Proteome-wide profiling of activated transcription factors with a concatenated tandem array of response elements

Chen 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 Engineering Research Center for Drugs, Beijing 102206, China; cCenter 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; dDepartment 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 that bind to specific Here, we report a method for determining DNA binding activity DNA sequences, or TF response elements (TFREs), and function as of multiple endogenous TFs simultaneously. By using a synthetic regulators of many cellular processes. Because of the low abundance DNA containing a concatenated tandem array of consensus TF of TFs, direct quantitative measurement of TFs on a proteome scale response elements (TFREs; catTFREs) for most known TF fam- remains a challenge. In this study, we report the development of an ilies, we succeeded in detecting more than 878 TFs from 11 cell affinity reagent that permits identification of endogenous TFs at the types, including 400 TFs from a single cell line. We further showed proteome scale. The affinity reagent is composed of a synthetic DNA that this method could quantitatively measure activated TF change containing a concatenated tandem array of the consensus TFREs in response to signaling events. We applied this method to eluci- (catTFRE) for the majority of TF families. By using catTFRE to enrich date drug effects by describing alterations of hundreds of activated TFs in response to drug treatments. We envision that this meth- TFs from cells, we were able to identify as many as 400 TFs from odology will find broad applications in discovering TF activation/ a single cell line and a total of 878 TFs from 11 cell types, covering

repression in signaling networks. BIOCHEMISTRY more than 50% of the products that code for the DNA-binding TFs in the genome. We further demonstrated that catTFRE pull-downs Results could quantitatively measure proteome-wide changes in DNA binding Design and Characterization of catTFRE for TF Enrichment. We re- activity of TFs in response to exogenous stimulation by using a label- fi ferred to TF binding database JASPAR to select consensus TFREs free MS-based quanti cation approach. Applying catTFRE on the for different TF families. To design the catTFRE construct, we used evaluation of drug effects, we described a panoramic view of TF 100 selected TFREs and placed two tandem copies of each se- activations and provided candidates for the elucidation of molecular quence with a spacer of three nucleotides in between, resulting in mechanisms of drug actions. We anticipate that the catTFRE affinity a total DNA length of 2.8 kb (Fig. 1C and Dataset S1). We syn- strategy will find widespread applications in biomedical research. thesized and cloned the catTFRE sequence into a pUC57 vector and prepared the catTFRE affinity reagent by PCR amplification TF activity profiling | transcriptional coregulator | drug effects screening with biotinylated primers. We then incubated nuclear extracts (NEs) with the biotinylated catTFRE. The resulting protein–DNA lmost all biological processes, ranging from reg- complexes were digested with trypsin and analyzed with MS. Iso- fi ulation to organ development, are controlled by the tran- tope-based and label-free quanti cation can be used in this work- A fl A B scriptional regulatory system (1). In the classic cell membrane- ow (Fig. 1 and ). fi to-nucleus signal transduction paradigm, transcription factors Next, we evaluated the ef ciency of catTFRE DNA pull-down μ (TFs) are the final effectors. They are activated and bind to con- for enrichment of TFs from 500 g of NE. Ninety-four TFs were fi sensus DNA sequences to execute specific transcriptional pro- identi ed in 1% of catTFRE pull-down eluate, whereas only 24 fi D grams in response to the signal. Thus, the ability to monitor TF TFs were identi ed in 1% of equivalent NE input (Fig. 1 and activity is important for the delineation of signal transduction Dataset S1), and 20 of the latter were recovered by catTFRE pathways when the cells are perturbed (e.g., treated with a drug), or pull-down. Areas under the curve (AUCs) of peptides as an in- when organs are under the influence of developmental cues. dication of TF abundance of the nine TFs were calculated, and Approximately 1,500 TF coding are reported to be in showed a significant enrichment by the catTFRE (Fig. 1E). To the (2). TFs can be grouped into different test the sensitivity of catTFRE pull down, we carried out the families depending on the structure of their DNA binding domains. experiments by using various amount of NEs ranging from 50 There are approximately 50 TF families (2), and each family prefers μg to 400 μg(Fig.2A). We detected more than 150 TFs from 50 to bind a specific DNA consensus sequence. For example, nuclear μg of NE, and more than 200 TFs from 400 μgofNE,dem- receptors (NRs) are ligand-modulated TFs that recognize one or onstrating that catTFRE strategy is a sensitive and high- two hormone response element sequences such as 5′-AGAACA-3′ throughput assay for the detection of TFs. or 5′-AGGTCA-3′ (3). Previous studies have demonstrated the We then evaluated how effective the catTFRE pull-down is in importance of linking an extracellular signaling event to the acti- the enrichment of endogenous TFs. We cloned a 2.8-kb DNA 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- Author contributions: C.D. and J.Q. designed research; C.D., D.W.C., M.L., C.L., S.Y.J., and ductions and gene functions (4). Y.W. performed research; C.D., D.L., and S.Y.J. contributed new reagents/analytic tools; The abundance of TFs in the cells is currently inferred from 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 mRNA profiling. Yang et al. identified 45 of 49 known NRs from the paper. several tissues in mice and linked NR expression to the circadian The authors declare no conflict of interest. (5). Bookout et al. surveyed the expression of all 49 mouse This article is a PNAS Direct Submission. NR mRNAs in 39 tissues (6). However, information obtained from 1To whom correspondence should be addressed. E-mail: [email protected]. mRNA profiling often cannot be directly translated into protein This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. levels, let alone the activity state of the TF population. 1073/pnas.1217657110/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1217657110 PNAS Early Edition | 1of6 Downloaded by guest on September 25, 2021 A B Label-Free and Stable Isotope Labeling by Amino Acids in Cell Culture Endogenous TFs Isotope labeled TFs Based Quantitative TF Screening by catTFRE Pull-Down. To test the Synthesizedeedd multipleTFRERE fi TF1 TF1 TF3 feasibility of label-free quantitative TF pro ling, we used same -plasmididd TF3 TF1 MS TFTF2 TF5 amount of catTFRE DNA (15 pmol) to isolate and identify en- TF3 TF5 TF2 PCR with Quantification TF4 nitoiB BBiotin-primers nitoiB TF4F44 TF4 TF55 dogenous TFs by using HeLa NE in the range of 0.25 to 2 mg total μ C TFRE 1 2 3 4 5 … CoR protein in 250 L of volume. As shown in Fig. 2 , all 14 selected tandem-TFRE nitoiB nitoiB TFs exhibit linear response to the amount of proteins used in the TF1 TF1 TFTF2F2F2 TF3 TF44 TTFTF55 … TFRE 1 2 3 4 5 … pull-down, whereas signals of nonspecific binding proteins, such as Nuclear Extract/ tandem Actin and HSP70, remain largely unchanged (Fig. 2C and Fig. TFRE DNA Whole Cell TF quantification Extract S1A). We also compared the dynamic response of three selected Light DNA – pull down κ Light Heavy proteins [nuclear factor kappa b (NF- B); nuclear sub- nitoiB nitoiB family 2, group C, member 2 (NR2C2); and CAMP responsive TFRE 1 2 3 4 5 … Heavy element binding protein (CREB-1)] by Western blotting. As shown

Multiple -TFRE Intensity D Trypsin digestion in Fig. 2 , the increased intensity of WB signals was consistent with IEF (optional)(p ) the increased amount of isolated proteins as more NE was used. MMassass SpectrometrySpectrometry M/ZZ Next, we tested whether this approach can be used to reveal C 2x AP1 2x YY1 2x CEBP1 dynamic changes of TF binding in response to extracellular stimuli. …TGACTCATCATGACTCA TCAGCCATC TCAGCCATC TCATTTCGCAATTCATTTCGCAATTCA… NF-κB TF is activated by various intra- and extracellular stimuli, Spacer Linker 100100 TF binding sites -- 2.8kb including TNF-α (7). We treated 293T cells with 10 ng/mL of TNF- D E α or vehicle control for 3 h and performed TF profiling for both 1% catTFRE 1% NE samples in parallel. As shown in Fig. S2 A and B, TFRE-bound NF- κB/p50 and bovine transcription factor p65 (RELA/p65) were in- creased five- and 13-fold, respectively, after TNF-α treatment, 74 20 4 which is consistent with the previous reports (8, 9). Jun, a TF ac- tivated by TNF-α (10), also exhibited an increased binding by three fold to catTFRE. The stable isotope labeling by amino acids in cell culture (SILAC)-based quantification was used to verify the label-free quantification results. We spiked the same amount of NE from Fig. 1. Outline of catTFRE pull-down strategy. (A) A tandem combination of α– TFRE with duplicated repeats was synthesized and amplified by PCR with SILAC-cultured HeLa cells in TNF- treated and vehicle control biotinylated primers. Biotinylated TFRE was then incubated with cell lysate samples. The isotope-labeled TFs should bind to catTFRE with the to enrich endogenous TFs. Samples were subjected to MS for measurement. same affinity in both samples and thereby serve as an internal (B) catTFRE pull-down coupled with label-free/based strategy. Peptides of standard (Fig. S2D). After normalization to isotope-labeled in- TFs with good signal response were selected for quantification by calculating ternal controls, SILAC quantification showed that NF-κB and Jun AUC. Isotope-labeled internal standard was spiked into samples, and the were increased by seven and three fold upon TNF-α stimulation, amount was determined after comparing peptide AUC with respective in- ternal standard, (C) Design of catTFRE DNA. Information of consensus TF binding sequence was grabbed from JASPAR Web site. Each TF binding site was synthesized duplicated and tandemly combined with a three-nucleotide A B D g spacer. (D) Advantages of catTFRE strategy in endogenous TF enrichment. A g µ catTFRE pGEX4T2 NE Input μ 25ul 50ul 100ul 200ul total of 500 g of NE was incubated with 15 pmol catTFRE or executed with 50µ 100µg 200µg 400 trypsin digestion directly. One percent of output was loaded on MS, 4 108 168 indentified TFs were counted, and peak areas of peptides were calculated. (E) Identifications and peptides AUC of TFs enriched by catTFRE or executed Total TF abundance Total with trypsin digestion directly. C

sequence (same length as catTFRE) from the pGEX4T2 plasmid as nonregulatory DNA control and carried out DNA pull-down experiments using the same amount of NEs and DNA. catTFRE and 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 control DNA (Fig. 2B and Dataset S1), suggesting that catTFRE was much more specific and effective in enriching and identifying TFs by design. To test how a single TFRE impacted TF binding of the TF SPC family, we made two deletion mutants named ΔNFY and ΔFox by 020 removing nuclear transcription factor Y (NFY) or Fox binding site from the original catTFRE sequence (Fig. S1B). Deletion of the Fig. 2. Sensitivity and quantitative feasibility evaluation of catTFRE strat- NFY binding site led to decreased binding of eight TFs to more egy. (A) TF identifications of serial amounts of NE incubated with 15 pmol than three fold among the 270 TFs detected (Dataset S1). NFYB catTFRE. TF SPCs are shown as a heat map of white to red. (B) TF enrichment and NFYC decreased by >10 fold, and NFYA decreased by seven and identification comparison between catTFRE and nonregulatory DNA fold (Fig. S1C). Deletion of the Fox binding site led to decreased pGEX4T2. Color density indicates total abundance of identified TFs. (C) binding of 17 TFs to more than three fold among the 270 TFs Quantitative feasibility and linearity of catTFRE strategy evaluated by ti- tration analysis. Serial amounts of NE were used as shown. Peptide AUC from detected (Dataset S1). FOXC1, FOXD2, FOXP1, and FOXP2 fi μ D 14 TFs and two nonspeci c binding proteins were calculated. AUCs of 25 L decreased DNA binding more than three fold (Fig. S1 ). We sample were set as 1 and others were normalized to their corresponding concluded that enrichment of TFs by catTFRE is largely de- peptide in 25 μL sample. (D) Western blotting analysis of the catTFRE output pendent on their specific TF binding sites. using antibodies as indicated.

2of6 | www.pnas.org/cgi/doi/10.1073/pnas.1217657110 Ding et al. Downloaded by guest on September 25, 2021 aresultthatisconsistentwiththelabel-freequantification 155 of 235 highly confident unconventional DNA binding proteins (Fig. S2C). reported in previous work (12) (Dataset S2).

In-Depth Analysis of TF Binding in Mammalian Cell Lines. To uncover Comparison of catTFRE Pull-Down and Protein/mRNA Profiling in HeLa the potential of catTFRE in TF profiling, we used 5 mg of NE and Cells. We used a faster mass spectrometer (Q-Exactive; Thermo) 30 pmol of catTFRE DNA to isolate and identify TFs from different and extended total MS measuring time to 16 h to improve the cell lines (Fig. 3A). Prefractionation of peptides into 12 fractions identification coverage and compared with a typical in-depth MS with isoelectric focusing resulted in the identification of 455 TFs profiling of HeLa cells. We identified 743 TFs among 3,866 gene from HeLa cells. We carried out similar experiments with 293T, products identified in catTFRE pull-down and 295 TFs among H1299, HeLa, HepG2, A549, U937, MCF7, PC3, SY5Y, and MEF 7,601 gene products from MS profiling. The enrichment of TFs C cells, and detected 207 to 460 TFs in these cell lines (Dataset S2). by catTFRE is profound (Fig. S3 ). A total of 487 TFs were fi Next, we applied catTFRE pull-down to mouse liver to evaluate identi ed exclusively in the catTFRE pull-down, whereas only17 fi TFs, such as STAT5A/B and STAT6, identified exclusively with whether catTFRE can be used for tissue TF pro ling. A total of 391 fi TFs were identified from mouse liver as a result (Fig. 3A). In all, we at least three unique peptides in the MS pro ling (Dataset S2). identified 878 TFs from 11 mammalian cell lines, representing more We concluded that these STATs did not bind DNA under our than half of all TF-coding genes in the genome. Notably, 29 of 50 experimental conditions, as we detected abundant amounts of STATs in other catTFRE pull-downs (Dataset S2). Forkhead family members and 42 of 48 predicted NRs were C We compared the catTFRE result with mRNA-seq in the lit- detected. Fig. 3 summarizes the coverage for each TF family in 11 erature (13). The mRNA-seq identified 859 TFs among the 10,936 mammalian cell lines. These results also demonstrate the wide dy- protein coding genes (fragments per kb of exon per million map- namic range of TF abundances, as the top 16 TFs contribute 25% of ped fragments > 1), whereas catTFRE pull-down identified 743 the total number of spectral counts (SPCs), an indicator of the TFs. The overlap between mRNA-seq and catTFRE is 579 TFs abundance of proteins, whereas 300 TFs of lower abundance to- (Fig. S3D and Dataset S2). There are total of 1,531 genome-coding D gether constitute only 1% of total SPCs (Fig. 3 ). TFs, of which mRNA-seq identified 56%, whereas catTFRE Transcription coregulators (CoRs) cooperate with TFs to in- identified 49% of them in HeLa cells. These data suggest that the tegrate diverse cellular signals and thereby mediate a coordinated depth of coverage for the TF subproteome by catTFRE pull-down transcriptional response (11). Although many CoRs do not directly and that of the TF subtranscriptome by mRNA-seq are compa- bind to DNA, they can be recruited to TFREs through interaction rable for HeLa cells. fi

with TFs. Considerable numbers of CoRs were identi ed in our TF BIOCHEMISTRY screening, suggesting that some TF–CoR interactions are pre- Analysis of Dynamic Changes of Global TF-DNA Binding Patterns After served in catTFRE DNA pull-down. A total of 497 CoRs were TNF-α Treatment. We then chose TNF-α signal transduction path- identified in the 11 tested cell types (Fig. 3B and Dataset S2). way to evaluate the potential of catTFRE in analysis of global TF Similarly to TFs, the presence and abundance of CoRs are widely alterations in response to exogenous stimulation. We performed distributed, with the 32 most abundant CoRs comprising half of the catTFRE pull-down with 293T cells treated with TNF-α for 15, 30, CoR SPCs (Fig. S3B). Moreover, the catTFRE strategy identified and 180 min, and detected a total of 234 TFs (Fig. 4A and Dataset S3). We arbitrarily chose more than threefold intensity change as significantly changed. Overall, 20 TFs were activated by TNF-α,13 of which were increased within 15 min, and seven TFs showed A Human Mouse B Human Mouse a delay in activation after 30 min. Meanwhile, binding of 15 TFs was suppressed by TNF-α, six of which were decreased within first

2293T H1299HeLa HepG2AA549 U937 MCF7PC3 SY5Y MEF Mliver 293T H1299HeLa HepG2A549A U937 MCF7PC3 SY5Y MEF Mliver 15 min, and another nine showed a delayed down-regulation after bZIP 30 min (Fig. 4A and Dataset S3). The remaining 199 TFs did not IPT/TIG General TF show significant changes upon TNF-α stimulation. Consistent with HLH κ Histone previous knowledge, TFs related to NF- B family and JNK/P38 deacetylase pathways were activated (14, 15) (Fig. S4A). In addition, several TF Krueppel associated families that have not previously been known to be involved in boxizinc Mediator finger TNF-α response exhibited marked changes. For example, binding Nuclear - co-activator /co- receptor repressor of the zing finger and BTB (ZBTB) and nuclear factor of activated Fork head PHD finger T cells (NFAT) family members was increased, whereas binding of α TBP-associated high mobility group (HMG) proteins was reduced upon TNF- factor B TOF TOF treatment (Fig. S4 ). 0 0.01 0 0.01 NF-κB is known as the strongest responder to TNF stimula- tion. We then sought out TF changes that are associated with C D NF-κB activation. To this end, we blocked NF-κB activation by preincubating 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 were inhibited by PDTC (Fig. 4B and Fig. S4 C and D). Up-regulated TFs ZBTB17 and 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 by PDTC (Fig. 4B, Fig. S4E,andDataset S3). This proof-of-concept study has demonstrated that catTFRE strategy is capable of sys- tematically detecting changes in TF DNA binding activity. Fig. 3. Differential TF expression pattern and coverage analysis of TF families among 11 cell types. (A) TF and (B) CoR profiling of 11 human cell Screening of TF DNA Binding Activity Change in Drug Actions. We types using catTFRE demonstrated heterogeneity in basal TF and CoR ex- tested whether catTFRE can be effectively used to study the mo- pression pattern. Normalized SPCs of TFs are shown as a heat map of white lecular effects of drug actions in K562 cells that contain the Phila- to red. FOT, fraction of total, i.e., percentage of a TF SPC to total. (C) Cov- delphia and the chimeric BCR-ABL1 gene. We chose erage analysis of TF families from 11 mammalian cells with catTFRE strategy. phorbol myristate acetate (PMA) (16), an activator of protein ki- (D) Cumulative protein mass from the highest to the lowest abundance TFs. nase C (17), and imatinib mesylate (Gleevec) (18), an inhibitor of

Ding et al. PNAS Early Edition | 3of6 Downloaded by guest on September 25, 2021 gene activation and repression by switching between antagonistic A B 15’ TNF- 15’ interaction pairs of –Max and Max–Mad (26). By using intensity- TNF- - + + based absolute quantification of protein amounts (27) as an indicator PDTC - - + 0’ 15’ 30’ 180’ of the absolute quantity of proteins, we found that Max is the most abundant protein that serves as an anchor in the Myc/Max/Mad network (Dataset S3). Mad is known to bind to Max to antagonize the Myc/Max complex (26)—the major oncogenic heterodimer— thereby inactivating Myc. We found that Mads [MAX dimerization protein 4 (MXD4) as the dominant and MXD3 as a minor variant] exhibit a dramatic increase in DNA binding when cells are treated with imatinib—they are up-regulated dramatically whereas Myc is moderately down-regulated (Fig. 5G). The result suggested a mech- anism for imatinib inactivation of Myc oncogenic activity (Fig. 5H). Discussion TFs belong to a group of proteins that are generally of low abun- fi 0 0.5 1.0 1.5 0 0.5 1.0 1.5 dance and usually underrepresented in proteome pro ling experi- ments. In this study, we design a DNA construct of tandem TF DNA response elements, termed catTFRE, and report its applications as Fig. 4. Systematical and quantitative analysis of TF profiling in TNF-α path- an affinity reagent to enrich DNA-bound TFs in mammalian cells way. (A) Kinetic TF activation pattern of 293T cells after TNF-α stimulation. 293T and tissues. Combined with sensitive MS measurements, we could α cells were treated with TNF- for different time. Relative amount of TFs com- identify as many as 150 TFs from 50 μg of NEs in 1 h of MS mea- pared with 0 min group are shown as a heat map of green to red that rep- surement. Sample prefractionation and longer MS running time can resents down-regulation and up-regulation, respectively. Accurate intensity of α further enhance the depth of TF coverage. For example, as many as TFs in 0-min group was set as 1. (B) 293T cells were treated with TNF- for 15 fi min in the presence or absence of PDTC. Relative amount of TFs compared with 455 and 391 TFs were identi ed from HeLa cells and mouse liver with total MS measuring time of 12 h, respectively. Among 878 vehicle control group are shown as a heat map of green to red that represents fi down-regulation and up-regulation respectively. Accurate intensity of TFs in identi ed TFs in 11 cell types, 110 were detected in at least 10 cell vehicle control group was set as 1. types. Two hundred ninety-four TFs were identified in no more than two cell types; we consider them as cell-specificTFs(Fig. S3A). By mutational analysis of the catTFRE sequence, we showed the BCR-ABL kinase, for many of their opposite effects in the that enrichment of TFs is indeed largely dependent upon the regulation of the K562 cells (19, 20). specific DNA sequences of the TFRE. The fact that the number of The K562 cells were treated with PMA or imatinib for 24 h, and TFs identified in experiments greatly exceeded the original design TF DNA binding was profiled with catTFRE. The PMA treatment of 100 TF families may be explained by the following: (i)the3-bp yielded 462 TF and 395 CoR identifications, of which 159 TFs and linkers may create additional binding sites, (ii) the tandem TFRE 92 CoRs were up-regulated and 113 TFs and 83 CoRs were down- may also create additional binding sites, and (iii)theflexibility of regulated, with more than threefold change (Dataset S3). The ima- TFs in TFRE recognition. We used the TF binding prediction tool tinib treatment yielded 406 TFs and 371 CoRs, of which 46 TFs and PROMO (28) to computationally analyze the catTFRE sequence 18 CoRs were up-regulated and 137 TFs and 146 CoRs were down- and find 132 “accidental” TF binding sites for human TFs and regulated, respectively, at more than three fold of change (Dataset more than 300 additional TF binding sites for TFs of other species S3). Analysis of the alteration of TFs with Integrated Pathway (Dataset S2). It has been known that each family of TFs binds Analysis indicates that PMA and imatinib play opposite roles in aspecific consensus sequence, but there are clear differences differentiation, development, and cell death. PMA activates the among members of a family (29–32). Our simplified generic design differentiation and development programs, but suppresses the “cell of TFRE may not reveal the subtle differences in DNA binding death” module, whereas imatinib suppresses differentiation and among members of a family. It will require a specialized design to development and activates the cell death module (Fig. 5 A and C). reveal these subtle differences. Complexity and flexibility in TFRE Integrated Pathway Analysis also revealed cell differentiation and recognition by TFs are starting to be appreciated, and our findings BCR-AML signaling as the primary altered pathway influenced by add more precedence to this topic. PMA and imatinib (Fig. 5 B and D). PMA stimulated binding of With the newer generation of mass spectrometers and MS mea- TFsthatareknowntofunctionincell differentiation such as acti- surement time of approximately 16 h, catTFRE pull-down is able to vator protein 1 (AP1), Finkel–Biskis–Jinkins osteosarcoma viral detect similar number of TFs that can be detected by mRNA-seq in oncogene (FOS), E-twenty six (ETS), ETS domain-containing pro- HeLa cells, proving a new tool for profiling TFs at protein level. tein Elk-1 (ELK), B-cell–activating transcription factor (BATF), and catTFRE pull down provides more direct information about TFs runt-related transcription factor (RUNX), whereas imatinib exe- than protein profiling and mRNA-seq, as it actually measures DNA cuted the opposite program (Fig. 5E and Fig. S4 F and G). binding “activity”; this is one step closer to profiling transcription Constitutive activation of STAT5 has been demonstrated as activity of TFs and one unique advantage over protein profiling a mechanism for the maintenance of chronic myeloid leukemia and mRNA-seq. (CML) characterized by the BCR-ABL fusion (21). We found that The quantitative nature of the catTFRE approach allows not DNA binding activity of STAT5 is suppressed when BCR-ABL is only confirmation of TF existence in a cell, but also monitoring inactivated by imatinib. In addition to known responders, imatinib of their dynamic change in response to exogenous stimulation, as dramatically activated tumor suppressors transcription factor demonstrated by the inducible TNF-α/NF-κB pathway. By using 4 () and GATA binding protein 5 (GATA5). catTFRE, we revealed alterations in binding of hundreds of TFs Activation of v-myc myelocytomatosis viral oncogene (MYC) by at the same time in addition to the well-known NF-κB factors. By BCR-AML was reported to be involved in CML progression (22) using a specific NF-κB inhibitor, we were also able to classify TFs and down-regulated by imatinib (23), but the mechanism of Myc into NF-κB–dependent and -independent categories. inactivation and alteration of its downstream pathway was not clear. Drugs for various therapeutic applications frequently have The DNA-binding patterns of Myc activator STAT5 (24) and re- “hidden phenotypes” that result from unexpected or unintended pressor E2F4 (25) upon imatinib treatment clearly suggest a mecha- activities, as a result of their binding to unknown targets or un- nism of Myc repression (Fig. 5F). known interactions between the intended drug target and other Myc, MYC associated factor X (MAX), and mitotic arrest de- biochemical pathways. Such unknown activities may be harmful, ficient protein (MAD) form a Myc/Max/Mad network that regulated leading to toxicity, or beneficial, suggesting new therapeutic

4of6 | www.pnas.org/cgi/doi/10.1073/pnas.1217657110 Ding et al. Downloaded by guest on September 25, 2021 A PMA B PMA Decreasing Increasing Tissue up regulated Development Lymphoid Differentiation Development down regulated

Cell Survival Cell Death Quantity Cell Development Transcription Tumor Hematopoiesis genesis Immune response & Hematological development Differentiation

Gleevec C Decreasing Increasing D Gleevec

Development Lymphocyte Differentiation Differentiation

Proliferation Cell Quantity Apoptosis

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E up regulated up regulated down regulated down regulated Gleevec PMA F BIOCHEMISTRY

ND ND ND

Gleevec STAT5 myc E2F4 myc

GHRepressor MAD MAD MYC MYCN MGA

MYC MAD Activator Adaptor MYCN MAXX MGA MAXX Gleevec E-boxes E-boxes

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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-regulation groups 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 of Myc repression: In imatinib-treated CML cells, Myc’s activator STAT5 was repressed whereas Myc’s repressor E2F4 was activated, leading the competitiveness loss of Myc in interacting with Max. Intensity-based absolute quantification of protein amounts (iBAQ) is the sum of all peptide peak intensities areas divided by the number of theoretically observable full tryptic peptides. ND, not detected. (G) Regulation of oncogenes and TF components of Myc/Max/Mad network induced by imatinib. (H) Schematic model of imatinib-triggered Myc/Max/Mad network switching. Mad proteins were dramatically activated and interacted with Max to antagonize Myc/Max complexes, resulting in transcription repression.

Ding et al. PNAS Early Edition | 5of6 Downloaded by guest on September 25, 2021 applications. Discoveries of new and useful properties of drugs are serve as a potent tool for elucidation of the molecular effects of usually made by serendipity, and the underlying mechanisms by drug actions, evaluation of drug efficacy, and concurrent discovery which a drug produces an effect are often not known. We believe of secondary drug effects. patterns of TF DNA binding can provide a diagnostic fingerprint of drug effects, and, in some cases, they provide hypotheses for the Materials and Methods cellular mechanisms of drug responses. Material and Chemicals. catTFRE DNA was synthesized by Genscript. Bio- The application of catTFRE for the treatment of K562 cells with tinylated catTFRE primers were synthesized by Sigma. Dynabeads (M-280 PMA and imatinib illustrated the aforementioned points. Quan- streptavidin) were purchased from Invitrogen. tification of the changes in TF DNA binding after drug treatment quickly pointed to the distinctive effect of PMA and imatinib in cell Nano-Liquid Chromatography/Tandem MS Analysis for Protein Identification differentiation, development, cell death, and BCR-AML signaling. and Label-Free Quantification. Tryptic peptides were separated on a C18 In addition, simultaneously monitoring most of the TF families and column, and were analyzed by LTQ-Orbitrap Velos (Thermo). Proteins were their CoRs permitted us to suggest that one mechanism of imatinib identified by using the National Center for Biotechnology Information search inhibition of CML is through down-regulation of Myc by un- engine against the human or mouse National Center for Biotechnology In- balanced DNA bind activities of STAT5 and E2F4. Decrease in formation RefSeq protein databases. Myc eventually triggered a molecular switch in the Max/Myc/Mad signaling network, whereby “off” position can results from co- ACKNOWLEDGMENTS. This work was supported by National Key Labora- ordinated down-regulation of Myc binding and up-regulation of tory of Proteomics Grant SKLP-K201001; National High-Tech Research and Mad binding (26). Development Program of China 863 Program Grant 2012AA020201; the Cancer Prevention and Research Institute of Texas (CPRIT) Grant RP110784; In summary, the catTFRE strategy presented here enables high- the National Institutes of Health Nuclear Receptor Signaling Atlas (NURSA) throughput identification and quantification of DNA binding ac- Grant U19-DK62434 (to J.Q.); and Natural Science Foundation of China tivity for most cellular TFs. We envision that this technology will Grant 31200582.

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