Connexin-dependent transcellular transcriptomic networks in mouse brain

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Progress in Biophysics and Molecular Biology 94 (2007) 169–185 www.elsevier.com/locate/pbiomolbio

Review

Connexin-dependent transcellular transcriptomic networks in mouse brain Dumitru A. Iacobas, Sanda Iacobas, David C. Spray Dominick P Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA Available online 3 April 2007

Abstract Microarray experiments have generally focused on magnitude of gene expression changes in pathological conditions, thereby using the method as a high throughput screen to identify candidate marker genes and/or to validate phenotypic differences. We have used novel strategies to extract additional information from array studies, including expression variability and coordination, from which organizational principles of transcriptomes are emerging. We have reported that the expression level, variability and coordination of numerous genes are regulated in brains of connexin43 null (Gja1/) mouse with respect to wildtype. Moreover, expression coordination with Gja1 in wildtype largely predicted the expression regulation in Gja1/ tissues. We now report a remarkable overlap between regulations in Gja1/ and connexin32 null (Gjb1/) brains, and that both differ markedly from those in connexin36 null (Gja9/) brain. Since in brain these three connexins are expressed in different cell types (Cx43 in astrocytes, ependymal and vascular cells, Gjb1 in oligodendrocytes, and Cx36 in neurons and microglia), and because astrocytes and oligodendrocytes (and possibly neurons and microglia) may form syncytia coupled by gap junction channels, these observations suggest the existence of distinct connexindependent panglial and neuronal transcriptomic networks. Such networks, where linkage partners are rearranged and strengths modified in brains of knockouts, may explain downstream and parallel ‘‘ripples’’ of phenotypic change resulting from single gene manipulations as illustrated by alterations in transcription factor networks resulting from deletion of Gja1 or Gjb1. The transcription factors also formed network hubs with genes from other functional categories, thus allowing regulation of one functional pathway through manipulation of another. r 2007 Elsevier Ltd. All rights reserved. Keywords: Gap junction; Gja1; Gjb1; Gja9; Transcription factors; Cx43; Cx32; Cx36

Contents 1. 2. 3. 4. 5. 6.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Premises of a connexin-dependent transcriptomic topology of the brain . Experimental design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Connexin-dependent regulomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transcriptomic similarities and differences between connexin null brains Role of gap junction proteins in controlling transcript abundance . . . . .

Corresponding author. Tel.: +1 718 430 4138; fax: +1 718 430 8594.

E-mail address: [email protected] (D.A. Iacobas). 0079-6107/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.pbiomolbio.2007.03.015

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7. 8. 9. 10. 11.

Connexin-dependent expressomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wildtype connexin-dependent expressomes predict connexin-dependent regulomes. Connexin43 related ‘‘seesaws’’ of transcription factors . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Supplementary Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction The connexins (Cx) comprise a family of about 20 gap junction channel forming proteins that exhibit cellspecific patterns of expression (see Willecke et al., 2002). Hereditary human diseases have been associated with mutations in several connexin genes, and dysfunctional phenotypes have been demonstrated to result from connexin deletions in mice (e.g., Willecke et al., 2002). Each type of brain cell expresses multiple connexins in a developmentally regulated manner. Thus, cortical astrocytes have been reported to express Gja1 (Cx43), Gja5 (Cx40), Gja7 (Cx45), Gjb2 (Cx26) and Gjb6 (Cx30), neurons have been reported to express Gja1, Gja4 (Cx37), Gja5, Gja7, Gja9 (Cx36), Gja12 (Cx47), Gjb1 (Cx32), Gjb2, Gjb5 (Cx31.1) and Gjb6, while oligodendrocytes may express Gja7, Gja12, Gjb1 and Gje1 (Cx29) (Altevogt and Paul, 2004; Nagy et al., 2004; Rouach et al., 2002; Sohl et al., 2005; Theis et al., 2005; Vandecasteele et al., 2006). However, Gjb1 (Cx32 in oligodendrocytes—the myelinating cells of CNS), Gja9 (Cx36, in neurons and, as recently evidenced (Dobrenis et al., 2005), in microglia) and Gja1 (Cx43, in astrocytes) appear to be the most abundant gap junction genes in neurons and glia of the neonatal mouse (Spray, 2005). Many studies have suggested that, in addition to forming gap junction channels, connexins (either alone or as components of the Nexus complex, Duffy et al., 2006) might participate in other processes, such as growth control (Naus, 2002; Naus et al., 2000; Kardami et al., 2007), migration (Xu et al., 2001), intercellular and cellmatrix adhesion (Lin et al., 2003), and intercellular signaling (Scemes et al., 2000; Iacobas et al., 2006a). With regard to intercellular signaling, reduction of Gja1 expression was found to be associated with changes in the expression of two types of purinergic receptors, thereby partially compensating in the maintenance of Ca2+ wave propagation among the astrocytes (Suadicani et al., 2003). With regard to growth control, it has long been proposed that gap junction genes might act as tumor suppressors (Sager, 1997), and recent data indicate that gene regulatory networks may be responsible (see Kardami et al., 2007 for full discussion).

2. Premises of a connexin-dependent transcriptomic topology of the brain Six homomeric or heteromeric gap junction proteins form a connexon or ‘‘hemichannel’’ (Spray et al., 2006) inserted in the plasma membrane. Connexons of adjacent cells may dock with each other to form homotypic or heterotypic gap junction channels, where it appears that the composition of the two connexons dictates the selective permeability of the gap junction channel to various solutes (see Moreno, 2004). The intercellular gap junction channels ensure cytoplasm continuity between coupled cells for molecules up to about 1 kDa (depending on which connexins form the channel). It is now well appreciated that both glia and neurons form gap junctions with cells of the same type; moreover, astrocytes and oligodendrocytes are coupled to one another through heterotypic gap junction channels (see Altevogt and Paul, 2004; Nagy et al., 2004; Sohl et al., 2005; Theis et al., 2005). Gap junctions between different types of glia, first observed in electron micrographs by Massa and Mugnaini (1982), appear to connect astrocytes and oligodendrocytes into a ‘‘panglial syncytium’’, where ions, metabolites and signaling molecules may be exchanged throughout the brain (Rash et al., 1997). Gap junction channels may also provide ‘‘transcriptomic continuity’’ between coupled cells. Mechanisms responsible for such continuity may include direct transfer of genetic material, as in the case of RNAi-sized

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oligonucleotides (Valiunas et al., 2005) or could result from secondary consequences of molecular or ionic exchange. For example, intercellular diffusion of signaling molecules (e.g., Ca2+, IP3 and cAMP) could modulate gene expression by altering phosphorylation state of transcription factors (Stains and Civitelli, 2005; Stains et al., 2003) or could affect affinity of binding of Cx43 to transcription factors (e.g., Ai et al., 2000; Fu et al., 2004; Gellhaus et al., 2004; Penes et al., 2005). This transcriptomic continuity suggests that the brain transcriptome might be organized into connexin-dependent transcellular networks, so that altering expression of a single gene would produce downstream and parallel ‘‘ripples’’ contributing to the phenotypic change in the coupled cells. Observations regarding the high degree of similarity between gene expression regulation in Gja1 null and Gjb1 null brains and much less similarity compared to genes regulated in Gja9/ brain (discussed below) indicate that these connexin-dependent transcellular networks might have various degrees of overlap. 3. Experimental design In order to test the hypothesis of connexin-dependent transcriptomic transcellular networks and determine the extent of the transcellular networks, we have further analyzed previously reported gene expression data in the brains of neonatal wildtype, Gja1 null and Gjb1 null mice (deposited in the National Center for Biotechnology Information Gene Expression Omnibus database, http://www.ncbi.nlm.nih.gov/geo, series GSE1954) and performed an additional gene expression experiment on P10 wildtype and Gja9 null mice (series GSE6355). We chose to study neonatal mice because Gja1 nulls die shortly after birth (due to a developmental cardiac abnormality, where hyperplasia blocks blood flow exiting from the right ventricular outflow tract to the lungs: Reaume et al., 1995), and on P10 s because Gja9 expression in mouse brain peaks between P7 and P10. For the experiments on neonates we have used the sample reference strategy (Iacobas et al., 2002a, b) and AECOM 27k mouse cDNA microarrays (http://microarray1k.aecom.yu.edu), probing redundantly 7455 distinct, well-annotated genes and a previously described protocol (Iacobas et al., 2005c). The experiment on P10 mice was performed using the ‘‘multiple yellow’’ strategy (Iacobas et al., 2006b) to optimize the cost efficiency, thus reducing the number of arrays to six for four biological replicas of each sample type. The hybridization protocol, the slide type and the scanner settings were uniform throughout the entire experiment. Briefly, 60 mg Trizol extracted RNA from each of the brains (or our mouse reference in the case of neonates) was reverse transcribed in the presence of fluorescent Cy3 and Cy5 dUTPs to obtain labeled cDNAs. Hybridization was done overnight at 50 1C. After washing (0.1% SDS and 1% SSC) to remove the non-hybridized cDNAs, each array was scanned at 750 V (635 nm) and 670 V (532 nm). In order to reduce the influence of the development on the comparison of the three knockout brains, we have differentially renormalized the data of the Gja9 null brain with the gene expression ratios of the P10 wildtype with respect to the neonatal wildtype brain. Quantified genes were classified into disjoint cohorts based on chromosomal location or primary molecular function of the encoded protein (CSD: cell cycle, shape, differentiation, death; CYT: cytoskeleton; ENE: energy metabolism; JAE: cell junction, adhesion, extracellular matrix; RNA: RNA processing; SIG: cell signaling; TIC: transport of small molecules and ions into the cells; TRA: transcription; TWC: transport of ions/molecules within the cells; UNK: function not yet assigned). 4. Connexin-dependent regulomes In our experiments, a gene was considered significantly regulated if the fold change x (i.e., absolute value of the expression ratio) between the compared brains was 41.5 and the p-value o0.05 (with Bonferroni type correction applied to the redundancy group of that gene: Iacobas et al., 2005a). A gene cohort was considered to be significantly regulated if the absolute value of the average fold change of all cohort genes exceeded 1.5. In addition, a cohort was considered significantly perturbed if the standard deviation of the expression ratios was more than 1.5, with high standard deviations indicating that the proportions of transcript abundances were significantly altered. Such alterations are expected to result in major effects on the functional pathways since the perturbed ‘‘stoichiometry’’ may introduce some ‘‘bottlenecks’’ in the dynamics of the functional pathways. We have previously reported that ablation of Gja1 significantly regulates the expression level of hundreds of genes (more than 10% of the sampled genome) in brain (Iacobas et al., 2003a, 2004, 2005a), heart (Iacobas

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Fig. 1. Gene expression regulation in neonatal Gja1/ (black bars), Gjb1/ (white bars) and P10 Gja9/ (gray bars) brains as compared to the brains of neonatal or P10 wildtype mice. (A) Percent of regulated genes in functional categories. (B) Percent of regulated genes in chromosomes. Note that the regulated genes are distributed in all functional categories (not restricted to JAE, the primary function of all connexins) and in all chromosomes (not restricted to chromosomes 10 locating Gja1, chromosome X locating Gjb1 or chromosome 2 locating Gja9). Although significantly reduced as percentages, the regulated genes in Gja1+/ and Gja9+/ (not shown) were also distributed in all functional categories and chromosomes. (C) Average fold change of functional gene cohorts. Note that no cohort was significantly regulated in any of the connexin-deficient brains. (D) Standard deviation of the expression ratios within functional gene cohorts. Note that while no cohort excepting RNA and SIG exceeded by little the cut-off in Gja9 null brain, all functional cohorts have been significantly perturbed in Gja1 null and Gjb1 null brains.

et al., 2005b, c) and cortical astrocytes (Iacobas et al., 2003b, 2004). Hundreds of genes were also found regulated in brains of Gjb1 null mice (Iacobas et al., 2005a). Now, we report that disruption of Gja9 also significantly regulates hundreds of genes. Somewhat surprisingly, the regulated genes in all these connexindeficient brains were neither restricted to the JAE cohort (the primary functional category of the connexins) nor to the chromosomes on which the connexin genes are located (chromosome 2 for Gja9, chromosome 10 for Gja1 and X for Gjb1). Instead, they belonged to all functional categories (Fig. 1A) and were located in all chromosomes (Fig. 1B). These results indicate that the brain transcriptome contains connexindependent regulomes that encompass genes in multiple cohorts, where the regulome is defined as the set of genes significantly regulated in a given pathological or experimental sample with respect to the physiological/ control sample.

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Interestingly, similar to our findings for Gja1 null brains (Iacobas et al., 2007), no functional gene cohort was significantly regulated as a whole (up- and down-regulations of individual genes were roughly balanced within each cohort) in Gja9 null and Gjb1 null brains (Fig. 1C). Although the percentage of regulated genes within each cohort was generally higher for Gja9 null brain than for Gja1 and Gjb1 null brains, the perturbation of gene cohorts was generally slightly smaller in Gja9 null brain than in Gja1 and Gjb1 null brains (Fig. 1D). 5. Transcriptomic similarities and differences between connexin null brains When we compared the regulomes of Gja1 null and Gjb1 null brains, we observed striking similarities, with more than 83% of the Gja1/ regulome overlapping that of Gjb1/; however, the regulome of Gja9 null brain appeared quite different from those of the other genotypes, with only 11–12% overlap. Moreover, as illustrated in Fig. 2A, the fold changes of the expression level in Gja1 null and Gjb1 null brains are very close, which is not the case for the Gja9 null brain (not shown). One way in which such large regulomes can be quantitatively compared is to calculate the reduced (Euclidean) fold-change distance between altered transcriptomes with respect to the corresponding wildtypes. Our calculations indicated that the reduced fold-change distances between the transcriptome of Gja9/ brain and those of Gjb1/ and Gja1/ brains (2.44 and 2.24, respectively) were significantly larger than the distance between the transcriptomes of Gjb1/ and Gja1/ brains (1.00). These differences were robust for all functional categories (Fig. 2B) and chromosomal locations (Fig. 2C). The finding of substantial similarity in the transcriptomes of Gja1 null and Gjb1 null brains was initially surprising, given the expression of these connexins in different neural cell populations. However, because gap junctions interconnect Cx32 and Cx43 expressing cell types (oligodendrocytes and astrocytes), this finding leads us to propose that the existence of similar Gja1-dependent and Gjb1-dependent panglial transcriptomic networks, both disjoint from the Gja9-dependent neuronal transcriptomic network. Such panglial signaling networks could explain the reduction in Cx43 expression in the EAE model of multiple sclerosis, where the myelin proteins of oligodendrocytes are targeted (Brand-Schieber et al., 2005) and could also explain white matter disturbances in patients with occulodentodigital dysplasia, which is due to mutations in the astrocyte gap junction protein Cx43 (see Loddenkemper et al., 2002). Such networks, where linkage partners are rearranged and strengths modified in knockouts, may explain downstream and parallel ‘‘ripples’’ of phenotypic change in the panglial syncytium resulting from altered expression of either gap junction gene. Although oligodendrocytes have not been reported to be coupled to neurons, and astrocyte–neuronal coupling has been reported only very rarely (e.g., Rozental et al., 2001), it has been reported that microglia may exhibit a low level of coupling with neurons (Dobrenis et al., 2005), allowing the possibility for a neuron–microglial pancellular transcriptome. Importantly, the panglial and neuronal networks may regulate each other in other ways, for instance by releasing of agonists such as ATP and glutamate (Fields, 2006; Fields and Burnstock, 2006). Another recently reported possibility is through the cytokine leukemia inhibitory factor (LIF) that is released by astrocytes in response to ATP liberated from axons firing action potentials and LIF promoting myelination by mature oligodendrocytes (Ishibashi et al., 2006). Such examples indicate that neuroglial interactions may extend from direct functional roles (e.g., Haydon, 2000; Nedergaard et al., 2003) to transcriptomic control. 6. Role of gap junction proteins in controlling transcript abundance In previous papers (Iacobas et al., 2002c, 2003b) we introduced the relative expression variability (REV) score as a statistical measure of the variability of transcript abundance in a set of biological replicas and the gene expression stability (GES percentile) as a priority score of transcription control, with GES ¼ 100 indicating the most controlled gene and GES  0 the least controlled gene. REV and GES scores may be averaged for each gene cohort, in each genotype. Since then, we have identified genes that are very stably or very unstably expressed in mouse cortical astrocytes (Iacobas et al., 2003b), neuroblastoma N2A cells (Iacobas et al., 2002c), brain (Iacobas et al., 2005a), heart (Iacobas et al., 2005c) or kidneys (Iacobas et al., 2006b). Because the transcription mechanisms are identical in all replicates but the local conditions may be different,

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Fig. 2. Similarities and dissimilarities between brains deficient in Gja1, Gjb1 or Gja9. (A) Log–log plot of expression ratios in Gjb1/ and Gja1/ brains with respect to the wildtype brain. Note the remarkable overlap of the 3730 ratios computed for each of the two connexindeficient brains. (B) Distribution of fold-change distances on functional categories. Reduced (Euclidean) fold-change distance between altered transcriptomes with respect to the corresponding wildtypes was calculated at the level of the entire transcriptome as well as at the level of each gene cohort: DðG;HÞ ftranscriptomeg 

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X 1 2 ðxðHÞ  xðGÞ i Þ ; Xftranscriptomeg i2ftranscriptomeg i

ðG;HÞ  Dfcohortg

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X 1 2 ðxðHÞ  xðGÞ i Þ , Xfcohortg i2fcohortg i

(1)

where: Xftranscriptomeg and Xfcohortg are the numbers of the distinct quantified genes, and xðaÞ i is the expression ratio of gene i in the genotype (a ¼ G,H) with respect to the corresponding wildtype brain (negative for down-regulation). (C) Distribution of fold-change distances on chromosomal locations. Note that the fold-change distances between the Gja9/ brain and Gja1/ (black bars) and Gjb1/ (gray bars) brains are over twice as large as that between Gja1/ and Gjb1/ (open bars) and the robustness of this observation for all functional categories and chromosomal locations.

the most plausible cause of this non-uniform expression variability among genes is the different sensitivity to local conditions resulting from different intensities of the homeostatic control of transcript abundance. We have hypothesized that the very stably expressed genes are essential for cell survival and/or phenotypic expression and the very unstably expressed genes empower the cell to adapt to a continuously changing environment. A strong argument in favor of highly variable expression favoring cell adaptation is our finding that genes with higher expression variability in the wildtype brain were more prone to be regulated in Gja1

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Fig. 3. Wildtype variome predicts connexin-deficient regulome. The absolute fold change in Gja1 null, Gjb1 null or Gja9 null brains were plotted against the relative expression variability (REV score) in the corresponding wildtype brain, i.e., neonatal for comparison with Gja1 null and Gjb1 null, and P10 for Gja9 null brains. Note that all three plots exhibit significant positive slopes of the linear fittings indicating the tendency of the higher variably expressed genes in the wildtype brain to have higher fold change in the connexin-deficient brains. Note also that the fraction of genes with absolute fold change larger than 1.5 (the cut-off to be considered as significantly regulated) out of all quantified genes increases with the REV score. Remarkably, the slopes of Gja1 null and Gjb1 null brains are very similar.

null, Gjb1 null and Gja9 null brains (illustrated in Fig. 3). By defining the ‘‘variome’’ as the set of the most variably expressed genes, these results of significant overlaps between the WT brain variome and the regulomes of the Gja1 null and Gjb1 null brain indicate that the wildtype/control variome predicts the regulome of the experimental/pathological condition. Remarkably, the slopes of the linear fittings of the absolute foldchange plots in Gja1 null and Gjb1 null brains against the REV scores in WT are almost identical, but they are quite different from that of the Gja9 null brain. This last observation again supports the high similarity of the transcriptomes of Gja1 null and Gjb1 null brains and their difference with respect to the transcriptome of Gja9 null brain. We have reported that ablation of Gja1 in brain and heart significantly regulates the expression variability of hundreds of genes. Disruption of Gja1 significantly reduced the overall expression variability

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Fig. 4. Role of major brain connexins in regulating the transcripts abundance variability. Average REV scores (A) and GES percentile (B) of functional cohorts. W00 ¼ neonatal wildtype, W10 ¼ P10 wildtype. Note in (A) the robust decrease of expression variability in all functional categories for each null brain with respect to the corresponding wildtype brain (black bars) as well as the decrease in all functional categories of expression variability for P10 wildtype with respect to the neonatal wildtype. Observe the close values of the average GES percentiles for all functional cohorts in all genotypes and that ablation of one connexin did not change significantly the hierarchy of cohort transcription control.

(Iacobas et al., 2005a–c), indicating a strengthened control of transcript abundances, presumably thereby limiting the propagation of the expression alterations. The reduction was robust for all functional categories and chromosomal locations. Here, we report that disruption of Gjb1 or Gja9 also significantly decreases the overall expression variability among the animals. Thus, compared to the corresponding wildtype, ablation of Gjb1 reduced the average expression variability by 49%, ablation of Gja1 by 29% and ablation of Gja9 by 16% (in addition to the 40% variability reduction observed when P10 and neonatal wildtype brains were compared). Also, the reduction was robust for all functional categories (Fig. 4A) and chromosomal locations (not shown). However, ablation of any of the three connexins did not alter the hierarchy of cohort transcription control since the average GES percentiles were rather similar (50) for all cohorts in each genotype (illustrated in Fig. 4B). These observations indicate the important role of gap junction proteins in regulating the homeostatic control of transcript abundances, the molecular mechanisms of which remain to be determined. 7. Connexin-dependent expressomes The expression variability among four biological replicas allowed us to study the expression coordination of any pair of genes. In our microarray experiments with four biological replicas, two genes, i and j, are considered significantly (po0.05) coexpressed in a given genotype G if the absolute value of the pair-wise Pearson correlation coefficient rðGÞ i;j of their relative expression levels across the four biological replicas was ðGÞ higher than 0.9. The coexpression can be synergistic (if rðGÞ i;j 40:9) or antagonistic (if ri;j o  0:9). The genes ðGÞ are considered independently expressed if jri;j jo0:05. We here define the expressome as the set of genes that are antagonistically or synergistically and the exclusome as the set of independently coexpressed genes with a gene of interest. Thus, one may consider the brain transcriptome as a superposition of numerous non-disjoint expressomes and exclusomes. Gene composition of expressomes depends on the genotype and may change during development. The size of individual expressomes (i.e., the number of the coexpressed partners for a given gene) is not uniform among genes. For instance, by analyzing the Pearson correlation coefficients of each of the 402 genes encoding transcription factors with each other gene of the selection in brains of neonatal wildtype mice, we

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found that on average, each transcription factor was synergistically expressed with 9.7% of the rest of 3729 quantified genes, antagonistically expressed with 8.6% and independently expressed with 7.0%. These values are very close to the average coordination indices for all genes in wildtype brain (Iacobas et al., 2005a). At extremes, some genes (e.g., zinc finger proteins 278 and 239, topoisomerase (DNA) III beta, RNA polymerase 1–3 and histone 1, H4 m) were significantly (synergistically or antagonistically) co-expressed with over 35% of the quantified genes, while other genes (e.g., DEAD (Asp–Glu–Ala–Asp) box polypeptide 50, histone deacetylase 10, replication factor C 1 and homeo box A5) were significantly co-expressed with less than 2% of the selection. Supplementary Table 1 contains the 35 most and least coordinately expressed transcription factors. Disruption of Gja1 significantly reduced the overall expression coordination (the average size of the expressomes), a result again found to be robust for all functional categories and pairs of functional categories (Iacobas et al., 2007). As may be noted from Supplementary Table 2 that presents the genes encoding transcriptomic factors with the highest change of the coordination degree in the Gja1 null brain as compared to the wildtype, the reduction was not uniform among genes and in few situations we found even an increase in the coordination degree. Thus, we identified 51 transcription factors whose coordination with other genes increased by more than 10% in Gja1 null brain with respect to the wildtype (e.g., cyclin D binding myb-like transcription factor 1 from 4.26% to 27.43%, replication factor C 1 from 1.72% to 23.91%) and nine genes with significant increase in Gjb1 null brain (e.g., homeo box A5 from 1.85% to 15.58%, ankyrin repeat domain 10 from 2.39% to 16.01%). One may also observe in Supplementary Table 2 that the genes exhibited the same tendency to increase or decrease the coordination degree in both Gja1 null and Gjb1 null brains with respect to the wildtype brain. These data indicate again the high complexity of the gene expression networks and the remarkable similarity between Gja1 null and Gjb1 null brains (here as change of coordination degree among transcription factors). The Gja1 expressome of the sampled wildtype brain transcriptome contains 51 synergistically and 23 antagonistically expressed transcription factors. Of the 2701 distinct gene pairs that can be formed with them, 36% were found to be synergistically expressed, 27.2% antagonistically expressed and none were independently expressed. Compared to the average coordination indices (see above paragraph), these values indicate the important role of Gja1 in the coexpression of transcription factors. Moreover, as illustrated in Fig. 5, all significant coordinations among synergistic as well as among antagonistic transcription factors with Gja1 were synergistic, all significant coordinations of synergistic partners of Gja1 with antagonistic partners of Gja1 were antagonistic, and all significant coordinations between synergistic or antagonistic partners of Gja1 and independent partners of Gja1 were independent. When the coordination among the same 74 transcription factors was computed in the Gja1 null brain, we found 8.7% synergistic pairs and 6.8% antagonistic pairs, while in the Gjb1 null brain we found 4.8% synergistic and 5.0% antagonistic. These findings indicate that in both Gja1 null and Gjb1 null brains there is a striking decrease in the average size of transcription factor expressomes compared to wildtype. 8. Wildtype connexin-dependent expressomes predict connexin-dependent regulomes As illustrated in Fig. 6, we found that Gja1 brain and heart expressomes of wildtype mice predict with remarkable accuracy (480%) the Gja1 brain and heart regulomes of Gja1/ mice (Iacobas et al., 2005a,b), providing additional evidence for our hypothesis of a connexin43-dependent transcriptomic network. Moreover, we found that, in general, genes that are synergistically coexpressed in tissues of control/wildtype genotype mice are most likely similarly regulated in tissues of diseased/ knockout mice, while genes antagonistically expressed in control/wildtype are most likely to be oppositely regulated in experimental/ knockout (examples for apoptotic genes in Iacobas et al., 2003b, motility genes in Iacobas et al., 2004 and for JAE (other than connexin) genes in Iacobas et al., 2006c). Therefore, we consider identification of expressomes in physiological conditions as a valuable tool to predict transcriptomic alterations in pathological conditions. 9. Connexin43 related ‘‘seesaws’’ of transcription factors For each gene i we determined the coordination profile C ðGÞ (i.e., the set of correlation coefficients between i the expression levels of that gene and each other gene in the four biological replicas of each genotype). The

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Fig. 5. Regulation of expression partnership among the transcription factors in brains of Gja1/ and Gjb1/ mice. Ninety-seven transcription factors were grouped in synergistically (51, Syn), antagonistically (23, Ant) or independently (23, Ind) expressed with Gja1 in the wildtype brain. The color of the spots indicates whether the genes labeling the intersecting columns and rows are synergistically (brown), antagonistically (blue) or independently expressed in the genotype pointed out in the upper corner of each triangle. Gray color indicates non-significant correlation. Note that most of the synergistically and antagonistically expressed partners of Gja1 in the wildtype brain are synergistically expressed among themselves (no significant antagonism or independence), that most of the synergistic partners of Gja1 are antagonistic partners to the antagonistic partners of Gja1 (no significant synergism or independence) and how disruption of Gja1 or Gjb1 alters the expression coordination of the genes that were synergistically or antagonistically expressed with Gja1 in the wildtype.

coordination profiles and the significant partnerships of all quantified distinct genes were then compared within each genotype to identify striking similarities and oppositions by computing their ‘‘overlap’’, OVL. Pairs of genes with striking similarity or opposition of their coordination profiles were termed ‘‘coordination seesaws’’. The OVL score is a stronger parameter by which to compare the coordination profiles than is the ‘‘similarity’’ (SIM: Iacobas et al., 2007) since it considers the entire coordination profile and not only the significant synergistic, antagonistic and independent partners of the two genes.

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Fig. 6. Expression coordination with Gja1 in the wildtype brain (A) and heart (B) predicts expression regulation in the Gja1 null brain and heart. Note the significantly high proportions of genes whose regulation (dot circles) or lack of regulation (the ‘‘mountains’’ standing on the rectangle between 0.05 and 0.05 as coordination with Gja1 in wildtype and 1.5 and 1.5 as fold change in the Gja1 null tissue) was accurately predicted (adapted from data reported in Iacobas et al., 2005a, b).

Supplementary Table 3 lists the genes encoding transcription factors with the most striking similarity and opposition with regard to coordination profile and coordination partnership with Gja1. Fig. 7 presents examples of transcription factors with striking similarity or opposition of coordination profile with Gja1 in the wildtype brain. All genes synergistically expressed with Gja1 were found to have positive COR, SIM and OVL scores, and all genes antagonistically expressed with Gja1 had negative COR, SIM and OVL scores, indicating that these scores do not provide contradictory results in evaluating the coordination similarity. The most stringent criterion was the similarity of the significant coordination partners, but it reduced the analysis to about onequarter of the quantified genes. We found no significant bias toward a particular chromosomal location of the most similarly or oppositely coordinated TRA genes with respect to Gja1. Disruption of Gja1 not only canceled the co-expression linkages of Gja1 but also affected the similarity/opposition of the coordination profiles of other genes (examples in Figs. 8A and B). This effect of altering coordination similarity of other gene pairs is not unique to the disruption of Gja1 but occurs also for the disruption of Gjb1 (examples in Figs. 8C and D). 10. Discussion We have found that deletion of any of the three gap junction genes alters the expression level, variability and coordination of large numbers of genes in the brain, located on all chromosomes and encoding proteins performing a wide variety of functions. Since these connexins are expressed only in non-overlapping cell types in brain but astrocytes are coupled to oligodendrocytes, we propose the existence of connexin-dependent transcellular transcriptomic networks. In such networks, alteration of a key gene in one cell type may produce downstream and parallel ‘‘ripples’’ contributing to the phenotypic change in both that cell type and of any other cell type to which it is connected by heterotypic gap junction channels. In addition, disruption of Gja1, Gjb1 or Gja9 significantly decreased the overall expression variability and coordination in brain, indicating both a decoupling of gene expression and an increased control of individual gene expression. In a separate recent study we also reported a significant reduction of expression variability in kidney of mice subjected for 1, 2 or 4 weeks to chronic constant hypoxia (Iacobas et al., 2006b). Moreover, we found that the least controlled genes in the wildtype brain are the most prone to be regulated in the connexindeficient mice. Together, these observations regarding regulation of transcription variability suggest that multiple compensatory and control mechanisms may operate to limit the extent of gene expression alteration in

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Fig. 7. Connexin43 related ‘‘seesaws’’ of transcription factors in the brain transcriptome of the wildtype mice. CR ¼ Pierson correlation coefficient of the sets of expression levels in the four biological replicas, COR ¼ correlation of coordination profiles (i.e., of the sets of 3730 Pierson correlation coefficients), OVL ¼ overlap of the coordination profiles, SIM ¼ similarity of the significant partnerships (i.e., sets of significant synergistically, antagonistically and independently expressed partners). Blm ¼ Bloom syndrome homolog (human), Myst3 ¼ MYST histone acetyltransferase (monocytic leukemia) 3, Daam2 ¼ disheveled associated activator of morphogenesis 2, Atbf1 ¼ AT motif binding factor 1, Surb7 ¼ SRB7 (suppressor of RNA polymerase B) homolog (Saccharomyces cerevisiae), Sap30 ¼ sin3 associated polypeptide, Foxn1 ¼ forkhead box N1, Klf16 ¼ Kruppel-like factor 16, Msh5 ¼ mutS homolog 5 (Escherichia coli), Mcm3ap ¼ minichromosome maintenance deficient 3 (S. cerevisiae) associated protein. Tables below panels contain the scores of the indicated gene pairs. Note the high similarity of the coordination profiles of Surb7 and Sap30 (both similar to that of Gja1) and of Klf16 and Msh5 (both opposite to that of Gja1) and the opposition of the coordination profiles of Surb7 and Foxn1, and of Klf16 and Mcm3ap. The overlap (OVL) was computed according to the relation 0 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi1 PU ðGÞ 2 ðGÞ 2 ðGÞ ðGÞ 2 j¼1 ððri;j Þ þ ðrk;j Þ Þ sin ð p=4  arctanðri;j =rk;j ÞÞC B ðGÞ ðGÞ ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi OVLðC i ; C k Þ  @1  2 (2) A  100%. PU ðGÞ 2 ðGÞ 2 j¼1 ððri;j Þ þ ðrk;j Þ Þ This overlap of the coordination profiles quantifies how close is the plot of the correlation coefficients of gene k with the genes against the ðGÞ correlation coefficients of gene i with the other genes to the mean line of the Cartesian axes rðGÞ i;j Ork;j . It takes values from 100% to 100%, with high positive values indicating similarity (i.e., the genes whose coordination profiles are compared exhibit similar co-expression with all other genes), high negative values indicating opposition (the two genes are oppositely co-expressed with the other genes) and close to zero values indicating neutrality (no bias toward similar or opposite co-expression with other genes).

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the organs of transgenic animals or in animals subjected to a major stress. In order to be effective, these compensatory and control mechanisms should operate through transcellular transcriptomic networks that may be connexin-dependent. We verified the hypothesis of connexin-dependent transcriptomic networks through coordination analysis of transcription factors expressed in the wildtype, Gja1 null and Gjb1 null brains. In this analysis, we computed the Pearson correlation coefficients of the expression levels of each pair of genes in four biological replicas of the same genotype and then compared the sets of correlation coefficients of individual genes with those of each other gene. A strong argument in favor of the hypothesis of intertwined gene regulatory networks was the finding that the Gja1 expressome (i.e., the set of significant synergistic, antagonistic and independent partners of Gja1) in brain and heart of wildtype mice predicts with remarkable accuracy the regulome (i.e., the set of up- and down-regulated genes) in the Gja1 null brain and heart (Fig. 6). In this review, we chose to focus on the transcription factors due to their fundamental role in controlling the expression of other genes. The analysis revealed the existence of an overlap of networks of transcription factors acting as nodes of gene expression coordination (exemplified in Supplementary Table 1), where linkage partners are rearranged and strengths modified in knockouts (as illustrated in Fig. 5 and Supplementary Table 2). It is noteworthy that the number of synergistic gene pairs was approximately equal to that of antagonistic pairs, indicating that the transcription factors have equally stimulatory and inhibitory effects. Since we found that any of the 3730 quantified genes displayed significant synergisms or antagonisms with at least one transcription factor, we assume that the union of all transcription factor expressomes includes the entire transcriptome, thus defining a transcription factor topology of the transcriptome. In addition, we identified several transcription factors whose coordination profiles in brain have striking similarity or opposition to that of Gja1 (listed in Supplementary Table 3). In a previous paper (Iacobas et al., 2006c), we hypothesized that such parallel (or anti-parallel for opposed) coordination profiles may play a major role in the process of ‘‘proof-reading’’, to insure that individual gene expression alterations have minimal impact on the entire transcriptome. A simple model of such organization is that of loosely linked ‘‘seesaws’’, where an expression alteration in one gene is transmitted as similar alteration to synergistically expressed partners and opposite to antagonistically expressed ones. Thus, perturbation of a single gene not only affects of expression of other genes but also profoundly changes the composition and linkage of ‘‘seesaw’’ networks. We identified numerous ‘‘seesaws’’ in the wildtype brain that were significantly altered by disruption of Gja1 or Gjb1. The ‘‘seesaws’’ may have both genes within the same functional category (e.g., synergistic Surb7:Sap30 and antagonistic Surb7:Foxn1 gene pairs) contributing redundancy and thereby stability of the transcriptome, or in distinct categories (e.g., synergistic Gja1:Rpa3 and antagonistic Gja1:Klf16 pairs, illustrated in Iacobas et al., 2006c) allowing regulation of one category through manipulation of the other. The calculation of Euclidean fold-change distance allowed us to compare the transcriptomic alterations induced in brain by disruption of each of Gja1, Gjb1 and Gja9. The analysis provided significantly smaller distances between Gja1 null and Gjb1 null brains than between Gja9 null and Gja1 null or Gjb1 null brains. In addition, we found very close linear fittings of the plots of the absolute fold change in Gja1 null and Gjb1 null brains against the expression variability in wildtype brain, both of which were significantly different from the linear fitting of the absolute fold change in Gja9 null brain plotted against the expression variability in wildtype brain. The striking similarity between Gja1 null and Gjb1 null brain transcriptomes raised the hypothesis of a possible extension of the transcriptomic networks to encompass the panglial syncytium. Such a panglial expressome should be possible in principle due to the cytoplasmic continuity through the gap junction coupling of astrocytes and oligodendrocytes both to themselves and to one another. 11. Concluding remarks To summarize, we observed that: (1) ablation of either Gja1, Gjb1 or Gja9 regulates the expression level, variability and coordination of hundreds of genes (410% of sampled genome) in the mouse brain; (2) the regulated genes are located on all chromosomes and encode proteins from all functional categories; (3) there is

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Fig. 8. Alteration of the transcription factors ‘‘seesaws’’ in Gja1 and Gjb1 null brains. Compare with the corresponding plots and similarity scores in Fig. 7C. COR ¼ correlation of the coordination profiles, SIM ¼ similarity of the significant partnership, OVL ¼ overlap of the coordination profiles. Surb7 ¼ SRB7 (suppressor of RNA polymerase B) homolog (S. cerevisiae), Sap30 ¼ sin3 associated polypeptide, Foxn1 ¼ forkhead box N1, Klf16 ¼ Kruppel-like factor 16, Msh5 ¼ mutS homolog 5 (E. coli), Mcm3ap ¼ minichromosome maintenance deficient 3 (S. cerevisiae) associated protein.

a remarkable overlap between the alterations in Gja1 null and Gjb1 null brains; (4) Gja9 null brain differs significantly from Gja1 null and Gjb1 null brains; (5) expression coordination with Gja1 in wildtype brain predicts with remarkable accuracy expression regulation in Gja1 null brain; (6) the overall expression variability and coordination were significantly reduced in Gja1 and Gjb1 null brains; (7) expression variability in the wildtype brain predicts the probability of a gene to be regulated in a connexin (43, 32 or 36)-deficient brain, with very similar results for Gja1 null and Gjb1 null brains; (8) there are numerous genes whose coordination profiles have striking similarity and opposition with that of Gja1; (9) the coordination profiles of numerous genes were altered in Gja1 and Gjb1 null brains. Considering that: Gja1 is expressed in astrocytes, Gjb1 in oligodendrocytes and Gja9 in neurons, that astrocytes and oligodendrocytes may form heterotypic gap junction channels with one another, whereas under

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normal conditions glial cells rarely if at all form gap junction channels with neurons and that gap junction channels provide transcriptomic continuity, our gene expression data suggest the following: (1) the existence of a (Gja1, Gjb1)-dependent transcriptomic network extending throughout the panglial syncytium; (2) this network is organized in parallel hierarchical modules of coordinately expressed genes that are also coordinated among themselves, thereby regulating and stabilizing the overall transcriptome; (3) such a network not only serves to limit major alterations in functional pathways, but also compensates for changes that can occur under extreme conditions, optimizing the transcriptome for both flexibility and functional preservation. Acknowledgment This study was supported by NIH Grants MH65495, HD32573 and NS41282. Appendix A. Supplementary Material Supplementary data associated with this article can be found in the online version at doi:10.1016/ j.pbiomolbio.2007.03.015

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