Matthew Zapf1 ‘11 and Matthew Anderson1,2

1Beth Israel Deaconess Medical Center, Department of Pathology; 2Principal Investigator

Angelman syndrome (AS) is a severe neurological disorder often involving epilepsy, autistic tendencies, mental retardation and ataxia. The genetic locus of the disorder has been identified as an inability to inherit a functional maternal copy of the gene UBE3A, which encodes for a ubiquitin ligase involved in proteasome-mediated degradation. Previous work localized largely in neuron synapses has identified putative mechanisms by which lack of maternal Ube3a causes various neuronal changes. Recently, the effect of the AS mutation on the transcriptome has been elucidated, suggesting nuclear Ube3a dysfunction in AS. To study the possible function of Ube3a on the transcriptome, we assessed hypotheses that the lack of Ube3a could result in increased levels of transcriptional repressor(s) that mediate the downregulated gene set.  Using the downregulated set of genes identified through an expression profiling study of AS mice, we proceeded to search for transcription factors involved in downregulation with the in silico methods cREMaG and PSCAN. Of the 17 hits identified, 12 demonstrated concurrent downregulation in the temporal window p7-p14 when assessed by temporally organized developmental gene expression profiling. Network analysis indicated that the expression level of many of the identified transcriptional factors is likely dependent on the concentrations of other identified transcription factors. Using this data, we suggest a model where a set of developmentally-related and expressionally-dependent transcription factors function as a toolkit gene set in mediating cerebellar development.  In this model, the lack of maternal Ube3a misregulates an intra-regulated transcription factor network employed in tissue development through the decrease of Sp1.


Angelman syndrome (AS) is a severe neurological disorder that affects between 1:10,000-1:40,000 newborns (Thomson, 2006; Clayton-Smith, 1993).  The clinical features of the disorder often include mental retardation, epileptic seizures, ataxia, consistent dysmorphic facial features, and language deficits especially involving speech (Williams, 2005).  The behavioral features of the disorder consist of easily provoked laughter, hyperactivity with sleep disturbances, and an affinity for social stimuli. These features provide a dependable basis for diagnosis (Clayton-Smith, 2003).

The genetic basis for AS has been well characterized as an inability for an individual to inherit a functional maternal copy of the gene UBE3A (Kishino, 1997; Horsthemke, 2008).  AS Mice, possessing this maternal genetic defect, show lower mRNA expression levels in the olfactory bulb, cerebellum and hippocampus (Jiang, 1998).  However, a recent proteomic analysis of AS mice revealed levels of Ube3a protein were lower than WT across the entire brain (Gustin, 2010).  At the intracellular level, Ube3a is expressed in the nucleus, presynaptic and post synaptic densities (Dindot, 2008).  Researchers have focused on these brain and neuron regions, which show maternal Ube3a expression, to study the pathogenesis of AS.

Ube3a has two well characterized modes of action: (1) as a steroid hormone co-activator involved in the transcriptional regulation of members of the nuclear hormone receptor superfamily, and (2) as an ubiquitin ligase, tagging substrate proteins for degradation (Greer, 2010; Nawaz, 1999).  Importantly, the misfunction of either mechanism has not been conclusively shown to cause AS (Nawaz, 1999).  Much research involving AS-associated Ube3a mutations has focused on the potential role of the protein at the synapse of neurons (Margolis, 2010; Greer, 2010, Yashiro, 2009; Weeber, 2003).  Previous work studying AS mouse hippocampal neurons found LTP and learning defects associated with reductions of active CaMKII in the postsynaptic density (Weeber, 2003).  Ube3a is also required for the experience-dependent maturation of excitatory cortical circuits in AS mice (Yashiro, 2009).  Along with aberrant synaptic plasticity and circuit maturation, morphological dendritic abnormalities have also been noted in AS mice at the synapse (Dindot, 2008).  Studies have recently identified potential molecular mechanisms by which reduced functional maternal Ube3a expression affects developmental and circuit properties of neurons at the synapse (Greer, 2010; Margolis, 2010).  However, recent evidence from the transcriptome of AS mice points toward the nucleus as an under-investigated locus.

A genome-wide expression profiling study revealed dozens of genes significantly differentially expressed in AS mice that are known to participate in signaling, nervous system development, and cell death (Low, 2010). This study exposed the neglected action of Ube3a on the transcriptome, suggesting a possible role for Ube3a in regulating the nucleus of cells.  With the knowledge that Ube3a can function as a ubiquinating enzyme or a coactivator of the nuclear hormone receptor superfamily, we hypothesized that Ube3a ubiquinates or transiently activates a molecule that affects the concentration of transcription factor(s) which modulate the expression of a subset of genes identified by the expression analysis.  AS mice demonstrated significantly lower expression of 57 genes, while showing higher expression of only 7 genes.  Therefore, we considered simple hypothesis 1: Ube3a could function to degrade a transcriptional repressor that regulates the AS mice downregulated gene set.  Without functional Ube3a in the cerebellar tissue, overabundance of that repressor results in the observed gene downregulation found in AS mice.  We also considered simple hypothesis 2: Ube3a could function to degrade multiple transcriptional repressors that together regulate the AS mice downregulated gene set.  Additionally, we considered a complex hypothesis: Ube3a could degrade or aid in activating transcription of a molecule whose concentration controls the expression levels of multiple transcription factors that together regulate the AS implicated gene set.

We applied in silico transcription factor motif finding software programs that are designed to analyze the genomic regions around genes to search for sequences that have high affinity for specific transcription factors.  Such genomic sequences with high affinity for transcription factors are called transcription factor binding sites (TFBSs).  Specifically, we analyzed the genomic region surrounding each of the downregulated genes in the AS mouse tissue set for common transcription factor binding motifs.  The in silico analysis was designed to identify transcription factors that might co-regulate the AS downregulated gene set to suggest the validity of simple hypotheses 1 and 2: Ube3a acts to influence expression of a set of genes by affecting levels of shared transcription factor or a set of shared transcription factors.  Analysis of identified target transcriptional regulators did not confirm the viability of simple hypotheses 1 or 2.  Network analysis and literature review of the in silico-identified target genes suggested the plausibility of the complex hypothesis: Ube3a affects the active concentration of a transcription factor that controls the expression of in silico-identified transcription factors.  We identified a set of tightly intraregulated transcriptional factors whose principal member, Sp1, is known to be positively regulated by functional Ube3a (Nawaz, 1999).


cREMaG identified 3 transcription factors likely to regulate the downregulated gene set

The cREMaG program was used to analyze the promoter region of 56 downregulated genes, searching for common transcription factor binding sites (TFBSs) likely to regulate multiple genes within the set.  The cREMaG software found TFBSs in significant subsets of downregulated genes that Lhx3, Mef2a and Pax4 were deemed likely to bind to.  All three transcription factors were identified as possible downregulated gene set regulators by the JASPAR TFBS database, while Mef2a was identified by both the JASPAR and TRANSFAC databases.  To identify transcription factors as possible regulators of the downregulated gene set, the average frequency of a given transcription factor binding site across the mouse genome was assessed along with the likelihood that the TFBS occurs on a given gene promoter region to compare to the experimental input.  For Lhx3, 55 binding sites were found (12.34 expected) and the promoters for 21 genes of the 54 (7.66 expected) were deemed likely to be regulated by the gene.  For the Mef2a matrix obtained from the JASPAR database, 54 binding sites were found (13.87 expected) and the promoters for 20 genes (8.2 expected) were deemed likely to be regulated by the gene.  For the Mef2a matrix obtained from the TRANSFAC database, 87 binding sites were found (18.53 expected) and the promoters for 31 genes (14.56 expected) were deemed likely to be regulated by the gene.  For Pax4, 58 binding sites were found (14.25 expected) and the promoters for 22 genes (8.38 expected) were deemed likely to be regulated by the gene.

Many of the downregulated genes were deemed likely to contain TFBSs for multiple identified transcription factors.  Each of the candidate transcription factors was predicted to regulate 20-22 of the 56 downregulated genes.  Lhx3, Pax4 and Mef2a were deemed likely to regulate 11 of the same genes from the downregulated gene set.  Taken together, this data suggests a co-regulation of a small subset of the downregulated genes by Lhx3, Pax4 and Mef2a.

Figure 1
Figure 1. Network analysis of in silico identified transcription factors groups regulators into developmental and gene expression related functional networks. Pathway analyses identified by the Core Analysis Program (IPA) using different subsets of in silico identified transcriptional regulators. (A) An illustration of a molecular pathway in which 11 of the 17 identified transcription factors and Ube3a are present. This pathway is known to be involved in gene expression and the development of the endocrine system. (B) An illustration of a molecular pathway in which the remaining 6 of the 17 identified transcription factors are present. This pathway is known to be involved in tissue development and gene expression. (C) An illustration of the molecular pathway created when the pathways identified in figures 2a and 2b are overlaid on each other. (D) The molecular pathway revealed by the identified tight interactive network of identified transcription factors. Sp1 is central to this pathway, directly and indirectly binding to and controlling expression of multiple in silico identified transcription factors.



PSCAN identified 14 transcription factors likely to regulate the set of downregulated genes

The PSCAN program was utilized to analyze the promoter region of 56 downregulated genes to search for transcription factor(s) that regulated multiple genes.  The PSCAN software found 15 Mus musculus TFBSs in a significant subset of downregulated genes using both the TRANSFAC and JASPAR databases.  The PSCAN software does not display the details of which subset of downregulated genes a given transcription factor is likely to regulate.  Rather, the software computes a p-value for the likelihood of a given TFBS regulating the set of downregulated genes.  The JASPAR database identified SP1, MZF1, MYF, REST, EWSR1-FLI1, PATZ1, PLAG1, PAX5 and EGR1.   The TRANSFAC database identified SP1, PAX4, PAX5, MAZR, TFAP4, E2F1, and NHLH1.  Two transcription factors (SP1, PAX5) were identified by both databases as likely to regulate genes in the downregulated set.

Network analysis of PSCAN and cREMaG identified transcription factors

The connections between identified transcription factors were studied due to the in silico-indicated overlap of Lhx3, Pax4 and Mef2a TFBSs on multiple downregulated genes and the PSCAN identification of many (15) transcription factors likely to regulate the set of downregulated genes.  Network analysis revealed that 11 of the 17 candidate transcription factors and Ube3a were present in a previously characterized molecular network implicated in gene expression and endocrine development (Figure 1a).  Additionally, the other 6 molecules were present in a molecular network implicated in gene expression and tissue development (Figure 1b). The two networks were merged and showed considerable degree of overlap (Figure 1c).

Expression analysis of in silico identified potential transcription factors in developing mouse cerebellar tissue

Network analysis revealed that all of the identified transcription factors interact in networks implicated in tissue development.  We hypothesized that identified transcription factors could act as toolkit genes that are redeployed in the development of the cerebellum.  In order to study the developmental expression patterns of this set of identified transcription factors in cerebellar tissue, the Cerebellar Development Transcriptome Database (CDT-DB) was mined for temporally organized mRNA expression levels of each gene.  Ten of the 17 identified transcription factors showed marked downregulation from p7 to p14 (Figure 2a) and 3 transcription factors showed marked upregulation during this temporal space (Figure 2b).  Taken together, the mRNA of 13 out of the 17 identified transcription factors were shown to be differentially-regulated from p7 to p14 in mouse cerebellar tissue.


Figure 2



Testing the simple degradation hypotheses

We identified a total of 17 transcription factors deemed likely to regulate the downregulated genes from Ube3m-/p+ mice using the in silico analysis tools cREMaG (3) and PSCAN (14).  Both software programs identified PAX4 gene as a likely regulator of the downregulated gene set.  Of the 17 identified transcription factors, 12 are characterized in the literature as negative regulators of transcription.  cREMaG software provides the gene subset that an identified transcription factor is likely to regulate and  PAX4, LHX3 and MEF2A were each indicated as likely to regulate between 20-22 of the downregulated genes. These predictions do not support simple hypothesis 1 because the misregulation of any one of them would not cause the downregulation of the gene set.  Nor do the predictions support simple hypothesis 2 because PAX4, MEF2A and LHX3 together were deemed likely to regulate 33 of the 57 downregulated genes, less than two thirds of the gene set.  The PSCAN software does not provide information as to which subset of downregulated genes that a candidate transcription factor is likely to regulate.  Therefore, we could not consider the predicted transcription factors from PSCAN in simple hypotheses 1 or 2.

Characterization of the in silico identified transcription factors and support for the complex hypothesis

Seven of the identified transcription factors were associated with neurological diseases that involve deficits in motor abilities similar to those apparent in AS, including Huntington’s disease, Parkinson’s disease, Jacobsen syndrome, multiple sclerosis and ataxia (Table 1).  Genome-wide association studies have uncovered specific genes such as NXN1 that are associated with multiple complex neurological disorders and behavioral predispositions like autism, schizophrenia and nicotine dependence (Kim, 2008; Rujescu, 2009; Bierut, 2007).  The association of a specific gene to complex behaviors and neurological disorders suggests that many neurally-employed genes likely serve complex roles in proper brain function and development that transcend their association to a single disorder.  Therefore, the high number of identified transcription factors associated with neurological diseases involving motor deficits supports the hypothesis that the misregulation of many of the identified transcription factors might contribute to the AS phenotype.


Table 1


Network analysis of the 17 transcription factors identified in silico indicated that 11 are deployed in controlling the development of the endocrine system (Figure 1a).  Interestingly, mutations in the paternal copy of UBE3A cause Prader-Willi syndrome, which shows high rates of endocrine dysfunction, suggesting that many of the identified transcription factors could be misregulated by a Ube3a mutation (Burman, 2001; Diene, 2010).  The function of 6 of the transcription factors has already been characterized in central nervous system development according to the IPA database (Table 1).  Due to the roles of many of the in silico-identified transcription factors in the development of the endocrine system and central nervous system, we explored the possibility that this gene set might also regulate the development of the cerebellum as a toolkit gene set.  We analyzed the developmental expression profiles of the identified transcription factors by mining the Cerebellar Development Transcriptome Database.  The mRNA of at least 13 of the identified transcription factors showed a downregulation or upregulation from days p7 to p14.  In light of the putative developmental roles of identified transcription factors in other tissues, the fact that 13 of the transcription factors showed marked expressional changes in this specific temporal window suggests their possible association in cerebellar development.

Evidence that all of the identified transcription factors are included in developmental pathways and many show similar spatio-temporal expressional regulation during cerebellar development suggests that identified transcription factors could act in an interactive network where the misregulation of a subset of transcription factors by a lack of functional Ube3a could result in expressional imbalances across the system.  In order to test this hypothesis, we mined the literature for evidence of identified transcription factors acting to regulate the expression of one another.  Intra-set regulation was indeed identified for many transcription factors in the literature.  For example, REST has been shown to negatively regulate SP1 as well as silence PAX4 (Plaisance, 2005; Kemp, 2003).  Sp1 and TFAP4 have been shown to enhance the E2F1 promoter (Ngwenya, 2003).  This data suggests an intra-set regulation network model, where the correct expression level of any transcription factor is dependent on the correct levels of all others.

Model for misregulation of an intraregulatory network of identified transcription factors by AS mutation

While many of the identified transcription factors show network interactions, eight of the transcription factors display a tight interactive network where they regulate the expression level of one another according to Ingenuity Pathway Analysis (Figure 1d).  This regulatory network of 8 identified transcription factors appears to be a good candidate for misregulation by maternal Ube3a absence. In fact, Ube3a expression has been shown to upregulate Sp1 in HeLa cells (Nawaz, 1999).  In this transcription factor intra-regulatory network, Sp1 plays a prominent role, interacting with Tfap4, Egr1 and Fli1 while directly activating E2f1 and indirectly repressing REST through GJDZ (Ku, 2009; Zhang, 2003; Czuwara-Ladykowska, 2001; Ngwenya, 2003; Plaisance, 2005; Kemp, 2003).  Sp1 could function as a lynchpin in this intradependent network of regulatory transcription factors.  By lowering the expression of Sp1, a host of transcription factors may be mis-expressed causing downstream expressional misregulation of transcription factor targets.

By exploring in silico results, we suggested how the misregulation of Sp1 in AS mice could cause downstream affects on the expression of genes controlled by a host of transcriptional factors.  While it is tempting to imagine that the ubiquination or hormone co-activator function of Ube3a directly controls the expression of these transcription factors in the nucleus, it is also possible that mutant Ube3a causes the downregulated gene set through mis-function outside of the nucleus.  Previous research has suggested that Ube3a mis-function leads to synaptic changes in neuronal cells (Margolis, 2010; Greer, 2010, Yashiro, 2009; Weeber, 2003).  Immediate early gene dysfunction caused by aberrant synaptic activity could possibly account for the expressional changes in the AS mouse (Okuno, 2011).  However, pieces of evidence support the hypothesis that lack of functional Ube3a affects the intraregulated set which causes the downregulation of genes in the AS mouse.  For example, Ube3a levels were shown to regulate the levels of Sp1 in HeLa cells, which lack synapses and other neuronal conditions that Ube3a misregulation have been shown to affect (Nawaz, 1999).  Also, the androgen receptor (AR), which Ube3a co-activates in its role as a nuclear hormone co-activator, was identified in the pathway analysis as an interacting partner to the in silico-identified transcription factor PATZ1 (Nawaz, 1999; Ramamoorthy, 2008).  The identified transcription factor network is intra-dependent in all functional concentrations and deployed in the development in other systems, and it largely shares common spatiotemporal expressional changes in cerebellar tissue.  If we consider the possible role of the indicated intraregulatory network in cerebellar development, we can speculate that the misregulation at juncture p7-p14 could not only affect direct transcription factor targets but also the correct upregulation and downregulation of cascades of genes involved in subsequent developmental processes that together contribute to the pathogenesis of Angelman syndrome.


Selection of co-regulated gene set

Low et al. performed Affymetrix Microarray gene expression analysis on mice that were maternally Ube3a deficient (Ube3m-/p+) compared to WT mice (2010).  Analysis revealed a set of 56 genes that showed at least a -1.5 fold change in expression in the Ube3m-/p+ mice compared to WT mice.  The downregulated gene set was also subjected to one-way ANOVA analysis comparing Ube3m-/p+ expression to WT expression and all 56 genes exhibited a significant difference in expression (p≤.05).  This set of 56 Ube3m-/p+ downregulated genes was used to identify transcription factors that would agree with the hypothesis that they shared a co-regulatory mechanism in in silico analyses.

cREMaG (cis-Regulatory Elements in the Mammalian Genome)

The cREMaG software was used to search the genetic area surrounding the downregulated genes for sequences common in the downregulated gene set that have affinity for specific transcription factors.  Ensembl gene identification numbers representing the Mus musculus Ube3m-/p+ downregulated gene set were entered into the cREMaG database interface, selecting for the identification and clustering of all known gene transcripts around their transcription start sites (TSSs) (Piechota, 2010).  For each gene, the non-coding area 10 kb upstream and 5 kb downstream of the TSS was analyzed using transcription factor binding site (TFBS) matrices from the public TRANSFAC and JASPAR databases (Bryne, 2008; Wingender, 2000).  Each TFBS match was assigned values corresponding to matrix score, conservation score, distance from gene start, and coding/non-coding values that weighed in their suppositious identification.

The TFBSs from each downregulated gene were compared against the expected background occurrence of those TFBSs in the Mus musculus genome.   Then, the number of downregulated genes that contained specific TFBSs was compared against the predicted background occurrence in all genes in the Mus musculus genome.  The difference between the number of downregulated genes indicated to contain a given TFBS and the expected frequency of Mus musculus genes predicted to contain that TFBS was computed, resulting in a z-score based on a Gaussian fold distribution.  The probability of obtaining a range of z-scores using a normal distribution was defined as the p-value of a given TFBS (Piechota, 2010).


Ref seq mRNA identification numbers representing the Mus musculus Ube3m-/p+ downregulated gene set were entered into the PSCAN database interface (Zambelli, 2009).  For each gene, the region 950 bp upstream from the TSS and 50 bp downstream of the transcription start site was analyzed with transcription factor binding site (TFBS) matrices from the public TRANSFAC and JASPAR databases.  TFBS with p-values of < .01 were considered significant as this p-value level effectively minimizes false positives (Zambelli, 2009).

Pathway analysis

Pathway analysis of identified transcription factors was conducted using the Ingenuity Pathway analysis software (Ingenuity System, Redwood City, CA, USA).

Temporal cerebellar development expression profiling

The Cerebellar Development Transcriptome Database (CDT-DB) was mined for temporally organized expression profiles of identified transcription factors (Sato, 2008).


Bierut LJ, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau OF, Swan GE, Rutter J, Bertelsen S, Fox L, Fugman D, Goate AM, Hinrichs AL, Konvicka K, Martin NG, Montgomery GW, Saccone NL, Saccone SF, Wang JC, Chase GA, Rice JP, Ballinger DG. (2007)  Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet. Jan 1; (1): 24-35.

Bryne JC, Valen E, Tang MH, Marstrand T, Winther O, da Piedade I, Krogh A, Lenhard B, Sandelin A. (2008).  JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update. Nucleic Acids Res. Jan; 36

Burman P, Ritzén EM, Lindgren AC. (2001). Endocrine dysfunction in Prader-Willi syndrome: a review with special reference to GH. Endocr Rev. Dec; 22 (6) :787-99.

Clayton-Smith J (1993).  Clinical research on Angelman syndrome in the United Kingdom: observations on 82 affected individuals. Am J Med Genet. Apr 1; 46 (1) :12-5.

Clayton-Smith J, Laan L. Angelman syndrome: a review of the clinical and genetic aspects. (2003). J Med Genet. Feb; 40 (2) :87-95.

Czuwara-Ladykowska J, Shirasaki F, Jackers P, Watson DK, Trojanowska M. (2001).  Fli-1 inhibits collagen type I production in dermal fibroblasts via an Sp1-dependent pathway. J Biol Chem. Jun 15; 276 (24) :20839-48.

Diene G, Mimoun E, Feigerlova E, Caula S, Molinas C, Grandjean H, Tauber M,. (2010). French Reference Centre for PWS. Endocrine disorders in children with Prader-Willi syndrome–data from 142 children of the French database. Horm Res Paediatr. 74 (2) :121-8.

Dindot SV, Antalffy BA, Bhattacharjee MB, Beaudet AL. (2008). The Angelman syndrome ubiquitin ligase localizes to the synapse and nucleus, and maternal deficiency results in abnormal dendritic spine morphology. Hum Mol Genet. Jan 1; 17 (1) :111-8.

Greer PL, Hanayama R, Bloodgood BL, Mardinly AR, Lipton DM, Flavell SW, Kim TK, Griffith EC, Waldon Z, Maehr R, Ploegh HL, Chowdhury S, Worley PF, Steen J, Greenberg ME. (2010)  The Angelman Syndrome protein Ube3A regulates synapse development by ubiquitinating arc. Cell. Mar 5; 140 (5) :704-16.

Gustin RM, Bichell TJ, Bubser M, Daily J, Filonova I, Mrelashvili D, Deutch AY, Colbran RJ, Weeber EJ, Haas KF. (2010). Tissue-specific variation of Ube3a protein expression in rodents and in a mouse model of Angelman syndrome. Neurobiol Dis. Sep; 39 (3) :283-91. PubMed PMID:20423730; PubMed Central PMCID: PMC2922926.

Horsthemke B, Wagstaff J., (2008). Mechanisms of imprinting of the Prader-Willi/Angelman region.  Am J Med Genet.  Aug; 146A (16): 2041-52.

Jiang YH, Armstrong D, Albrecht U, Atkins CM, Noebels JL, Eichele G, Sweatt JD, Beaudet AL. (1998). Mutation of the Angelman ubiquitin ligase in mice causes increased cytoplasmic p53 and deficits of contextual learning and long-term potentiation. Neuron. Oct; 21 (4) :799-811. PubMed PMID:9808466.

Kemp DM, Lin JC, Habener JF. Regulation of Pax4 paired homeodomain gene by neuron-restrictive silencer factor. (2003). J Biol Chem. Sep 12; 278 (37) :35057-62.

Kim HG, Kishikawa S, Higgins AW, Seong IS, Donovan DJ, Shen Y, Lally E, Weiss LA, Najm J, Kutsche K, Descartes M, Holt L, Braddock S, Troxell R, Kaplan L, Volkmar F, Klin A, Tsatsanis K, Harris DJ, Noens I, Pauls DL, Daly MJ, MacDonald ME, Morton CC, Quade BJ, Gusella JF. (2008) Disruption of neurexin 1 associated with autism spectrum disorder.  Am J Hum Genet. Jan; 82 (1): 199-207.

Kishino T, Lalande M, Wagstaff J. (1997)  UBE3A/E6-AP mutations cause Angelman syndrome. Nat Genet. Jan; 15 (1) :70-3.

Ku WC, Chiu SK, Chen YJ, Huang HH, Wu WG, Chen YJ. (2009). Complementary quantitative proteomics reveals that transcription factor AP-4 mediates E-box-dependent complex formation for transcriptional repression of HDM2. Mol Cell Proteomics. Sep; 8 (9) :2034-50.

Lein ES, Hawrylycz MJ, Ao N, Ayres M, Bensinger A, Bernard A, Boe AF, Boguski MS, Brockway KS, Byrnes EJ, Chen L, Chen L, Chen TM, Chin MC, Chong J, Crook BE, Czaplinska A, Dang CN, Datta S, Dee NR, Desaki AL, Desta T, Diep E, Dolbeare TA, Donelan MJ, Dong HW, Dougherty JG, Duncan BJ, Ebbert AJ, Eichele G, Estin LK, Faber C, Facer BA, Fields R, Fischer SR, Fliss TP, Frensley C, Gates SN, Glattfelder KJ, Halverson KR, Hart MR, Hohmann JG, Howell MP, Jeung DP, Johnson RA, Karr PT, Kawal R, Kidney JM, Knapik RH, Kuan CL, Lake JH, Laramee AR, Larsen KD, Lau C, Lemon TA, Liang AJ, Liu Y, Luong LT, Michaels J, Morgan JJ, Morgan RJ, Mortrud MT, Mosqueda NF, Ng LL, Ng R, Orta GJ, Overly CC, Pak TH, Parry SE, Pathak SD, Pearson OC, Puchalski RB, Riley ZL, Rockett HR, Rowland SA, Royall JJ, Ruiz MJ, Sarno NR, Schaffnit K, Shapovalova NV, Sivisay T, Slaughterbeck CR, Smith SC, Smith KA, Smith BI, Sodt AJ, Stewart NN, Stumpf KR, Sunkin SM, Sutram M, Tam A, Teemer CD, Thaller C, Thompson CL, Varnam LR, Visel A, Whitlock RM, Wohnoutka PE, Wolkey CK, Wong VY, Wood M, Yaylaoglu MB, Young RC, Youngstrom BL, Yuan XF, Zhang B, Zwingman TA, Jones AR. (2007). Genome-wide atlas of gene expression in the adult mouse brain. Nature. Jan 11; 445 (7124) :168-76.

Low D, Chen KS. Genome-wide gene expression profiling of the Angelman syndrome mice with Ube3a mutation. (2010). Eur J Hum Genet. Nov; 18 (11) :1228-35.

Margolis SS, Salogiannis J, Lipton DM, Mandel-Brehm C, Wills ZP, Mardinly AR, Hu L, Greer PL, Bikoff JB, Ho HY, Soskis MJ, Sahin M, Greenberg ME. (2010). EphB-mediated degradation of the RhoA GEF Ephexin5 relieves a developmental brake on excitatory synapse formation. Cell. Oct 29; 143 (3) :442-55.

Martin D, Tawadros T, Meylan L, Abderrahmani A, Condorelli DF, Waeber G, Haefliger JA. (2003). Critical role of the transcriptional repressor neuron-restrictive silencer factor in the specific control of connexin36 in insulin-producing cell lines. J Biol Chem. Dec 26; 278 (52) :53082-9.

Morrow EM. (2010). Genomic copy number variation in disorders of cognitive development. J Am Acad Child Adolesc Psychiatry. Nov; 49 (11) :1091-104.

Nawaz Z, Lonard DM, Smith CL, Lev-Lehman E, Tsai SY, Tsai MJ, O’Malley BW. (1999). The Angelman syndrome-associated protein, E6-AP, is a coactivator for the nuclear hormone receptor superfamily. Mol Cell Biol. Feb; 19 (2) :1182-9.

Ngwenya S, Safe S. (2003). Cell context-dependent differences in the induction of E2F-1 gene expression by 17 beta-estradiol in MCF-7 and ZR-75 cells. Endocrinology. May; 144 (5) :1675-85.

Okuno H. (2011).  Regulation and function of immediate-early genes in the brain: Beyond neuronal activity markers.  Neurosci Res. Mar; 69 (3): 175-86.

Piechota M, Korostynski M, Przewlocki R. (2010). Identification of cis-regulatory elements in the mammalian genome: the cREMaG database. PLoS One. Aug 31; 5 (8) :e12465.

Plaisance V, Niederhauser G, Azzouz F, Lenain V, Haefliger JA, Waeber G, Abderrahmani A. (2005). The repressor element silencing transcription factor (REST)-mediated transcriptional repression requires the inhibition of Sp1. J Biol Chem. Jan 7; 280 (1) :401-7.

Ramamoorthy S, Nawaz Z. (2008) E6-associated protein (E6-AP) is a dual function coactivator of steroid hormone receptors.  Nucl Recept Signal. Apr 18; 6: e006.

Rujescu D, Ingason A, Cichon S, Pietiläinen OP, Barnes MR, Toulopoulou T, Picchioni M, Vassos E, Ettinger U, Bramon E, Murray R, Ruggeri M, Tosato S, Bonetto C, Steinberg S, Sigurdsson E, Sigmundsson T, Petursson H, Gylfason A, Olason PI, Hardarsson G, Jonsdottir GA, Gustafsson O, Fossdal R, Giegling I, Möller HJ, Hartmann AM, Hoffmann P, Crombie C, Fraser G, Walker N, Lonnqvist J, Suvisaari J, Tuulio-Henriksson A, Djurovic S, Melle I, Andreassen OA, Hansen T, Werge T, Kiemeney LA, Franke B, Veltman J, Buizer-Voskamp JE; GROUP Investigators, Sabatti C, Ophoff RA, Rietschel M, Nöthen MM, Stefansson K, Peltonen L, St Clair D, Stefansson H, Collier DA. (2009) Disruption of the neurexin 1 gene is associated with schizophrenia.  Hum Mol Genet. Mar 1; 18 (5): 9888-96.

Sato A, Sekine Y, Saruta C, Nishibe H, Morita N, Sato Y, Sadakata T, Shinoda Y, Kojima T, Furuichi T. (2008). Cerebellar development transcriptome database (CDT-DB): profiling of spatio-temporal gene expression during the postnatal development of mouse cerebellum. Neural Netw. Oct; 21 (8) :1056-69.

Thomson AK, Glasson EJ, Bittles AH. (2006). A long-term population-based clinical and morbidity profile of Angelman syndrome in Western Australia: 1953-2003. Disabil Rehabil. Mar 15; 28 (5) :299-305.

Weeber EJ, Jiang YH, Elgersma Y, Varga AW, Carrasquillo Y, Brown SE, Christian JM, Mirnikjoo B, Silva A, Beaudet AL, Sweatt JD. (2003). Derangements of hippocampal calcium/calmodulin-dependent protein kinase II in a mouse model for Angelman mental retardation syndrome. J Neurosci. Apr 1; 23 (7) :2634-44.

Williams CA. (2005). Neurological aspects of the Angelman syndrome. Brain Dev. Mar; 27 (2) :88-94.

Wingender E, Chen X, Hehl R, Karas H, Liebich I, Matys V, Meinhardt T, Prüss M, Reuter I, Schacherer F. (2000). TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res. Jan 1; 28 (1) :316-9.

Yashiro K, Riday TT, Condon KH, Roberts AC, Bernardo DR, Prakash R, Weinberg RJ, Ehlers MD, Philpot BD. (2009). Ube3a is required for experience-dependent maturation of the neocortex. Nat Neurosci. Jun; 12 (6) :777-83.

Zambelli F, Pesole G, Pavesi G. (2009). Pscan: finding over-represented transcription factor binding site motifs in sequences from co-regulated or co-expressed genes. Nucleic Acids Res. Jul 1; 37 (Web Server issue) :W247-52.

Zhang X, Liu Y. 2003. Suppression of HGF receptor gene expression by oxidative stress is mediated through the interplay between Sp1 and Egr-1. Am J Physiol Renal Physiol. Jun; 284 (6) :F1216-25.