language to code for and adapt elements of complex feedback loops and other information processing circuits for improved cellular function and behavior.

Acknowledgements The authors would like to thank Prakriti Mudvari for editorial assistance, and Mark Mazaitis for the production of the graphics.

References [1] P. Andersen, E.I. Moser, Brain temperature and hippocampal function, Hippocampus 5 (1995) 491–498. [2] P. Anderson, N. Kedersha, Stress granules, Curr. Biol. 19 (2009) R397–398. [3] M.A. Aon, S. Cortassa, F.G. Akar, B. O’Rourke, Mitochondrial criticality: a new concept at the turning point of life or death, Biochim. Biophys. Acta 1762 (2006) 232–240. [4] A. Athanasiadis, A. Rich, S. Maas, Widespread A-to-I RNA editing of Alucontaining mRNAs in the human transcriptome, PLoS Biol. 2 (2004) e391. [5] S.N. Bhattacharyya, R. Habermacher, U. Martine, E.I. Closs, W. Filipowicz, Relief of microRNA-mediated translational repression in human cells subjected to stress, Cell 125 (2006) 1111–1124. [6] G. Biamonti, J.F. Caceres, Cellular stress and RNA splicing, Trends Biochem. Sci. 34 (2009) 146–153. [7] M.J. Blow, R.J. Grocock, S. van Dongen, A.J. Enright, E. Dicks, P.A. Futreal, R. Wooster, M.R. Stratton, RNA editing of human microRNAs, Genome Biol. 7 (2006) R27. [8] B. Boeckmann, A. Bairoch, R. Apweiler, M.C. Blatter, A. Estreicher, E. Gasteiger, M.J. Martin, K. Michoud, C. O’Donovan, I. Phan, S. Pilbout, M. Schneider, The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003, Nucleic Acids Res. 31 (2003) 365–370. [9] H.P. Cam, E.S. Chen, S.I. Grewal, Transcriptional scaffolds for heterochromatin assembly, Cell 136 (2009) 610–614. [10] X. Cao, G. Yeo, A.R. Muotri, T. Kuwabara, F.H. Gage, Noncoding RNAs in the mammalian central nervous system, Annu. Rev. Neurosci. 29 (2006) 77– 103. [11] H.H. Chang, M. Hemberg, M. Barahona, D.E. Ingber, S. Huang, Transcriptomewide noise controls lineage choice in mammalian progenitor cells, Nature 453 (2008) 544–547. [12] K.Y. Chang, A. Ramos, The double-stranded RNA-binding motif, a versatile macromolecular docking platform, FEBS J. 272 (2005) 2109–2117. [13] M.T. Cheah, A. Wachter, N. Sudarsan, R.R. Breaker, Control of alternative RNA splicing and gene expression by eukaryotic riboswitches, Nature 447 (2007) 497–500. [14] J. Cheng, P. Kapranov, J. Drenkow, S. Dike, S. Brubaker, S. Patel, J. Long, D. Stern, H. Tammana, G. Helt, V. Sementchenko, A. Piccolboni, S. Bekiranov, D.K. Bailey, M. Ganesh, S. Ghosh, I. Bell, D.S. Gerhard, T.R. Gingeras, Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution, Science 308 (2005) 1149–1154. [15] S. Chowdhury, C. Ragaz, E. Kreuger, F. Narberhaus, Temperature-controlled structural alterations of an RNA thermometer, J. Biol. Chem. 278 (2003) 47915–47921. [16] D.R. Clutterbuck, A. Leroy, M.A. O’Connell, C.A. Semple, A bioinformatic screen for novel A-I RNA editing sites reveals recoding editing in BC10, Bioinformatics 21 (2005) 2590–2595. [17] F. Crick, Central dogma of molecular biology, Nature 227 (1970) 561–563. [18] T.R. Dawson, C.L. Sansam, R.B. Emeson, Structure and sequence determinants required for the RNA editing of ADAR2 substrates, J. Biol. Chem. 279 (2004) 4941–4951. [19] J. DeCerbo, G.G. Carmichael, Retention and repression: fates of hyperedited RNAs in the nucleus, Curr. Opin. Cell Biol. 17 (2005) 302–308. [20] D.E. Draper, Protein–RNA recognition, Annu. Rev. Biochem. 64 (1995) 593–620. [21] M.A. Faghihi, F. Modarresi, A.M. Khalil, D.E. Wood, B.G. Sahagan, T.E. Morgan, C.E. Finch, G. St Laurent 3rd, P.J. Kenny, C. Wahlestedt, Expression of a noncoding RNA is elevated in Alzheimer’s disease and drives rapid feed-forward regulation of beta-secretase, Nat. Med. 14 (2008) 723–730. [22] G.J. Faulkner, Y. Kimura, C.O. Daub, S. Wani, C. Plessy, K.M. Irvine, K. Schroder, N. Cloonan, A.L. Steptoe, T. Lassmann, K. Waki, N. Hornig, T. Arakawa, H. Takahashi, J. Kawai, A.R. Forrest, H. Suzuki, Y. Hayashizaki, D.A. Hume, V. Orlando, S.M. Grimmond, P. Carninci, The regulated retrotransposon transcriptome of mammalian cells, Nat. Genet. 41 (2009) 563–571. [23] Fejes-Toth, Post-transcriptional processing generates a diversity of 5 -modified long and short RNAs, Nature 457 (2009) 1028–1032. [24] A. Fire, S. Xu, M.K. Montgomery, S.A. Kostas, S.E. Driver, C.C. Mello, Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans, Nature 391 (1998) 806–811.

[25] S. Gasser, S. Orsulic, E.J. Brown, D.H. Raulet, The DNA damage pathway regulates innate immune system ligands of the NKG2D receptor, Nature 436 (2005) 1186–1190. [26] T.R. Gingeras, R. Higuchi, L.J. Kricka, Y.M. Lo, C.T. Wittwer, Fifty years of molecular (DNA/RNA) diagnostics, Clin. Chem. 51 (2005) 661–671. [27] C.B. Guan, H.T. Xu, M. Jin, X.B. Yuan, M.M. Poo, Long-range Ca2+ signaling from growth cone to soma mediates reversal of neuronal migration induced by slit-2, Cell 129 (2007) 385–395. [28] J. Hasler, K. Strub, Alu RNP and Alu RNA regulate translation initiation in vitro, Nucleic Acids Res. 34 (2006) 2374–2385. [29] B. Hoopengardner, T. Bhalla, C. Staber, R. Reenan, Nervous system targets of RNA editing identified by comparative genomics, Science 301 (2003) 832– 836. [30] Q. Hu, Y.S. Kwon, E. Nunez, M.D. Cardamone, K.R. Hutt, K.A. Ohgi, I. GarciaBassets, D.W. Rose, C.K. Glass, M.G. Rosenfeld, X.D. Fu, Enhancing nuclear receptor-induced transcription requires nuclear motor and LSD1-dependent gene networking in interchromatin granules, Proc. Natl. Acad. Sci. U.S.A. 105 (2008) 19199–19204. [31] S. Huang, G. Eichler, Y. Bar-Yam, D.E. Ingber, Cell fates as high-dimensional attractor states of a complex gene regulatory network, Phys. Rev. Lett. 94 (2005) 128701. [32] D. Huertas, A. Cortes, J. Casanova, F. Azorin, Drosophila DDP1, a multiKH-domain protein, contributes to centromeric silencing and chromosome segregation, Curr. Biol. 14 (2004) 1611–1620. [33] L. Ingleby, R. Maloney, J. Jepson, R. Horn, R. Reenan, Regulated RNA editing and functional epistasis in Shaker potassium channels, J. Gen. Physiol. 133 (2009) 17–27. [34] J.E. Jepson, R.A. Reenan, RNA editing in regulating gene expression in the brain, Biochim. Biophys. Acta 1779 (2008) 459–470. [35] J.M. Johnson, S. Edwards, D. Shoemaker, E.E. Schadt, Dark matter in the genome: evidence of widespread transcription detected by microarray tiling experiments, Trends Genet. 21 (2005) 93–102. [36] A.M. Kallman, M. Sahlin, M. Ohman, ADAR2 A → I editing: site selectivity and editing efficiency are separate events, Nucleic Acids Res. 31 (2003) 4874– 4881. [37] S. Katayama, Y. Tomaru, T. Kasukawa, K. Waki, M. Nakanishi, M. Nakamura, H. Nishida, C.C. Yap, M. Suzuki, J. Kawai, H. Suzuki, P. Carninci, Y. Hayashizaki, C. Wells, M. Frith, T. Ravasi, K.C. Pang, J. Hallinan, J. Mattick, D.A. Hume, L. Lipovich, S. Batalov, P.G. Engstrom, Y. Mizuno, M.A. Faghihi, A. Sandelin, A.M. Chalk, S. Mottagui-Tabar, Z. Liang, B. Lenhard, C. Wahlestedt, Antisense transcription in the mammalian transcriptome, Science 309 (2005) 1564–1566. [38] Y. Kawahara, M. Megraw, E. Kreider, H. Iizasa, L. Valente, A.G. Hatzigeorgiou, K. Nishikura, Frequency and fate of microRNA editing in human brain, Nucleic Acids Res. 36 (2008) 5270–5280. [39] D.D. Kim, T.T. Kim, T. Walsh, Y. Kobayashi, T.C. Matise, S. Buyske, A. Gabriel, Widespread RNA editing of embedded alu elements in the human transcriptome, Genome Res. 14 (2004) 1719–1725. [40] S. Kishore, S. Stamm, The snoRNA HBII-52 regulates alternative splicing of the serotonin receptor 2C, Science 311 (2006) 230–232. [41] H. Kitano, Towards a theory of biological robustness, Mol. Syst. Biol. 3 (2007) 137. [42] K.V. Kuchibhotla, C.R. Lattarulo, B.T. Hyman, B.J. Bacskai, Synchronous hyperactivity and intercellular calcium waves in astrocytes in Alzheimer mice, Science 323 (2009) 1211–1215. [43] E.S. Lander, L.M. Linton, B. Birren, C. Nusbaum, M.C. Zody, J. Baldwin, K. Devon, K. Dewar, M. Doyle, W. FitzHugh, R. Funke, D. Gage, K. Harris, A. Heaford, J. Howland, L. Kann, J. Lehoczky, R. LeVine, P. McEwan, K. McKernan, J. Meldrim, J.P. Mesirov, C. Miranda, W. Morris, J. Naylor, C. Raymond, M. Rosetti, R. Santos, A. Sheridan, C. Sougnez, N. Stange-Thomann, N. Stojanovic, A. Subramanian, D. Wyman, J. Rogers, J. Sulston, R. Ainscough, S. Beck, D. Bentley, J. Burton, C. Clee, N. Carter, A. Coulson, R. Deadman, P. Deloukas, A. Dunham, I. Dunham, R. Durbin, L. French, D. Grafham, S. Gregory, T. Hubbard, S. Humphray, A. Hunt, M. Jones, C. Lloyd, A. McMurray, L. Matthews, S. Mercer, S. Milne, J.C. Mullikin, A. Mungall, R. Plumb, M. Ross, R. Shownkeen, S. Sims, R.H. Waterston, R.K. Wilson, L.W. Hillier, J.D. McPherson, M.A. Marra, E.R. Mardis, L.A. Fulton, A.T. Chinwalla, K.H. Pepin, W.R. Gish, S.L. Chissoe, M.C. Wendl, K.D. Delehaunty, T.L. Miner, A. Delehaunty, J.B. Kramer, L.L. Cook, R.S. Fulton, D.L. Johnson, P.J. Minx, S.W. Clifton, T. Hawkins, E. Branscomb, P. Predki, P. Richardson, S. Wenning, T. Slezak, N. Doggett, J.F. Cheng, A. Olsen, S. Lucas, C. Elkin, E. Uberbacher, M. Frazier, R.A. Gibbs, D.M. Muzny, S.E. Scherer, J.B. Bouck, E.J. Sodergren, K.C. Worley, C.M. Rives, J.H. Gorrell, M.L. Metzker, S.L. Naylor, R.S. Kucherlapati, D.L. Nelson, G.M. Weinstock, Y. Sakaki, A. Fujiyama, M. Hattori, T. Yada, A. Toyoda, T. Itoh, C. Kawagoe, H. Watanabe, Y. Totoki, T. Taylor, J. Weissenbach, R. Heilig, W. Saurin, F. Artiguenave, P. Brottier, T. Bruls, E. Pelletier, C. Robert, P. Wincker, D.R. Smith, L. Doucette-Stamm, M. Rubenfield, K. Weinstock, H.M. Lee, J. Dubois, A. Rosenthal, M. Platzer, G. Nyakatura, S. Taudien, A. Rump, H. Yang, J. Yu, J. Wang, G. Huang, J. Gu, L. Hood, L. Rowen, A. Madan, S. Qin, R.W. Davis, N.A. Federspiel, A.P. Abola, M.J. Proctor, R.M. Myers, J. Schmutz, M. Dickson, J. Grimwood, D.R. Cox, M.V. Olson, R. Kaul, N. Shimizu, K. Kawasaki, S. Minoshima, G.A. Evans, M. Athanasiou, R. Schultz, B.A. Roe, F. Chen, H. Pan, J. Ramser, H. Lehrach, R. Reinhardt, W.R. McCombie, M. de la Bastide, N. Dedhia, H. Blocker, K. Hornischer, G. Nordsiek, R. Agarwala, L. Aravind, J.A. Bailey, A. Bateman, S. Batzoglou, E. Birney, P. Bork, D.G. Brown, C.B. Burge, L. Cerutti, H.C. Chen, D. Church, M. Clamp, R.R. Copley, T. Doerks, S.R. Eddy, E.E. Eichler, T.S. Furey, J. Galagan, J.G. Gilbert, C. Harmon, Y. Hayashizaki, D. Haussler, H. Hermjakob, K. Hokamp, W. Jang, L.S. Johnson, T.A. Jones, S. Kasif, A. Kaspryzk, S. Kennedy, W.J.

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

[44]

[45]

[46]

[47]

[48]

[49]

[50]

[51]

[52] [53] [54] [55] [56] [57] [58]

[59]

[60] [61] [62]

[63] [64]

[65] [66] [67]

[68]

Kent, P. Kitts, E.V. Koonin, I. Korf, D. Kulp, D. Lancet, T.M. Lowe, A. McLysaght, T. Mikkelsen, J.V. Moran, N. Mulder, V.J. Pollara, C.P. Ponting, G. Schuler, J. Schultz, G. Slater, A.F. Smit, E. Stupka, J. Szustakowski, D. Thierry-Mieg, J. Thierry-Mieg, L. Wagner, J. Wallis, R. Wheeler, A. Williams, Y.I. Wolf, K.H. Wolfe, S.P. Yang, R.F. Yeh, F. Collins, M.S. Guyer, J. Peterson, A. Felsenfeld, K.A. Wetterstrand, A. Patrinos, M.J. Morgan, P. de Jong, J.J. Catanese, K. Osoegawa, H. Shizuya, S. Choi, Y.J. Chen, Initial sequencing and analysis of the human genome, Nature 409 (2001) 860–921. E.S. Lein, M.J. Hawrylycz, N. Ao, M. Ayres, A. Bensinger, A. Bernard, A.F. Boe, M.S. Boguski, K.S. Brockway, E.J. Byrnes, L. Chen, T.M. Chen, M.C. Chin, J. Chong, B.E. Crook, A. Czaplinska, C.N. Dang, S. Datta, N.R. Dee, A.L. Desaki, T. Desta, E. Diep, T.A. Dolbeare, M.J. Donelan, H.W. Dong, J.G. Dougherty, B.J. Duncan, A.J. Ebbert, G. Eichele, L.K. Estin, C. Faber, B.A. Facer, R. Fields, S.R. Fischer, T.P. Fliss, C. Frensley, S.N. Gates, K.J. Glattfelder, K.R. Halverson, M.R. Hart, J.G. Hohmann, M.P. Howell, D.P. Jeung, R.A. Johnson, P.T. Karr, R. Kawal, J.M. Kidney, R.H. Knapik, C.L. Kuan, J.H. Lake, A.R. Laramee, K.D. Larsen, C. Lau, T.A. Lemon, A.J. Liang, Y. Liu, L.T. Luong, J. Michaels, J.J. Morgan, R.J. Morgan, M.T. Mortrud, N.F. Mosqueda, L.L. Ng, R. Ng, G.J. Orta, C.C. Overly, T.H. Pak, S.E. Parry, S.D. Pathak, O.C. Pearson, R.B. Puchalski, Z.L. Riley, H.R. Rockett, S.A. Rowland, J.J. Royall, M.J. Ruiz, N.R. Sarno, K. Schaffnit, N.V. Shapovalova, T. Sivisay, C.R. Slaughterbeck, S.C. Smith, K.A. Smith, B.I. Smith, A.J. Sodt, N.N. Stewart, K.R. Stumpf, S.M. Sunkin, M. Sutram, A. Tam, C.D. Teemer, C. Thaller, C.L. Thompson, L.R. Varnam, A. Visel, R.M. Whitlock, P.E. Wohnoutka, C.K. Wolkey, V.Y. Wong, M. Wood, M.B. Yaylaoglu, R.C. Young, B.L. Youngstrom, X.F. Yuan, B. Zhang, T.A. Zwingman, A.R. Jones, Genome-wide atlas of gene expression in the adult mouse brain, Nature 445 (2007) 168–176. N. Leulliot, G. Varani, Current topics in RNA–protein recognition: control of specificity and biological function through induced fit and conformational capture, Biochemistry 40 (2001) 7947–7956. G. Lev-Maor, A. Goren, N. Sela, E. Kim, H. Keren, A. Doron-Faigenboim, S. Leibman-Barak, T. Pupko, G. Ast, The “alternative” choice of constitutive exons throughout evolution, PLoS Genet. 3 (2007) e203. E.Y. Levanon, E. Eisenberg, R. Yelin, S. Nemzer, M. Hallegger, R. Shemesh, Z.Y. Fligelman, A. Shoshan, S.R. Pollock, D. Sztybel, M. Olshansky, G. Rechavi, M.F. Jantsch, Systematic identification of abundant A-to-I editing sites in the human transcriptome, Nat. Biotechnol. 22 (2004) 1001–1005. K. Levanon, E. Eisenberg, G. Rechavi, E.Y. Levanon, Letter from the editor: Adenosine-to-inosine RNA editing in Alu repeats in the human genome, EMBO Rep. 6 (2005) 831–835. C.L. Li, W.Z. Yang, Y.P. Chen, H.S. Yuan, Structural and functional insights into human Tudor-SN, a key component linking RNA interference and editing, Nucleic Acids Res. 36 (2008) 3579–3589. J.B. Li, E.Y. Levanon, J.K. Yoon, J. Aach, B. Xie, E. Leproust, K. Zhang, Y. Gao, G.M. Church, Genome-wide identification of human RNA editing sites by parallel DNA capturing and sequencing, Science 324 (2009) 1210–1213. L. Lin, S. Shen, A. Tye, J.J. Cai, P. Jiang, B.L. Davidson, Y. Xing, Diverse splicing patterns of exonized Alu elements in human tissues, PLoS Genet. 4 (2008) e1000225. D. Long, R. Lee, P. Williams, C.Y. Chan, V. Ambros, Y. Ding, Potent effect of target structure on microRNA function, Nat. Struct. Mol. Biol. 14 (2007) 287–294. B.M. Lunde, C. Moore, G. Varani, RNA-binding proteins: modular design for efficient function, Nat. Rev. Mol. Cell Biol. 8 (2007) 479–490. S. Maas, Y. Kawahara, K.M. Tamburro, K. Nishikura, A-to-I RNA editing and human disease, RNA Biol. 3 (2006) 1–9. Macbeth, et al., Science 309 (2005) 1534. J.S. Mattick, A new paradigm for developmental biology, J. Exp. Biol. 210 (2007) 1526–1547. J.S. Mattick, M.F. Mehler, RNA editing, DNA recoding and the evolution of human cognition, Trends Neurosci. 31 (2008) 227–233. M.F. Mehler, J.S. Mattick, Noncoding RNAs and RNA editing in brain development, functional diversification, and neurological disease, Physiol. Rev. 87 (2007) 799–823. T.R. Mercer, M.E. Dinger, S.M. Sunkin, M.F. Mehler, J.S. Mattick, Specific expression of long noncoding RNAs in the mouse brain, Proc. Natl. Acad. Sci. U.S.A. 105 (2008) 716–721. K.V. Morris, Non-coding RNAs, epigenetic memory, and the passage of information to progeny, RNA Biol. 6 (2009). S.B. Nelson, K. Sugino, C.M. Hempel, The problem of neuronal cell types: a physiological genomics approach, Trends Neurosci. 29 (2006) 339–345. M.A. Newman, J.M. Thomson, S.M. Hammond, Lin-28 interaction with the Let7 precursor loop mediates regulated microRNA processing, RNA 14 (2008) 1539–1549. K. Nishikura, Editor meets silencer: crosstalk between RNA editing and RNA interference, Nat. Rev. Mol. Cell Biol. 7 (2006) 919–931. P.H. Olsen, V. Ambros, The lin-4 regulatory RNA controls developmental timing in Caenorhabditis elegans by blocking LIN-14 protein synthesis after the initiation of translation, Dev. Biol. 216 (1999) 671–680. G.W. Owttrim, RNA helicases and abiotic stress, Nucleic Acids Res. 34 (2006) 3220–3230. R. Parker, U. Sheth, P bodies and the control of mRNA translation and degradation, Mol Cell. 25 (2007) 635–646. J.B. Patterson, D.C. Thomis, S.L. Hans, C.E. Samuel, Mechanism of interferon action: double-stranded RNA-specific adenosine deaminase from human cells is inducible by alpha and gamma interferons, Virology 210 (1995) 508–511. R. Peng, B.T. Dye, I. Perez, D.C. Barnard, A.B. Thompson, J.G. Patton, PSF and p54nrb bind a conserved stem in U5 snRNA, RNA 8 (2002) 1334–1347.

97

[69] K.S. Pollard, S.R. Salama, N. Lambert, M.A. Lambot, S. Coppens, J.S. Pedersen, S. Katzman, B. King, C. Onodera, A. Siepel, A.D. Kern, C. Dehay, H. Igel, M. Ares Jr., P. Vanderhaeghen, D. Haussler, An RNA gene expressed during cortical development evolved rapidly in humans, Nature 443 (2006) 167– 172. [70] A.G. Polson, B.L. Bass, Preferential selection of adenosines for modification by double-stranded RNA adenosine deaminase, EMBO J. 13 (1994) 5701–5711. [71] K.V. Prasanth, S.G. Prasanth, Z. Xuan, S. Hearn, S.M. Freier, C.F. Bennett, M.Q. Zhang, D.L. Spector, Regulating gene expression through RNA nuclear retention, Cell 123 (2005) 249–263. [72] L. Rajkowitsch, R. Schroeder, Dissecting RNA chaperone activity, RNA 13 (2007) 2053–2060. [73] R.A. Reenan, Molecular determinants and guided evolution of species-specific RNA editing, Nature 434 (2005) 409–413. [74] S.L. Reichow, T. Hamma, A.R. Ferre-D’Amare, G. Varani, The structure and function of small nucleolar ribonucleoproteins, Nucleic Acids Res. 35 (2007) 1452–1464. [75] A. Rybak, H. Fuchs, L. Smirnova, C. Brandt, E.E. Pohl, R. Nitsch, F.G. Wulczyn, A feedback loop comprising lin-28 and let-7 controls pre-let-7 maturation during neural stem-cell commitment, Nat. Cell Biol. 10 (2008) 987–993. [76] Y.T. Sasaki, T. Ideue, M. Sano, T. Mituyama, T. Hirose, MENepsilon/beta noncoding RNAs are essential for structural integrity of nuclear paraspeckles, Proc. Natl. Acad. Sci. U.S.A. 106 (2009) 2525–2530. [77] A.D. Scadden, The RISC subunit Tudor-SN binds to hyper-edited doublestranded RNA and promotes its cleavage, Nat. Struct. Mol. Biol. 12 (2005) 489–496. [78] S. Sengupta, R. Parihar, S. Ganesh, Satellite III non-coding RNAs show distinct and stress-specific patterns of induction, Biochem. Biophys. Res. Commun. 382 (2009) 102–107. [79] S.Y. Seong, P. Matzinger, Hydrophobicity: an ancient damage-associated molecular pattern that initiates innate immune responses, Nat. Rev. Immunol. 4 (2004) 469–478. [80] I. Shamovsky, M. Ivannikov, E.S. Kandel, D. Gershon, E. Nudler, RNA-mediated response to heat shock in mammalian cells, Nature 440 (2006) 556–560. [81] Y. Shav-Tal, D. Zipori, PSF and p54(nrb)/NonO—multi-functional nuclear proteins, FEBS Lett. 531 (2002) 109–114. [82] G. St Laurent 3rd, C. Wahlestedt, Noncoding RNAs: couplers of analog and digital information in nervous system function? Trends Neurosci. 30 (2007) 612–621. [83] R. Stefl, M. Xu, L. Skrisovska, R.B. Emeson, F.H. Allain, Structure and specific RNA binding of ADAR2 double-stranded RNA binding motifs, Structure 14 (2006) 345–355. [84] B. Tian, P.C. Bevilacqua, A. Diegelman-Parente, M.B. Mathews, The doublestranded-RNA-binding motif: interference and much more, Nat. Rev. Mol. Cell Biol. 5 (2004) 1013–1023. [85] I. Tirosh, S. Reikhav, A.A. Levy, N. Barkai, A yeast hybrid provides insight into the evolution of gene expression regulation, Science 324 (2009) 659– 662. [86] L. Valente, K. Nishikura, ADAR gene family and A-to-I RNA editing: diverse roles in posttranscriptional gene regulation, Prog. Nucleic Acid Res. Mol. Biol. 79 (2005) 299–338. [87] L. Valente, K. Nishikura, RNA binding-independent dimerization of adenosine deaminases acting on RNA and dominant negative effects of nonfunctional subunits on dimer functions, J. Biol. Chem. 282 (2007) 16054–16061. [88] J.C. Venter, M.D. Adams, E.W. Myers, P.W. Li, R.J. Mural, G.G. Sutton, H.O. Smith, M. Yandell, C.A. Evans, R.A. Holt, J.D. Gocayne, P. Amanatides, R.M. Ballew, D.H. Huson, J.R. Wortman, Q. Zhang, C.D. Kodira, X.H. Zheng, L. Chen, M. Skupski, G. Subramanian, P.D. Thomas, J. Zhang, G.L. Gabor Miklos, C. Nelson, S. Broder, A.G. Clark, J. Nadeau, V.A. McKusick, N. Zinder, A.J. Levine, R.J. Roberts, M. Simon, C. Slayman, M. Hunkapiller, R. Bolanos, A. Delcher, I. Dew, D. Fasulo, M. Flanigan, L. Florea, A. Halpern, S. Hannenhalli, S. Kravitz, S. Levy, C. Mobarry, K. Reinert, K. Remington, J. Abu-Threideh, E. Beasley, K. Biddick, V. Bonazzi, R. Brandon, M. Cargill, I. Chandramouliswaran, R. Charlab, K. Chaturvedi, Z. Deng, V. Di Francesco, P. Dunn, K. Eilbeck, C. Evangelista, A.E. Gabrielian, W. Gan, W. Ge, F. Gong, Z. Gu, P. Guan, T.J. Heiman, M.E. Higgins, R.R. Ji, Z. Ke, K.A. Ketchum, Z. Lai, Y. Lei, Z. Li, J. Li, Y. Liang, X. Lin, F. Lu, G.V. Merkulov, N. Milshina, H.M. Moore, A.K. Naik, V.A. Narayan, B. Neelam, D. Nusskern, D.B. Rusch, S. Salzberg, W. Shao, B. Shue, J. Sun, Z. Wang, A. Wang, X. Wang, J. Wang, M. Wei, R. Wides, C. Xiao, C. Yan, A. Yao, J. Ye, M. Zhan, W. Zhang, H. Zhang, Q. Zhao, L. Zheng, F. Zhong, W. Zhong, S. Zhu, S. Zhao, D. Gilbert, S. Baumhueter, G. Spier, C. Carter, A. Cravchik, T. Woodage, F. Ali, H. An, A. Awe, D. Baldwin, H. Baden, M. Barnstead, I. Barrow, K. Beeson, D. Busam, A. Carver, A. Center, M.L. Cheng, L. Curry, S. Danaher, L. Davenport, R. Desilets, S. Dietz, K. Dodson, L. Doup, S. Ferriera, N. Garg, A. Gluecksmann, B. Hart, J. Haynes, C. Haynes, C. Heiner, S. Hladun, D. Hostin, J. Houck, T. Howland, C. Ibegwam, J. Johnson, F. Kalush, L. Kline, S. Koduru, A. Love, F. Mann, D. May, S. McCawley, T. McIntosh, I. McMullen, M. Moy, L. Moy, B. Murphy, K. Nelson, C. Pfannkoch, E. Pratts, V. Puri, H. Qureshi, M. Reardon, R. Rodriguez, Y.H. Rogers, D. Romblad, B. Ruhfel, R. Scott, C. Sitter, M. Smallwood, E. Stewart, R. Strong, E. Suh, R. Thomas, N.N. Tint, S. Tse, C. Vech, G. Wang, J. Wetter, S. Williams, M. Williams, S. Windsor, E. Winn-Deen, K. Wolfe, J. Zaveri, K. Zaveri, J.F. Abril, R. Guigo, M.J. Campbell, K.V. Sjolander, B. Karlak, A. Kejariwal, H. Mi, B. Lazareva, T. Hatton, A. Narechania, K. Diemer, A. Muruganujan, N. Guo, S. Sato, V. Bafna, S. Istrail, R. Lippert, R. Schwartz, B. Walenz, S. Yooseph, D. Allen, A. Basu, J. Baxendale, L. Blick, M. Caminha, J. Carnes-Stine, P. Caulk, Y.H. Chiang, M. Coyne, C. Dahlke, A. Mays, M. Dombroski, M. Donnelly, D. Ely, S. Esparham, C. Fosler, H. Gire, S. Glanowski, K. Glasser, A. Glodek, M. Gorokhov, K. Graham, B. Gropman, M. Harris, J. Heil, S. Henderson, J. Hoover, D.

98

G. St. Laurent III et al. / Neuroscience Letters 466 (2009) 89–98

Jennings, C. Jordan, J. Jordan, J. Kasha, L. Kagan, C. Kraft, A. Levitsky, M. Lewis, X. Liu, J. Lopez, D. Ma, W. Majoros, J. McDaniel, S. Murphy, M. Newman, T. Nguyen, N. Nguyen, M. Nodell, S. Pan, J. Peck, M. Peterson, W. Rowe, R. Sanders, J. Scott, M. Simpson, T. Smith, A. Sprague, T. Stockwell, R. Turner, E. Venter, M. Wang, M. Wen, D. Wu, M. Wu, A. Xia, A. Zandieh, X. Zhu, The sequence of the human genome, Science 291 (2001) 1304–1351. [89] S.R. Viswanathan, G.Q. Daley, R.I. Gregory, Selective blockade of microRNA processing by Lin28, Science 320 (2008) 97–100. [90] Q. Wang, Z. Zhang, K. Blackwell, G.G. Carmichael, Vigilins bind to promiscuously A-to-I-edited RNAs and are involved in the formation of heterochromatin, Curr. Biol. 15 (2005) 384–391.

[91] J.H. Yang, X. Luo, Y. Nie, Y. Su, Q. Zhao, K. Kabir, D. Zhang, R. Rabinovici, Widespread inosine-containing mRNA in lymphocytes regulated by ADAR1 in response to inflammation, Immunology 109 (2003) 15–23. [92] Z. Zhang, G.G. Carmichael, The fate of dsRNA in the nucleus: a p54(nrb)containing complex mediates the nuclear retention of promiscuously A-to-I edited RNAs, Cell 106 (2001) 465–475. [93] K. Zhang, X. Chen, J. Wu, K. Sakaki, T. Saunders, D. Rutkowski, S. Back, R. Kaufman, Endoplasmic reticulum stress activates cleavage of CREBH to induce a systemic inflammatory response, Cell 124 (2006) 587– 599.