proteome-wide profiling of activated transcription ... · proteome-wide profiling of activated...

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Proteome-wide proling of activated transcription factors with a concatenated tandem array of transcription factor response elements Chen Ding a,b,c , Doug W. Chan c , Wanlin Liu a,b , Mingwei Liu a,b , Dong Li a,b , Lei Song a,b , Chonghua Li d , Jianping Jin d , Anna Malovannaya c , Sung Yun Jung c , Bei Zhen a,b , Yi Wang c , and Jun Qin a,b,c,1 a State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100850, China; b National Engineering Research Center for Protein Drugs, Beijing 102206, China; c Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030; d Department of Biochemistry and Molecular Biology, University of Texas Medical School at Houston, University of Texas Health Science Center at Houston, Houston, TX 77030 Edited by Robert G. Roeder, The Rockefeller University, New York, NY, and approved March 4, 2013 (received for review October 16, 2012) Transcription factors (TFs) are families of proteins that bind to specic DNA sequences, or TF response elements (TFREs), and function as regulators of many cellular processes. Because of the low abundance of TFs, direct quantitative measurement of TFs on a proteome scale remains a challenge. In this study, we report the development of an afnity reagent that permits identication of endogenous TFs at the proteome scale. The afnity reagent is composed of a synthetic DNA containing a concatenated tandem array of the consensus TFREs (catTFRE) for the majority of TF families. By using catTFRE to enrich TFs from cells, we were able to identify as many as 400 TFs from a single cell line and a total of 878 TFs from 11 cell types, covering more than 50% of the gene products that code for the DNA-binding TFs in the genome. We further demonstrated that catTFRE pull-downs could quantitatively measure proteome-wide changes in DNA binding activity of TFs in response to exogenous stimulation by using a label- free MS-based quantication approach. Applying catTFRE on the evaluation of drug effects, we described a panoramic view of TF activations and provided candidates for the elucidation of molecular mechanisms of drug actions. We anticipate that the catTFRE afnity strategy will nd widespread applications in biomedical research. TF activity proling | transcriptional coregulator | drug effects screening A lmost all biological processes, ranging from cell cycle reg- ulation to organ development, are controlled by the tran- scriptional regulatory system (1). In the classic cell membrane- to-nucleus signal transduction paradigm, transcription factors (TFs) are the nal effectors. They are activated and bind to con- sensus DNA sequences to execute specic transcriptional pro- grams in response to the signal. Thus, the ability to monitor TF activity is important for the delineation of signal transduction pathways when the cells are perturbed (e.g., treated with a drug), or when organs are under the inuence of developmental cues. Approximately 1,500 TF coding genes are reported to be in the human genome (2). TFs can be grouped into different families depending on the structure of their DNA binding domains. There are approximately 50 TF families (2), and each family prefers to bind a specic DNA consensus sequence. For example, nuclear receptors (NRs) are ligand-modulated TFs that recognize one or two hormone response element sequences such as 5-AGAACA-3or 5-AGGTCA-3(3). Previous studies have demonstrated the importance of linking an extracellular signaling event to the acti- vation of TFs. For example, assigning Forkhead box (Fox) P3 to a signaling module that is crucial for regulatory T-cell de- velopment has accelerated our understanding of signal trans- ductions and gene functions (4). The abundance of TFs in the cells is currently inferred from mRNA proling. Yang et al. identied 45 of 49 known NRs from several tissues in mice and linked NR expression to the circadian clock (5). Bookout et al. surveyed the expression of all 49 mouse NR mRNAs in 39 tissues (6). However, information obtained from mRNA proling often cannot be directly translated into protein levels, let alone the activity state of the TF population. Here, we report a method for determining DNA binding activity of multiple endogenous TFs simultaneously. By using a synthetic DNA containing a concatenated tandem array of consensus TF response elements (TFREs; catTFREs) for most known TF fam- ilies, we succeeded in detecting more than 878 TFs from 11 cell types, including 400 TFs from a single cell line. We further showed that this method could quantitatively measure activated TF change in response to signaling events. We applied this method to eluci- date drug effects by describing alterations of hundreds of activated TFs in response to drug treatments. We envision that this meth- odology will nd broad applications in discovering TF activation/ repression in signaling networks. Results Design and Characterization of catTFRE for TF Enrichment. We re- ferred to TF binding database JASPAR to select consensus TFREs for different TF families. To design the catTFRE construct, we used 100 selected TFREs and placed two tandem copies of each se- quence with a spacer of three nucleotides in between, resulting in a total DNA length of 2.8 kb (Fig. 1C and Dataset S1). We syn- thesized and cloned the catTFRE sequence into a pUC57 vector and prepared the catTFRE afnity reagent by PCR amplication with biotinylated primers. We then incubated nuclear extracts (NEs) with the biotinylated catTFRE. The resulting proteinDNA complexes were digested with trypsin and analyzed with MS. Iso- tope-based and label-free quantication can be used in this work- ow (Fig. 1 A and B). Next, we evaluated the efciency of catTFRE DNA pull-down for enrichment of TFs from 500 μg of NE. Ninety-four TFs were identied in 1% of catTFRE pull-down eluate, whereas only 24 TFs were identied in 1% of equivalent NE input (Fig. 1D and Dataset S1), and 20 of the latter were recovered by catTFRE pull-down. Areas under the curve (AUCs) of peptides as an in- dication of TF abundance of the nine TFs were calculated, and showed a signicant enrichment by the catTFRE (Fig. 1E). To test the sensitivity of catTFRE pull down, we carried out the experiments by using various amount of NEs ranging from 50 μg to 400 μg (Fig. 2A). We detected more than 150 TFs from 50 μg of NE, and more than 200 TFs from 400 μg of NE, dem- onstrating that catTFRE strategy is a sensitive and high- throughput assay for the detection of TFs. We then evaluated how effective the catTFRE pull-down is in the enrichment of endogenous TFs. We cloned a 2.8-kb DNA Author contributions: C.D. and J.Q. designed research; C.D., D.W.C., M.L., C.L., S.Y.J., and Y.W. performed research; C.D., D.L., and S.Y.J. contributed new reagents/analytic tools; C.D., W.L., D.L., L.S., J.J., A.M., B.Z., and Y.W. analyzed data; and C.D., Y.W., and J.Q. wrote the paper. The authors declare no conict of interest. This article is a PNAS Direct Submission. 1 To whom correspondence should be addressed. E-mail: [email protected]. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1217657110/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1217657110 PNAS Early Edition | 1 of 6 BIOCHEMISTRY

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Page 1: Proteome-wide profiling of activated transcription ... · Proteome-wide profiling of activated transcription ... transcription factor response elements ... Mass Spectrometry

Proteome-wide profiling of activated transcriptionfactors with a concatenated tandem array oftranscription factor response elementsChen Dinga,b,c, Doug W. Chanc, Wanlin Liua,b, Mingwei Liua,b, Dong Lia,b, Lei Songa,b, Chonghua Lid, Jianping Jind,Anna Malovannayac, Sung Yun Jungc, Bei Zhena,b, Yi Wangc, and Jun Qina,b,c,1

aState Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100850, China; bNational EngineeringResearch Center for Protein Drugs, Beijing 102206, China; cCenter for Molecular Discovery, Verna and Marrs McLean Department of Biochemistryand Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030; dDepartment of Biochemistryand Molecular Biology, University of Texas Medical School at Houston, University of Texas Health Science Center at Houston, Houston, TX 77030

Edited by Robert G. Roeder, The Rockefeller University, New York, NY, and approved March 4, 2013 (received for review October 16, 2012)

Transcription factors (TFs) are families of proteins that bind to specificDNA sequences, or TF response elements (TFREs), and function asregulators of many cellular processes. Because of the low abundanceof TFs, direct quantitative measurement of TFs on a proteome scaleremains a challenge. In this study, we report the development of anaffinity reagent that permits identification of endogenous TFs at theproteome scale. The affinity reagent is composed of a synthetic DNAcontaining a concatenated tandem array of the consensus TFREs(catTFRE) for the majority of TF families. By using catTFRE to enrichTFs from cells, we were able to identify as many as 400 TFs froma single cell line and a total of 878 TFs from 11 cell types, coveringmore than 50% of the gene products that code for the DNA-bindingTFs in the genome.We further demonstrated that catTFRE pull-downscould quantitatively measure proteome-wide changes in DNA bindingactivity of TFs in response to exogenous stimulation by using a label-free MS-based quantification approach. Applying catTFRE on theevaluation of drug effects, we described a panoramic view of TFactivations and provided candidates for the elucidation of molecularmechanisms of drug actions. We anticipate that the catTFRE affinitystrategy will find widespread applications in biomedical research.

TF activity profiling | transcriptional coregulator | drug effects screening

Almost all biological processes, ranging from cell cycle reg-ulation to organ development, are controlled by the tran-

scriptional regulatory system (1). In the classic cell membrane-to-nucleus signal transduction paradigm, transcription factors(TFs) are the final effectors. They are activated and bind to con-sensus DNA sequences to execute specific transcriptional pro-grams in response to the signal. Thus, the ability to monitor TFactivity is important for the delineation of signal transductionpathwayswhen the cells are perturbed (e.g., treatedwith a drug), orwhen organs are under the influence of developmental cues.Approximately 1,500 TF coding genes are reported to be in

the human genome (2). TFs can be grouped into differentfamilies depending on the structure of their DNA binding domains.There are approximately 50 TF families (2), and each family prefersto bind a specific DNA consensus sequence. For example, nuclearreceptors (NRs) are ligand-modulated TFs that recognize one ortwo hormone response element sequences such as 5′-AGAACA-3′or 5′-AGGTCA-3′ (3). Previous studies have demonstrated theimportance of linking an extracellular signaling event to the acti-vation of TFs. For example, assigning Forkhead box (Fox) P3 toa signaling module that is crucial for regulatory T-cell de-velopment has accelerated our understanding of signal trans-ductions and gene functions (4).The abundance of TFs in the cells is currently inferred from

mRNA profiling. Yang et al. identified 45 of 49 known NRs fromseveral tissues in mice and linked NR expression to the circadianclock (5). Bookout et al. surveyed the expression of all 49 mouseNRmRNAs in 39 tissues (6). However, information obtained frommRNA profiling often cannot be directly translated into proteinlevels, let alone the activity state of the TF population.

Here, we report a method for determining DNA binding activityof multiple endogenous TFs simultaneously. By using a syntheticDNA containing a concatenated tandem array of consensus TFresponse elements (TFREs; catTFREs) for most known TF fam-ilies, we succeeded in detecting more than 878 TFs from 11 celltypes, including 400 TFs from a single cell line. We further showedthat this method could quantitatively measure activated TF changein response to signaling events. We applied this method to eluci-date drug effects by describing alterations of hundreds of activatedTFs in response to drug treatments. We envision that this meth-odology will find broad applications in discovering TF activation/repression in signaling networks.

ResultsDesign and Characterization of catTFRE for TF Enrichment. We re-ferred to TF binding database JASPAR to select consensus TFREsfor different TF families. To design the catTFRE construct, we used100 selected TFREs and placed two tandem copies of each se-quence with a spacer of three nucleotides in between, resulting ina total DNA length of 2.8 kb (Fig. 1C and Dataset S1). We syn-thesized and cloned the catTFRE sequence into a pUC57 vectorand prepared the catTFRE affinity reagent by PCR amplificationwith biotinylated primers. We then incubated nuclear extracts(NEs) with the biotinylated catTFRE. The resulting protein–DNAcomplexes were digested with trypsin and analyzed with MS. Iso-tope-based and label-free quantification can be used in this work-flow (Fig. 1 A and B).Next, we evaluated the efficiency of catTFRE DNA pull-down

for enrichment of TFs from 500 μg of NE. Ninety-four TFs wereidentified in 1% of catTFRE pull-down eluate, whereas only 24TFs were identified in 1% of equivalent NE input (Fig. 1D andDataset S1), and 20 of the latter were recovered by catTFREpull-down. Areas under the curve (AUCs) of peptides as an in-dication of TF abundance of the nine TFs were calculated, andshowed a significant enrichment by the catTFRE (Fig. 1E). Totest the sensitivity of catTFRE pull down, we carried out theexperiments by using various amount of NEs ranging from 50μg to 400 μg (Fig. 2A). We detected more than 150 TFs from 50μg of NE, and more than 200 TFs from 400 μg of NE, dem-onstrating that catTFRE strategy is a sensitive and high-throughput assay for the detection of TFs.We then evaluated how effective the catTFRE pull-down is in

the enrichment of endogenous TFs. We cloned a 2.8-kb DNA

Author contributions: C.D. and J.Q. designed research; C.D., D.W.C., M.L., C.L., S.Y.J., andY.W. performed research; C.D., D.L., and S.Y.J. contributed new reagents/analytic tools;C.D., W.L., D.L., L.S., J.J., A.M., B.Z., and Y.W. analyzed data; and C.D., Y.W., and J.Q. wrotethe paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.1To whom correspondence should be addressed. E-mail: [email protected].

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1217657110/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1217657110 PNAS Early Edition | 1 of 6

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sequence (same length as catTFRE) from the pGEX4T2 plasmidas nonregulatory DNA control and carried out DNA pull-downexperiments using the same amount of NEs and DNA. catTFREand control DNA pull-downs identified 276 and 172 TFs, re-spectively; 194 TFs showed enrichment of >10 fold in catTFRE,whereas only five TFs showed enrichment of >10 fold in controlDNA (Fig. 2B and Dataset S1), suggesting that catTFRE wasmuch more specific and effective in enriching and identifying TFsby design.To test how a single TFRE impacted TF binding of the TF

family, we made two deletion mutants named ΔNFY and ΔFox byremoving nuclear transcription factor Y (NFY) or Fox binding sitefrom the original catTFRE sequence (Fig. S1B). Deletion of theNFY binding site led to decreased binding of eight TFs to morethan three fold among the 270 TFs detected (Dataset S1). NFYBand NFYC decreased by >10 fold, and NFYA decreased by sevenfold (Fig. S1C). Deletion of the Fox binding site led to decreasedbinding of 17 TFs to more than three fold among the 270 TFsdetected (Dataset S1). FOXC1, FOXD2, FOXP1, and FOXP2decreased DNA binding more than three fold (Fig. S1D). Weconcluded that enrichment of TFs by catTFRE is largely de-pendent on their specific TF binding sites.

Label-Free and Stable Isotope Labeling by Amino Acids in Cell CultureBased Quantitative TF Screening by catTFRE Pull-Down. To test thefeasibility of label-free quantitative TF profiling, we used sameamount of catTFRE DNA (15 pmol) to isolate and identify en-dogenous TFs by using HeLa NE in the range of 0.25 to 2 mg totalprotein in 250 μL of volume. As shown in Fig. 2C, all 14 selectedTFs exhibit linear response to the amount of proteins used in thepull-down, whereas signals of nonspecific binding proteins, such asActin and HSP70, remain largely unchanged (Fig. 2C and Fig.S1A). We also compared the dynamic response of three selectedproteins [nuclear factor kappa b (NF-κB); nuclear receptor sub-family 2, group C, member 2 (NR2C2); and CAMP responsiveelement binding protein (CREB-1)] byWestern blotting. As showninFig. 2D, the increased intensity ofWB signals was consistentwiththe increased amount of isolated proteins as more NE was used.Next, we tested whether this approach can be used to reveal

dynamic changes of TF binding in response to extracellular stimuli.NF-κB TF is activated by various intra- and extracellular stimuli,including TNF-α (7).We treated 293T cells with 10 ng/mLof TNF-α or vehicle control for 3 h and performed TF profiling for bothsamples in parallel. As shown in Fig. S2A andB, TFRE-boundNF-κB/p50 and bovine transcription factor p65 (RELA/p65) were in-creased five- and 13-fold, respectively, after TNF-α treatment,which is consistent with the previous reports (8, 9). Jun, a TF ac-tivated by TNF-α (10), also exhibited an increased binding by threefold to catTFRE.The stable isotope labeling by amino acids in cell culture

(SILAC)-based quantification was used to verify the label-freequantification results. We spiked the same amount of NE fromSILAC-cultured HeLa cells in TNF-α–treated and vehicle controlsamples. The isotope-labeledTFs should bind to catTFREwith thesame affinity in both samples and thereby serve as an internalstandard (Fig. S2D). After normalization to isotope-labeled in-ternal controls, SILAC quantification showed that NF-κB and Junwere increased by seven and three fold upon TNF-α stimulation,

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Fig. 1. Outline of catTFRE pull-down strategy. (A) A tandem combination ofTFRE with duplicated repeats was synthesized and amplified by PCR withbiotinylated primers. Biotinylated TFRE was then incubated with cell lysateto enrich endogenous TFs. Samples were subjected to MS for measurement.(B) catTFRE pull-down coupled with label-free/based strategy. Peptides ofTFs with good signal response were selected for quantification by calculatingAUC. Isotope-labeled internal standard was spiked into samples, and theamount was determined after comparing peptide AUC with respective in-ternal standard, (C) Design of catTFRE DNA. Information of consensus TFbinding sequence was grabbed from JASPAR Web site. Each TF binding sitewas synthesized duplicated and tandemly combined with a three-nucleotidespacer. (D) Advantages of catTFRE strategy in endogenous TF enrichment. Atotal of 500 μg of NE was incubated with 15 pmol catTFRE or executed withtrypsin digestion directly. One percent of output was loaded on MS,indentified TFs were counted, and peak areas of peptides were calculated.(E) Identifications and peptides AUC of TFs enriched by catTFRE or executedwith trypsin digestion directly.

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Fig. 2. Sensitivity and quantitative feasibility evaluation of catTFRE strat-egy. (A) TF identifications of serial amounts of NE incubated with 15 pmolcatTFRE. TF SPCs are shown as a heat map of white to red. (B) TF enrichmentand identification comparison between catTFRE and nonregulatory DNApGEX4T2. Color density indicates total abundance of identified TFs. (C)Quantitative feasibility and linearity of catTFRE strategy evaluated by ti-tration analysis. Serial amounts of NE were used as shown. Peptide AUC from14 TFs and two nonspecific binding proteins were calculated. AUCs of 25 μLsample were set as 1 and others were normalized to their correspondingpeptide in 25 μL sample. (D) Western blotting analysis of the catTFRE outputusing antibodies as indicated.

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a result that is consistent with the label-free quantification(Fig. S2C).

In-Depth Analysis of TF Binding in Mammalian Cell Lines. To uncoverthe potential of catTFRE in TF profiling, we used 5 mg of NE and30 pmol of catTFREDNA to isolate and identify TFs fromdifferentcell lines (Fig. 3A). Prefractionation of peptides into 12 fractionswith isoelectric focusing resulted in the identification of 455 TFsfrom HeLa cells. We carried out similar experiments with 293T,H1299, HeLa, HepG2, A549, U937, MCF7, PC3, SY5Y, andMEFcells, and detected 207 to 460 TFs in these cell lines (Dataset S2).Next, we applied catTFRE pull-down to mouse liver to evaluatewhether catTFRE can be used for tissue TF profiling. A total of 391TFs were identified frommouse liver as a result (Fig. 3A). In all, weidentified 878 TFs from11mammalian cell lines, representingmorethan half of all TF-coding genes in the genome. Notably, 29 of 50Forkhead family members and 42 of 48 predicted NRs weredetected. Fig. 3C summarizes the coverage for each TF family in 11mammalian cell lines. These results also demonstrate the wide dy-namic range of TF abundances, as the top 16 TFs contribute 25%ofthe total number of spectral counts (SPCs), an indicator of theabundance of proteins, whereas 300 TFs of lower abundance to-gether constitute only 1% of total SPCs (Fig. 3D).Transcription coregulators (CoRs) cooperate with TFs to in-

tegrate diverse cellular signals and thereby mediate a coordinatedtranscriptional response (11). AlthoughmanyCoRs do not directlybind to DNA, they can be recruited to TFREs through interactionwith TFs. Considerable numbers of CoRswere identified in our TFscreening, suggesting that some TF–CoR interactions are pre-served in catTFRE DNA pull-down. A total of 497 CoRs wereidentified in the 11 tested cell types (Fig. 3B and Dataset S2).Similarly to TFs, the presence and abundance of CoRs are widelydistributed, with the 32most abundantCoRs comprising half of theCoR SPCs (Fig. S3B). Moreover, the catTFRE strategy identified

155 of 235 highly confident unconventional DNA binding proteinsreported in previous work (12) (Dataset S2).

Comparison of catTFRE Pull-Down and Protein/mRNA Profiling in HeLaCells. We used a faster mass spectrometer (Q-Exactive; Thermo)and extended total MS measuring time to 16 h to improve theidentification coverage and compared with a typical in-depth MSprofiling of HeLa cells. We identified 743 TFs among 3,866 geneproducts identified in catTFRE pull-down and 295 TFs among7,601 gene products from MS profiling. The enrichment of TFsby catTFRE is profound (Fig. S3C). A total of 487 TFs wereidentified exclusively in the catTFRE pull-down, whereas only17TFs, such as STAT5A/B and STAT6, identified exclusively withat least three unique peptides in the MS profiling (Dataset S2).We concluded that these STATs did not bind DNA under ourexperimental conditions, as we detected abundant amounts ofSTATs in other catTFRE pull-downs (Dataset S2).We compared the catTFRE result with mRNA-seq in the lit-

erature (13). The mRNA-seq identified 859 TFs among the 10,936protein coding genes (fragments per kb of exon per million map-ped fragments > 1), whereas catTFRE pull-down identified 743TFs. The overlap between mRNA-seq and catTFRE is 579 TFs(Fig. S3D andDataset S2). There are total of 1,531 genome-codingTFs, of which mRNA-seq identified 56%, whereas catTFREidentified 49% of them in HeLa cells. These data suggest that thedepth of coverage for the TF subproteome by catTFRE pull-downand that of the TF subtranscriptome by mRNA-seq are compa-rable for HeLa cells.

Analysis of Dynamic Changes of Global TF-DNA Binding Patterns AfterTNF-α Treatment. We then chose TNF-α signal transduction path-way to evaluate the potential of catTFRE in analysis of global TFalterations in response to exogenous stimulation. We performedcatTFRE pull-down with 293T cells treated with TNF-α for 15, 30,and 180 min, and detected a total of 234 TFs (Fig. 4A and DatasetS3). We arbitrarily chose more than threefold intensity change assignificantly changed. Overall, 20 TFs were activated by TNF-α, 13of which were increased within 15 min, and seven TFs showeda delay in activation after 30 min. Meanwhile, binding of 15 TFswas suppressed by TNF-α, six of which were decreased within first15 min, and another nine showed a delayed down-regulation after30 min (Fig. 4A and Dataset S3). The remaining 199 TFs did notshow significant changes upon TNF-α stimulation. Consistent withprevious knowledge, TFs related to NF-κB family and JNK/P38pathways were activated (14, 15) (Fig. S4A). In addition, several TFfamilies that have not previously been known to be involved inTNF-α response exhibited marked changes. For example, bindingof the zing finger and BTB (ZBTB) and nuclear factor of activatedT cells (NFAT) family members was increased, whereas binding ofhigh mobility group (HMG) proteins was reduced upon TNF-αtreatment (Fig. S4B).NF-κB is known as the strongest responder to TNF stimula-

tion. We then sought out TF changes that are associated withNF-κB activation. To this end, we blocked NF-κB activation bypreincubating the cells with an inhibitor ammonium pyrrolidine-dithiocarbamate (PDTC) before TNF-α treatment. As expected,TF members of NF-κB family but not JNK/P38 pathway wereinhibited by PDTC (Fig. 4B and Fig. S4 C and D). Up-regulatedTFs ZBTB17 and NFATs had the same response pattern as NF-κB, indicating that these TFs behave similarly as NF-κB. In con-trast, the decrease in HMG family members was not affected byPDTC (Fig. 4B, Fig. S4E, and Dataset S3). This proof-of-conceptstudy has demonstrated that catTFRE strategy is capable of sys-tematically detecting changes in TF DNA binding activity.

Screening of TF DNA Binding Activity Change in Drug Actions. Wetested whether catTFRE can be effectively used to study the mo-lecular effects of drug actions in K562 cells that contain the Phila-delphia chromosome and the chimeric BCR-ABL1 gene. We chosephorbol myristate acetate (PMA) (16), an activator of protein ki-nase C (17), and imatinib mesylate (Gleevec) (18), an inhibitor of

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Fig. 3. Differential TF expression pattern and coverage analysis of TFfamilies among 11 cell types. (A) TF and (B) CoR profiling of 11 human celltypes using catTFRE demonstrated heterogeneity in basal TF and CoR ex-pression pattern. Normalized SPCs of TFs are shown as a heat map of whiteto red. FOT, fraction of total, i.e., percentage of a TF SPC to total. (C) Cov-erage analysis of TF families from 11 mammalian cells with catTFRE strategy.(D) Cumulative protein mass from the highest to the lowest abundance TFs.

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the BCR-ABL kinase, for many of their opposite effects in theregulation of the K562 cells (19, 20).The K562 cells were treated with PMA or imatinib for 24 h, and

TF DNA binding was profiled with catTFRE. The PMA treatmentyielded 462 TF and 395 CoR identifications, of which 159 TFs and92 CoRs were up-regulated and 113 TFs and 83 CoRs were down-regulated, with more than threefold change (Dataset S3). The ima-tinib treatment yielded 406 TFs and 371 CoRs, of which 46 TFs and18 CoRs were up-regulated and 137 TFs and 146 CoRs were down-regulated, respectively, at more than three fold of change (DatasetS3). Analysis of the alteration of TFs with Integrated PathwayAnalysis indicates that PMA and imatinib play opposite roles indifferentiation, development, and cell death. PMA activates thedifferentiation and development programs, but suppresses the “celldeath” module, whereas imatinib suppresses differentiation anddevelopment and activates the cell death module (Fig. 5 A and C).Integrated Pathway Analysis also revealed cell differentiation andBCR-AML signaling as the primary altered pathway influenced byPMA and imatinib (Fig. 5 B and D). PMA stimulated binding ofTFs that are known to function in cell differentiation such as acti-vator protein 1 (AP1), Finkel–Biskis–Jinkins osteosarcoma viraloncogene (FOS), E-twenty six (ETS), ETS domain-containing pro-tein Elk-1 (ELK), B-cell–activating transcription factor (BATF), andrunt-related transcription factor (RUNX), whereas imatinib exe-cuted the opposite program (Fig. 5E and Fig. S4 F and G).Constitutive activation of STAT5 has been demonstrated as

a mechanism for the maintenance of chronic myeloid leukemia(CML) characterized by the BCR-ABL fusion (21).We found thatDNA binding activity of STAT5 is suppressed when BCR-ABL isinactivated by imatinib. In addition to known responders, imatinibdramatically activated tumor suppressors E2F transcription factor4 (E2F4) and GATA binding protein 5 (GATA5).Activation of v-myc myelocytomatosis viral oncogene (MYC) by

BCR-AML was reported to be involved in CML progression (22)and down-regulated by imatinib (23), but the mechanism of Mycinactivation and alteration of its downstreampathwaywas not clear.The DNA-binding patterns of Myc activator STAT5 (24) and re-pressor E2F4 (25) upon imatinib treatment clearly suggest a mecha-nism of Myc repression (Fig. 5F).Myc, MYC associated factor X (MAX), and mitotic arrest de-

ficient protein (MAD) form aMyc/Max/Mad network that regulated

gene activation and repression by switching between antagonisticinteraction pairs ofMyc–Max andMax–Mad (26). By using intensity-based absolute quantification of protein amounts (27) as an indicatorof the absolute quantity of proteins, we found that Max is the mostabundant protein that serves as an anchor in the Myc/Max/Madnetwork (Dataset S3). Mad is known to bind to Max to antagonizethe Myc/Max complex (26)—the major oncogenic heterodimer—thereby inactivating Myc. We found that Mads [MAX dimerizationprotein 4 (MXD4) as the dominant and MXD3 as a minor variant]exhibit a dramatic increase in DNA binding when cells are treatedwith imatinib—they are up-regulated dramatically whereas Myc ismoderately down-regulated (Fig. 5G). The result suggested a mech-anism for imatinib inactivation of Myc oncogenic activity (Fig. 5H).

DiscussionTFs belong to a group of proteins that are generally of low abun-dance and usually underrepresented in proteome profiling experi-ments. In this study, we design aDNAconstruct of tandemTFDNAresponse elements, termed catTFRE, and report its applications asan affinity reagent to enrich DNA-bound TFs in mammalian cellsand tissues. Combined with sensitive MS measurements, we couldidentify as many as 150 TFs from 50 μg of NEs in 1 h of MS mea-surement. Sample prefractionation and longerMS running time canfurther enhance the depth of TF coverage. For example, as many as455 and 391 TFs were identified from HeLa cells and mouse liverwith total MS measuring time of 12 h, respectively. Among 878identified TFs in 11 cell types, 110 were detected in at least 10 celltypes. Twohundred ninety-four TFswere identified in nomore thantwo cell types; we consider them as cell-specific TFs (Fig. S3A).By mutational analysis of the catTFRE sequence, we showed

that enrichment of TFs is indeed largely dependent upon thespecific DNA sequences of the TFRE. The fact that the number ofTFs identified in experiments greatly exceeded the original designof 100 TF families may be explained by the following: (i) the 3-bplinkers may create additional binding sites, (ii) the tandem TFREmay also create additional binding sites, and (iii) the flexibility ofTFs in TFRE recognition. We used the TF binding prediction toolPROMO (28) to computationally analyze the catTFRE sequenceand find 132 “accidental” TF binding sites for human TFs andmore than 300 additional TF binding sites for TFs of other species(Dataset S2). It has been known that each family of TFs bindsa specific consensus sequence, but there are clear differencesamong members of a family (29–32). Our simplified generic designof TFRE may not reveal the subtle differences in DNA bindingamong members of a family. It will require a specialized design toreveal these subtle differences. Complexity and flexibility in TFRErecognition by TFs are starting to be appreciated, and our findingsadd more precedence to this topic.With the newer generation of mass spectrometers and MS mea-

surement time of approximately 16 h, catTFRE pull-down is able todetect similar number of TFs that can be detected by mRNA-seq inHeLa cells, proving a new tool for profiling TFs at protein level.catTFRE pull down provides more direct information about TFsthan protein profiling and mRNA-seq, as it actually measures DNAbinding “activity”; this is one step closer to profiling transcriptionactivity of TFs and one unique advantage over protein profilingand mRNA-seq.The quantitative nature of the catTFRE approach allows not

only confirmation of TF existence in a cell, but also monitoringof their dynamic change in response to exogenous stimulation, asdemonstrated by the inducible TNF-α/NF-κB pathway. By usingcatTFRE, we revealed alterations in binding of hundreds of TFsat the same time in addition to the well-known NF-κB factors. Byusing a specific NF-κB inhibitor, we were also able to classify TFsinto NF-κB–dependent and -independent categories.Drugs for various therapeutic applications frequently have

“hidden phenotypes” that result from unexpected or unintendedactivities, as a result of their binding to unknown targets or un-known interactions between the intended drug target and otherbiochemical pathways. Such unknown activities may be harmful,leading to toxicity, or beneficial, suggesting new therapeutic

0’ 15’

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Fig. 4. Systematical and quantitative analysis of TF profiling in TNF-α path-way. (A) Kinetic TF activation pattern of 293T cells after TNF-α stimulation. 293Tcells were treated with TNF-α for different time. Relative amount of TFs com-pared with 0 min group are shown as a heat map of green to red that rep-resents down-regulation and up-regulation, respectively. Accurate intensity ofTFs in 0-min group was set as 1. (B) 293T cells were treated with TNF-α for 15min in the presence or absence of PDTC. Relative amount of TFs comparedwithvehicle control group are shown as a heat map of green to red that representsdown-regulation and up-regulation respectively. Accurate intensity of TFs invehicle control group was set as 1.

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A

Differentiation

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& development

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Fig. 5. Bioinformatics analysis of TF regulations induced by drugs. Functional classification of altered TFs in (A) PMA and (C) imatinib. Down-regulationgroups are indicated in blue and up-regulation groups are in brown. (B and D) Volcano plot of cellular pathways responded to PMA and imatinib treatment.Z-score stands for the pathway ratio of drug-treated group to control group. (E) Alteration of differentiation, development, and proliferation-related ca-nonical TF pathways induced by PMA and imatinib. (F) Alteration of STAT5 and E2F4 in response to imatinib treatment. Cartoon shows possible mechanism ofMyc repression: In imatinib-treated CML cells, Myc’s activator STAT5 was repressed whereas Myc’s repressor E2F4 was activated, leading the competitivenessloss of Myc in interacting with Max. Intensity-based absolute quantification of protein amounts (iBAQ) is the sum of all peptide peak intensities areas dividedby the number of theoretically observable full tryptic peptides. ND, not detected. (G) Regulation of oncogenes and TF components of Myc/Max/Mad networkinduced by imatinib. (H) Schematic model of imatinib-triggered Myc/Max/Mad network switching. Mad proteins were dramatically activated and interactedwith Max to antagonize Myc/Max complexes, resulting in transcription repression.

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applications. Discoveries of new and useful properties of drugs areusually made by serendipity, and the underlying mechanisms bywhich a drug produces an effect are often not known. We believepatterns of TFDNAbinding can provide a diagnostic fingerprint ofdrug effects, and, in some cases, they provide hypotheses for thecellular mechanisms of drug responses.The application of catTFRE for the treatment of K562 cells with

PMA and imatinib illustrated the aforementioned points. Quan-tification of the changes in TF DNA binding after drug treatmentquickly pointed to the distinctive effect of PMAand imatinib in celldifferentiation, development, cell death, and BCR-AML signaling.In addition, simultaneouslymonitoringmost of theTF families andtheir CoRs permitted us to suggest that onemechanism of imatinibinhibition of CML is through down-regulation of Myc by un-balanced DNA bind activities of STAT5 and E2F4. Decrease inMyc eventually triggered a molecular switch in the Max/Myc/Madsignaling network, whereby “off” position can results from co-ordinated down-regulation of Myc binding and up-regulation ofMad binding (26).In summary, the catTFRE strategy presented here enables high-

throughput identification and quantification of DNA binding ac-tivity for most cellular TFs. We envision that this technology will

serve as a potent tool for elucidation of the molecular effects ofdrug actions, evaluation of drug efficacy, and concurrent discoveryof secondary drug effects.

Materials and MethodsMaterial and Chemicals. catTFRE DNA was synthesized by Genscript. Bio-tinylated catTFRE primers were synthesized by Sigma. Dynabeads (M-280streptavidin) were purchased from Invitrogen.

Nano-Liquid Chromatography/Tandem MS Analysis for Protein Identificationand Label-Free Quantification. Tryptic peptides were separated on a C18column, and were analyzed by LTQ-Orbitrap Velos (Thermo). Proteins wereidentified by using the National Center for Biotechnology Information searchengine against the human or mouse National Center for Biotechnology In-formation RefSeq protein databases.

ACKNOWLEDGMENTS. This work was supported by National Key Labora-tory of Proteomics Grant SKLP-K201001; National High-Tech Research andDevelopment Program of China 863 Program Grant 2012AA020201; theCancer Prevention and Research Institute of Texas (CPRIT) Grant RP110784;the National Institutes of Health Nuclear Receptor Signaling Atlas (NURSA)Grant U19-DK62434 (to J.Q.); and Natural Science Foundation of ChinaGrant 31200582.

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