Chronic cocaine-regulated epigenomic changes in mouse nucleus accumbens
- Jian Feng1,
- Matthew Wilkinson1,
- Xiaochuan Liu1,
- Immanuel Purushothaman1,
- Deveroux Ferguson1,
- Vincent Vialou1,
- Ian Maze1,
- Ningyi Shao1,
- Pamela Kennedy1,
- JaWook Koo1,
- Caroline Dias1,
- Benjamin Laitman1,
- Victoria Stockman1,
- Quincey LaPlant1,
- Michael E Cahill1,
- Eric J Nestler1Email author and
- Li Shen1Email author
© Feng et al.; licensee BioMed Central Ltd. 2014
Received: 8 August 2013
Accepted: 22 April 2014
Published: 22 April 2014
Increasing evidence supports a role for altered gene expression in mediating the lasting effects of cocaine on the brain, and recent work has demonstrated the involvement of chromatin modifications in these alterations. However, all such studies to date have been restricted by their reliance on microarray technologies that have intrinsic limitations.
We use next generation sequencing methods, RNA-seq and ChIP-seq for RNA polymerase II and several histone methylation marks, to obtain a more complete view of cocaine-induced changes in gene expression and associated adaptations in numerous modes of chromatin regulation in the mouse nucleus accumbens, a key brain reward region. We demonstrate an unexpectedly large number of pre-mRNA splicing alterations in response to repeated cocaine treatment. In addition, we identify combinations of chromatin changes, or signatures, that correlate with cocaine-dependent regulation of gene expression, including those involving pre-mRNA alternative splicing. Through bioinformatic prediction and biological validation, we identify one particular splicing factor, A2BP1(Rbfox1/Fox-1), which is enriched at genes that display certain chromatin signatures and contributes to drug-induced behavioral abnormalities. Together, this delineation of the cocaine-induced epigenome in the nucleus accumbens reveals several novel modes of regulation by which cocaine alters the brain.
We establish combinatorial chromatin and transcriptional profiles in mouse nucleus accumbens after repeated cocaine treatment. These results serve as an important resource for the field and provide a template for the analysis of other systems to reveal new transcriptional and epigenetic mechanisms of neuronal regulation.
Alterations in gene expression contribute importantly to the long-lasting changes that drugs of abuse induce in the brain’s reward circuitry . Numerous studies to date have utilized gene expression microarrays to obtain an unbiased view of such alterations, and several transcription factors have been implicated in mediating some of these effects. Moreover, several target genes discovered with these approaches have been directly implicated in the complex cellular and behavioral plasticity induced in this reward circuitry associated with drug addiction. However, relatively little information is yet available concerning the detailed molecular steps through which such alterations in gene expression are induced, and available information is limited by the reliance to date on microarray technology.
Recently, epigenetic regulation, such as multiple histone modifications and DNA methylation, has emerged as a key mechanism of addiction-related phenomena [2–6]. Drugs of abuse such as cocaine have been shown to alter the expression levels of several histone- and DNA-modifying enzymes within key brain reward regions, such as the nucleus accumbens (NAc) [7–10]. Importantly, these enzyme changes, which include altered levels of certain histone deacetylases and histone lysine methyltransferases, are associated with cocaine-induced changes in histone acetylation or lysine methylation at many specific candidate genes, which are already known to be involved in cocaine action [9, 11]. Recently, cross-talk has been demonstrated between regulation of histone acetylation and lysine methylation in NAc . While many gene-specific histone changes are in a direction commensurate with the altered enzyme expression levels, a large subset of observed changes are in the opposite direction, which further underscores the complexity of chromatin regulation in vivo.
To extend these candidate gene studies, we recently mapped cocaine-induced changes in the genome-wide binding of pan-acetylated H3, pan-acetylated H4, and dimethylated H3 (at both K9 and K27) in NAc by use of ChIP-chip assays (chromatin immunoprecipitation followed by promoter microarrays) . This study identified hundreds of novel gene targets of cocaine, but was inherently limited in several important ways. First, ChIP-chip by design restricts analysis to proximal promoter regions of genes only, even though we know that much chromatin regulation occurs in other genic, as well as intergenic, regions. Second, recent evidence indicates that net levels of gene transcription result from the complex interplay of large numbers of chromatin modifications, which act in concert in ways that remain incompletely understood [14, 15]. Third, genome-wide characterizations of gene expression in brain have to date relied mainly on microarrays, as opposed to RNA-seq, which provides unprecedented advantages such as more precise measurement of levels of transcripts and their splicing isoforms . Finally, recent evidence from in vitro non-nervous tissues has suggested that alternative splicing is regulated by chromatin modifications at specific genes . As alternative splicing is a process by which pre-mRNAs are differentially spliced, and lead to the expression of several mRNAs from a single gene, it provides an essential mechanism that expands and diversifies the proteome . However, little is known about the contribution of alternative splicing to cocaine action or how it is influenced by epigenetic regulation in brain.
To address these limitations, we carried out a more comprehensive analysis of the cocaine-induced transcriptome and epigenome in the mouse NAc. We used ChIP-seq (ChIP followed by next-generation sequencing), which offers several advantages over ChIP-chip , to characterize numerous chromatin modifications within this brain region in response to repeated cocaine administration. We focused on several transactivation marks (H3K4me1, H3K4me3, and H3K36me3) and repression marks (H3K9me2, H3K9me3, and H3K27me3). These histone modifications were selected to cover enhancer (H3K4me1), promoter (H3K4me3, H3K27me3), gene body (H3K36me3), and intergenic (H3K9me2, H3K9me3) regions [20, 21]. We also analyzed binding of RNA polymerase II (RNA pol II). These ChIP-seq data were then overlaid onto RNA-seq data to capture cocaine-induced changes in gene expression, including those resulting from regulation of pre-mRNA alternative splicing.
Our findings identify many chromatin signatures - unique combinations of histone modifications that predict cocaine regulation of gene expression, a large portion of which is mediated by previously uncharacterized changes in alternative splicing. The robustness of this epigenomic analysis is further demonstrated by its ability to predict the involvement of a novel splicing factor, termed A2BP1 (also known as RBFOX1 or FOX-1), in cocaine action.
Cocaine-regulated transcriptomic changes in mouse nucleus accumbens
To characterize the transcriptome of mouse NAc, we used RNA-seq to measure the expression levels of all polyA containing transcripts in NAc of mice treated chronically with cocaine or saline (control); we used a standard treatment regimen of daily 20 mg/kg intraperitoneal doses of cocaine for 7 days with animals analyzed 24 h after the last dose, a procedure known to induce numerous, highly validated molecular and cellular adaptations to the drug . This procedure is also behaviorally relevant, as it induced locomotor sensitization, an extensively validated form of behavioral plasticity to repeated cocaine administration (Additional file 1). To account for inter-animal variations, we obtained three biological replicates for each treatment group, with each replicate representing NAc pooled from five animals. Samples were sequenced by an Illumina HiSeq2000 machine. We obtained 93 to 97 million short reads of 100 bp from each replicate. Of these, 65 to 67% were successfully aligned to a reference gene database (Ensembl: Mus musculus, NCBIM37.62) by TopHat . The quality of the data were assessed by the RNA-SeQC package, which revealed that approximately 95% and 81% of the mapped reads are intragenic and exonic, respectively, and that the sequencing data are not overrepresented by mitochondrial reads (Additional file 2). Overall, our aligned short reads represent 21,892,637,222 and 21,717,236,397 transcribed nucleotides for the mouse NAc transcriptome under cocaine and saline treatment, respectively. These data are sufficient to provide on average of approximately 183× coverage for mouse exomes under both conditions.
We used the Cufflinks package  to perform differential analysis for changes in gene expression. For our initial analysis, we used stringent false discovery rate (FDR) cutoffs of <10%, fold change >1.25, and Reads Per Kilobase transcript per Million reads (RPKM) >1, and identified 92 genes (61 increased, 31 decreased; Additional file 3) that are differentially expressed in NAc after repeated cocaine administration (see Materials and methods). To confirm that the expression changes identified reflect the actions of repeated, not acute, cocaine treatment, we performed RNA-seq on NAc obtained from mice treated with a single dose of cocaine, with animals analyzed 24 h later. The data were analyzed the same way and passed all quality assessments mentioned above (Additional file 2). We identified 55 genes (42 increased, 13 decreased; Additional file 3) that are differentially expressed in NAc in response to a single cocaine dose, only 4 of which overlapped with the chronic cocaine-regulated genes. In addition, two of the four genes showed the opposite direction of regulation. We therefore conclude that the vast majority of gene expression changes induced by repeated cocaine are very different from those induced by acute cocaine.
Combining the genes that show chronic cocaine-induced changes in alternative promoter usage or alternative splicing yields 2,998 genes that are differentially spliced, which represent 35% of all differentially expressed genes. To understand the functional roles of these differentially spliced genes, we performed gene ontology (GO) analysis [27, 28] and identified 110 (FDR <10%, Fisher’s exact test) enriched GO terms (Additional file 6). We created an enrichment map  (Figure 1B) to represent these functional terms and found that the differentially spliced genes are associated with very diverse functions and cellular components. With respect to molecular functions, two major groups are involved in nucleotide binding and ion binding, with one minor group participating in protein localization. In terms of cellular components, four major groups are involved in membrane enclosed lumen, actin cytoskeleton, cell junction, and membrane bounded vesicle, with four minor groups involving chromatin remodeling complex, synapse, neuron projection, and mitochondrion. These results suggest that differentially transcribed or spliced genes play substantial roles in the transcriptional perturbations induced in NAc by chronic cocaine.
Cocaine-regulated epigenomic changes in mouse nucleus accumbens
The lasting behavioral abnormalities induced by chronic cocaine treatment have been attributed, in part, to epigenomic changes involving post-translational modifications to histone tails [2, 29]. We thus chose six histone modifications (H3K4me1, H3K4me3, H3K9me2, H3K9me3, H3K27me3, and H3K36me3), as well as total RNA pol II, to investigate the epigenomic changes induced in mouse NAc by repeated cocaine exposure. They were chosen to ensure coverage of gene promoters, gene bodies, enhancers, as well as intergenic regions and to reflect mechanisms of gene activation and repression (see Background). We used three biological replicates for each mark, with each replicate again representing tissue pooled from five animals. After mapping the reads to the mouse reference genome, we removed the ones that are redundant at the same location and strand (Additional file 7). We thereby obtained uniquely aligned, non-redundant reads with a total number varying from 8 to 465 million for each of the seven marks under each condition (Additional file 8). Overall, these ChIP-seq data represent a highly informative collection of histone modifications and RNA pol II enrichment, comprising total read counts of 1,105,314,297 and 1,114,544,836 (Additional file 8) under cocaine and saline treatment, respectively.
e classified all differential sites into several categories based on their genomic locations (Figure 3D) and found that the seven marks can generally be divided into the following groups: 'promoter-centric' (H3K4me3), 'balanced' (H3K4me1 and H3K27me3), 'genebody-centric' (H3K36me3 and RNA pol II), and 'genebody + intergenic' (H3K9me2 and H3K9me3). The distributions of the differential sites are very similar to that of basal peaks (Additional file 11) for these marks. To further elucidate the potential functions of these chromatin changes, we performed molecular pathway analysis through IPA (Ingenuity Systems)  for the genes that are associated with the differential sites. In total, 248 canonical pathways were found to be enriched (P < 0.05, Fisher’s exact test). To identify the most important pathways, we ranked them by co-occurrence score among the seven marks and examined the top 30 (Additional files 12 and 13). We found that many of the top ranked pathways have been previously implicated in drug addiction, for example, 'CREB signaling in neurons' (top 2, score = 37.5), 'axonal guidance signaling' (top 7, score = 28.2), 'synaptic long term potentiation' (top 8, score = 27.8), and 'WNT/β-catenin signaling' (top 9, score = 26.5). In addition to the canonical pathways, we created three customized gene lists to represent additional knowledge of addiction pathophysiology and also found them to be enriched: 'actin cytoskeleton' (score = 25.0); 'synaptic plasticity' (score = 17.3); and 'growth factors' (score = 2.2). This further demonstrates that the chromatin changes we identified are highly specific to brain functions that contribute to addiction. Interestingly, the two marks that are associated with gene activation, H3K4me3 and RNA pol II, show much more pronounced enrichment than the other five marks among the top 30 pathways (Additional files 12 and 13).
Chromatin signatures associated with pre-mRNA alternative splicing
We next determined that the chromatin differential sites show sharp proximity to exons within a 10 kb window (Additional file 14), suggesting a possible role for the seven marks in cocaine-mediated pre-mRNA alternative splicing. Recent in vitro investigations showed that the splicing of exons into a mature mRNA occurs co-transcriptionally . Previous studies have also demonstrated that some histone marks can act as beacons in exon definition [34, 35] or play important roles in recruiting splicing factors to pre-mRNAs [36, 37]. We found in our dataset that all seven marks show local enrichment at exons to varying degrees and are correlated with transcriptional levels under basal conditions (Additional file 15). More specifically, exonic enrichments of H3K4me1, H3K4me3, H3K36me3, and total RNA pol II are positively correlated with transcriptional levels; that of H3K9me2, H3K9me3, and H3K27me3 are negatively correlated.
Despite these correlations, the interplay between histone marks and splicing regulators is complicated. For example, the repressive mark H3K9me3 has recently been found to facilitate the inclusion of variant exons of several genes via a mechanism that involves decreased RNA pol II elongation rate . Another study  investigated the roles of several histone marks in selection of two mutually exclusive exons between two human cell lines, and found that two groups (H3K27me3, H3K4me3, and H3K9me1 versus H3K36me3 and H3K4me1) of histone marks regulate the two exons in the opposite direction. However, how extensive and how exactly the histone modifications influence alternative splicing in vivo, especially under pathological conditions (for example, after chronic cocaine exposure), is unknown.
RNA-seq provides unique advantages for alternative splicing analysis . Some programs achieve this goal by looking at the read counts of individual exons [39, 40]. However, transcripts often share common exons whose read counts thus convey nothing unique about each transcript’s expression levels. On the other hand, each transcript must contain unique exonic regions, which provide information about the transcript’s expression level. The program we used - Cufflinks - assigns reads proportionally to each individual transcript by solving an optimization problem on the unique exonic read counts [24, 25]. In line with this approach, we needed a method to describe the chromatin changes associated with each transcript. We therefore devised a systematic approach called 'chromatin signature' that allowed us to profile the epigenomic changes associated with each transcript in a unified fashion (Additional files 7 and 16). In this analysis, it was important to use our broader Cufflinks evaluation of differential transcriptional changes, since it reduces false negative discovery rates, while overlaying such data with multiple chromatin endpoints achieves the higher stringency needed to reduce false positive discovery.
Briefly, we first classified all exons into six different types (Additional file 16): 'promoter', 'canonical', 'variant', 'alternative acceptor', 'alternative donor', and 'polyA'. Each exon type represents a unique combination of exon-intron boundaries. We also derived the neighboring intronic regions (150 bp) for each exon, which are also implicated in splicing regulation . In total, we defined 335,779 and 441,648 unique exonic and intronic regions on the genome and calculated each mark’s enrichment difference between cocaine and saline at each region (Additional file 7). In addition, we included the 500 bp intergenic regions upstream of the TSS and the enhancers defined by H3K4me1 peaks (Additional file 7) to complement a chromatin signature. We removed the shared regions (that is, canonical exons and introns at canonical acceptor and donor sites) from consideration as they do not convey distinct chromatin information between transcripts. This results in 13 different types of regions (Additional file 16) upon which chromatin modifications are defined for a transcript. Based on the resulting chromatin signatures, we constructed a matrix of approximately 77,000 (coding transcripts) × 91 (7 marks × 13 genomic regions; Additional files 7 and 16) to represent the splicing-related chromatin modifications mediated by cocaine for the entire mouse transcriptome.
Chromatin signature-associated protein regulators
We next used these chromatin signatures to infer the types of chromatin-associated proteins that may convert the epigenetic information into transcriptional change. The 29 signature clusters we identified represent groups of transcripts that share common chromatin modification patterns. This co-expression indicates that each of the clusters is co-regulated by a few common protein regulators that may interact with chromatin during transcription. We focused on transcription factors and splicing factors that may be involved in this process. To illustrate our approach, an example is given in Figure 5C-F. Briefly, the region-mark combinations that show significant chromatin change (P < 1E-10, one group t-test) in the same direction are first identified from each cluster. Each transcript is then analyzed and only the regions that show a significant chromatin binding difference (P < 0.05, one-tailed Student’s t-test) between cocaine and saline are picked for further analysis. The sequences of the same type of region (such as variant exon) from the same cluster are pooled and motif analysis (Additional files 7 and 20) by MEME  is performed. In this manner, we found 32 and 58 uniquely identifiable motifs from the intragenic and intergenic regions, representing potential splicing and transcription factors that control the 29 clusters, respectively.
A2BP1 is an important regulator of cocaine responses
As noted above, our motif analysis identified A2BP1 as a potentially important splicing factor that regulates clusters 8 and 9 (Figure 6). A2BP1 belongs to a family of RNA binding proteins that is composed of two other homolog splicing factors, RBFOX2 (RBM9 or FOX-2) and RBFOX3 (HRNBP3, NEUN, or FOX-3). Human A2BP1 was first identified through its interaction with Ataxin-2, the protein mutated in spinocerebellar ataxia type II . Mutations in the human A2BP1 gene have since been associated with several other neurological syndromes, including mental retardation, epilepsy, and autism spectrum disorders [54–57]. Recent studies also implicate A2BP1 in regulating neuronal excitability as well as neuronal adaptations to stress [58, 59]. Our RNA-seq data demonstrated that A2bp1 is highly expressed in mouse NAc (RPKM = 90, >97% of the genome in NAc). By binding to the CAUGCA motif, A2BP1 controls many neuronally regulated exons . Indeed, some A2BP1-dependent alternative exons have already shown dysregulated splicing in human autism spectrum disorders .
In clusters 8 and 9 (Figure 6), the site of discovery for the A2BP1 motif is located in promoter exons where H3K4me3 shows increased binding after cocaine. This indicates an interaction between this splicing factor and the histone tail modification, which has not to date been documented. We first validated such H3K4me3 enrichment at selected loci from clusters 8 and 9 with ChIP-quantitative PCR (Additional file 21). Next, we experimentally examined the physical binding between the two molecules. A co-immunoprecipitation assay demonstrated a significant enrichment of A2BP1 in the H3K4me3 immunoprecipitation pulldown from NAc extracts (Figure 2A). Notably, this binding between A2BP1 and H3K4me3 appeared to be specific, since no A2BP1 was detected in the IgG pulldown control samples.
We then performed a genome-wide scan  for the A2BP1 motif obtained from our analysis on the regions where the chromatin signatures were defined in this study, and found 37,993 hits (motif match P < 1E-4). We further intersected the genes whose exons or introns contain a predicted A2BP1 binding motif (n = 11,874) with the genes that contain H3K4me3 differential sites (n = 3,994) and found the overlap (n = 2,463) to be statistically significant (Figure 2B; P = 6E-45, Fisher’s exact test). This finding further strengthened the enrichment of A2BP1 at cocaine-regulated H3K4me3 binding sites. Moreover, the genes that display a significant A2BP1 and H3K4me3 interaction (n = 2,463) also show substantial overlap (Figure 2B; P = 1E-25, Fisher’s exact test) with cocaine-regulated genes (n = 2,866), including those displaying differential expression or alternative splicing. IPA analysis of the 478 cocaine-regulated, A2BP1-H3K4Me3 interaction genes (Figure 2B; Additional file 22) revealed 174 functional terms to be enriched (P = 0.05, Fisher’s exact test; Additional file 23), with the top five terms (Figure 2B) relating to neurite formation and synapse dynamics.
Though our RNA-seq data did not show significant cocaine regulation of A2bp1’s mRNA levels, we used western blotting to test whether chronic cocaine treatment alters A2BP1 at the protein level in NAc. Consistent with the mRNA finding, we did not observe a significant change of A2BP1 from whole NAc lysates (data not shown). However, chronic cocaine induced a >2.5-fold increase in A2BP1 protein levels in nuclear lysates (Figure 2C). This nucleus relocation is consistent with previous findings from cultured neurons that depolarization induces nuclear migration of A2BP1, which increases the splicing of A2BP1 target genes .
To gain further insight into the functional importance of A2BP1 in behavioral responses to cocaine, we carried out conditioned place preference (CPP) assays in mice with a local knockout of A2bp1 from NAc. CPP provides an indirect measure of drug reward. Adult floxed A2bp1 (A2bp1 loxP/loxP ) mice  were injected intra-NAc with an adeno-associated virus (AAV) vector expressing Cre-GFP or GFP alone. Though AAV-GFP-injected A2bp1 loxP/loxP mice developed a significant cocaine preference at a moderate drug dose (7.5 mg/kg), AAV-Cre-GFP-injected A2bp1 loxP/loxP mice displayed no place conditioning (Figure 2D). Thus, knockdown of A2bp1 in NAc decreased the rewarding effects of cocaine.
Lastly, we selected representative predicted A2BP1target genes within clusters 8 and 9 and tested whether conditional A2bp1 knockdown in NAc affects their expression. Consistent with our RNA-seq data, by use of Nanostring validation with independent tissue samples, we confirmed increased expression of Rps6kb2 and Zfp26, as well as decreased expression of Dvl1 and Ece2 (Figure 2E). Importantly, all of these chronic cocaine-triggered expression changes were lost when A2bp1 was conditionally knocked down in NAc; in fact, Ece2 displayed cocaine regulation in the opposite direction in the absence of A2bp1 (Figure 2F). These findings further support the importance of this splicing factor in cocaine action as inferred from our bioinformatic analyses.
Conclusion and discussion
Results of the present study provide the most complete profiling to date of the cocaine-induced transcriptome and epigenome in NAc. We defined the binding patterns of six histone modifications and of RNA pol II genome-wide under repeated cocaine and saline conditions and correlated these patterns with the repeated cocaine-induced transcriptome. We show that different histone modifications act in a combinational fashion to create chromatin signatures that correlate with altered gene expression and, more specifically, with dramatic cocaine regulation of alternative splicing. These findings not only provide fundamentally new insight into the mechanisms by which repeated exposure to cocaine regulates gene transcription in NAc, but they also provide important information concerning the basic mechanisms of transcriptional regulation in the brain in vivo.
Genome-wide mapping of histone modifications has emerged as a powerful means for characterizing the functional consequences of chromatin structure . However, most available studies are derived from cultured cell systems during differentiation, development, or reprogramming. Whether similar rules defined in these homogeneous cell populations in vitro also apply to the brain in vivo is the key step to expand future epigenetic research. Our profiling of multiple histone marks in mouse NAc thus presents a much needed public reference resource for the neuro-epigenome, as well as detailed knowledge of global chromatin changes that occur in a discrete region of adult brain in response to repeated cocaine administration. We found that the basal patterns of the six histone marks studied are similar to those demonstrated in simpler systems. However, within these constraints, cocaine induced robust modifications in each of these marks at numerous genes and non-genic loci. We also found that the various histone marks carry different weights for transcriptional regulation, and that the combinatory pattern of modifications (chromatin signature) ultimately defines the transcriptional response. Our expectation is that analysis of still additional histone modifications will yield an ever more comprehensive and accurate epigenetic regulation network. Selective analysis of the cocaine-induced epigenomes of the several neuronal and non-neuronal cell types in NAc, something not yet technically feasible, would further improve our understanding of such networks. Nevertheless, our findings to date highlight the power of histone modification profiling for identifying diverse functional groups and target genes involved in cocaine action.
An unexpected finding of our study is the dominant contribution of changes in alternative splicing induced in NAc in response to chronic cocaine. In contrast to approximately 100 genes that show cocaine regulation of total transcript levels, we demonstrated an order of magnitude more genes that display altered splicing. These data indicate that previous studies that relied on microarray analysis and thereby focused on total gene transcription without discrimination of isoform differences dramatically underestimated the degree to which cocaine modifies the NAc transcriptome. Alternative pre-mRNA splicing is a major source of protein diversity in higher eukaryotes, a process particularly important for genes expressed in the brain [18, 63, 64]. Though there have been sporadic papers on splicing regulation of particular genes in addiction models [65–67], the present study is the first comprehensive analysis of splicing regulation in response to chronic cocaine. Given the fact that products of different splicing isoforms often serve unique cellular functions, the characterization of individual transcripts instead of the whole gene represents an important advance for understanding the molecular adaptations that underlie cocaine action.
Though alternative splicing was traditionally thought to be a post-transcriptional event, based largely on the primary sequence of the RNA, recent research has demonstrated that pre-mRNA splicing is intimately linked to transcription and the chromatin architecture of the gene . The spliceosome is proposed to physically link to the transcriptional machinery through interactions between splicing factors and RNA pol II, and specific histone modifications have been shown to regulate alternative splicing in cell culture. For example, depolarization of cultured neurons triggers the skipping of exon 18 of the neural cell adhesion gene, a change accompanied by H3K9 hyper-acetylation around the exon . The effect of depolarization can be further potentiated by treating the cells with a histone deacetylase inhibitor. As another example, the fibroblast growth factor receptor 2 (Fgfr2) gene is alternatively spliced into two isoforms, Fgfr2-IIIb and -IIIc. The gene is enriched with H3K36me3 and H3K4me1 along the alternatively spliced region in mesenchymal cells where exon IIIc is transcribed, and with H3K27me3 and H3K4me3 in epithelial cells where exon IIIb is transcribed. Importantly, modulation of H3K36me3 or H3K4me3 levels by overexpression or down-regulation of their respective histone methyltransferases changes the tissue-specific alternative splicing pattern in a predictable fashion in cultured cells . These observations suggest that localized changes in histone modification signatures along an alternatively spliced region can change splicing outcome. Furthermore, it provides a novel means of regulating gene transcription (splicing) through epigenetic manipulation.
However, studies to date have been mainly performed in cell culture with a candidate gene approach. How histone modifications relate to alternative splicing at a more global level, within the brain in vivo and in response to environmental stimuli, remains unknown. By obtaining genome-wide maps of several histone modifications within a discrete region of brain under chronic cocaine conditions coupled with genome-wide analysis of alternative splicing patterns, we have identified 29 chromatin signatures that differentially predict alterations in gene expression and, more specifically, regulation of alternative splicing. The genes are highly concentrated in certain functional groups. These findings indicate that control of pre-mRNA alternative splicing by histone modifications is a general feature of biological regulation. Moreover, an unbiased motif analysis inferred unique sets of transcription factors and splicing factors that are associated with individual chromatin signatures.
As a proof of principle, we selected to further analyze one candidate splicing factor, A2BP1, which has not previously been studied in cocaine action. A2BP1 is a neuron-specific splicing factor that promotes either exon inclusion or skipping. It has been implicated in several neurodevelopmental and neuropsychiatric disorders such as autism spectrum disorder, mental retardation, epilepsy, bipolar disorder, and schizophrenia . The protein kinase WNK3 binds to A2BP1 and suppresses its splicing activity through a kinase activity-dependent cytoplasmic re-localization of A2BP1 . Our observation of nuclear translocation of A2BP1 after repeated cocaine exposure suggests a robust role of A2BP1 in alternative splicing even though there is no change in total cellular levels of the protein. Increased nuclear levels of A2BP1 might facilitate adaptive alterations of pre-mRNA splicing of A2BP1 target transcripts that affect cocaine responses. Analysis of brain-specific A2bp1 knockout mice revealed altered synaptic transmission, increased membrane excitability, and a predisposition to seizures . Though few changes are seen in total transcript abundance, A2bp1-deficient brain displays a variety of splicing changes related to genes mediating synaptic transmission and membrane excitability. Similar implication of A2BP1 targets in neural transmission, neuronal development, and maturation genes has been demonstrated in autism spectrum disorder and human neural stem cell studies [61, 69]. Through bioinformatic analysis, our genome-wide data predicted that A2BP1 associates with H3K4me3 in concert with the regulated splicing of target genes after repeated cocaine administration. Indeed, we verified that A2BP1 is associated with H3K4me3 in NAc in response to repeated cocaine administration. Moreover, we show that conditional knockdown of A2bp1 from the adult NAc dramatically impairs rewarding responses to cocaine, and we confirmed regulation of several predicted A2BP1 target genes in NAc whose regulation by repeated cocaine is lost upon knockdown of this splicing factor. In the future, it will be interesting to further investigate the mechanisms by which cocaine triggers A2BP1 translocation to the nucleus and the means underlying A2BP1 regulation of its gene targets, work which will contribute to a better understanding of the molecular mechanism of cocaine action.
It is important to emphasize that sequencing data obtained from brain is inherently noisier than that obtained from simpler systems such as cultured cells. One prominent example is Ttr, which encodes transthyretin, important for thyroid hormone and retinol transport. It is highly enriched in choroid plexus , although expression in retina and certain central neurons has been reported [72, 73]. As can be seen from Additional file 24, although our differential analysis shows that chronic cocaine regulates Ttr expression in NAc, this conclusion must be viewed with caution given the great variability in the cocaine and saline replicates. We therefore analyzed our entire differential gene list for genes that show similar large variance. Only three and two of the regulated genes show such variability in acute and chronic data, respectively, which underscores the importance of utilizing multiple statistical tests when evaluating RNA-seq datasets. The analyses also demonstrate that the differential lists reported in this study are generally sound, as substantiated further by the several levels of validation provided. Meanwhile, the source of the variability seen in Ttr and a small fraction of other genes remains unknown. One possible source of variability might be dissections of NAc. To gain insight into this possibility, we analyzed classes of genes known to be expressed either at very high levels or at relatively low levels in NAc versus surrounding brain regions, including the choroid plexus (Additional file 25). Among a list of over 100 choroid plexus-enriched genes  compared to striatum, only Ttr shows high variability; all of the others are consistently depleted in our datasets. The data also reveal strong consistency across replicates for NAc-enriched and -depleted genes. Thus, while dissecting a micronucleus from brain by necessity introduces some variability, these data argue for considerable consistency in our dissections. The analysis does, however, highlight systematic differences in expression levels of some genes seen across experiments: replicates are highly consistent within one experiment (for example, acute saline) but vary more between experiments (for example, acute versus chronic saline). Such 'batch' effects may reflect the different basal state of animals used at different times of experimentation, variability that is inherent in any in vivo experiment.
In any event, the results of this study confirm the important insight provided by the multiple platforms of analysis undertaken to better understand how repeated exposure to cocaine alters gene expression in NAc. By further mining these data, and carrying out similar analyses at different time points of cocaine exposure and cocaine treatment paradigms with additional epigenetic marks, it will be possible to ultimately explore the complete complex program of gene regulation that underlies important aspects of drug addiction.
Materials and methods
Cocaine treatment and nucleus accumbens dissection
Adult male C57BL/6 J mice (Jackson) 8 weeks old were used in this study. They were housed five per cage on a 12-h light-dark cycle at constant temperature (23°C) with free access to food and water ad libitum. Animals were habituated for at least 1 week before experimentation. For repeated cocaine treatment, animals received daily intraperitoneal injections for seven consecutive days of cocaine (Sigma-Aldrich, St. Louis, MO, USA) at 20 mg/kg body weight ('repeated cocaine'). Mice were used 24 h after the final injection. For acute cocaine treatment, mice received only one injection of cocaine at 20 mg/kg body weight on day seven after six daily intraperitoneal saline injections. Control mice for all groups received daily saline injections for seven days. Bilateral 14-gauge NAc punches were taken from each animal 24 h after the last injection. All animal protocols were approved by the Institutional Animal Care and Use Committee of Mount Sinai.
Locomotor activity assay
Mouse locomotor activity was tested as previously described . In brief, mice were injected with saline or cocaine (20 mg/kg) at the same time each day and placed in standard rat cages located inside a Photobeam Activity System (San Diego Instruments, San Diego, CA, USA). On day 0, mice were habituated to the cage for 30 minutes and then given a saline injection. On days 1 to 7, mice were given injections of cocaine. Horizontal ambulation was measured for 30 minutes after all injections.
Brain samples were homogenized in Trizol and processed according to the manufacturer’s instructions. RNA was purified with RNeasy Micro columns and Bioanalyzer confirmed that the RNA integrity numbers were >8.0. Total RNA (4 μg) was used for mRNA library construction following instructions of Illumina mRNA sample prep kit (catalog number RS-100-0801). Please refer to Additional file 7 for details. The RNA-seq read alignment and differential analysis were done using TopHat  and Cufflinks  packages. For our initial analysis, cutoffs were set as FDR <10%, fold change >1.25, and RPKM >1 for treatment and control groups. For subsequent broader analyses, we used an FDR cutoff of only <10%.
ChIP was performed as previously described [9, 13]. Antibodies were all ChIP grade from Abcam, Cambridge, MA, USA. Around 10 nanograms of input DNA or pull-down DNA were used for sequencing library preparation following the instructions of Illumina’s ChIP-seq sample prep kit (catalog number IP-102-1001). Please refer to Additional file 7 for details. The ChIP-seq read alignment was done using Illumina’s CASAVA pipeline. Please refer to Additional file 7 for details on further filtering. Differential analysis was done by diffReps  with window size 200 bp and moving size 20 bp. A FDR <10% was used as the significance cutoff. Global visualization for the ChIP-seq data was accomplished with a program called ngs.plot  (Additional file 7). Basal level peak calling was carried out using MACS  with the three saline replicates pooled and DNA input samples used as background.
The reference gene database was analyzed to extract the genomic coordinates for the six types of exons and neighboring intronic regions. These genomic coordinates were then compared against the ChIP-seq alignment files to determine the fold changes that were further assembled into chromatin signatures for clustering (see Additional file 7 for more details).
Nuclear protein isolation, co-immunoprecipitation, and western blotting
Nuclear protein isolation was done following a published protocol . Please refer to Additional file 7 for details. Immunoprecipitation was performed following a standard protocol with H3K4me3 antibody from Millipore, Billerica, MA, USA. Either nuclear protein or immunoprecipitated proteins were used for western blotting as described previously . Antibodies used in this experiment were A2BP1 (1:500; Abcam) and histone 3 (1:1,000; Abcam).
High quality RNA (RNA integrity number >8) was selected based on bioanalyzer examination. Up to 1,000 ng of total RNA samples were submitted to NanoString for analysis with the Gene Expression Assay. The code set was designed by the company with unique sequences. Raw counts for each assay were collected and normalized with the NanoString data analysis software nSolver. Both positive control and reference housekeeping genes were utilized for normalization of read counts.
A2bp1knockout mice, stereotaxic surgery, and conditional place preference
Adult (6 to 8 weeks old) A2bp1loxP/loxP mice were purchased from Jackson (stock number 014089) . AAV-Cre and AAV-GFP vectors were used, and stereotaxic intra-NAc injections were performed, as reported . Viral injection sites were verified by confirming the GFP signal in NAc slices under the microscope. Viral knockdown of A2bp1 was confirmed using quantitative PCR.
A standard, unbiased CPP procedure was utilized as described . In brief, 3 to 4 weeks after viral injection, when AAV-mediated expression is maximal, animals were pretested for 20 minutes in a photo-beam monitored box with free access to environmentally distinct chambers. The mice were then arranged into control and experimental groups with balanced pretest scores. Then mice underwent four 30-minute training sessions (saline in the morning and cocaine in the afternoon) over two days. On the test day, mice had 20 minutes of unrestricted access to all chambers and a CPP score was assigned by subtracting the time spent in the cocaine-paired chamber from the time spent in the saline-paired chamber. Cocaine was injected intraperitoneally at 7.5 mg/kg.
All the ChIP-seq and RNA-seq data have been deposited into the Gene Expression Omnibus with accession number GSE42811 with the exception of cocaine replicates 1 and 2 and saline replicates 1, 2, and 3 of the H3K9me3 ChIP-seq, which were previously deposited in the Gene Expression Omnibus, submission GSE24850.
conditioned place preference
green fluorescent protein
polymerase chain reaction
- RNA polII:
RNA polymerase II
transcriptional start site.
This work was supported by P01 DA008227 from the National Institute on Drug Abuse (EJN).
- Nestler EJ: Molecular basis of long-term plasticity underlying addiction. Nat Rev Neurosci. 2001, 2: 119-128. 10.1038/35053570.View ArticlePubMedGoogle Scholar
- Robison AJ, Nestler EJ: Transcriptional and epigenetic mechanisms of addiction. Nat Rev Neurosci. 2011, 12: 623-637. 10.1038/nrn3111.View ArticlePubMedPubMed CentralGoogle Scholar
- Day JJ, Sweatt JD: DNA methylation and memory formation. Nat Neurosci. 2010, 13: 1319-1323. 10.1038/nn.2666.View ArticlePubMedPubMed CentralGoogle Scholar
- Peixoto L, Abel T: The role of histone acetylation in memory formation and cognitive impairments. Neuropsychopharmacology. 2013, 38: 62-76. 10.1038/npp.2012.86.View ArticlePubMedGoogle Scholar
- Jonkman S, Kenny PJ: Molecular, Cellular, and Structural Mechanisms of Cocaine Addiction: A Key Role for MicroRNAs. Neuropsychopharmacology. 2013, 38: 198-211. 10.1038/npp.2012.120.View ArticlePubMedGoogle Scholar
- Feng J, Nestler EJ: Epigenetic mechanisms of drug addiction. Curr Opin Neurobiol. 2013, 23: 521-528. 10.1016/j.conb.2013.01.001.View ArticlePubMedPubMed CentralGoogle Scholar
- Renthal W, Maze I, Krishnan V, Covington HE, Xiao G, Kumar A, Russo SJ, Graham A, Tsankova N, Kippin TE, Kerstetter KA, Neve RL, Haggarty SJ, McKinsey TA, Bassel-Duby R, Olson EN, Nestler EJ: Histone deacetylase 5 epigenetically controls behavioral adaptations to chronic emotional stimuli. Neuron. 2007, 56: 517-529. 10.1016/j.neuron.2007.09.032.View ArticlePubMedGoogle Scholar
- LaPlant Q, Vialou V, Covington HE, Dumitriu D, Feng J, Warren BL, Maze I, Dietz DM, Watts EL, Iniguez SD, Koo JW, Mouzon E, Renthal W, Hollis F, Wang H, Noonan MA, Ren Y, Eisch AJ, Bolanos CA, Kabbaj M, Xiao G, Neve RL, Hurd YL, Oosting RS, Fan G, Morrison JH, Nestler EJ: Dnmt3a regulates emotional behavior and spine plasticity in the nucleus accumbens. Nat Neurosci. 2010, 13: 1137-1143. 10.1038/nn.2619.View ArticlePubMedPubMed CentralGoogle Scholar
- Maze I, Covington HE, Dietz DM, LaPlant Q, Renthal W, Russo SJ, Mechanic M, Mouzon E, Neve RL, Haggarty SJ, Ren Y, Sampath SC, Hurd YL, Greengard P, Tarakhovsky A, Schaefer A, Nestler EJ: Essential role of the histone methyltransferase G9a in cocaine-induced plasticity. Science. 2010, 327: 213-216. 10.1126/science.1179438.View ArticlePubMedPubMed CentralGoogle Scholar
- Malvaez M, Sanchis-Segura C, Vo D, Lattal KM, Wood MA: Modulation of chromatin modification facilitates extinction of cocaine-induced conditioned place preference. Biol Psychiatry. 2010, 67: 36-43. 10.1016/j.biopsych.2009.07.032.View ArticlePubMedPubMed CentralGoogle Scholar
- Kumar A, Choi K-H, Renthal W, Tsankova NM, Theobald DEH, Truong H-T, Russo SJ, LaPlant Q, Sasaki TS, Whistler KN, Neve RL, Self DW, Nestler EJ: Chromatin remodeling is a key mechanism underlying cocaine-induced plasticity in striatum. Neuron. 2005, 48: 303-314. 10.1016/j.neuron.2005.09.023.View ArticlePubMedGoogle Scholar
- Kennedy PJ, Feng J, Robison AJ, Maze I, Badimon A, Mouzon E, Chaudhury D, Damez-Werno DM, Haggarty SJ, Han MH, Bassel-Duby R, Olson EN, Nestler EJ: Class I HDAC inhibition blocks cocaine-induced plasticity by targeted changes in histone methylation. Nat Neurosci. 2013, 16: 434-440. 10.1038/nn.3354.View ArticlePubMedPubMed CentralGoogle Scholar
- Renthal W, Kumar A, Xiao G, Wilkinson M, Covington HE, Maze I, Sikder D, Robison AJ, LaPlant Q, Dietz DM, Russo SJ, Vialou V, Chakravarty S, Kodadek TJ, Stack A, Kabbaj M, Nestler EJ: Genome-wide analysis of chromatin regulation by cocaine reveals a role for sirtuins. Neuron. 2009, 62: 335-348. 10.1016/j.neuron.2009.03.026.View ArticlePubMedPubMed CentralGoogle Scholar
- Jenuwein T, Allis CD: Translating the histone code. Science. 2001, 293: 1074-1080. 10.1126/science.1063127.View ArticlePubMedGoogle Scholar
- Zhou VW, Goren A, Bernstein BE: Charting histone modifications and the functional organization of mammalian genomes. Nat Rev Genet. 2011, 12: 7-18.View ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10: 57-63. 10.1038/nrg2484.View ArticlePubMedPubMed CentralGoogle Scholar
- Luco RF, Allo M, Schor IE, Kornblihtt AR, Misteli T: Epigenetics in alternative pre-mRNA splicing. Cell. 2011, 144: 16-26. 10.1016/j.cell.2010.11.056.View ArticlePubMedPubMed CentralGoogle Scholar
- Li Q, Lee JA, Black DL: Neuronal regulation of alternative pre-mRNA splicing. Nat Rev Neurosci. 2007, 8: 819-831. 10.1038/nrn2237.View ArticlePubMedGoogle Scholar
- Park PJ: ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet. 2009, 10: 669-680.View ArticlePubMedPubMed CentralGoogle Scholar
- Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M: An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489: 57-74. 10.1038/nature11247.View ArticleGoogle Scholar
- Barski A, Cuddapah S, Cui K, Roh T-Y, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K: High-resolution profiling of histone methylations in the human genome. Cell. 2007, 129: 823-837. 10.1016/j.cell.2007.05.009.View ArticlePubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25: 1105-1111. 10.1093/bioinformatics/btp120.View ArticlePubMedPubMed CentralGoogle Scholar
- DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire M-D, Williams C, Reich M, Winckler W, Getz G: RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012, 28: 1530-1532. 10.1093/bioinformatics/bts196.View ArticlePubMedPubMed CentralGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010, 28: 511-515. 10.1038/nbt.1621.View ArticlePubMedPubMed CentralGoogle Scholar
- Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012, 7: 562-578. 10.1038/nprot.2012.016.View ArticlePubMedPubMed CentralGoogle Scholar
- Merico D, Isserlin R, Stueker O, Emili A, Bader GD: Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010, 5: e13984-10.1371/journal.pone.0013984.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4: 44-57.View ArticleGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37: 1-13. 10.1093/nar/gkn923.View ArticleGoogle Scholar
- Maze I, Nestler EJ: The epigenetic landscape of addiction. Ann N Y Acad Sci. 2011, 1216: 99-113. 10.1111/j.1749-6632.2010.05893.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Shen L, Shao N, Liu X, Nestler E: ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics. 2014, 15: 284-10.1186/1471-2164-15-284.View ArticlePubMedPubMed CentralGoogle Scholar
- Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, Koche RP, Lee W, Mendenhall E, O'Donovan A, Presser A, Russ C, Xie X, Meissner A, Wernig M, Jaenisch R, Nusbaum C, Lander ES, Bernstein BE: Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature. 2007, 448: 553-560. 10.1038/nature06008.View ArticlePubMedPubMed CentralGoogle Scholar
- Shen L, Shao N-Y, Liu X, Maze I, Feng J, Nestler EJ: diffReps: detecting differential chromatin modification sites from ChIP-seq data with biological replicates. PLoS One. 2013, 8: e65598-10.1371/journal.pone.0065598.View ArticlePubMedPubMed CentralGoogle Scholar
- Ingenuity Pathway Analysis. [http://www.ingenuity.com/]
- Andersson R, Enroth S, Rada-Iglesias A, Wadelius C, Komorowski J: Nucleosomes are well positioned in exons and carry characteristic histone modifications. Genome Res. 2009, 19: 1732-1741. 10.1101/gr.092353.109.View ArticlePubMedPubMed CentralGoogle Scholar
- Spies N, Nielsen CB, Padgett RA, Burge CB: Biased chromatin signatures around polyadenylation sites and exons. Mol Cell. 2009, 36: 245-254. 10.1016/j.molcel.2009.10.008.View ArticlePubMedPubMed CentralGoogle Scholar
- Sims RJ, Millhouse S, Chen CF, Lewis BA, Erdjument-Bromage H, Tempst P, Manley JL, Reinberg D: Recognition of trimethylated histone h3 lysine 4 facilitates the recruitment of transcription postinitiation factors and pre-mRNA splicing. Mol Cell. 2007, 28: 665-676. 10.1016/j.molcel.2007.11.010.View ArticlePubMedPubMed CentralGoogle Scholar
- Luco RF, Pan Q, Tominaga K, Blencowe BJ, Pereira-Smith OM, Misteli T: Regulation of alternative splicing by histone modifications. Science. 2010, 327: 996-1000. 10.1126/science.1184208.View ArticlePubMedPubMed CentralGoogle Scholar
- Saint-André V, Batsché E, Rachez C, Muchardt C: Histone H3 lysine 9 trimethylation and HP1γ favor inclusion of alternative exons. Nat Struct Mol Biol. 2011, 18: 337-344. 10.1038/nsmb.1995.View ArticlePubMedGoogle Scholar
- Katz Y, Wang ET, Airoldi EM, Burge CB: Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010, 7: 1009-1015. 10.1038/nmeth.1528.View ArticlePubMedPubMed CentralGoogle Scholar
- Anders S, Reyes A, Huber W: Detecting differential usage of exons from RNA-seq data. Genome Res. 2012, 22: 2008-2017. 10.1101/gr.133744.111.View ArticlePubMedPubMed CentralGoogle Scholar
- Chasin LA: Searching for splicing motifs. Adv Exp Med Biol. 2007, 623: 85-106. 10.1007/978-0-387-77374-2_6.View ArticlePubMedGoogle Scholar
- Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren JY, Li WW, Noble WS: MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009, 37: W202-W208. 10.1093/nar/gkp335.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhu H, Hasman RA, Barron VA, Luo G, Lou H: A nuclear function of Hu proteins as neuron-specific alternative RNA processing regulators. Mol Biol Cell. 2006, 17: 5105-5114. 10.1091/mbc.E06-02-0099.View ArticlePubMedPubMed CentralGoogle Scholar
- Yano M, Okano HJ, Okano H: Involvement of Hu and heterogeneous nuclear ribonucleoprotein K in neuronal differentiation through p21 mRNA post-transcriptional regulation. J Biol Chem. 2005, 280: 12690-12699. 10.1074/jbc.M411119200.View ArticlePubMedGoogle Scholar
- Merzdorf CS: Emerging roles for zic genes in early development. Dev Dyn. 2007, 236: 922-940. 10.1002/dvdy.21098.View ArticlePubMedGoogle Scholar
- Kuroyanagi H: Fox-1 family of RNA-binding proteins. Cell Mol Life Sci. 2009, 66: 3895-3907. 10.1007/s00018-009-0120-5.View ArticlePubMedPubMed CentralGoogle Scholar
- Fujii R, Takumi T: TLS facilitates transport of mRNA encoding an actin-stabilizing protein to dendritic spines. J Cell Sci. 2005, 118: 5755-5765. 10.1242/jcs.02692.View ArticlePubMedGoogle Scholar
- Fujii R, Okabe S, Urushido T, Inoue K, Yoshimura A, Tachibana T, Nishikawa T, Hicks GG, Takumi T: The RNA binding protein TLS is translocated to dendritic spines by mGluR5 activation and regulates spine morphology. Curr Biol. 2005, 15: 587-593. 10.1016/j.cub.2005.01.058.View ArticlePubMedGoogle Scholar
- McClellan KA, Ruzhynsky VA, Douda DN, Vanderluit JL, Ferguson KL, Chen D, Bremner R, Park DS, Leone G, Slack RS: Unique requirement for Rb/E2F3 in neuronal migration: evidence for cell cycle-independent functions. Mol Cell Biol. 2007, 27: 4825-4843. 10.1128/MCB.02100-06.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen D, Pacal M, Wenzel P, Knoepfler PS, Leone G, Bremner R: Division and apoptosis of E2f-deficient retinal progenitors. Nature. 2009, 462: 925-929. 10.1038/nature08544.View ArticlePubMedPubMed CentralGoogle Scholar
- Ferguson D, Koo JW, Feng J, Heller E, Rabkin J, Heshmati M, Renthal W, Neve R, Liu X, Shao N, Sartorelli V, Shen L, Nestler EJ: Essential role of SIRT1 signaling in the nucleus accumbens in cocaine and morphine action. J Neurosci. 2013, 33: 16088-16098. 10.1523/JNEUROSCI.1284-13.2013.View ArticlePubMedPubMed CentralGoogle Scholar
- Davis S, Bozon B, Laroche S: How necessary is the activation of the immediate early gene zif268 in synaptic plasticity and learning?. Behav Brain Res. 2003, 142: 17-30. 10.1016/S0166-4328(02)00421-7.View ArticlePubMedGoogle Scholar
- Shibata H, Huynh DP, Pulst SM: A novel protein with RNA-binding motifs interacts with ataxin-2. Hum Mol Genet. 2000, 9: 1303-1313. 10.1093/hmg/9.9.1303.View ArticlePubMedGoogle Scholar
- Bhalla K, Phillips HA, Crawford J, McKenzie OL, Mulley JC, Eyre H, Gardner AE, Kremmidiotis G, Callen DF: The de novo chromosome 16 translocations of two patients with abnormal phenotypes (mental retardation and epilepsy) disrupt the A2BP1 gene. J Hum Genet. 2004, 49: 308-311.View ArticlePubMedGoogle Scholar
- Barnby G, Abbott A, Sykes N, Morris A, Weeks DE, Mott R, Lamb J, Bailey AJ, Monaco AP: Candidate-gene screening and association analysis at the autism-susceptibility locus on chromosome 16p: evidence of association at GRIN2A and ABAT. Am J Hum Genet. 2005, 76: 950-966. 10.1086/430454.View ArticlePubMedPubMed CentralGoogle Scholar
- Martin CL, Duvall JA, Ilkin Y, Simon JS, Arreaza MG, Wilkes K, Alvarez-Retuerto A, Whichello A, Powell CM, Rao K: Cytogenetic and molecular characterization of A2BP1/FOX1 as a candidate gene for autism. Am J Med Genet B Neuropsychiatr Genet. 2007, 144: 869-876.View ArticleGoogle Scholar
- Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, Yamrom B, Yoon S, Krasnitz A, Kendall J, Leotta A, Pai D, Zhang R, Lee Y-H, Hicks J, Spence SJ, Lee AT, Puura K, Lehtimäki T, Ledbetter D, Gregersen PK, Bregman J, Sutcliffe JS, Jobanputra V, Chung W, Warburton D, King M-C, Skuse D, Geschwind DH, Gilliam TC, et al: Strong association of de novo copy number mutations with autism. Science. 2007, 316: 445-449. 10.1126/science.1138659.View ArticlePubMedPubMed CentralGoogle Scholar
- Gehman LT, Stoilov P, Maguire J, Damianov A, Lin CH, Shiue L, Ares M, Mody I, Black DL: The splicing regulator Rbfox1 (A2BP1) controls neuronal excitation in the mammalian brain. Nat Genet. 2011, 43: 706-711. 10.1038/ng.841.View ArticlePubMedPubMed CentralGoogle Scholar
- Amir-Zilberstein L, Blechman J, Sztainberg Y, Norton WH, Reuveny A, Borodovsky N, Tahor M, Bonkowsky JL, Bally-Cuif L, Chen A, Levkowitz G: Homeodomain protein otp and activity-dependent splicing modulate neuronal adaptation to stress. Neuron. 2012, 73: 279-291. 10.1016/j.neuron.2011.11.019.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee JA, Tang ZZ, Black DL: An inducible change in Fox-1/A2BP1 splicing modulates the alternative splicing of downstream neuronal target exons. Genes Dev. 2009, 23: 2284-2293. 10.1101/gad.1837009.View ArticlePubMedPubMed CentralGoogle Scholar
- Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH: Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011, 474: 380-384. 10.1038/nature10110.View ArticlePubMedPubMed CentralGoogle Scholar
- Grant CE, Bailey TL, Noble WS: FIMO: scanning for occurrences of a given motif. Bioinformatics. 2011, 27: 1017-1018. 10.1093/bioinformatics/btr064.View ArticlePubMedPubMed CentralGoogle Scholar
- Licatalosi DD, Darnell RB: Splicing regulation in neurologic disease. Neuron. 2006, 52: 93-101. 10.1016/j.neuron.2006.09.017.View ArticlePubMedGoogle Scholar
- Ranum LP, Cooper TA: RNA-mediated neuromuscular disorders. Annu Rev Neurosci. 2006, 29: 259-277. 10.1146/annurev.neuro.29.051605.113014.View ArticlePubMedGoogle Scholar
- Hishimoto A, Liu QR, Drgon T, Pletnikova O, Walther D, Zhu XG, Troncoso JC, Uhl GR: Neurexin 3 polymorphisms are associated with alcohol dependence and altered expression of specific isoforms. Hum Mol Genet. 2007, 16: 2880-2891. 10.1093/hmg/ddm247.View ArticlePubMedGoogle Scholar
- Mains RE, Kiraly DD, Eipper-Mains JE, Ma XM, Eipper BA: Kalrn promoter usage and isoform expression respond to chronic cocaine exposure. BMC Neurosci. 2011, 12: 20-10.1186/1471-2202-12-20.View ArticlePubMedPubMed CentralGoogle Scholar
- Moyer RA, Wang D, Papp AC, Smith RM, Duque L, Mash DC, Sadee W: Intronic polymorphisms affecting alternative splicing of human dopamine D2 receptor are associated with cocaine abuse. Neuropsychopharmacology. 2011, 36: 753-762. 10.1038/npp.2010.208.View ArticlePubMedGoogle Scholar
- Schor IE, Rascovan N, Pelisch F, Allo M, Kornblihtt AR: Neuronal cell depolarization induces intragenic chromatin modifications affecting NCAM alternative splicing. Proc Natl Acad Sci U S A. 2009, 106: 4325-4330. 10.1073/pnas.0810666106.View ArticlePubMedPubMed CentralGoogle Scholar
- Fogel BL, Wexler E, Wahnich A, Friedrich T, Vijayendran C, Gao FY, Parikshak N, Konopka G, Geschwind DH: RBFOX1 regulates both splicing and transcriptional networks in human neuronal development. Hum Mol Genet. 2012, 21: 4171-4186. 10.1093/hmg/dds240.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee AY, Chen W, Stippec S, Self J, Yang F, Ding XJ, Chen S, Juang YC, Cobb MH: Protein kinase WNK3 regulates the neuronal splicing factor Fox-1. Proc Natl Acad Sci U S A. 2012, 109: 16841-16846. 10.1073/pnas.1215406109.View ArticlePubMedPubMed CentralGoogle Scholar
- Marques F, Sousa J, Coppola G, Gao F, Puga R, Brentani H, Geschwind D, Sousa N, Correia-Neves M, Palha J: Transcriptome signature of the adult mouse choroid plexus. Fluids Barriers CNS. 2011, 8: 10-10.1186/2045-8118-8-10.View ArticlePubMedPubMed CentralGoogle Scholar
- Li X, Buxbaum J: Transthyretin and the brain re-visited: Is neuronal synthesis of transthyretin protective in Alzheimer's disease?. Mol Neurodegener. 2011, 6: 79-10.1186/1750-1326-6-79.View ArticlePubMedPubMed CentralGoogle Scholar
- Hovatta I, Zapala M, Broide R, Schadt E, Libiger O, Schork N, Lockhart D, Barlow C: DNA variation and brain region-specific expression profiles exhibit different relationships between inbred mouse strains: implications for eQTL mapping studies. Genome Biol. 2007, 8: R25-10.1186/gb-2007-8-2-r25.View ArticlePubMedPubMed CentralGoogle Scholar
- Bowyer J, Patterson T, Saini U, Hanig J, Thomas M, Camacho L, George N, Chen J: Comparison of the global gene expression of choroid plexus and meninges and associated vasculature under control conditions and after pronounced hyperthermia or amphetamine toxicity. BMC Genomics. 2013, 14: 147-10.1186/1471-2164-14-147.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nussbaum C, Myers RM, Brown M, Li W, Liu XS: Model-based Analysis of ChIP-Seq (MACS). Genome Biol. 2008, 9: 9-Google Scholar
- Ohnishi YN, Ohnishi YH, Hokama M, Nomaru H, Yamazaki K, Tominaga Y, Sakumi K, Nestler EJ, Nakabeppu Y: FosB is essential for the enhancement of stress tolerance and antagonizes locomotor sensitization by DeltaFosB. Biol Psychiatry. 2011, 70: 487-495. 10.1016/j.biopsych.2011.04.021.View ArticlePubMedPubMed CentralGoogle Scholar
- Vialou V, Robison AJ, LaPlant QC, Covington HE, Dietz DM, Ohnishi YN, Mouzon E, Rush AJ, Watts EL, Wallace DL: [Delta] FosB in brain reward circuits mediates resilience to stress and antidepressant responses. Nat Neurosci. 2010, 13: 745-752. 10.1038/nn.2551.View ArticlePubMedPubMed CentralGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate - a practical and powerful approach to multiple testing. J R Stat Soc B-Method. 1995, 57: 289-300.Google Scholar
- Jensen ST, Liu JS: Bayesian clustering of transcription factor binding motifs. J Am Stat Assoc. 2008, 103: 188-200. 10.1198/016214507000000365.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.