A protein–protein interaction network of transcription factors acting during liver cell proliferation

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Genomics 91 (2008) 347 – 355 www.elsevier.com/locate/ygeno

A protein–protein interaction network of transcription factors acting during liver cell proliferation Jin Gao a , Wen-Xi Li b , Si-Qian Feng c , Yun-Sheng Yuan a , Da-Fang Wan b , Wei Han b,d , Yan Yu a,b,⁎ a

b

Shanghai Municipality Key Laboratory of Veterinary Biotechnology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China National Key Laboratory for Oncogenes and Related Genes, Cancer Institute of Shanghai Jiao Tong University, Shanghai 200032, People's Republic of China c Program in Molecular and Cell Biology, University of Maryland at College Park, College Park, MD 20742, USA d Laboratory of Regenerative Medicine, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China Received 27 February 2007; accepted 20 December 2007 Available online 5 February 2008

Abstract Liver regeneration is a complex process that involves a multitude of cellular functions, including primarily cell proliferation, apoptosis, inflammation, and metabolism. A number of signaling pathways that control these processes have been identified, and cross communication between them by direct protein–protein interactions has been shown to be crucial in orchestrating liver regeneration. Previously, we have identified a group of transcription factors capable of regulating liver cell growth and that may be involved in liver cancer development. The expression of some of their mouse counterpart genes was altered dramatically after liver injury and regeneration induced by CCl4 in mice. In an effort to elucidate the molecular basis for liver regeneration through protein–protein interactions (PPI), a matrix mating Y2H approach was produced to generate a PPI network between a set of 32 regulatory proteins. Sixty-four interactions were identified, including 4 that had been identified previously. Ten of the interactions were further confirmed with GST pull-down and coimmunoprecipitation assays. Information provided by this PPI network may shed further light on the molecular mechanisms that regulate liver regeneration at the protein interaction level and ultimately identify regulatory factors that may serve as candidate drug targets for the treatment of liver diseases. © 2007 Elsevier Inc. All rights reserved. Keywords: Protein–protein interaction; Liver regeneration; Cell proliferation; Transcription factor; Matrix mating Y2H assay

As one of the main focuses of proteomic research, protein– protein interactions (PPI) play crucial roles in signal transduction, cell growth, apoptosis, and other cellular events that regulate cell proliferation and tissue regeneration [1,2]. Unlike genomic research, proteomic studies must recognize that protein expression and protein interactions are unique events with spatial and temporal characteristics. Thus, interactions of physiological consequence must be defined and analyzed in a specific cellular system in which the time frame is recognized. A series of proteome-scale PPI networks has been investigated in several species, including Saccharomyces cerevisiae [3], Drosophila melanogaster [4], Caenorhabditis elegans [5], and Homo ⁎ Corresponding author. Fax: +86 21 34205833. E-mail address: [email protected] (Y. Yu). 0888-7543/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.ygeno.2007.12.007

sapiens [6–8] using the yeast two-hybrid (Y2H) system [9,10]. The Y2H system is reliably and easily adapted to high-throughput technology and provides the most widely used method for studies of protein–protein interaction. Liver has an extraordinary capacity to regenerate in response to abrupt reduction of liver cell mass associated with toxic injury. The regenerative mechanism presumably evolved to protect animals in the wild from catastrophic liver damage caused by food toxins. Liver regeneration following partial hepatectomy or CCl4 administration are experimental models frequently used to study this process, which is associated with the proliferation of all existing mature cellular populations of the organ, including hepatocytes, biliary epithelial cells, Kupffer cells, and other cell types [11,12]. Most accepted views about the mechanisms that regulate liver regeneration suggest the process requires activation of

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J. Gao et al. / Genomics 91 (2008) 347–355 Table 1 (continued)

Table 1 The Y2H interaction scored from three reporter selections Bait

Prey

SD/−His−Trp− Leu+3-AT (mM)

Bait

SD/−His−Trp− LacZ PPI Leu−Ade strength

0 2 6 10 15 ATF3 ATF3 ATF3 ATF4 CNOT3 CSK DPF2 FHL2 FHL2 FHL2 FHL2 FHL2 FHL2 FHL2 FHL2 FHL2 FHL2 FHL2 FHL2 GNB2 GNL1 GNL1 HCNGP ID3 NFKBIA PIP5K2C PTP4A1 REA RHO6 RHO6 SIAHBP1 SIAHBP1 TCFL4 TCFL4 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF212 ZNF3 ZNF3 ZNF3 ZNF3 ZNF331 ZNF331 ZNF408 ZNF408 ZNF408 ZNF408

ATF3 FHL2 ID3 ZNF212 ID3 ID3 FHL2 ATF3 CSK DPF2 FHL2 HCNGP ID3 SIAHBP1 STAT3 ZNF331 ZNF408 ZNF626 ZNFN1A4 ID3 GDI1 PTP4A1 SIAHBP1 ID3 ATF4 PIP5K2C CREB3L2 FHL2 CREB3L2 ID3 HCNGP ID3 HCNGP ID3 ATF3 CREB3L2 CSK DPF2 DUSP6 FHL2 GNL1 HBP1 HCNGP NFKBIA NKX2−3 PTP4A1 RHO6 ZNF212 ZNF3 ZNF331 ZNF408 FHL2 ID3 ZNF212 ZNF3 FHL2 ZNF408 FHL2 HCNGP ID3 ZNF408

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ + + + + − + + + − + + + + − + + + + − − − + − + + + + + + + + + + + + − + + + + + + + + + + + + + + − + + + + + + + + +

+ − + − − − − + + − + + + + − + + − + − − − + − − − + + − − + + + − + − − + + + − − + + + + − + − − + − − + + + + + − − +

+ − − − − − − + + − + + + + − + + − + − − − + − − − + − − − + + + − − − − + − − − − + + + − − − − − + − − + − + − + − − +

− − − − − − − + + − + + + + − − + − − − − − + − − − + − − − + − + − − − − + − − − − + + + − − − − − + − − − − + − + − − +

Prey

+ + + − + + − + + − + + + + − − + + − + − + + + − − + − + + − + + + + − + + + + + + + + + + + + + − + + + − − + + + − + +

− + − − − − − + + − + + + + − + + + + − − − − + − − + − + + − + + − − − − + − + − + − + + + − − − − + − − − − + − + − − +

II III II I II II I IV IV I IV IV IV IV I II IV III II II I II II III I I IV I III III I III IV II II I II IV II III II III II IV IV III II II II I IV II II I I IV II IV I II IV

SD/−His−Trp− Leu+3-AT (mM)

SD/−His−Trp− LacZ PPI Leu−Ade strength

0 2 6 10 15 ZNF626 ID3 ZNFN1A4 FHL2 ZNFN1A4 NFKBIA

+ − − − + + − − + + − −

− − −

+ + +

− + +

II III III

Results of the Y2H screen using HIS, LacZ, and ADE selections are summarized, in which + means positive in the selection, e.g., growth in the media without histidine (and with various concentrations of 3−AT) or adenine or the appearance of blue colony, and − means negative in the selection.

dozens of different pathways. These pathways do not act independently but involve simultaneous and sequential modes of operation and occur in different liver cell types [13]. The essential circuitry required for liver regeneration involves three primary types of pathways: cytokine, growth factor, and metabolic networks that link liver function with cell growth and proliferation [13]. The final players of these signaling pathways are transcription factors that control the expression of genes involved in cell death, cell growth, cell movement, and metabolism. For the complex process of liver regeneration to be completed, a multilevel series of protein–protein interactions must be coordinated. In the following report, we have utilized the matrix mating Y2H assay to develop a protein interaction network among 32 human proteins known to be relevant to liver regeneration or liver cell proliferation. All the selected 32 genes were capable of either stimulating or repressing the growth of the human liver cancer cell SMMC7721 in cell culture [14]. The expression of the mouse counterparts of some of these genes was altered dramatically after liver injury in a CCl4-induced liver regeneration model, suggesting their functional roles in the regulation of this process. The unique PPI network that has been described provides useful information for future studies of the molecular basis for liver cell proliferation at protein interaction level and potential targets for therapeutic intervention in liver cell injury. Results Potential regulators of liver cell proliferation identified from two independent large-scale screens In a previous study [14], a group of transcription factors that simulated or repressed the growth of human hepatoma cells and mouse embryonic fibroblasts was identified. More than 100 transcription factors with growth regulatory capacity were identified. In a separate investigation, cDNA microarray technology was used to identify key regulators of liver regeneration in mice following CCl4-induced hepatic injury. A group of transcription factor genes was shown to be either up-regulated or downregulated dramatically during the regeneration process, suggesting their respective roles in regulating liver regeneration (details of this study will be reported separately). We have consolidated the results of these two studies and selected 27 transcription factor genes that appeared to have roles

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in the control of cell growth and liver regeneration and of which there was dominant expression in the liver (GenBank database). In addition, five other genes that encode signal transducers, namely DUSP6, CSK, PTP4A1, PIP5K2C, and CALM1, were selected. These five genes appeared to regulate the growth of SMMC7721 hepatoma cells [14] and the expression of their respective mouse counterparts was altered dramatically during liver regeneration that followed CCl4-induced hepatic injury. Importantly, these genes can express in both the cytoplasm and the nucleus, suggesting they might modulate transcription activity by direct protein–protein interaction. Altogether, 32 ORFs were used as interacting partners in the matrix mating Y2H assay to identify all possible interactions between any two gene products. ORFs of ZNF408, HBP1, ZNFN1A4, ZNF626, ZNF212, CNOT3, EPS8L2, FOXM1, and CSK used in this study encoded truncated protein products that were missing their C-terminal ends. Truncation, however, did not seem to affect the capacity to interact with partners in our experiments (Table 2).

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A PPI network of liver cell proliferation and regeneration established using a matrix mating Y2H technique To conduct the matrix mating Y2H assay, selected ORFs were inserted into the pGADT7 (containing the activation domain— AD) and pGBKT7 (containing the DNA-binding domain—BD) vectors separately to construct a total of 32 bait and 32 prey plasmids, and each AD and BD plasmid was transformed into appropriate yeast strains to express the corresponding proteins. Every pair of bait and prey genes was coexpressed in yeast via mating, and possible interactions between them were evaluated using three types of reporters: (1) the activation of the HIS reporter gene expression in growth media lacking histidine and with various concentrations of 3-AT, (2) the activation of the LacZ reporter gene expression in the presence of β-galactosidase, and (3) the activation of the ADE reporter gene expression in growth medium lacking adenine. After the initial selection, we found that three bait ORFs and two prey ORFs were autoactivators and were not used in further studies.

Fig. 1. Protein interaction strength assessed based on the Y2H screen. Based on the Y2H screen of three reporters, all the identified interactions were ranked into four levels, Grades I, II, III, and IV, indicated by different colors, of which Grade IV is the strongest interaction. Gray background indicates autoactivators. Blue, Grade I; peach, Grade II; pink, Grade III; red, Grade IV.

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Using the matrix mating Y2H assay, we performed a total of 870 mating experiments and identified 64 interactions among the candidate genes. Table 1 summarizes the Y2H selection results of three reporters from each mating experiment; the order of screens is such that the HIS and LacZ selections were carried out simultaneously at first, then the ADE selection, and finally the HIS with 3-AT selection. From the initial 870 HIS selections, 64 positive interaction pairs were generated. These were able to grow in the medium lacking histidine and some colonies appeared blue. We then tested the interaction strength using ADE selection and increasing concentrations of 3-AT. Forty-eight mating pairs exhibited the ADE-positive phenotype with growth in medium lacking adenine. Fifty-four, 35, 24, and 19 mating pairs survived at 2, 6, 10, and 15 mM 3-AT, respectively (Table 1). Based on the results derived from the matrix mating Y2H screen, we were able to differentiate the strength of protein– protein interactions by scoring various phenotypes from the three reporter genes used for each mating. We ranked the strength of protein interactions into four levels (Fig. 1). Grade IV interactions were the strongest and their mating colonies were LacZ positive and ADE positive and survived at high concentrations of 3-AT (15 mM). Grade III interactions were those of mating colonies that were positive for all three reporters but failed to grow in 15 mM 3AT. Grade II interactions were those of mating colonies positive for only two reporters, and survival rate in the presence of 3-AT was not considered, and the Grade I interactions were the least strong interactions and involved mating colonies that grew only in medium that lacked histidine. Based on this standard, a protein– protein interaction network was drawn that comprised 64 interactions from the matrix mating Y2H assay. Included were 16 Grade IV, 11 Grade III, 23 Grade II, and 14 Grade I interactions (Fig. 2). This PPI network is believed to be the first available for

transcriptional regulators that have potential roles in liver cell proliferation and regeneration. Network interactions validated using α-glutathione S-transferase (GST) pull-down and coimmunoprecipitation (co-IP) assays To validate the Y2H-derived PPI network further, interactions of all four grades were selected for confirmation using both GST pull-down and co-IP assay methods. Selected cDNAs were PCR amplified from previous mating vectors and subcloned into the pET and pGEX vectors for their expression in bacterial cells and for subsequent use in GST pull-down assay. Appropriate cDNAs were also cloned into the pCMV-HA and pCMV-MYC vectors for expression in eukaryotic cells and for subsequent use in co-IP experiments. To achieve a high level of target gene expression in human embryonic kidney (HEK) 293T cells, transfection protocols using electroporation, Lipofectin, and Ca3(PO4)2 were tried to transfect transiently and express cDNAs in HEK 293T cells. Overall the Ca3(PO4)2-mediated transfection method worked better and provided more reproducible results. For gene expression in bacterial cells, we tested different induction conditions in the presence of isopropyl-β-D-thiogalactopyranoside (IPTG). Proteins were expressed in soluble forms as fusion proteins with either a GST or a HIS tag. Using both coimmunoprecipitation and GST pulldown methods, 13 interactions derived from the matrix mating Y2H assay were analyzed, from which 10 interactions, including one that was redundant, were confirmed as positives by either coIP or GST pull-down method (Fig. 3). The reconfirmation rate of 10 of 13 provides confidence that the matrix mating Y2H assay was a reliable and reproducible

Fig. 2. A PPI network of transcription factors associated with liver cell proliferation. Direct protein interactions are depicted as lines connecting two dots of proteins, in which the blue lines symbolize the protein interactions reconfirmed with GST pull-down or co-IP assays. Red dots are proteins that formed dimeric interactions.

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Fig. 3. Verification of PPIs identified from the Y2H assay. (Left) The reconfirmation results of GST pull-down assay are summarized for the protein pairs (1) GSTPIP5K2C and HIS-PIP5K2C, (2) GST-ZNF3 and HIS-ZNF3, (3) GST-ZNF3 and HIS-ZNF212, (4) GST-ATF3 and HIS-ATF3, (5) GST-ZNF212 and HIS-ZNF3, (6) GST-ZNF408 and HIS-ZNF408, (7) GST-ZNF212 and HIS-ZNF212. Escherichia coli-expressed corresponding proteins were used as positive controls; some of the proteins expressed as double bands on SDS–PAGE (lane A). GST nonfusion proteins were used as negative controls (lane B). Interacting proteins are detected in lane C, shown by the same sizes and patterns of pulled-down proteins compared to those in lane A, except for (4), for which only a single band is resolved. (Right) The results of co-IP assay are summarized, for the protein pairs (8) HA-FHL2 and HIS-ZNF408, (9) HA-FHL2 and HIS-ATF3, (10) HA-FHL2 and HIS-FHL2, (11) HAHCNGP and c-MYC-SIAHBP1. 293T cell-expressed corresponding proteins are shown in lane D as size markers, and interacting proteins are detected in lane E.

method for screening protein–protein interactions in a highthroughput manner. Each interaction pair in our screen was repeated at least twice (data not shown). However, proof of physiological significance of any protein interaction requires further functional study in vivo, and we are in the process of investigating the effects of overexpression or RNAi-based knockdown of gene expression of several interacting pairs in cell growth assays. The extent of growth based on nutrient selection (HIS and ADE) and the appearance of blue color did not correlate well with the reconfirmation rate utilizing GST pull-down or co-IP assays. The two nonconfirmed pairs of interactions were classified as Grade IV interactions (ZNF408 to ZNF408 and FHL2 to ID3). However, additional co-IP, GST pull-down, or other methods are required to examine a larger number of interacting pairs. In summary, we have constructed a PPI network of transcription factors that are believed to regulate liver cell proliferation and regeneration. The network comprises a total of 64 interactions of which 4 were previously identified (dimeric interactions of ZNF408 [15], ATF3 [16], ID3 [17], and FHL2 [18]) and 60 were newly discovered. Ten interaction pairs were verified using either GST pull-down or co-IP assay. Since the genes selected for this PPI network appeared to regulate liver cell growth in vitro and in vivo, we believe we have developed the first PPI network related to the regulation of liver cell proliferation and regeneration that should provide valuable new information for future studies of the molecular basis of liver regeneration at the protein interaction level. Discussion Large-scale human PPI networks at the level of the whole genome have been published previously [6–8]. Most of the candidates selected in our studies were not included in the study

of Stelzl et al. [6]. Our results were comparable with those of Rual et al. [7] except for the ATF3-ATF4 and SIAHBP1SIAHBP1 interactions. All other interactions detected by Rual et al. were reconfirmed by our results if both interacting partners were included (Table 2). We also compared our data set with other individual studies of protein interaction and found consistency in many cases. For example, self-interactions identified in our studies of ZNF408 [15], ATF3 [16], ID3 [17], and FHL2 [18] were identified by others. Unlike genome-wide studies, small-scale PPI networks define specific cellular functions under physiological conditions. From our study, FHL2 interacts with a number of proteins, including ATF3, DPF2, and ZNF331, and these interacting partners of FHL2 were not suggested either by Stelzl et al. [6] or by Rual [7]. FHL2 is an important transcriptional factor regulating cell growth and survival and is known to be related to the development and progression of cancer and osteoporosis [19]. The name FHL2 is an abbreviation of the name Four and a Half LIM Domain protein. It has been suggested that the LIM domain provides a protein– protein binding interface [19]. Previous work [19–21] demonstrated that FHL2 could interact with many proteins. Our results confirmed some of these interactions and detected new partners for FHL2 (e.g., DPF2, ZNFN1A4, ZNF331, and ZNF408). Taken together, FHL2 interacts with more than 10 partner proteins, suggesting the hypothesis that FHL2 may exert its regulatory function by binding to different partner proteins and plays key roles in communicating with various signaling pathways that control cell proliferation and survival. We currently are investigating the function of FHL2 in response to diverse stimuli through interaction with different partner proteins using the liver regeneration model. It is our belief that any complex network of interactions can be deciphered only within the context of a physiologically relevant model system.

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Table 2 Comparison of our PPI results with those of other publications No. Gene name Accession No.

ORF SMMC7721 NIH3T3 regulation Interacting proteins detected sequence regulation effect effect in our study

PPIs in the Nature and Cell papers

1 2 3 4 5 6 7 8 9 10

ATF3 ATF4 CALM1 CNOT3 CREB3L2 CSK DPF2 DUSP6 EPS8L2 FHL2

NM_001030287 NM_001675 NM_006888 NM_014516 NM_194071 NM_004383 NM_006268 NM_001946 NM_022772 NM_001450

F F F P F P F F P F

S R R R S S R R R R

R R S S R S S R S ND

ATF4n; othersc; NAm ATF3n; othersc; NAm Nonen; NAcm Othersn; NAcm NAncm NAncm Nonen; NAcm Othersn; NAcm NAncm NAncm

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

FOXM1 GDI1 GNB2 GNL1 HBP1 HCNGP ID3 NFKBIA NKX2−3 PIP5K2C PTP4A1 REA RHO6 SIAHBP1 STAT3 TCFL4 ZNF212 ZNF3 ZNF331 ZNF408 ZNF626 ZNFN1A4

NM_021953 NM_001493 NM_005273 NM_005275 NM_012257 NM_013260 NM_002167 NM_020529 NM_145285 NM_024779 NM_003463 NM_007273 NM_014470 NM_014281 NM_003150 NM_198205 NM_012256 NM_032924 NM_018555 NM_024741 NM_145297 NM_022465

P F F F P F F F F F F F F F F F P F F P P P

R R S R R S R R R R R R R R S S R R R R R R

S R S R S S S ND R S R R S S S S S ND R R ND S

ATF3, FHL2, others ZNF212, NFKBIA None ID3 ZNF212, PTP4A1, RHO6 FHL2, ZNF212, ID3 ZNF212, FHL2 ZNF212 None ATF3, ZNF408, ZNF3, ID3, ZNFN1A4, FHL2, others None GNL1 ID3 GDI1, PTP4A1, ZNF212 ZNF212 SIAHBP1, others ID3, FHL2, ZNF408, others ZNFN1A4, ATF4, ZNF212 ZNF212 PIP5K2C ZNF212, GNL1, CREB3L2 FHL2 ID3, FHL2, CREB3L2 ID3, HCNGP, FHL2 FHL2 HCNGP, ID3 FHL2, ZNF408, ZNF3, ZNF212, others ZNF3, ZNF212, ID3, FHL2 FHL2, ZNF408, ZNF212 FHL2, HCNGP, ZNF408, others ID3, FHL2 FHL2, NFKBIA

Nonen; NAcm Othersn; NAc; Nonem Nonen; otherscm Nonen; NAcm Othersn; NAcm SIAHBP1n; othersc; NAm ID3n; NAcm Othersnc; NAm NAncm NAncm NAnc; othersm Othersnm; NAc NAncm HCNGP, SIAHBP1n; otherscm Othersn; NAcm Othersn; NAcm Nonen; NAcm Nonen; NAcm NAncm ZNF408n; NAcm Nonen; NAcm NAncm

Genes selected for generating this PPI network were able to either stimulate (S) or repress (R) the growth of SMMC7721 human liver tumor cells and/or NIH3T3 mouse fibroblast cells. “ND” means growth effects were not determined. Superscripts “n”, “c”, and “m” indicate the interacting partners found in Nature (Ref. [7]), Cell (Ref. [6]), and Molecular Systems Biology (Ref. [8]) publications. “None” means no interacting protein was found. “NA” means the corresponding proteins were not studied in that publication. “Others” means the identified interacting proteins were not included in our searching list.

Although the process of liver regeneration has been well investigated, a number of pivotal questions remain to be addressed. One is the mechanism by which liver regeneration is terminated. Current opinion suggests that the antiproliferative TGFβ and related TGFβ family members play a crucial role [22]. We searched public PPI databases for interaction partners of ATF3 and STAT3, which are believed to be involved in liver regeneration, and found they interact with many growth regulators (Fig. 4A). ATF3 is a member of the mammalian activation transcription factor/cAMP-responsive element-binding protein family of transcription factors [23–25]. TGFβ binds to its receptor, resulting in phosphorylation of SMAD3, which activates expression of the ATF3 gene. The stress signal, acting through p38 kinase, can also induce ATF3. Previous work has demonstrated that ATF3 expression correlates with the increased metastasis in melanoma and in breast cancer cells [26,27] and that antisense knockdown of ATF3 reduces the ability of HT29 colon cancer cells to invade through Matrigel in vitro [28], suggesting ATF3 is a cancer promoter. However, ATF3 may also function as a tumor suppressor in Ras-mediated tumorigenesis [29]. Differ-

ences in function regarding regulation of the cell cycle have been shown. AFT3 has been shown in some studies to induce arrest of cell growth [29,30], and in another, ATF3 played a role promoting the cell cycle at the G1 to S transition [31]. ATF3 has been demonstrated to both stabilize and antagonize the activity of p53 [32,33]. The function of ATF3 seems to vary, depending on its cellular context, in a manner similar to those of NF-κB, p53, and TGFβ [34–38]. Allen et al. concluded that ATF3 induced DNA synthesis in hepatocytes [31] and this result was confirmed by our earlier large-scale screen for growth regulators ([14], Table 2). It has been reported that ATF3 mRNA increased immediately after partial hepatectomy in rats and that its greatest expression occurred 2 h after surgery [39]. Our cDNA microarray data indicated that the ATF3 mRNA level doubled 12 h after CCl4 administration (data not shown). All these data indicate that ATF3 is a cell cycle stimulator. When liver is exposed to toxins or otherwise is injured, ATF3 expression increases rapidly, leading to a signaling cascade and hepatocyte proliferation [40]. ATF3 can repress its own expression during the late stages of liver

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regeneration [25], resulting in a level of expression that is lower than normal (confirmed by our microarray data, not shown). In the present study, we observed a Grade IV interaction between ATF3 and FHL2 and postulated that such interaction may inhibit the function of ATF3 through formation of a protein complex with FHL2. FHL2 was identified as a growth repressor in our large-scale screen for growth regulators [14], and the

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microarray data demonstrated that FHL2 was down-regulated during the early phase of liver regeneration induced by CCl4 and then up-regulated later. These observations could support a growth-repressing function of FHL2 by sequestering ATF3 and preventing its action in the signaling pathways that stimulate cell growth. By formation of a FHL2/ATF3 complex, a mechanism would be provided for terminating hepatocyte proliferation.

Fig. 4. Possible roles of the PPI network in terminating hepatic proliferation. (a) All available interacting partners for ATF3 and STAT3 were searched in PubMed and are summarized here (interactions between their partner proteins are not included). (b) ATF3 and STAT3 are two known growth stimulators for liver cells. Increased ATF3 switches on the expression of a set of genes that are necessary for hepatocyte proliferation. It can also repress its own expression. When liver regeneration is ending, ATF3 returns to its normal level of expression; FHL2 may form a complex with ATF3 to abolish its function on DNA synthesis. STAT3 can be phosphorylated and activated by the IL-6 receptor through JAK-mediated tyrosine phosphorylation. FHL2 may interact with STAT3 to inhibit its function in activating downstream target gene expression that is necessary for hepatocyte proliferation.

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Another hepatocyte growth stimulator, STAT3, is known to be involved in the IL-6 signaling pathway [12]. IL-6 can bind to the IL-6 receptor on hepatocytes and activate Janus kinase (JAK). The activated JAK triggers mitogen-activated protein kinase (MAPK) and STAT3 pathways through JAK-mediated phosphorylation of tyrosine. The STAT3 transcription factor dimerizes and translocates to the nucleus, where it activates transcription of ≈36% of the immediate-early target genes. Therefore, STAT3 promotes hepatocyte proliferation, progression, and switching on of liver regeneration. In our PPI network, STAT3 interacts with FHL2 and may serve to induce growth arrest at the end of the liver regeneration process (Fig. 4B). Most of the 32 ORFs used in the PPI network described had demonstrated growth repression effects on liver cells [14]. From all 64 protein interactions, there are only 2 interactions in which both partners were growth stimulators in our screen. The other 62 interactions involved at least one partner that was a growth repressor. On this basis, we believe that the PPI network presented here provides a functional network with the capacity to regulate the termination of hepatocyte proliferation during liver regeneration. Further studies are required to address the molecular specificity of these interactions and their respective physiological impacts on liver regeneration. Materials and methods

as a positive control and the pGADT7-T and pGBKT7-Lam pairs were used as negative controls in the mating assay. Bait-to-prey mating was performed extensively between every bait and prey pair, excluding autoactivators; thus, a total of 870 mating experiments were conducted among 32 baits and 32 preys.

Target gene expression in eukaryotic and prokaryotic cells After YTH screening, positive ORFs were subcloned into pCMV-MYC and pCMV-HA vectors (Clontech) for expression in eukaryotic cells. For expression in prokaryotic cells, those ORFs were cloned into pET28a(+) or pET30a(+) vector (Novagen) and pGEX-4T-1 (Pharmacia) or pGEX(N) vector (Pharmacia; modified by inserting a NcoІ site into pGEX-4T-2 between the BamHІ and the EcoRІ sites at the polylinker). Final plasmids were confirmed by sequencing. HEK 293T cells (Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences) were maintained in DMEM supplemented with 10% fetal bovine serum (Invitrogen). The plasmid DNAs were prepared using SDS-lysis buffer and purified using the polyethylene glycol precipitation method (Molecular Cloning; Lab Protocol), and DNA was then transfected into HEK 293T cells using a Ca3(PO4)2 method (Molecular Cloning; Lab Protocol). Protein extracts were prepared by harvesting cells in 100-mm plates 30 h after transfection and lysing them with 1 ml lysis buffer containing 50 mM Tris–HCl, pH 7.5, 150 mM NaCl, 1% Nonidet P-40, 0.5% sodium deoxycholate, and 0.5% protease inhibitor cocktail (Sigma), followed by co-IP and Western blot analysis. The gene expression in prokaryotic cells was induced by the presence of 0.5 mM IPTG and incubation for 3 h and then cells were spun down and resuspended in 2 ml PBS (pH 8.0) containing 1 mg/ml lysozyme (Amresco), 5 μg/ml RNase A (Sigma), 5 μg/ml DNase I (Sigma), and 0.5% Triton X-100 per 50 ml of cell culture, followed by sonication of the cell suspension and centrifugation at 17,500 rpm (≈34,000 ×g) for 30 min. The resulting protein extracts were used for SDS–PAGE and Western blot analysis.

cDNA selection GST pull-down and co-IP assays cDNA clones of transcription factors were selected from a previous study based on their growth regulatory effects [14]. Each clone had the capacity either to stimulate or to repress growth of SMMC7721 and NIH3T3 cells in vitro and some of these selected genes had mouse counterparts that were differentially expressed during liver regeneration. We also included several signal transducer encoding genes that produced similar effects. All cDNAs were cloned by RTPCR using the transfection plasmids from a previous study as templates [14], and the final clones were confirmed by sequencing analysis.

Construction of Y2H bait and prey plasmids and the expression of fusion genes in yeast Bait and prey plasmids were constructed by inserting PCR fragments of selected cDNAs into pGADT7 and pGBKT7 vectors (Clontech) at the NcoІ– XhoІ and EcoRІ–XhoІ restriction sites, respectively. Final plasmids were confirmed by sequencing. Bait plasmids in pGBKT7 were used to transform AH109 yeast cells and prey plasmids in pGADT7 were used to transform Y187 yeast cells according to the user manual from Matchmaker Y2H System 3 (Clontech). Autoactivating clones were determined by patching the transformed yeast cells containing single bait or prey plasmids on SD/−Leu/−His+X-gal or SD/−Trp/−His+X-gal. Positive clones were removed and the remaining negative clones were used for the subsequent screen.

Matrix mating Y2H assay The yeast mating procedure was carried out according to the user manual from Matchmaker Y2H System 3 (Clontech). Yeast colonies expressing each bait and prey fusion protein were mixed in a 1.5-ml Eppendorf tube containing 0.5 ml YPDA and shaken vigorously at 30 °C for 24 h. The resulting cells were centrifuged and transferred onto SD/−Leu−Trp plates as patches and then incubated at 30 °C for another 3 days. At the end of this incubation, grown colonies expressing both bait and prey proteins were picked and transferred onto fresh SD/−Leu−Trp −His+X-gal plates to screen for positive interactions. All the positive mating pairs were then transferred simultaneously onto SD/−Leu−Trp−His plates containing different concentrations of 3-AT (Sigma) and SD/−Leu−Trp−His−Ade plates, to test for their interaction strength. The pGADT7-T and pGBKT7-53 pairs were used

The GST pull-down assay was carried out according to the user's manual provided by the manufacturer (Novagen). Briefly, 500-μl extracts of each partner protein (one expressed as a GST fusion protein and the other expressed as a HIS fusion protein) were mixed, incubated with 10 μl GST MAG agarose beads (Novagen), and rotated vertically at 4 °C overnight. The protein complex was separated with a MAG stand (Novagen), the GST–agarose beads were washed three times with washing buffer, and finally, the protein complex was resuspended in 50 μl of 2× loading buffer for Western blot analysis. GST nonfusion protein was used as a negative control. Escherichia coli expressed GST- and HIS-fusion proteins were mixed and bound to a GST column. The protein complexes were eluted from the column and the HIS-fusion interacting partner proteins were detected using monoclonal anti-HIS (Novagen) after SDS–PAGE separation. The co-IP assay was conducted according to Sigma's co-IP protocol with modifications. In brief, the appropriate monoclonal antibody (about 5 μg) was diluted with phosphate buffer (pH 7.4) and mixed with 25 μl of prepared protein A–Sepharose (Amersham). After 1 h of rotation at 4°C, the agarose beads were collected and incubated overnight at 4°C with 250 µl of HEK 293T and E. coli cell lysate containing interacting partner proteins. The agarose beads were collected again, washed three times with lysis buffer, and then resuspended in 25 μl loading buffer for Western blot analysis. 293T cell extracts containing HAand HIS-fusion proteins or HA- and c-MYC-fusion proteins were precipitated with monoclonal anti-HA antibodies (Convance) and the precipitates were subjected to Western blot using anti-HIS and anti-c-MYC monoclonal antibodies (Sigma), respectively.

Acknowledgments We thank Yong-Ping Cai and Chao Xie for their generous help with some of the experiments and data analysis and Dr. Bud Tennant (Cornell University) for critical comments on revising the manuscript. This work was funded by the Chinese Human Liver Proteome Project: 2004BA711A19-08.

J. Gao et al. / Genomics 91 (2008) 347–355

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