The Pharmacogenomics Journal (2007) 7, 38–47 & 2007 Nature Publishing Group All rights reserved 1470-269X/07 $30.00 www.nature.com/tpj ORIGINAL ARTICLE

Ethanol-responsive : identification of transcription factors and their role in metabolomics

RK Uddin and SM Singh Transcription factors (TFs) and their combinatorial control on cis-regulatory elements play critical role in the co-expression of genes. This affects the Department of Biology and Division of Medical interaction of genes in the transcriptome and thus may affect signals that Genetics, The University of Western Ontario, cascade through cellular pathways. Using a combination of bioinformatic London, Ontario, Canada approaches, we sought to identify such common combinations of TFs in a set of ethanol-responsive (ER) genes and assess the role of ethanol in affecting Correspondence: Dr SM Singh, Department of Biology and multiple pathways through their co-regulation. Our results show that the Division of Medical Genetics, The University of metallothionein genes are regulated by TF motifs cAMP responsive element Western Ontario, London, Ontario, N6A 5B7 binding (CREB) and metal-activated 1 and Canada. primarily involved in zinc ion homeostasis. We have also identified new target E-mail: [email protected] genes, Synaptojanin 1 and tryptophan hydroxylase 1, potentially regulated by this module. Altered arrangement of TF-binding sites in the module may direct the action of these and other target genes in intracellular signaling cascades, cell growth and/or maintenance. In addition to CREB, other key TFs identified are ecotropic viral integration site-1 and SP1. These modulate the contribution of the target ER genes in regulation and or programmed cell death. Multiple lines of evidence confirm the above findings and indicate that different groups of ER genes are involved in different biological processes and their co-regulation most likely results from different sets of regulatory modules. These findings associate the role of the ER genes studied and their potential TF modules with alcohol response pathways and phenotypes. The Pharmacogenomics Journal (2007) 7, 38–47. doi:10.1038/sj.tpj.6500394; published online 2 May 2006

Keywords: ethanol-responsive genes; bioinformatics; transcription factors; cis-regulatory modules; pathways

Introduction

The completion of a number of genome-sequencing projects, including and mouse, now offers a number of novel challenges. We have begun with such questions as which encodes which protein. Results have permitted us to investigate the interaction of gene products inside the signaling networks required for proper to accomplish cellular function(s). Conse- quently, identifying which regulatory factor or combination of factors activates or represses a specific gene is a prerequisite for understanding cell fate and Received 13 December 2005; revised 27 February 2006; accepted 1 March 2006; function. In this context, cis-acting-regulatory elements are important molecular published online 2 May 2006 switches that are turned on or off partly by specific trans-acting factors. Their Transcription factors in ethanol-responsive genes RK Uddin and SM Singh 39

interactions allow transcriptional regulation of a dynamic tributing to such phenotypes as alcohol preference, depen- network of gene activities controlling various biological dence and alcoholism, among others. The goal of this study processes such as , apoptosis, cell growth is to perform a comprehensive analysis on a set of ER genes8 and proliferation. Further, these interactions may be altered as a model for such studies. We plan to identify the by a variety of exposures and challenges, including alcohol. regulatory elements, TF motifs and cis-regulatory modules The physiological effects of alcohol are known to include (CRM) associated with these genes. The results will be drunkenness, toxicity and addiction leading to alcohol- portrayed onto pathway information from published litera- related health and societal problems.1–3 These effects are ture and will be used to associate genes and their regulatory mediated by alcohol’s effect on the expression of a large models with biological processes and functions. This will number of genes.4–8 We have established that alcohol causes advance our understanding of what genes and factors these altered expression of genes belonging to a number of ER genes are interacting with and how ER signals lead to a cellular pathways including stress response, ethanol meta- cascading effect. bolism, protein modification, gene regulation and cell signaling.7,8 Consequently, these genes affect multiple cellular events contributing both positively and negatively Results and discussion to a large number of biological pathways in ethanol response cascades.9 Therefore, an understanding of regula- Literature and promoter sequence analysis tory gene networks in ethanol response cascades is very Our initial analysis with Bibliosphere recognized 43 genes in critical. To this end, a functional analysis of cis-acting the literature fulfilling the condition that at least two of the elements harbored in the promoter sequences and their input genes were co-cited within an abstract. These genes corresponding transcription factors (TFs) is desirable. were found co-cited with over 450 other genes most of A central structural feature of the regulatory logic of cis- which were known TFs. Application of the regulatory regions is their combinatorial nature.10 In higher (GO) filter ‘biological process’ (to the results) categorized the eukaryotes, most promoters and gene-regulatory regions are input genes into subgroups according to their z-score comprised of an integrated network of modular or ‘compo- (direction and distance of deviations of an item from its site’ TF-binding sites (TFBS)10–12 whose specific arrangement distribution’s mean) values (Table 1). Genes in each determines the expression specificity of the gene(s). A set of subgroup are likely to represent a functionally correlated distinct TFBS that make up a regulatory constituent is called group based on their common GO annotation, the rationale a cis-regulatory module. Multiple TFs bind to composite being that most pathways belong to particular biological modules in a linked or coordinated manner. Control of this processes.13 Major GO biological process categories contain- network is hierarchical and progressive. It is likely that ing two or more input genes and significant z-scores were different sets of TFs bind to different sets of genes of various ‘zinc ion homeostasis,’ ‘regulation of / activ- functional categories. Precise understanding of which TFs ity,’ and ‘regulation of cell cycle and apoptosis.’ The top participate in a module to co-regulate which ethanol- scoring category was ‘zinc ion homeostasis’ with a z-score of responsive (ER) gene set, and how each participating TF 19.78 which included the metallothionein genes metal- itself is activated in the ER pathway, has now become a lothionein 1 (Mt1) and metallothionein 2 (Mt2). Each gene necessity. New strategies are required that aim to identify was then separately reanalyzed in Bibliosphere applying a both the cis-regulatory sequences of any given gene and the higher stringency (see Materials and methods) to identify trans-acting-regulatory factors that recognize these elements TFs that are co-cited in the literature with the genes in each as their target site(s). This is needed to elucidate the subgroup. Results presented in column 4 of Table 1 show mechanism underlying alcohol’s physiological effect con- that some TFs are subgroup specific e.g. Mtf1 (subgroup 1),

Table 1 Subgroup of ethanol-responsive genes with the GO annotations likely to represent functionally correlated groups

Subgroup (biological process) Significant z-score Input gene names Co-cited transcription factors

1. Zinc ion homeostasis 19.78 Mt1, Mt2 Mtf1a, Trp53a, Nr3c1a, Nfkb1, Usf1, Sp1, Jun and Stat3 2. Negative regulation of enzyme or 8.99–7.88 Gadd45g, Cdkn1a kinase or activity 3. Regulation of cell cycle 4.7 Erbb3, Cdkn1a, Gadd45g, Maff, Mafk, Mafg, Trp53a, Nfkb1a, Jun, Stat3, Btg3 , Mybl2, Esr2, Gata5, Sox10,Ar 4. Apoptosis/programmed cell death 4.15 Sgk, Gadd45g, Cdkn1a, sgk3, Trp53a, Nr3c1a, Nfkb1a, Sp1a, Jun, Trp53inp1 Stat3, Cebpb, Creb1, Catnb, E2f1, Sp3a, Mybl2, Nr4a3, Nr3c2, Cebpa, Lef4

Abbrevaiation: GO: gene ontology. Note: Transcription factors (TF) in bold face contain on at least one of the input gene promoters. aBinding site for this TF present in at least two of the input gene promoters.

The Pharmacogenomics Journal Transcription factors in ethanol-responsive genes RK Uddin and SM Singh 40 6 8 8 8 8 8 8 6 6 7 7

Mafk (subgroup 3) and Creb1 (subgroup 4) while few others À À À À À À À À À À À 10 10 10 10 10 10 10 10 10 10 are common in each group e.g. Trp53, Nr3c1, Nfkb1 and 10 0.2 Â Â Â Â Â Â Â Â Â Â Â Stat3. It is very likely that these TFs may perform a 0.00 ¼ ¼ P 2.4 1.7 7.9 7.9 7.9 7.9 7.9 7.9 4.8 8.0 8.0 spatiotemporal role in regard to these ER genes. P ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ P P P P P P P P P P Genes belonging to each subgroup were subjected to P extensive sequence and statistical analysis using Genomatix 14

tools FrameWorker and ModelInspector, and databases Significance score such as GPD (Genomatix promoter database, release 3, www.genomatix.de) in order to identify statistically signifi- cant combinations of TFs that may function as a CRM(s) and direct an effect on different cellular pathways. 50) ¼ FrameWorker returns CRM models that are common to the input sequences and that satisfy to the user parameters. These models contain elements that occur in the same order and within a 50 bp distance range in all (or a subset of) the (bp) ( input sequences. FrameWorker also determines the specifi- city of models by scanning each generated model with a background promoter sequence set of 5000 human promo-

ters. The results of this search are used to check whether the 1; Mt2: metallothionein 2; MTF1: metal-activated transcription factor models can also be found in a set of randomly selected promoters. The specificity score (P-value) is the probability of obtaining an equal or greater number of sequences with a model match in a randomly drawn sample of the same size as the input sequence set. The lower this probability is, the higher the specificity of the model. Based on the above criteria we selected CRM models (Table 2) with the best Presence of CRM Distance range scores and P-values from each of the ‘biological process’ subgroups. When selected models were further tested and scanned against the Genomatix human, mouse, or rat In input genes In % of promoters promoters using ModelInspector, the search results were also evaluated by calculation of z-scores for GO groups (categories ‘biological process’ and ‘molecular function’). This evaluated whether the genes identified by the model were functionally related.

Zinc ion homeostasis

The FrameWorker results showed that the top-scoring CRM -regulatory modules; EKLF: erythroid Kruppel-like factor; Erbb3: v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian); cis models in this category consisted of one two-element model (Zih_1), five three-element models (Zhi_2-6), and one four- element model (Zih_7) (Table 2). Interestingly, a number of TFBS motifs appeared common in different models, such as CREB, metal-activated transcription factor 1 (MTF1), E2FF, AHRR, EGRF and TBPF. Searching the mouse promoter database with ModelInspector revealed that models Zih_2, -regulatory module Zih_3, Zih_4, Zih_5, Zih_6 and Zih_7 did not receive any hit Cis (CRM) model with any promoter sequence. Moreover, literature analysis also verified the irrelevancy of these models. For example, Ccr_2 V$CREB+V$MAZF+V$EGRF+V$TBPF Cdkn1a, Maff 40 7–46 1.00/1.00 Zih_2Zih_3 V$CREB+V$MTF1+V$EKLFZih_4 V$HESF+V$E2FF+V$TBPFZih_5 V$E2FF+V$AHRR+V$MTF1Zih_6 V$E2FF+V$EGRF+V$MTF1Zih_7 V$E2FF+V$AHRR+V$TBPF V$E2FF+V$AHRR+V$EGRF+V$MTF1Ccr_3 Mt1, V$CREB+V$SP1F+V$EGRF+V$TBPF Mt2 Mt1, Mt1, Mt2 Mt2 Mt1, Mt2Apo_3 Mt1, Mt2 V$CREB+V$MZF1+V$IRFF Cdkn1a, Mt1, Maff Mt2 100 100 100 100 100 40 100 Sgk, Sgk3 6–47 26–50 6–36 9–36 25–49 1.00/1.00 1.00/1.00 26–50 8–46 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 40 1.00/1.00 10–44 1.00/1.00 the TF motif AHRR predicted in models Zih_4, Zih_6 and name Apo_1 V$CREB+V$SP1FApo_2 V$CREB+V$SP1F+V$DMTF Cdkn1a, Sgk3, Cdkn1a, Trp53inp1 Sgk3 60 40 7–42 10–42 0.75/1.00 0.67/1.00 Zih_7, is known as a negative regulator of AH (AHR), which mediates most of the toxic and biochemical -regulatory modules present in gene set belonging to each biological process subgroup 15,16 effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin. Major cis involvement of AHRR has been reported in the dioxin/ AHR-signaling pathway17,18 which has no relevancy with zinc ion homeostasis. Similarly, erythroid Kruppel-like factor (EKLF), a TF in model Zih_2, is specific to erythroid Potential cells and binds to the CACCC element in promoters.19 The tissue distribution of EKLF indicates that its expression is 1. Zinc ion homeostasis Zih_1 V$CREB+V$MTF1 Mt1, Mt2 100 46–47 1.00/1.00 limited to hematopoietic organs and intimately involved in Table 2 Subgroup (biological process) Model 2. Regulation of cell cycle3. Ccr_1 Apoptosis/programmed cell death V$EVI1+V$EVI1Abbrevaiation: Cdkn1a: -dependent kinaseEVI1: inhibitor ecotropic 1A viral (p21); integration site-1; CRM: Maff:1; v- Sgk: musculoaponeurotic serum/glucocorticoid-regulated fibrosarcoma oncogene kinase. family,All protein the F (avian); elements Mt1: in metallothionein the CRM were contained within 50 bp range. Erbb3, Cdkn1a, Maff, Btg3 80 5–41 1.00/1.00

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the establishment and/or maintenance of the erythroid cell have the potential to bind to a large number of gene phenotype.19–21 promoters including Mt1 and Mt2 whose function stretch Model Zih_1, however, generated a total of 18 matches in beyond zinc ion homeostasis (Table 3). These TFs may affect 15 sequences in the mouse promoter database by ModelIn- a number of cellular pathways affecting intracellular-signal- spector. Once ModelInspector identifies significant hits ing cascades and finally contributing to cell growth and/or from the promoter database match for a model, it also maintenance. evaluates GO biological process and function that is to be MTF1 is best known to be a required TF for the basal and represented by the gene set containing the model and metal (e.g. zinc) induced transcription of metallothionein calculates significance (z-score). For model Zih_1, category genes Mt1 and Mt238 and also believed to play a generalized ‘zinc ion homeostasis’ was again overrepresented and role in regulating genes involved in protection against heavy evaluated with the highest z-score (e.g. 43.00 and 70.26, metals and oxidative stress.39–42 Therefore, any model respectively) consisting of Mt1 and Mt2 genes (Table 3). This involving metallothionein genes in the ‘zinc-ion home- analysis also identified a new target gene, Synaptojanin 1 ostasis’ category should include MTF1 transcription factor. (Synj1), for module Zih_1 in the ‘intracellular-signaling On the other hand, CREB is known to control gene cascade’ category and the gene tryptophan hydroxylase 1 expression for a variety of functions in the central nervous (Tph1) in ‘metal ion binding’ category. Although not much system.43 Exposure to ethanol causes changes in basal and is known about Synj1 in relation to ethanol or alcohol receptor-stimulated cAMP production, CREB phosphoryla- response, it is known to be involved in the regulation of tion and CRE-mediated gene expression in brain cell synaptic vesicle function22 and has been studied as a lines.44–46 Previous studies have suggested that CREB might potential candidate gene for psychiatric disorders including be associated with both anxiety and alcohol preference.47,48 bipolar disorder.23,24 Haplodeficiency of the CREB gene and ethanol-induced Interestingly, the Tph1 gene product is known as a rate- decreases in CREB function have been shown to be limiting enzyme in the biosynthesis of serotonin and its associated with increased alcohol drinking in mice.43 Thus, activity is most abundant in the brain.25 Alterations in brain it is very likely that changes in CREB expression via ethanol serotonin (5-hydroxytryptamine) levels as well as distur- may affect the functioning of the CRM modules Zih_1 and bances in central serotonergic transmission are important the after effect will likely be seen not only in metal ion contributing factors in the pathogenesis of many psychiatric homeostasis but also in ethanol response pathways. disorders26,27 including alcoholism.28–30 Tryptophan hydro- It is known that different cis-elements, trans-acting xylase 1 alone has been reported to have a genetic factors, and/or use of alternative promoters can control association with and represent a major candidate gene for the quantitative and spatiotemporal-specific expression of various psychiatric disorders31 such as bipolar disorder,32 genes.49,50 Distance variability is also known to influence alcohol dependence,33 affective disorders and alcoholism.34– the selectivity of TFs to particular cis-elements.51 Therefore, 36 However, no reports on TFs involved in the expression of it is plausible that CREB and MTF1, as in model Zih_1, Tph1 were found. One possibility regarding how this gene regulate the expression of Synj1, Tph1, Mt1 and Mt2, whose might be activated involves the TFs MTF and CREB as in function contribute to ‘metal ion binding and homeostasis’. model Zih_1 (Figure 1a). Metallothionein genes Mt1 and On the other hand, they may join in an alternate config- Mt2, which we found to share the same regulatory module uration and regulate the expression of Mt1 and Mt2 along Zih_1, have been reported to have association with alcohol with many other genes (Table 3) to exert their effect on preference in mice.37 Further studies are required to other cellular processes such as ‘intracellular signaling investigate the exact mechanism by which Synj1 and Tph1 cascade’ and ‘cell growth and maintenance.’ genes may contribute in alcohol-related behavior. Reanalysis of model Zih_1 by changing the distance range Regulation of cell cycle of the motifs from 5–50 to 5–100 bp reiterated above Extensive analysis with the ER genes in subgroup 3 findings providing further proof of the major involvement suggested three potential models: Ccr_1, Ccr_2 and Ccr_3. of Mt1 and Mt2 genes in ‘zinc ion homeostasis’ through this Model Ccr_1 is represented by TF ecotropic viral integration model. Modified model Zih_1_mod yielded a total of 714 site-1 (EVI1) which binds as a dimer in the promoters of 80% non-redundant matches in 703 sequences in the database. of the genes (Table 2). The other two models consist of motif In addition, the result showed a global involvement of the CREB, SP1F, EGRF, TBPF and MAZF, however, their binding TF motifs of Zih_1_mod in ‘intracellular signaling cascade’ elements are present in only 40% of the promoters. and ‘cell growth and/or maintenance.’ In the former Interestingly, growth arrest DNA-damage-inducible 45 gam- category, in addition to Mt1 and Mt2, 35 other genes were ma has been identified as a target gene of proto-oncogene found to share the same model whereas in the latter EVI1.52 Since a majority of the ER genes (e.g. v-erb-b2 category, 117 other genes were identified as potential targets erythroblastic leukemia viral oncogene homolog 3 (avian) of this model. (Erbb3), cyclin-dependent kinase inhibitor 1A (p21) These findings suggest that the TFs CREB and MTF1 in (Cdkn1a), v-maf musculoaponeurotic fibrosarcoma onco- their original pattern can bind to gene promoters which gene family, protein F (avian) (Maff) and B-cell translocation solely function in ‘zinc/metal ion homeostasis.’ However, in gene 3 (Btg3)) contain the CRM model Ccr_1 with a alternate binding position and orientation, CREB and MTF1 statistically significant score, this makes EVI1 the most

The Pharmacogenomics Journal Transcription factors in ethanol-responsive genes RK Uddin and SM Singh 42

Table 3 Significant association of selected CRM models and associated genes with different GO categories. New target gene(s) of each model is also shown

CRM model Significant GO group z-score Original input genes New target genes

GO category ‘biological process’ Zih_1 Zinc ion homeostasis 43.00 Mt1, Mt2 Nitric oxide-mediated signal transduction 37.23 Mt1, Mt2 Metal ion homeostasis 17.47 Mt1, Mt2 Inorganic cation homeostasis 12.28 Mt1, Mt2 Metal ion homeostasis 11.64 Mt1, Mt2 Cation homeostasis 10.07 Mt1, Mt2 Ion homeostasis 9.79 Mt1, Mt2 Cell ion homeostasis 9.79 Mt1, Mt2 Cell homeostasis 8.92 Mt1, Mt2 Intracellular signaling cascade 4.25 Mt1, Mt2 Synj1

GO category ‘molecular function’ Metal ion binding 5.03 Mt1, Mt2 Tph1

GO Category ‘biological process’ Zih_1_mod Zinc ion homeostasis 6.10 Mt1, Mt2 Nitric oxide-mediated signal transduction 5.19 Mt1, Mt2

Intracellular-signaling cascade 4.52 Mt1, Mt2 Als2, Centg2, Dok5, Pard6b, Bcl10, Gnb1, Cipp, Mcf2l, Pdlim3, Snx1, Snx14, Rab6b, Gna11, Spred2, Tshr, Dok3, 5830461H18Rik, Rapgef3, Synj1, 2700099C19Rik, Def8, Grid2ip, Ksr, Pdzk6, Arfgef2, AA691260, Gnai2, Fyn, Shc2, Rgs6, Dgkg, Rab12, Apba1, Rasal1, Rasd2

Cell growth and/or maintenance 4.17 Mt1, Mt2 Slc39a10, Erbb4, Sept2, Centg2, Hdlbp, Etnk2, D930050H05Rik, 5930412E23Rik, Slc30a1, Crat, Anapc2, 1700013L23Rik, Timm13a, Mcm8, Pax6, Rad51, Rbms1, Mrps26, E2f1, Atp9a, Pard6b, Rims4, Ccna2, Abcd3, Pitx2, D3Ertd330e, Bcl10, Khdrbs1, Kif17, Pla2g2a, Clcn6, Park7, Nfia, Ccni, Sfxn5, Gfpt1, Ipo8, Napa, Pold1, Trpc2, Stx4a, Ccnd1, Rps19, Mcf2 l, Atp11a, Pdlim3, Srpr, Anln, Ccnb2, Snx1, Snx14, Rab6b, Shprh, Hey2, Epb4.1l2, Sec63, Vps26, Abca5, Mrc2, Hist1h2bc, Hist1h2ba, Hexb, Sap18, Mscp, Nefl, Lrp10, Mtss1, Ap2m1, Clcn2, C630015F21Rik, Grik1, Synj1, Vil2, Chd1, 2310051D06Rik, Hmga1, Pacsin1, Hdac1, Kcng3, Hspa9a, Kcnn2, Tcof1, Camk2a, Atp9b, Nolc1, 2700099C19Rik, Ophn1, Kif4, Copg2, Slc7a5, Pkn1, Gan, Hps4, Atp5j2, Grid2ip, Atp5b, Ddit3, Tcirg1, 4933400A11Rik, Ltf, Hist3h2ba, Myh10, Tcf7, Adamts2, Dach1, Cd47, Cpeb1, Arntl, Slc5a2, Igf2, Fyn, Rab12, Cdc25c, Apba1, Lzts2, Kifc3, Tnfsf13

GO category ‘biological process’ Ccr_1 Regulation of cell cycle — Erbb3, Cdkn1a, Maff, Btg3 Apo_1 Apoptosis/programmed cell death — Cdkn1a, Sgk3, Trp53inp1 Apo_3 Apoptosis 7.01 Sgk, Sgk3 Programmed cell death 6.93 Sgk, Sgk3 Cell death 6.60 Sgk, Sgk3 Protein amino-acid phosphorylation 5.89 Sgk, Sgk3 Phosphate metabolism 5.07 Sgk, Sgk3

Abbrevaiations: Btg3: B-cell translocation gene3; Cdkn1a: cyclin-dependent kinase inhibitor 1A (p21); CRM: cis-regulatory modules; Erbb3: v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian); GO: gene ontology; Maff: v-maf musculoaponeurotic fibrosarcoma oncogene family, protein F (avian); Mt1: metallothionein 1; Mt2: metallothionein 2; Sgk: serum/glucocorticoid-regulated kinase; Trp53inp1: transformation related protein 53 inducible nuclear protein 1.

The Pharmacogenomics Journal Transcription factors in ethanol-responsive genes RK Uddin and SM Singh 43

a P872298

Mt1 618 NT_078575 Mus musculus 618 bp

P879657 Mt2 601 NT_078575 Mus musculus 601 bp

100 bp V$MTF1 V$CREB

b

P844542 Erbb3 601 NT_081856 Mus musculus 601 bp

P912896 Cdkn1a NT_039649 624 Mus musculus 624 bp

P945001 Maff NT_039621 736 Mus musculus 736 bp

P935984 Btg3 NT_039625 601 Mus musculus 601 bp

100 bp V$EVI1

c P912896 Cdkn1a NT_039649 624 Mus musculus 624 bp

P881981 660 Sgk3 NT_039169 Mus musculus 660 bp

P965980 1 11 1111 1 1 Trp53inp1 NT_039258 601 Mus musculus 601 bp

100 bp V$SP1F V$CREB

Figure 1 Organization of transcription factor (TF) motifs in the promoter region of the ethanol-responsive (ER) genes in biological process category (a) ‘zinc ion homeostasis,’ (b) ‘regulation of cell cycle’ and (c) ‘apoptosis/programmed cell death. prominent candidate TF as a regulator of these genes Apo_1, Apo_2 and Apo_3 (Table 2). Although TF motif CREB involved in cell cycle regulation (Figure 1b). However, is present in all of them, model Apo_1 (Figure 1c) further analysis is required to validate the involvement of representing CREB and SP1 is present in three of the five model Ccr_1 in the co-regulation of these genes in this ER genes (60%). Interestingly, Bibliosphere analysis also context. identified these two TFs co-cited with this group of ER genes. Previous findings show that the transcriptional activation of Apoptosis/programmed cell death serum/glucocorticoid-regulated kinase (Sgk) depends on an Similar analysis with the ER genes in subgroup ‘apoptosis/ intact Sp1-binding site within the proximal promoter.53 programmed cell death’ revealed three potential models, Numerous studies have also shown that SP1 binding within

The Pharmacogenomics Journal Transcription factors in ethanol-responsive genes RK Uddin and SM Singh 44

the Cdkn1a promoter is required for its expression in and Synj1, which acts in intracellular-signaling cascades. different cell and tissue environments.54–57 Similarly, CREB Alternate configurations of CREB and MTF1 may direct the is known to be an important transcriptional co-activator function of the Mt1 and Mt2 genes along with over 100 that acts with other factors to regulate gene expression of others, to act in an ‘intracellular-signaling cascade’ and ‘cell Cdkn1a58,59 and play a role in many cell differentiation and growth and maintenance.’ We have found that regulatory signal transduction pathways.60 CREB has also been identi- models involving CREB and SP1F can modulate the expres- fied in model Zih_1 in the Mt1 and Mt2 genes and is known sion of Cdkn1a, Sgk3 and transformation related protein 53 to be involved in alcohol-related phenotypes. These findings inducible nuclear protein 1 (Trp53inp1) in apoptosis and confirm the validity of our result and suggest that CREB and programmed cell death. These predictions seem logical, SP1, as in model Apo_1, may participate in the co-regulation since the action of ethanol has been implicated to affect a of this group of genes whose co-expression may contribute number of cellular systems including signal transduction, to ethanol’s effect on apoptosis or programmed cell death. apoptosis and cell differentiation.8,9,70 The Mt2 gene has Aside from the serotonergic and cAMP-mediated path- previously been shown to be associated with ethanol ways as discussed above in relation to Tph1 and CREB preference.37 We have used multiple methods in combina- correspondingly, there are a number of other biochemical tion to verify these results repeatedly. Therefore, the final pathways known to be modified by alcohol use. These result is supported by several independent but complemen- include pathways for alcohol metabolism involving alcohol tary lines of evidence, such as literature searches with dehydrogenase and aldehyde dehydrogenase and their incorporated GO ranking for ER genes, promoter sequence many variants,61–65 the gabaergic system involving g- analysis and statistical analysis for motifs, sequence analysis amino-butyric acid receptors,66,67 the dopaminergic system against promoter databases, GO ranking for models with involving dopamine receptors,66–69 and the glutamate statistical scoring, and additional manual literature searches receptor system.67 We did not find any regulatory elements for final screening. Future research on these TFs will help us or modules in our current research that may be involved in understand how they themselves are activated, whether by such pathways. However, ER genes known to be involved in initial ethanol trigger or by other signals triggered by each of these pathways could be studied together following ethanol, and will join TF elements together in a module the approach discussed in this manuscript. In addition, an that acts to activate the ER genes. extensive comparative analysis should be carried out. This may reveal some interesting information about the regula- Materials and methods tory control of these genes in the transcriptome and provide more insight into the regulatory mechanism of alcohol Gene selection for analysis action on biochemical pathways. A total of 53 ER genes were selected (supplementary Table 1) which were identified through differential display and Conclusion microarray analysis, based on their differential expression Earlier we proposed9 that ethanol’s action on genes and in mouse brain in response to ethanol in a previous study by cellular pathways is eventuated as a ‘domino effect’ where Treadwell and Singh.8 The results are based on total RNA the action of one or a set of genes affects the action of others isolated from whole brains, which were collected 6 h after which again cascades through the pathways and generates a intraperitoneal injection of (6 g/kg) ethanol (25%) or saline global effect on metabolomics. Here, we sought a solution to for weight-matched control from C57BL/6J (B6) and DBA/2J a part of that bigger puzzle. It is important to understand the (D2) strains of mice.8 The microarray results were confirmed links in each interaction to understand the complete by reverse transcription-polymerase chain reaction-(PCR) network. Genes that are responsive to ethanol and function and real-time PCR as appropriate. in certain pathways must be regulated by common mechan- isms such as cooperative TFs containing regulatory modules. Literature analysis In this research, we have identified such CRM for sets of ER Each selected gene belonged to one or more GO annotation genes belonging to different GO biological pathways and groups.9 This analysis was performed to subgroup our initial shown how different models of CRM may affect their gene set into overrepresented GO groups based on the contribution towards different pathways. Only a subgroup results from literature analysis. It was performed using of all co-expressed genes could be shown to be related to co- BiblioSphere (version 5.13, Genomatix Software, www.ge- regulation but this is due to the facts that co-expression can nomatix.de) system, which integrates literature mining be based on a variety of co-regulatory mechanisms and (scientific abstracts) with GO annotation analysis. Locuslink complete functional and biological information is not yet identifiers of the 53 genes (supplementary Table 1) were available for many of the genes. However, our analyses show introduced into the BiblioSphere and the GO filter ‘biolo- the presence of statistically significant and distinct TF gical process’ was applied to the result set. Each subgroup models contributing to different biological processes. In was scored by a z-score. Only subgroups with significant z- the ‘zinc ion homeostasis’ category, the genes Mt1 and Mt2 score value and meaningful association with the pathways are activated by CREB and MTF1. We have also identified potentially affected by ethanol were selected (Table 1). two new target genes of this model: Tph1, which may also Genes in each selected subgroup pertaining to specific function in the same pathway as the metallothionein genes, GO biological process category were reanalyzed with

The Pharmacogenomics Journal Transcription factors in ethanol-responsive genes RK Uddin and SM Singh 45

Bibliosphere for identification of TFs co-cited with as many Functional association of the input genes as possible. Some of these TFs may be PubMed literature search was performed to identify any potential candidates for common regulators. Selection connection between identified TF gene and ethanol. Biblio- criteria for TFs were; first, each TF gene must be co-cited sphere was used to project the results obtained onto logical with at least two input genes; second, each TF gene must be biological pathways. at least two times co-cited with one input gene and finally, the co-citation must be at the sentence level in the abstract describing some function, that is co-citation was restricted Abbreviations to sentences with order ‘gene y function word y gene.’ Btg3 B-cell translocation gene 3 Cdkn1a cyclin-dependent kinase inhibitor 1A (p21) CRM cis-regulatory modules Transcription factor modeling ER ethanol responsive Promoter sequences were extracted using the program Erbb3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 Gene2Promoter (Genomatix software suite 3.4.1, www.ge- (avian) GO gene ontology nomatix.de) using default settings, 500 bp upstream and Mt1 metallothionein 1 100 bp downstream of the transcription start site. Genes Mt2 metallothionein 2 belonging to a GO biological process group were analyzed Maff v-maf musculoaponeurotic fibrosarcoma oncogene family, together as a subset. It is known that the co-regulation of protein F (avian) Sgk serum/glucocorticoid-regulated kinase mammalian genes usually depends on sets of TFs rather than TF transcription factor 71 individual factors alone and cis-acting-regulatory elements TFBS transcription factor binding sites are often organized into defined frameworks of two51 or Tph1 tryptophan hydroxylase 1 more TFBS72 and clusters of such motifs73 Therefore, the Trp53inp1 transformation related protein 53 inducible nuclear protein 1 ‘FrameWorker’ task of GEMS Launcher (version 4.1, Geno- matix software, www.genomatix.de) was used to construct significantly common conserved CRMs generally consisting of two or more cis-elements where potential TF motifs bind Acknowledgments on the promoter sequences of the input gene set. For this purpose, first, the promoter sequences of the genes were Special thanks to Morgan Kleiber for her thorough and critical scanned for matches to the MatInspector TF matrix family review, which has helped to improve this manuscript. This work was supported by grants from the National Science and Engineering library version 5 (all vertebrate section). The TF matches Research Council, the Ontario Mental Health Foundation and the found were then used as basic motifs for the extraction of Canadian Institute of Health Research. common CRM models by FrameWorker. The search was carried out setting the quorum constraint (the lower limit of sequences within the input set that has to contain the common CRM) from 40 to 100%. The distance range within Duality of interest which the cis-elements will be contained was set to 5–50 bp. CRM models with the best FW-scores and P-values were None declared. selected for ModelInspector analysis. References

ModelInspector. (Genomatix software, www.genomatix.de) 1 Crabbe JC. Genetic contributions to addiction. Annu Rev Psychol 2002; uses these models to scan DNA sequences for matches to 53: 435–462. these models. This way we can also verify the specificity of 2 Ryabinin AE. Role of hippocampus in alcohol-induced memory impairment: implications from behavioral and immediate early gene the models generated by FrameWorker. This approach also studies. Psychopharmacology (Berlin) 1998; 139: 34–43. has the ability to identify other potential target genes of 3 Torres G, Horowitz JM. Individual and combined effects of ethanol and FrameWorker defined CRM models. For DNA sequences, cocaine on intracellular signals and gene expression. Prog Neuropsycho- Genomatix mouse promoter database was selected which pharmacol Biol Psychiatry 1996; 20: 561–596. 4 Fan L, Bellinger F, Ge YL, Wilce P. Genetic study of alcoholism and novel contained promoters for annotated mouse genes. The gene expression in the alcoholic brain. Addict Biol 2004; 9: 11–18. threshold for the output of model matches was set to 5 Lewohl JM, Wang L, Miles MF, Zhang L, Dodd PR, Harris RA. Gene 100% (i.e., all elements of the model have to be present in expression in human alcoholism: microarray analysis of frontal cortex. the match for a model to appear in the output). Alcohol Clin Exp Res 2000; 24: 1873–1882. 6 Mayfield RD, Lewohl JM, Dodd PR, Herlihy A, Liu J, Harris RA. Patterns of gene expression are altered in the frontal and motor cortices of human alcoholics. J Neurochem 2002; 81: 802–813. Model optimization 7 Murphy BC, Chiu T, Harrison M, Uddin RK, Singh SM. Examination of The FastM51 task of GEMS Launcher was used to optimize ethanol responsive liver and brain specific gene expression, in the selected models by changing the distance variability mouse strains with variable ethanol preferences, using cDNA expression between the TFBS. The new distance range was set to arrays. Biochem Genet 2002; 40: 395–410. 8 Treadwell JA, Singh SM. Microarray analysis of mouse brain gene 5–100. Each modified model was further assessed by expression following acute ethanol treatment. Neurochem Res 2004; 29: ModelInspector. 357–369.

The Pharmacogenomics Journal Transcription factors in ethanol-responsive genes RK Uddin and SM Singh 46

9 Uddin RK, Treadwell JA, Singh SM. Towards unraveling ethanol-specific 32 Lai TJ, Wu CY, Tsai HW, Lin YM, Sun HS. Polymorphism screening and neuro-metabolomics based on ethanol responsive genes in vivo. haplotype analysis of the tryptophan hydroxylase gene (TPH1) and Neurochem Res 2005; 30: 1179–1190. association with bipolar affective disorder in Taiwan. BMC Med Genet 10 Michelson AM. Deciphering genetic regulatory codes: a challenge for 2005; 6: 14. functional genomics. Proc Natl Acad Sci USA 2002; 99: 546–548. 33 Sun HS, Fann CS, Lane HY, Chang YT, Chang CJ, Liu YL et al. A 11 Remenyi A, Scholer HR, Wilmanns M. Combinatorial control of gene functional polymorphism in the promoter region of the tryptophan expression. Nat Struct Mol Biol 2004; 11: 812–815. hydroxylase gene is associated with alcohol dependence in one 12 Smith JL, Freebern WJ, Collins I, De Siervi A, Montano I, Haggerty CM et aboriginal group in Taiwan. Alcohol Clin Exp Res 2005; 29: 1–7. al. Kinetic profiles of p300 occupancy in vivo predict common features 34 Nielsen DA, Virkkunen M, Lappalainen J, Eggert M, Brown GL, Long JC of promoter structure and coactivator recruitment. Proc Natl Acad Sci et al. A tryptophan hydroxylase gene marker for suicidality and USA 2004; 101: 11554–11559. alcoholism. Arch Gen Psychiatr 1998; 55: 593–602. 13 Seifert M, Scherf M, Epple A, Werner T. Multievidence microarray 35 Paik I, Toh K, Kim J, Lee C. TPH gene may be associated with suicidal mining. Trends Genet 2005; 21: 553–558. behavior, but not with schizophrenia in the Korean population. Hum 14 Cartharius K, Frech K, Grote K, Klocke B, Haltmeier M, Klingenhoff A et Hered 2000; 50: 365–369. al. MatInspector and beyond: promoter analysis based on transcription 36 Chung IW, Kim H, Sribney W, Hong JB, Lee CH, Lee KY et al. factor binding sites. Bioinformatics 2005; 21: 2933–2942. Tryptophan hydroxylase polymorphism is associated with age of onset 15 Korkalainen M, Linden J, Tuomisto J, Pohjanvirta R. Effect of TCDD on of alcoholism related behaviors. Alcohol 2005; 36: 1–3. mRNA expression of genes encoding bHLH/PAS in rat 37 Loney KD, Uddin KR, Singh SM. Strain-specific brain metallothionein II hypothalamus. Toxicology 2005; 208: 1–11. (MT-II) gene expression, its ethanol responsiveness, and association 16 Tsuchiya Y, Nakajima M, Itoh S, Iwanari M, Yokoi T. Expression of aryl with ethanol preference in mice. Alcohol Clin Exp Res 2003; 27: 388–395. hydrocarbon receptor repressor in normal human tissues and induci- 38 Heuchel R, Radtke F, Georgiev O, Stark G, Aguet M, Schaffner W. The bility by polycyclic aromatic hydrocarbons in human tumor-derived cell transcription factor MTF-1 is essential for basal and heavy metal-induced lines. Toxicol Sci 2003; 72: 253–259. metallothionein gene expression. EMBO J 1994; 13: 2870–2875. 17 Soneda S, Fukami M, Fujimoto M, Hasegawa T, Koitabashi Y, Ogata T. 39 Solis WA, Childs NL, Weedon MN, He L, Nebert DW, Dalton TP. Association of micropenis with Pro185Ala polymorphism of the gene for Retrovirally expressed metal response element-binding transcription aryl hydrocarbon receptor repressor involved in dioxin signaling. Endocr factor-1 normalizes metallothionein-1 gene expression and protects J 2005; 52: 83–88. cells against zinc, but not cadmium, toxicity. Toxicol Appl Pharmacol 18 Evans BR, Karchner SI, Franks DG, Hahn ME. Duplicate aryl hydrocarbon 2002; 178: 93–101. receptor repressor genes (ahrr1 and ahrr2) in the zebrafish Danio rerio: 40 Wang Y, Wimmer U, Lichtlen P, Inderbitzin D, Stieger B, Meier PJ et al. structure, function, evolution, and AHR-dependent regulation in vivo. Metal-responsive transcription factor-1 (MTF-1) is essential for embryo- Arch Biochem Biophys 2005; 441: 151–167. nic liver development and heavy metal detoxification in the adult liver. 19 Miller IJ, Bieker JJ. A novel, erythroid cell-specific murine transcription FASEB J 2004; 18: 1071–1079. factor that binds to the CACCC element and is related to the Kruppel 41 Guerrerio AL, Berg JM. Metal ion affinities of the domains of family of nuclear proteins. Mol Cell Biol 1993; 13: 2776–2786. the metal responsive element-binding transcription factor-1 (MTF1). 20 Drissen R, von Lindern M, Kolbus A, Driegen S, Steinlein P, Beug H et al. Biochemistry 2004; 43: 5437–5444. The erythroid phenotype of EKLF-null mice: defects in hemoglobin 42 Bi Y, Palmiter RD, Wood KM, Ma Q. Induction of metallothionein I by metabolism and membrane stability. Mol Cell Biol 2005; 25: 5205– phenolic antioxidants requires metal-activated transcription factor 1 5214. (MTF-1) and zinc. Biochem J 2004; 380: 695–703. 21 Chen X, Bieker JJ. Stage-specific repression by the EKLF transcriptional 43 Pandey SC, Chartoff EH, Carlezon Jr WA, Zou J, Zhang H, Kreibich AS et activator. Mol Cell Biol 2004; 24: 10416–10424. al. CREB gene transcription factors: role in molecular mechanisms of 22 Verstreken P, Koh TW, Schulze KL, Zhai RG, Hiesinger PR, Zhou Y et al. alcohol and drug addiction. Alcohol Clin Exp Res 2005; 29: 176–184. Synaptojanin is recruited by endophilin to promote synaptic vesicle 44 Asher O, Cunningham TD, Yao L, Gordon AS, Diamond I. Ethanol uncoating. Neuron 2003; 40: 733–748. stimulates cAMP-responsive element (CRE)-mediated transcription via 23 Stopkova P, Vevera J, Paclt I, Zukov I, Lachman HM. Analysis of SYNJ1, a CRE-binding protein and cAMP-dependent protein kinase. J Pharmacol candidate gene for 21q22 linked bipolar disorder: a replication study. Exp Ther 2002; 301: 66–70. Psychiatr Res 2004; 127: 157–161. 45 Constantinescu A, Gordon AS, Diamond I. cAMP-dependent protein 24 Saito T, Guan F, Papolos DF, Lau S, Klein M, Fann CS et al. Mutation kinase types I and II differentially regulate cAMP response element- analysis of SYNJ1: a possible candidate gene for 21q22- mediated gene expression: implications for neuronal responses to linked bipolar disorder. Mol Psychiatr 2001; 6: 387–395. ethanol. J Biol Chem 2002; 277: 18810–18816. 25 Hamon M, Bourgoin S, Artaud F, El Mestikawy S. The respective roles of 46 Yang X, Diehl AMW, Wand GS. Ethanol exposure alters the phosphor- tryptophan uptake and tryptophan hydroxylase in the regulation of ylation of cyclic AMP responsive element binding protein and cyclic serotonin synthesis in the central nervous system. J Physiol (Paris) 1981; AMP responsive element binding activity in rat cerebellum. J Pharmacol 77: 269–279. Exp Ther 1996; 278: 338–346. 26 Malhotra AK, Murphy Jr GM, Kennedy JL. Pharmacogenetics of 47 Pandey SC, Mittal N, Lumeng L, Li TK. Involvement of the cyclic AMP- psychotropic drug response. Am J Psychiatr 2004; 161: 780–796. responsive element binding protein gene transcription factor in genetic 27 Wong AH, Van Tol HH. The dopamine D4 receptors and mechanisms of preference for alcohol drinking behavior. Alcohol Clin Exp Res 1999; 23: antipsychotic atypicality. Prog Neuropsychopharmacol Biol Psychiatr 1425–1434. 2003; 27: 1091–1099. 48 Misra K, Pandey SC. Differences in basal levels of CREB and NPY in nucleus 28 Gorwood P, Lanfumey L, Hamon M. Alcohol dependence and accumbens regions between C57BL/6 and DBA/2 mice differing in inborn polymorphisms of serotonin-related genes. Med Sci (Paris) 2004; 20: alcohol drinking behavior. J Neurosci Res 2003; 74: 967–975. 1132–1138. 49 Yamaguchi-Shinozaki K, Shinozaki K. Organization of cis-acting reg- 29 Johnson BA. An overview of the development of medications including ulatory elements in osmotic- and cold-stress-responsive promoters. novel anticonvulsants for the treatment of alcohol dependence. Expert Trends Plant Sci 2005; 10: 88–94. Opin Pharmacother 2004; 5: 1943–1955. 50 Kleinjan DA, van Heyningen V. Long-range control of gene expression: 30 McBride WJ, Lovinger DM, Machu T, Thielen RJ, Rodd ZA, Murphy JM emerging mechanisms and disruption in disease. Am J Hum Genet 2005; et al. Serotonin-3 receptors in the actions of alcohol, alcohol 76: 8–32. reinforcement, and alcoholism. Alcohol Clin Exp Res 2004; 28: 51 Klingenhoff A, Frech K, Quandt K, Werner T. Functional promoter 257–267. modules can be detected by formal models independent of overall 31 Zill P, Buttner A, Eisenmenger W, Moller HJ, Ackenheil M, Bondy B. nucleotide sequence similarity. Bioinformatics 1999; 15: 180–186. Analysis of tryptophan hydroxylase I and II mRNA expression in the 52 Yatsula B, Lin S, Read AJ, Poholek A, Yates K, Yue D et al. Identification human brain: a post-mortem study. J Psychiatr Res 2005; in press [Epub of binding sites of EVI1 in mammalian cells. J Biol Chem 2005; 280: ahead of print]. 30712–30722.

The Pharmacogenomics Journal Transcription factors in ethanol-responsive genes RK Uddin and SM Singh 47

53 Alliston TN, Maiyar AC, Buse P, Firestone GL, Richards JS. Follicle 62 Chen CC, Lu RB, Chen YC, Wang MF, Chang YC, Li TK et al. Interaction stimulating hormone-regulated expression of serum/glucocorticoid- between the functional polymorphisms of the alcohol-metabolism inducible kinase in rat ovarian granulosa cells: a functional role for genes in protection against alcoholism. Am J Hum Genet 1999; 65: the Sp1 family in promoter activity. Mol Endocrinol 1997; 11: 795–807. 1934–1949. 63 Vasiliou V, Pappa A. Polymorphisms of human aldehyde dehydro- 54 Savickiene J, Treigyte G, Magnusson KE, Navakauskiene R. p21 (Waf1/ genases. Consequences for drug metabolism and disease. Pharmacology Cip1) and FasL gene activation via Sp1 and NFkappaB is required for 2000; 61: 192–198. leukemia cell survival but not for cell death induced by diverse stimuli. 64 Jornvall H, Hoog JO, Persson BPares X. Pharmacogenetics of the alcohol Int J Biochem Cell Biol 2005; 37: 784–796. dehydrogenase system. Pharmacology 2000; 61: 184–191. 55 Savickiene J, Treigyte G, Pivoriunas A, Navakauskiene R, Magnusson KE. 65 Whitfield JB. Alcohol and gene interactions. Clin Chem Lab Med 2005; Sp1 and NF-kappaB transcription factor activity in the regulation of the 43: 480–487. p21 and FasL promoters during promyelocytic leukemia cell monocytic 66 Koob GF, Roberts AJ, Schulteis G, Parsons LH, Heyser CJ, Hyytia P et al. differentiation and its associated apoptosis. Ann NY Acad Sci 2004; Neurocircuitry targets in ethanol reward and dependence. Alcohol Clin 1030: 569–577. Exp Res 1998; 22: 3–9. 56 Vaque JP, Navascues J, Shiio Y, Laiho M, Ajenjo N, Mauleon I et al. 67 Oscar-Berman M, Shagrin B, Evert DL, Epstein C. Impairments of brain antagonizes Ras-mediated growth arrest in leukemia cells through the and behavior: the neurological effects of alcohol. Alcohol Health Res inhibition of the Ras-ERK-p21Cip1 pathway. J Biol Chem 2005; 280: World 1997; 21: 65–75. 1112–1122. 68 Bowirrat A, Oscar-Berman M. Relationship between dopaminergic 57 Sakaguchi M, Miyazaki M, Takaishi M, Sakaguchi Y, Makino E, Kataoka neurotransmission, alcoholism, and Reward Deficiency syndrome. Am N et al. S100C/A11 is a key mediator of Ca(2+)-induced growth J Med Genet B Neuropsychiatr Genet 2005; 132: 29–37. inhibition of human epidermal keratinocytes. J Cell Biol 2003; 163: 825– 69 Heidbreder CA, Andreoli M, Marcon C, Thanos PK, Ashby Jr CR, 835. Gardner EL. Role of dopamine D3 receptors in the addictive properties 58 Chowdhury IH, Farhadi A, Wang XF, Robb ML, Birx DL, Kim JH. Human of ethanol. Drugs Today (Barc) 2004; 40: 355–365. T-cell leukemia virus type 1 Tax activates cyclin-dependent kinase 70 Uddin RK, Singh SM. The effect of ethanol may be realized via the cis- inhibitor p21/Waf1/Cip1 expression through a -independent regulatory sequences of the genes involved in apoptosis, cell growth mechanism: Inhibition of cdk2. Int J 2003; 107: 603–611. and proliferation. Mol Brain Res 2006 (in Press). 59 Coqueret O, Gascan H. Functional interaction of STAT3 transcription 71 Elkon R, Linhart C, Sharan R, Shamir R, Shiloh Y. Genome-wide in silico factor with the cell cycle inhibitor p21WAF1/CIP1/SDI1. J Biol Chem identification of transcriptional regulators controlling the cell cycle in 2000; 275: 18794–18800. human cells. Genome Res 2003; 13: 773–780. 60 Kawasaki H, Eckner R, Yao TP, Taira K, Chiu R, Livingston DM et al. 72 Fessele S, Maier H, Zischek C, Nelson PJ, Werner T. Regulatory Distinct roles of the co-activators p300 and CBP in retinoic-acid-induced context is a crucial part of gene function. Trends Genet 2002; 18: F9-cell differentiation. Nature 1998; 393: 284–289. 60–63. 61 Tagliabracci CE, Singh SM. Genetic regulation of gene-specific mRNA 73 Kel A, Kel-Margoulis O, Babenko V, Wingender E. Recognition of by ethanol in vivo and its possible role in ethanol preference in a cross NFATp/AP-1 composite elements within genes induced upon the with RI lines in mice. Biochem Genet 1996; 34: 219–238. activation of immune cells. J Mol Biol 1999; 288: 353–376.

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