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Genome Res. 13:2708-2716, 2003 ©2003 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/03 $5.00 Methods Toward a Functional Annotation of the Human Genome Using Artificial Transcription FactorsToolGen, Inc., Yuseong-gu, Daejeon, South Korea, 305-390
We have developed a novel, high-throughput approach to collecting randomly perturbed gene-expression profiles from the human genome.A human 293 cell library that stably expresses randomly chosen zinc-finger transcription factors was constructed, and the expression profile of each cell line was obtained using cDNA microarray technology.Gene expression profiles from a total of 132 cell lines were collected and analyzed by (1) a simple clustering method based on expression-profile similarity, and (2) the shortest-path analysis method.These analyses identified a number of gene groups, and further investigation revealed that the genes that were grouped together had close biological relationships.The artificial transcription factor-based random genome perturbation method thus provides a novel functional genomic tool for annotation and classification of genes in the human genome and those of many other organisms.
Systematic approaches to achieve rapid and accurate functional annotation of a large number of uncharacterized genes are urgently needed in this postgenomic era. Identifying the functions of gene products is also a critical step toward utilization of genomic information in the drug discovery process. A number of methods have been developed to identify the functions of novel genes. For example, methods for large-scale phenotypic analysis in yeast (Tong et al. 2001
Recent advances in the genome-wide gene expression-profiling technology using DNA microarrays have made this approach a powerful one for the high-throughput analysis of tens of thousands of genes. The gene expression profile of a specific gene is valuable because it is a signature of the state of the cell, such as its response to environmental stress or disease. To understand the function of a specific gene of interest, it is helpful to know the expression profile of that gene under a variety of conditions. Several studies have established that genes whose products have similar functions or are involved in different steps of the same pathway share similar expression profiles and can be grouped together on the basis of their expression signature (Eisen et al. 1998
A pioneering study by Hughes et al. (2000
Unlike for yeast, however, specific gene activation or deletion on a large scale in mammalian cells has been technically challenging. Several technologies that involve specific down-regulation of human genes have been developed, including antisense RNA (Cho et al. 2001
Recent advances in the field of artificial transcription-factor technology allow one to build tens of thousands of active transcription factors with ease (Segal and Barbas III 2001 In this study, we present a novel, high-throughput method with which to acquire functional genomic data. Our approach involves the building of a large-scale gene-expression profile database for the human genome. To achieve this, we have used a number of preassembled ZFP-transcription factors (ZFP-TFs). By performing large-scale microarray experiments with cell lines that express these ZFP-TFs, we demonstrate that each ZFP-TF regulates a distinct set of genes in the human genome, thus verifying that our method perturbs the gene expression program in an unbiased manner. The gene expression profiles obtained were then subjected to bioinformatic analyses to build a number of coregulated gene groups. Inspection of these groups identified a number of genes whose functional relationships were evident, proving the validity of this approach.
Random Genome Perturbation Using ZFP-TFs From a number of preassembled ZFP-TF collections in our laboratory, we randomly picked a group of ZFP-TFs, and then established stable HEK-293 cell lines that express each individual ZFP-TF in a Doxycycline (Dox)-dependent manner (Fig. 1). Therefore, upon the addition of Dox, a unique ZFP-TF is expressed, and in turn, it could regulate a unique set of genes. To obtain the global gene expression signature affected by each ZFP-TF, genome-scale gene analysis was performed for a total of 132 cell lines, using a cDNA microarray that contained 7458 known human genes (Fig. 1). The identities of ZFP-TFs used are shown in Supplemental Table 1, available online at www.genome.org.
Overall, different ZFP-TFs showed unique global gene expression signatures, in agreement with our hypothesis that random perturbation of the genome can be attained with randomly chosen ZFP-TFs (See clustergram in Supplemental Fig. 1). It should be noted that the gene expression profiles obtained by ZFP-TFs disappeared when we deleted functional domains or introduced mutations into the DNA-binding domains, which destroyed DNA-binding ability (Supplemental Fig. 2). We then characterized some of the basic properties of gene regulation by several ZFP-TFs. First, we tested whether a particular transcription profile obtained with a given ZFP-TF was cell-type specific. To this end, we compared gene expression profiles generated by one ZFP-TF, F2840-p65, in the following cell types: (1) 293 cells, a noncancerous human embryonic kidney cell line that stably expressed the F2840-p65 ZFP-TF, and (2) HeLa cells, a human cervical carcinoma cell line, in which the F2840-p65 ZFP-TF was transiently transfected. Comparison of the microarray data revealed that the insulin gene was highly up-regulated in both cell lines (Fig. 2A, arrows). In addition, similar sets of genes were regulated in both cell types (Fig. 2A, bottom; Supplemental Table 2). However, we noted that there were also differences in the gene regulation pattern between 293 and HeLa cells (Supplemental Table 2), which may reflect differences in chromatin structure or DNA content (karyotype) between two cell lines. Another ZFP tested also showed similar, yet not identical expression profiles in 293 and HeLa cells (Supplemental Fig. 3). Thus, ZFP-TF appears to regulate resembling sets of genes when introduced into cell lines made from different cell types.
Second, we performed a time-course experiment using one of our stable cell lines that expresses a ZFP transcriptional activator, F475-p65. Time-course analysis revealed induction of a few genes at early time point (Figs. 2B, 3 hrs), and an increase in the number of genes as time progresses (Fig. 2B). In addition to the time course experiment, we also performed ZFP-TF induction, followed by treatment of protein-translation inhibitor Cycloheximide (see Methods). Genes induced at all time points, and whose expression pattern is not significantly affected by Cycloheximide treatment, were considered as primary target genes. From this, we identified three genes (Fig. 2B; also see Supplemental Fig. 4). In silico analysis identified ZFP-binding sites in the proximal promoter region for all three genes analyzed (Supplemental Fig. 4). This ZFP-TF was designed originally to regulate VEGF in another study (Bae et al. 2003  ). In agreement with the previous study, VEGF was identified as a primary target in this analysis. Therefore, this method appears to be useful for identifying target genes of ZFP-TFs.
This result also suggests that a pathway analysis can be performed if time-course microarray experiments are performed for a number of ZFP-TF-expressing stable cell lines. Because ZFP-TF expression is tightly controlled by the addition of Dox, a rigorous time-course analysis is possible using our cell library system. This will eventually help in the building of a hierarchical map or transcriptional network of gene expression.
Analysis of ZFP-TF Expression-Profiling Data Set Reveals a Number of Gene Groups With Functional Relationships
We found that the analysis of our ZFP-TF-driven data set produced very precise groupings, as demonstrated by the histone clusters shown in Figure 3. Previous microarray studies have shown that all histone genes are grouped together, both in yeast and human cell lines (Eisen et al. 1998
For the other gene groups, whose components did not, at first glance, show close functional relationships, we tried to identify any possible functional connection using an extensive search and study of the PUBMED literature database. We identified several gene groups to which we could assign putative functional relationships on the basis of their descriptions in the literature. Some of these groups are shown in Table 1. For example, ID4 and caspase 5 constitute a coregulated group with a Pearson coefficient of 0.85. From the literature search, we found that ID4 can induce apoptosis (Andres-Barquin et al. 1999
The tight clustering of genes with similar function suggests that this method is successful in grouping genes with close biological relationships. It should be noted that because we set a highly stringent cut off value, many of the groups we obtained contained only a small number of genes.
Verification of Functional Relationships Using Shortest-Path Analysis
An example of SP analysis is shown in Figure 4, a gene cluster that includes insulin-like growth factor-2 (IGF-2). Although this gene cluster was also identified in the simple grouping method (Supplemental Table 3, group 17), SP analysis led to the identification of the Shc gene as a new member in this group. Thorough literature analysis revealed that the members of this gene cluster are functionally inter-related. First, it has been shown that protein phosphatase 2A (PP2A) is involved in the insulin/IGF-1 signal-transduction pathway (Ugi et al. 2002
This group also contains an uncharacterized gene (Fig. 4, putative gene product) with some similarity to the Drosophila furry gene (Cong et al. 2001 ). On the basis of extensive relationship among other members of this group and their role in IGF signaling, we predict that this uncharacterized gene will also play an important role in IGF signaling. Further experiments are required to determine the biological function of this protein.
Experimental Validation of Gene Groups: Spi-B-Melanoma Antigens
In this report, we demonstrated that the human genome can be randomly perturbed by ZFP-TFs, and that the resulting expression profiles can provide novel information about the function of genes. Using conventional clustering, on the basis of expression profile similarity and the recently introduced SP analysis, we were able to identify many groups of genes with close functional relationships. SP analysis of ZFP-TF-derived expression data sets also revealed Spi-B as a novel regulator of the melanoma antigen gene family, and this prediction was verified experimentally. There are important advantages of using ZFP-TFs as genome-wide regulators of gene expression. First, ZFP-TFs can be used to both down-regulate and up-regulate target genes. Therefore, compared with the antisense or RNAi approach, in which only down-regulation is possible, ZFP-TFs should allow a more comprehensive analysis of coclustered genes. Second, the number of finger domains included in the ZFP-TF can regulate the specificity of ZFP. Depending upon whether three- or six-finger ZFPs are used, the number of regulated genes in a cell varies. Three- or four-finger proteins, as used in this study, are not highly specific; they can modulate several genes when introduced into cells. Whereas this could be a disadvantage in terms of specific regulation, in cases in which a large number of gene perturbations are required, it is an advantage. For example, a gene expression profile obtained from a single ZFP-TF with broad specificity can reveal information about several genetic pathways, and this information can be easily categorized with the use of bioinformatic analyses. This is a clear advantage over the antisense or RNAi approach, in which the number of antisense or RNAi molecules required to regulate the entire genome is theoretically the same as the number of genes in the genome. Third, the universality of transcription-factor action makes this strategy easily applicable to many other eukaryotic and prokaryotic genomes.
In addition to the multiple gene regulation by ZFP-TFs, there may be other reasons why our ZFP-TF-based random perturbation method was successful in identifying a number of coregulated gene groups with a relatively few experiments. For example, by affecting different genes in the same gene group, one would get similar patterns of gene perturbation. This would reduce the number of perturbing agents required to obtain coregulated gene groups. In fact, the work by Hughes et al (2000 We also note the limitation of our approach. As we set a highly stringent cut-off value to discard false positives, the majority of gene groups consist only of a pair of genes. Therefore, many true associations among multiple genes could have been missed. Overall, we have shown the utility of ZFP-TF-based microarray technology in identifying novel functional relationships among genes. More array experiments, along with the use of microarray chips that cover the entire human genome, would be required for a complete functional analysis of the human genome. In addition, time-course expression experiments for each cell line will provide another level of information, which will eventually help in building pathway maps of human cellular gene expression.
Construction of Stable Cell Lines That Express ZFP-TFs We selected ZFP-TFs from our premade collection without any bias or preference. Human embryonic kidney (HEK) cell lines stably expressing ZFP-TFs were generated as follows: Plasmids encoding ZFP-TFs were stably introduced into FlpTRex-293 cell lines (Invitrogen) essentially as described in the manufacturer's protocol. Briefly, the HindIII-XhoI fragment from the pLFD-p65, pLFD-VP16, or pLFD-Kid vectors (Bae et al. 2003 ), which contain DNA segments that encode ZFP-TFs, were subcloned individually into pcDNA5/FRT/TO (Invitrogen). The resulting plasmids were cotransfected along with pOG44 (Invitrogen) into FlpTRex-293 cells to induce a site-specific integration event. Stable integrants were then screened. The resulting cell lines expressed ZFP-TFs upon the addition of Dox. A total of 132 cell lines were subjected to gene expression microarray experiments. The identities of ZFP-TFs used for the experiments are shown in Supplemental Table 1. It should be noted that for some cell lines, we isolated stable clones after random transfection. Thus, the identities of ZFP-TFs are not known.
DNA Microarray In the Cycloheximide experiment, Dox treatment in cells was carried out for 3 h, after which they were cultured for an additional 3 h with 20 µg/mL cycloheximide. This sample was compared with the sample collected after a 6-hour culture in the presence of Dox, but without cycloheximide treatment. Also, 6 h after Dox treatment in cells, They were cultured with 20 µg/mL cycloheximide for another 6 h. Similarly, this sample was compared with the sample cultured for 12 h with Dox alone. To identify primary target genes, genes induced at all time points and whose increase were not significantly decreased by cycloheximide treatment, were considered as primary target genes. Other induced genes at any time point were considered as secondary effects.
Data Analysis To isolate gene groups with strong similarities in their expression profiles, we processed the data with the following algorithm.
SP analysis was performed essentially as described in Zhou et al. (2002
The detailed information on microarray data analysis and clustergrams of individual groups are available at www.toolgen.com
Experimental Validation
We thank the members of Toolgen laboratory for their support, and Drs. H.C. Shin, K.H. Bae, W. Seol, and K.-S. Park for providing materials. We also thank K. LaMarco and C. Sohn for carefully reading our manuscript. This work was partially supported by the National Research Laboratory Program (MI-0104-00-0048). The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.1397903.
1 Corresponding author. [Supplemental material is available online at www.genome.org. The microarray data from this study have been submitted to GEO under the accession nos.GSM10013-GSM10044 and GSM10069-GSM10168.]
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www.toolgen.com; Detailed information on microarray data.
Received April 2, 2003;
accepted in revised format September 18, 2003.
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ABSTRACT
RESULTS
30 amino acid residues and a zinc ion. Each finger typically recognizes a 3-bp sequence, and three or more zinc fingers are tandemly linked to build a zinc finger protein (ZFP) that recognizes a stretch of nine or more base pairs. We built novel ZFPs by shuffling appropriate zinc fingers to match the DNA target sites of interest. Thus, unlike previous protocols, such as phage display selection, this approach is easily scalable. Thousands of highly active ZFPs can be constructed simultaneously. For example, shuffling 20 domains to make three-finger proteins would yield 8000 (= 20 x 20 x 20) ZFPs in a single step (Bae et al. 2003
1 target protein (Ozbun et al. 2001
subunits. Cell 86: 445-452.
and 
receptors and their endogenous ligands: Evidence for two opioid peptide recognition cores. Brain Res. 700: 89-98.
