Human transcription factor protein interaction networks

Helka Göös Institute of Biotechnology, HiLIFE, University of Helsinki Matias Kinnunen Institute of Biotechnology, HiLIFE, University of Helsinki Leena Yadav University of Eastern Finland Zenglai Tan Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland Kari Salokas https://orcid.org/0000-0002-4471-6698 Qin Zhang University of Oulu Gong-hong Wei University of Oulu Markku Varjosalo (  markku.varjosalo@helsinki. ) University of Helsinki https://orcid.org/0000-0002-1340-9732

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Posted Date: February 23rd, 2021

DOI: https://doi.org/10.21203/rs.3.rs-228624/v1

License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Human transcription factor protein interaction networks

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3 Helka Göös1, Matias Kinnunen1, Leena Yadav1, Zenglai Tan2#, Kari Salokas1#, Qin Zhang2, Gong- 4 Hong Wei2,3, Markku Varjosalo1* 5 6 1Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland 7 2Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, 8 Finland 9 3Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan 10 University Shanghai Cancer Center, Shanghai Medical College of Fudan University, Shanghai, 11 China 12 13 14 # equal contribution 15 16 * Correspondence, lead contact: Markku Varjosalo, [email protected] 17 18 19 20 Abbreviations: ACTB, Beta-actin; ACTBL, Beta-actin-like protein 2; AP-MS, affinity purification

21 mass spectrometry; BioID, proximity-dependent biotinylation; DBD, DNA binding domain; GO-BP,

22 Gene Ontology Biological Process; GTF, general transcription factor; HAT, histone acetyl

23 transferase; KDM2B, Lysine-specific demethylase 2B; NFI, Nuclear Factor 1 family TFs; NLS,

24 nuclear localisation signal; NM1, Nuclear myosin 1; NR, nuclear receptor; NSL, non-specific lethal;

25 PIC, pre-initiation complex; Pol-II, RNA polymerase II; PPI, protein-protein interaction; TBP,

26 TATA-binding protein; TF, transcription factor

27 28 Abstract

29 In participation of transcriptional regulation, transcription factors (TFs) interact with several other

30 proteins. Here, we identified 7233 and 2176 protein-protein interactions for 110 different human TFs

31 through proximity-dependent biotinylation (BioID) and affinity purification mass spectrometry (AP-

32 MS), respectively. The BioID analysis resulted more high-confident interactions, highlighting the

33 transient and dynamic nature of many of the TF interactions.

34 Using clustering and correlation analyses, we identified subgroups of TFs associated with specific

35 biological functions, such as RNA-splicing, actin signalling or chromatin remodeling. We also

36 observed 203 TF-TF interactions, of which 175 were interactions with Nuclear Factor 1 (NFI) -family

37 members, indicating uncharacterized cross-talk between NFI signalling and numerous other TF

38 signalling. Moreover, TF interactions with basal transcription machinery were mainly observed

39 through TFIID and SAGA complexes.

40 This study, not only, provides a rich resource of human TF interactions, but also act as starting point

41 directing future studies aimed at understanding TF mediated transcription.

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47 Keywords: Transcription factors, Protein-protein interactions, transcriptional regulation, NFIA,

48 splicing, interaction proteomics, mass spectrometry

49 50 Introduction

51 ‘The central dogma’ states that genetic sequence information from DNA is transcribed to RNA and

52 subsequently translated into proteins. These processes are tightly regulated and employ a plethora of

53 proteins. Transcription, the first step, is regulated by transcription factors (TFs), which represent one

54 of the largest families of the human genes. In human, 6–9% (~1,400-1,900) of proteins are predicted

55 to regulate gene expression through DNA binding (1-3, https://www.proteinatlas.org), and the most

56 recent manual curation identified 1639 likely human TFs4.

57 Complex and multilayer regulation of transcription involves not only direct binding of TFs to a target

58 gene’s regulatory element(s) but there exists a complicated interplay between TFs and TF binding

59 proteins. These include several cofactors, the Mediator complex, basal transcription machinery, TF-

60 activity modulating enzymes (such as phosphatases and kinases), dimerization partners, subunits and

61 inhibitory proteins5-8. Moreover, as the chromosomal DNA is packed into chromatin to prevent

62 uncontrolled transcription, TFs also interact with several chromatin remodeling proteins. The formed

63 complexes are necessary to regulate the accessibility of DNA to allow chromatin opening and thereby

64 gene transcription.

65 TFs play crucial roles in regulating numerous cellular mechanisms and are key regulators of tissue

66 growth and embryonic development - processes which may cause cancer and other disorders when

67 aberrantly controlled. Therefore, understanding the TF network at systems level would form an

68 important crucial foundation for future studies as well as for therapeutic applications7. While the

69 binding of TFs to DNA is extensively studied, for the most part we are still lacking a global

70 understanding of the TF protein-protein interactions (PPIs) and their roles in the regulation of

71 transcription. Therefore, we sought to fill this knowledge gap by using the recently developed state-

72 of-the-art PPI identification methods, which allow unprecedented sensitivity and depth. In this study,

73 we systemically characterized the PPIs of a selected set of 110 human TFs using affinity purification

74 mass spectrometry (AP-MS) and proximity-dependent biotinylation (BioID) mass spectrometry. We 75 identified 7233 PPIs in the BioID analysis and 2176 PPIs in the AP-MS analysis. Most of the detected

76 interactions were nuclear and linked to transcription and transcriptional regulation. These interactions

77 paint a picture on how transcription factors are activated or repressed, and also add experimental

78 evidence on the potential relevance of transient interactions in the advent of transcription related

79 nuclear condensates and phase separation.

80 This large interactome network of TFs allowed us to recognise several interactome subgroups of TFs,

81 such as TFs linked to actin and myosin signalling, TFs linked to mRNA splicing and TFs linked to

82 chromatin remodeling. In addition, we observed that most of the studied TFs interacted with nuclear

83 factor 1 (NFI) TFs, which are essential for several developmental and oncogenic processes. In sum,

84 this work represents a rich resource to direct future studies aimed at understanding TF mediated

85 transcription and how the TF formed interactions regulate important cellular phenomena in both

86 health and disease.

87

88 89 Results

90 Identification of TF protein-protein interactions

91 To systematically investigate the protein-protein interactions formed by the human TFs, we selected

92 a representative set of 110 TF genes from different TF families (Table S1A), which were then

93 subjected to two independent mass spectrometry based interactome analysis methods. (Figure 1A).

94 First, the stable TF complexes were purified using single-step Strep-tag affinity purification (AP-

95 MS). Secondly, a proximity-dependent labelling approach (BioID) utilizing a minimal biotin ligase

96 tag BirA* was used to detected also transient and proximal interactions of the TFs9,10. The expression

97 of studied TFs were adjusted on the corresponding transgenic cell lines to close-to-physiological level

98 by the tetracycline inducible and adjustable Tet-On expression system11.

99 In total, we identified 7,233 high-confidence PPIs using BioID analysis and 2,176 PPIs using AP-MS

100 method (Table S1 B-C; Figure 1B, C). For initial sanity check for the obtained TF interactomes, we

101 mapped the interactors to their known subcellular localisations from the Cell Atlas12. This analysis

102 revealed that > 80% of the TF interactors were detected to have nuclear localisation (yellow nodes;

103 Figure 1B; Table S2), confirming the expected nuclear compartmentalisation of the studied TFs and

104 their interactors. Remarkably, the majority (>75%) of the interactions within the TF interactome were

105 previously unreported (Figure 1C; Table S1). On average, we identified 65 PPIs/TF in the BioID data

106 and 21 PPIs/TF in the AP-MS (Figure 1D). Higher number of identified interactions by BioID

107 compared to AP-MS agrees with many recently reported medium- and large-scale interatomic

108 studies13-16, but interestingly, our results suggest that TFs do prefer to form more transient or proximal

109 interactions than stable protein complexes. This finding is consistent with the phase separation model

110 for TF interactions, where interactions incorporated in TF condensates are dynamic17-20.

111 The BioID method has been suggested to be efficient for studying transient interactions9, and this is

112 supported by our results, strongly suggesting that BioID is the method-of-choice for studying TF 113 interactions (Figure 2A). Most of the TFs show more detected high-confidence interactions with the

114 BioID-method, with only few exceptions with Krüppel-like factor (KLF) family of transcription

115 factors (Figures 2A and 1C). There were prominent differences in number of detected PPIs between

116 different TF -families; for example, KLFs, SPs, TLXs, HNFs, SOXs and PAXs had over 100 PPIs on

117 average, whereas NFACs, IRFs, STATs, GLIs, ETVs and TEADs had fewer than 50 PPIs on average

118 (Figure 2B; Table S3). Some families, such as KLFs, PAXs, FOXs and NFIs, had similar number of

119 PPIs between the members, but few families, including SPs, TEADs, ELKs and ETVs, had high

120 variability in number of PPIs per bait (Figure 1D).

121 The most common TF interactor observed in our study was lysine-specific demethylase 2B

122 (KDM2B), which interacted with 62 TFs (Table S1B). In addition, two lysine methyltransferases

123 were among the top five of the most frequent TF preys (KMT2D: 58 PPIs and KDM6A: 53 PPIs),

124 which highlights the importance of the histone modification homeostasis in the regulation of

125 transcription. The detected interactions of lysine methyltransferases with TFs are highly specific and

126 very rarely detected in large-scale studies with other key cellular signalling molecules16,21,22, and

127 hardly ever detected as contaminants23. Other common TF interactors were NFIA (54 PPIs), TLE1

128 (53 PPIs), CIC (52 PPIs) and several zinc finger proteins (50–52 PPIs). In addition, the well-

129 established corepressors BCOR (48 PPIs) and NCOR2 (48 PPIs) were high on the list. Not

130 surprisingly, the most frequently observed TF interactors were transcriptional activators and

131 repressors.

132 To obtain a glimpse to the biological nature of TF interactions, we performed Gene Ontology

133 biological process (GO-BP) enrichment analysis for all BioID interactions (Figure 3A, Table S4). As

134 expected, BP terms linked to transcription and its regulation were significantly enriched. The most

135 significantly enriched term was ‘transcription, DNA-templated’ with a p-value of 6.09 x 10-104. This

136 was followed by biological processes linked to positive and negative regulation of transcription with

137 p-values of less than 1.34 x10-62. 138 Comparison to other studies

139 As BioID can capture transient and proximal interactions, most experimental validation methods,

140 such as coimmunoprecipitation, are not sufficiently sensitive to validate the results. We, therefore,

141 compared the identified PPIs with previously published interactions. In a medium scale analysis, Li

142 et al. screened the PPIs of 59 TFs by tagging them with a C-terminal SFB-tag (S protein-tag, Flag-

143 tag and Streptavidin binding peptide) and identified the interacting proteins using tandem affinity

144 purification coupled to MS6. This analysis identified 2,156 PPIs. Fourteen of the TFs analysed in their

145 study were included in our set (CREB1, ETS1, FOS, FOXI1, FOXL1, FOXQ1, IRF3, MEF2A, MYC,

146 NFKB1, PPARG, STAT3, TEAD2 and TP53). Their approach is close to our AP-MS analysis, which

147 identifies more stable protein-protein interactions and protein complexes than transient interactions.

148 Therefore, it is not surprising that the overlap between our BioID PPIs and their PPIs was low; only

149 6% of our PPIs were covered by their study (Table S1B). A comparison with our AP-MS results

150 revealed more common interactions; 26% of our AP-MS PPIs were detected by their approach (Table

151 S1C). These differences detected between Li et al.’s PPIs and those we identified are most likely due

152 to the transient nature of TF interactions and the different tagging strategies used.

153 Next, we compared our PPIs to public interaction databases such as PINA2, STRING, IntAct and

154 Biogrid (Tables S1B-C). Overall, 21% (1,525/ 7,233) of our BioID PPIs and 16% (345/ 2,176) of our

155 AP-MS interactions were also found in public databases or from the affinity-based TF interatomic

156 study conducted by Li et al. (Figure 1B;6) The PPIs of several TFs, such as SOX2, MYC, TYY1,

157 PAX6, HNF4a and GATA2, overlapped with more than 50 known interactions in the databases,

158 whereas the PPIs of other TFs, such as ZIC3, ELK4, EN1 and NHLH1, did not overlap with any

159 known PPIs from the databases (Table S1B).

160

161 Clustering of transcription factor protein-protein interactions 162 TFs are often classified according to their DNA-binding domains (DBDs). The DBD distribution of

163 studied TFs compared to all human TFs is shown in Figure 3B. The majority of the studied TFs had

164 C2H2 zing finger (ZF) or Homeodomain DBD, which are the most common DBDs among the human

165 TFs4. To study whether the identified PPIs of the various TFs correlated with their DBD- families,

166 we performed a hierarchical clustering of baits by their prey intensities and compared that to bait

167 DBDs. Only a modest correlation was seen between the PPIs and DBDs TLX and LHX homeodomain

168 TFs and KLFs and TYY1 -C2H2 ZF TFs clustered together, but no other correlations with DBDs

169 were observed (Figure 3C).

170 Next, we wanted to see if PPI clustering correlated with TF amino acid sequence. For that we aligned

171 the full amino acid sequences and compared these to the hierarchical PPI clustering (Figure S1A).

172 The sequence alignment comparison to PPI clustering revealed multiple clusters with similarities in

173 PPIs and sequences (Figure S1A), including the clusters of ELFs, NFIs, LHXs and KLFs.

174 In addition, the DNA-binding motifs of the studied TFs were aligned using the matrix-clustering tool

175 RSAT (Figure S1B)24 and compared to PPI clustering (Figure S1C). Several TF clusters also exhibited

176 similarity between DNA-binding motifs and protein interactomes (Figure S1C), including the clusters

177 of MYB, TBR and PAXs and the clusters of IRFs, TEADs and STATs.

178

179 TF interactions with basal transcription machinery and Mediator complex

180 Eukaryotic gene transcription is mostly executed by RNA polymerase II (Pol-II), which binds in

181 conserved core promoters. In addition to Pol-II, the core promoters also bind the SAGA complex and

182 the basal transcription machinery (known also as pre-initiation complex, PIC), which is composed of

183 Pol-II, Mediator complex and general TFs (GTFs). The GTFs are TATA-binding protein (TBP),

184 TFIIA, TFIIB, TFIID, TFIIE, TFIIF and TFIIH (Table S5;25,26. To assess how the studied TFs

185 interacted with PIC components, we retrieved the GTFs and Mediator complex members from the 186 CORUM protein complex database and compared them to the identified PPIs (Table S5). We

187 observed multiple interactions with both TFIID and SAGA complex components, but only few

188 interactions with Mediator complex members (Table S5) and we did not detect interactions between

189 the studied TFs and TFIIA, TFIIB, TFIIF, TFIIH or Pol-II complex components. This indicated that

190 under the given conditions, TF activity from enhancers to core promoter and PIC is mainly mediated

191 by TFIID, SAGA and Mediator complexes.

192 TF interactions with Nuclear factors

193 Interestingly, we found 203 TF-TF (bait-bait) interactions in our TF interactome (Figure 4A). Most

194 of these interactions (175) were TF interactions with NFI family members (NFIA, NFIB, NFIC and

195 NFIX; Figure 4B). A total of 54 TFs interacted with one or more NFIs (Figure 4B): 52 TFs interacted

196 with NFIA, 33 with NFIB, 12 with NFIC and 6 with NFIX (Figure 4B). In addition, all four NFIs

197 formed bidirectional interactions (when used reciprocally as bait proteins) to each other in both the

198 AP-MS and BioID analyses (Figure 4B). NFIs had also multiple other shared interactions than the

199 above mentioned interactions with TFs (Figure S2A).

200 From all eight studied SOX family members of TFs, only SOX4 had no interactions with NFI proteins

201 (Figure 4A). This suggested a previously unknown crosstalk between SOX transcription factors and

202 NFI signalling. Moreover, we found that all five studied PAXs interacted with NFIs (Figure 4B and

203 S2B). In fact, PAX9 was the only TF in set that interacted with all four NFIs. To our knowledge, no

204 link between PAX9 and NFIs has been reported before. In addition to SOXs and PAXs, four (out of

205 six) LHXs, six (out of ten) KLFs and all three studied TLXs interacted with NFIs (Figure 4B). These

206 and the other detected TF-NFI interactions (Figure 4B) indicated that NFIs take part in multiple

207 cellular processes with other TFs. 208 GO-BP enrichment analysis of NFIs’ interactomes using the total BioID interactome as a background

209 showed transcription-related BP terms to be significantly enriched, indicating NFIs importance in

210 transcription regulation in general (Table S4).

211 Given the fact that it interacted in our analyses with 52 TFs, it is possible that NFIA could regulate

212 the transcriptional activity of other TFs. To test this, we generated luciferase-based reporter (DNA

213 binding domains extracted from JASPAR27) assays for selected TFs interacting with NFIA. The

214 reporter assays which displayed induction after introduction of the corresponding TF were chosen for

215 NFIA via siRNA-mediated knockdown experiments. Knockdown efficiency was first confirmed by

216 western blotting using a specific antibody against NFIA (Figure 4C). Next, the effect of NFIA

217 depletion on selected reporter activity was tested in the presence and absence of the corresponding

218 TF (Figure 4C). Interestingly, both KLF4 assays, detecting the repressive and activating response of

219 KLF4, showed altered luciferase activity after NFIA silencing: both the repressive and activating

220 responses after the KLF4 induction were reduced (Figure 4C). In addition, SOX2 and PAX6 showed

221 reduced activity after the NFIA silencing, while EN1 activity was increased after the depletion of

222 NFIA (Figure 4C).

223 To further examine whether NFIA regulates genome-wide chromatin binding of its interacting TFs,

224 we performed SOX2 chromatin-immunoprecipitation and sequencing (ChIP-seq) upon depletion of

225 NFIA as an example. This analysis revealed the substantial loss of 6,921 SOX2 binding sites, while

226 only 362 sites remained and 1,341 were gained (Figure 4D; Table S6). Consistent with this global

227 shift in the genomic binding profile, depletion of NFIA led to a drastic difference in pathway

228 enrichment for SOX2 target genes (Figure S3), suggesting that NFIA might contribute to the

229 pathophysiological function of its interacting TFs on a genome-wide scale.

230 In addition, we tested the activity changes with some of the TFs that we did not detect to interact with

231 NFIA. The activity of some of the TFs were affected by NFIA silencing (Figure S4A), while others 232 were not (Figure S4B). This might indicate that, in addition to a direct regulation of other TFs’

233 activity, NFIA might regulate some other TFs’ activity indirectly, without a physical PPI.

234 Besides NFI-TF interactions, 20 TFs interacted with TYY1 and 16 TFs interacted with ELF1 and/or

235 ELF2 (Figure S5). Twelve of the baits that interacted with TYY1 and ELF1 or ELF2 were the same,

236 indicating similarities in their interactomes.

237 Prey-prey correlation analysis reveals several biological clusters

238 The TFs prey-prey correlation analysis, using ProHits-Viz28, revealed 15 biological clusters (Figure

239 5A, Table S7). This analysis revealed clusters of preys that were often seen together between baits,

240 suggesting that they might be part of the same complex or colocalize or both. Baits driving the same

241 cluster had a similarity in interactomes, indicating possible shared or similar biological roles. The

242 preys belonging to different clusters and the baits driving the clusters are shown in Table S7. Next,

243 we describe some of the interesting clusters found in this correlation analysis.

244 Actin and myosin linked protein cluster

245 The second cluster was mainly formed of proteins linked to actin and myosin signalling. This cluster

246 consisted of 54 proteins, 36 of which were linked to actin and myosin signalling (Figure 5B, Table

247 S7). In addition, 10 proteins were linked to ATP synthesis and mitochondrial respiration (Table S7).

248 Cluster 2 was mainly driven by FOS interactions as FOS interacted with all 54 preys (Fig 5B). Besides

249 FOS, the cluster was also driven by STAT1 (18 interactions), FOXL1 (17 interactions) and 29 other

250 baits with less than 10 interactions (Table S7). Interestingly, FOS interacted with all 36 actin- and

251 myosin-linked proteins in the cluster; STAT1 interacted with 17 and FOXL1 with 6 actin and myosin

252 linked proteins (Table S7; Figure 5B). Other baits did not have actin- and myosin-linked interactions.

253 FOS and STAT1 uniquely interacted for example with beta-actin (ACTB) and beta-actin-like protein

254 2 (ACTBL2) (Table S7; Figure 5B). In addition, FOS and STAT1 interacted with ARP3, FOS with

255 ARPC3 and STAT1 with ARPC4, all members of the Arp2/3 complex which mediates the actin 256 polymerisation in the nucleus and cytoplasm29,30. Interestingly, no other TFs interacted with Arp2/3

257 proteins (Table S1B). Furthermore, FOS and STAT1 were found to interact with MYO1C, isoform 3

258 of which is also known as nuclear myosin 1 (NM1, Figure 5B). FOS and STAT1 also interacted with

259 various other myosins and myosin-linked proteins, such as the myosin chains MYL6B and MYH14,

260 as well as several unconventional myosins (Tables S1 and S5; Figure 5B).

261 To explore FOS and STAT1 interactomes further, we performed GO-BP enrichment analysis on all

262 TF BioID interactomes. This resulted in high enrichment of terms linked to actin filaments and cell-

263 cell adhesion (p-values <0.001; Table S5), which further highlights the link from FOS and STAT1 to

264 actin and myosin signalling among the TFs studied.

265 Preys linked to RNA splicing and processing in Clusters 10 and 11

266 Next, we found significant enrichment of proteins linked to mRNA splicing and processing in

267 Clusters 10 and 11 (Figure 5A). Cluster 10 consisted of 29 preys and Cluster 11 of 34 preys, of which

268 23 and 20, respectively, were linked to RNA splicing and processing (Figure 6A, Table S7). GO-BP

269 analysis showed a significant enrichment of proteins linked to ‘mRNA splicing, via spliceosome’ (p-

270 value 2.70 x 10-11 in Cluster 10 and 6.7 x 10-13 in Cluster 11). Cluster 10 was driven mainly by GATA1

271 (28 interactions), GATA3 (20 interactions) and SP7 (20 interactions) (Figure 6A). Cluster 11

272 consisted almost totally of SP7 interactions; SP7 interacted with all 34 proteins (Figure 6A, Table

273 S7).

274 In our dataset SP7 was the only protein to interact with core spliceosomal components SNRPA

275 (SnRNPa, U1A), RU17 (SnRP70, U1-70), CD2B2 (U5-52K) and RSMB (snRNP-B). These newly

276 identified splicing related interactions indicated that GATA1, GATA3 and especially SP7 are related

277 to splicing and RNA processing. This was also evident in GO-BP enrichment analysis of GATA1 and

278 SP7 interactions using all the TF interactions from our study as a background; GO-BP term “mRNA

279 splicing, via spliceosome” was again significantly enriched (p-values 2.52 x 10-4 for GATA1 and 1.25 280 x 10-6 for SP7; Table S4). These new splicing related interactions for GATAs and SP7, and possible

281 roles in regulation of splicing are highly intriguing and require further studies.

282 TF interactions with chromatin modulating complexes in Cluster 14

283 Preys in Cluster 14 (Figure 5A, Figure 6B, Table S7) had clear biological roles in chromatin

284 modulation, especially in histone H4 and H3 modifications. Of 57 preys, 36 were directly linked to

285 histone and chromatin signalling (Figure 6B, Table S7). These included ten members and one putative

286 regulator of the INO80 chromatin remodeling complex (Figure 6B), seven members of the non-

287 specific lethal (NSL) complex and nine members of the MLL1-WDR5 histone-3-lysine-4-(H3K4)

288 methyltransferase complex.

289 Cluster 14 was mainly formed of TYY1 interactions; TYY1 interacted with all 57 preys in the cluster

290 (Figure 6B, Table S7), while ELF1 interacted with 24, ELF4 with 24, ELF2 with 20 and HNF4A with

291 15 preys. Other baits driving the cluster are listed in Table S7.

292 TYY1 is known to be part of the INO80 complex31. Predictably, we found TYY1 to interact with 10

293 subunits of the INO80 complex (Figure 6B; Table S7). TYY1 interactions with INO80 complex

294 members appear to be very stable as many of the interactions were also detected in the AP-MS data

295 (Table S1C). Besides TYY1, we found that ELF4 interacted with eight INO80 complex members and

296 with its putative regulator UCHL (Figure 6B).

297 Cluster 14 also contained seven out of nine members of the NSL histone acetyltransferase complex.

298 In addition, WDR5 was identified outside Cluster 14 (Figure 6B). In total, we found that TYY1 and

299 MYC interacted with WDR5 and all seven identified subunits of the NSL complex, whereas ELF1,

300 ELF2, ELF4 and HNF4A interacted with five subunits (Figure 6B, Table S7). Histone

301 acetyltransferase KAT8 (main unit of the NSL complex, also known as MYST1), was found to

302 interact with TYY1, ELF1, ELF2, ELF4, HNF4a and MYC. 303 The top four baits in cluster 14 (TYY1, ELF1, ELF4 and ELF2) were also clustered together in bait-

304 prey clustering, indicating similarities in protein interactomes (Figure 3C). In addition, 12 other TFs

305 studied formed bait-bait interactions with TYY1, ELF1 and ELF2 (Figure S6). These results suggest

306 that TYY1, ELF1 and ELF2 may have shared or similar biological functions. Of all five ELFs (1-5)

307 studied, ELF1, ELF2 and ELF4 were the most connected and shared the greatest number of

308 interactions, including interactions with TYY1 (Figure S6).

309 TF interactions with histone acetyltransferase complexes in Cluster 15

310 Closer analysis of Cluster 15 (Figure 5A) revealed accessory chromatin modulating complexes,

311 especially histone acetylation complexes. BP-GO analysis resulted in the significant enrichment of

312 terms linked to histone H2A, histone H4 and histone H3 acetylation (p-values of less than 7.19 x 10-

313 22).

314 In total, Cluster 15 consisted of 50 preys of which 40 were directly linked to histone acetylation

315 (Figure 6C, Table S7). These included 19 members of the SAGA complex and 14 members of the

316 NuA4/Tip60 HAT complex A (Figure 6C). The cluster was mainly driven by MYC, which had

317 interactions with all 50 preys. KLF6 interacted with 29, HNF4A with 21, KLF8 with 20, ELF4 with

318 17, TYY1 with 13 and ELF1 with 11 preys. Other baits with less than 10 interactions are listed in

319 Table S7.

320 MYC interacted with all 19 subunits of the SAGA complex identified in our data (Table S7).

321 Furthermore, KLF6 was found to interact with 16, and KLF8 and HNF4a with eight SAGA complex

322 subunits (Table S7). In addition, 14 of 15 NuA4/Tip60 HAT complex A subunits were identified in

323 Cluster 15. MYC was found to interact with all 14 identified subunits, while HNF4a and KLF6

324 interacted with nine subunits, and KLF8 and ELF4 with eight subunits of this complex (Figure 6C,

325 Table S7). 326 Discussion

327 Chromatin opening, transcription, RNA splicing, RNA processing and their regulation are often

328 studied as separate processes. However, our understanding of the simultaneous and co-transcriptional

329 nature of these processes has exploded in recent years32-36. In our analyses, TFs were found to interact

330 with proteins involved in chromatin remodeling, transcription, mRNA splicing and RNA processing,

331 highlighting the cooperative nature and close proximity of these processes. This also showed that TFs

332 are central in regulating these interconnected processes. The most common interaction partners for

333 the TFs studied were histone-modifying enzymes, signifying that histone modification and chromatin

334 accessibility regulation are central to all these transcriptional subprocesses.

335 Studied TFs interact with basal transcription machinery mainly via TFIID, SAGA and Mediator

336 complexes

337 Mediator complex, SAGA complex and most of the GTFs are multimeric protein complexes, which

338 are needed for Pol-II promoter recognition and transcription initiation37-39. In most studied models,

339 the PIC assembly starts with the binding of TBP to TATA- or TATA-like core promoters40. TBP

340 belongs to two GTF complexes: TFIID and SAGA. It has been indicated that both TFIID and SAGA

341 participate in the transcription of various genes simultaneously41, but that the regulation of expression

342 might be dominated by either one of them42,43. Different promoters are alleged to prefer either TFIID

343 or SAGA, and it has been indicated that the activity of SAGA/TATA-like promoters might be more

344 dependent on the presence of transcriptional activators (regulated genes) than the activity of

345 TFIID/TATA-promoters (housekeeping genes)44. However, this is still controversial, as the depletion

346 of either SAGA or TFIID complex members decreased the transcription of both regulated and

347 housekeeping genes25,41.

348 We observed multiple interactions with both TFIID and SAGA components, supporting the theory

349 that both are needed for the transcription of regulated genes. In phase separation condensates 350 enhancer-bound TFs are physically separated from PIC with multiple cofactor complexes17-20. Our

351 data indicated that mainly TFIID and SAGA serve as these cofactors (Table S5).

352 Interestingly, we detected only few interactions with Mediator complex (Table S5), even though

353 Mediator is generally thought to mediate the regulatory signals between TFs to Pol-II45,46. The

354 mediator complex is reported to interact with multiple TFs, and it is thought to form phase separation

355 condensates with many TFs20,46. However, it is not comprehensively known how directly TFs interact

356 with Mediator complex members. Multiple TFs are seen to colocalize with Mediator complex

357 members in phase separation complexes in vitro20. However, our data indicated that the interactions

358 between TFs and Mediator complex members might be mediated through other proteins, such as

359 histone modifiers. We suggest that for these studied TFs and under the given conditions, the signal is

360 primarily transferred to the Mediator complex and to PIC via other cofactors, such as SAGA, TFIID

361 or other chromatin remodeling complexes.

362 TFs’ interactions with the Nuclear Factor I family members

363 Interestingly, we detected a total of 203 bait-bait interactions within the studied TFs (Figure 4A),

364 most of which (175) were interactions with NFI family members. NFIs are CCAAT-box-binding TFs

365 that have similar DBDs and bind as hetero- or homodimers to the same common consensus

366 sequence47-49. There are four NFI family members (NFIA, NFIB, NFIC and NFIX) in humans and

367 most vertebrates50,51. Originally, the NFIs were identified as essential for adenovirus replication52, but

368 over the years they have been found to control a variety of genes in cancer and in development53-58.

369 For example, NFIs are found to have multiple translocations leading to oncogenic gene fusion

370 proteins in several cancer types58, and knockout studies of NFIA, NFIB, NFIC, NFIX have revealed

371 their necessity in lung, central nervous system, brain, tooth, skeletal and muscle development53-55,57,59-

372 61. 373 In our data, especially SOXs, PAXs, LHXs, KLFs and TLXs had multiple interactions with NFIs

374 (Table S1B). These interactions indicated that NFIs take part in many cellular processes with other

375 TFs. NFI family members have been found to interact with some individual TFs, such as with few

376 SOX family proteins62,63. However, TF-NFI interactions in this scale have not been reported before.

377 Given the important role of NFIs in regulation of developmental processes and their impact on cancer

378 development, the high number of TF-NFI interactions might indicate that the activity of NFIs is

379 generally regulated by other TFs, or vice versa. To test this, we generated the reporter gene assays

380 for selected NFIA interacting TFs and discovered that RNAi silencing resulted in altered TF activity

381 (Figure 4C). This strengthens the theory that NFIs have extensive and not a well characterised role in

382 the regulation of other TFs’ activity in gene expression regulation.

383 Moreover, the widely altered DNA binding of SOX2 after the NFIA silencing (Figure 4D) indicated

384 that NFIA-TF PPI might be essential for genome-wide biding of certain TFs, and thus, be crucial for

385 the normal regulatory functions of NFIA-interacting TFs.

386 FOS and STAT1 interact with nuclear actin and myosin related proteins

387 Nuclear actin is associated to chromatin remodeling complexes and is part of the Pol-II transcription

388 machinery64. Actin dynamics have also been directly linked to gene transcription regulation65,66.

389 In our analysis, the interactomes of FOS and STAT1 were found to be enriched with proteins linked

390 to nuclear actin and myosin signalling. FOS and STAT1 interacted uniquely, e.g., with beta-actin

391 (ACTB), ACTBL, and MYO1C, isoform 3 of which is also known as NM1 (Table S7, Figure 5B).

392 NM1 and ACTB are suggested to be associated with each other and to play important roles in Pol-II

393 transcription64,67. ACTB is also part of several chromatin remodeling complexes, such as BAF, Tip60

394 and INO8068. Indeed, we also detected FOS interacting with 13 BAF complex members (Table S1B,

395 Figure 5B), suggesting that FOS activity is regulated trough BAF complex and actin dynamics. FOS 396 has been reported to be linked to BAF signalling before69,70, but how actin is involved in this process

397 remains largely unknown.

398 STAT1 activity is known to be regulated by actin cytoskeleton and extracellular matrix proteins71.

399 Interestingly, STAT1 did not show any interactions with BAF complex members, indicating a

400 different mechanism or different mediating proteins between STAT1 and actin proteins.

401 SP7 interacts with RNA-splicing proteins

402 In our analysis, proteins involved in RNA-splicing were enriched in interactomes of SP7, GATA1

403 and GATA3. In addition, we found that SP7 and GATA3 interacted with EP300 (p300) along with

404 other members of the p300-CBP-p270-SWI/SNF HAT complex (Figure 6A, Table S1B).

405 TFs are known to affect RNA splicing in three ways; they can bind to RNA to recruit coregulators

406 that also take part in splicing, block the associations of splicing factors with mRNA, and/or influence

407 the transcription elongation rates, which are known to impact on splicing by skipping the weak 3'

408 splice sites at a high rate72. One way to alter the elongation rate is through TF-mediated recruitment

409 of EP300 which induces the histone acetylation of nearby promoters, increases the elongation rate

410 and promotes exon skipping73. Therefore, SP7 and GATA3 interaction to EP300 suggest that they

411 may be connected to the p300 chromatin remodeling complex and, thus, to the regulation of

412 elongation rate.

413 Some TFs, such as steroid hormone receptors, nuclear receptors (NRs) and certain non-NR TFs, are

414 known to regulate mRNA splicing by recruiting splicing-linked coregulators72,74. One of these

415 coregulators is RBM14 (also known as CoAA), which is an NCOA6 (also known as TRBP) binding

416 protein75. We found that, along with other splicing-related proteins, both RBM14 and NCOA6

417 interacted with SP7 (Figure 6A, Table S1B). This might indicate that SP7 recruits similar splicing-

418 regulation-related coregulators. 419 SP7 interactions with four core spliceosomal components, and all other splicing related components

420 suggested that SP7 has a largely unstudied role in recruiting the splicing machinery to the nascent

421 pre-mRNA – a role that needs to be studied more. Like some other C2H2 zinc finger TFs, such as

422 CTCF, VEZF1, MAZ and WT1, that are known to regulate mRNA splicing72, SP7 might participate

423 in pre-mRNA splicing.

424 TF interactions with chromatin modulating complexes

425 As expected, multiple TFs had interactions to chromatin modulating proteins. We found several TFs

426 to interact e.g. with INO80, NSL, SAGA and NuA4/Tip60 HAT complexes (Table S1B). TF

427 interactions to these complexes will be next discussed in more detail.

428 INO80 ATP-dependent chromatin remodeling complex activates transcription31, regulates genomic

429 stability through DNA repair76, contributes to DNA replication77 and, by shifting the nucleosomes,

430 remodels the chromatin78,79. Predictably, TYY1, that is known to be part of INO80 complex, had

431 multiple interactions with INO80 complex members in our analyses. More interestingly, we found

432 that ELF4 had also multiple interactions with INO80 members. To our knowledge, ELFs have not

433 been previously linked to INO80 signalling or TYY1 and this should be studied further.

434 Next, we found that various TFs, such as TYY1, ELF1, ELF2, ELF4, HNF4a and MYC, interacted

435 with members of NSL histone acetyltransferase complex. The interactions between TYY1 and three

436 NSL complex subunits (MCRS1, HCFC1, WDR5) have been reported (PINA2;80), but the role of

437 TYY1 in NSL regulation is still unknown. Moreover, the roles of ELF1, ELF2 and ELF4 in the NSL

438 complex (as in the INO80 complex) and their interactions with WDR5 are not well understood and

439 require further study.

440 As mentioned earlier, the SAGA complex is a multi-module complex that has an important role in

441 Pol-II recruitment for all expressed genes81. In addition, SAGA takes part in mRNA synthesis and

442 export, maintenance of DNA integrity and histone modifications, such as histone acetylation, 443 succinylation and ubiquitylation81-87. In our data, multiple interactions were detected between SAGA

444 complex members and the studied TFs (e.g. MYC, KLF6, KL8, HNF4a; Tables S1B and S5). MYC

445 connections to SAGA are known88 and KLF6 interaction with one of the subunits, TAF9, is reported

446 in PINA2 database. In addition, KLF6 is known to interact with HDAC3 in preadipocyte

447 differentiation89. However, we found no other connections between KLF6 and SAGA complex or

448 histone modification. Our data indicated that KLF6 connections to SAGA are bona fide and should

449 be studied further.

450 Finally, we found MYC, HNF4a, KLF6, KLF8 and ELF4 to interact with the NuA4/Tip60 HAT

451 complex (Tables S1B and S7) that plays essential roles in cell cycle control, transcription and DNA

452 repair, and act in the N-terminal acetylation of histones H4 and H2A90. NuA4/Tip60 HAT complex,

453 along with other HAT complexes, is known to participate in MYC-signaling91. Accordingly, we found

454 14 interactions between MYC and NuA4/Tip60 HAT complex (Tables S1B and S7). Like that of

455 MYC, HNF4a’s association with the NuA4/Tip60 HAT complex is reported in a previous study92.

456 Interestingly, we found also strong connection (eight to nine interactions) between KLF6, KLF8 or

457 ELF4 and NuA4/Tip60 HAT complex. However, as mentioned earlier with regard to the SAGA

458 complex, not much is known about the link between KLF6 link and histone modification. Therefore,

459 KLF6’s role in HAT complexes remains largely unstudied and requires further investigations.

460 Taken together, TYY1, ELF4, ELF1, ELF2 (Cluster14) and MYC, KLF6, KLF8 and HNF4a (Cluster

461 15) had several interactions with chromatin remodeling complexes. Some research has been

462 conducted on the contributions of TYY1, MYC and HNF4a to histone modification and chromatin

463 remodeling, but the roles of ELFs and KLFs in chromatin remodeling remain largely unexplored.

464 Interestingly, even though chromatin remodeling and histone modifications are known to be

465 important for almost all TF signaling and most of the studied TFs interact with proteins that mediate

466 these processes, only a fraction of TFs seemed to have interactions with almost-complete histone-

467 modifying or chromatin remodeling complexes. These interactions include TYY1 and ELF1 468 interactions with the INO80 complex; TYY1 and MYC interactions with the NSL complex; MYC

469 and KLF6 interactions with the SAGA complex; and MYC, HNF4a, KLF6, KLF8 and ELF4

470 interactions with the NuA4/Tip60 HAT complex. This observation suggests that these TFs act in

471 close relation to these complexes and take part in them at least in certain conditions.

472 Conclusions

473 While TF binding to DNA is much studied, there is still a lack of comprehensive systems levels

474 understanding of human TF protein interactions. The protein interactions of other human large

475 proteins families, such as kinases and phosphatases, have been studied on the systems level16,21,22,93,

476 but the TFs protein interactions remain globally unstudied. This study provides so far, the most

477 comprehensive systems-level analysis of human TFs identifying the largest reported cohort of TF

478 PPIs and serving as a rich resource for further research and development of pharmaceutical treatment

479 for TF-related diseases. It also allows to profile TFs protein interactomes in the context of more than

480 100 TF interactomes. Moreover, this is the first large-scale study to identify the dynamic PPIs of TFs

481 using transient and proximal interactions catching BioID method. Finally, as defects in TF signalling

482 often lead to severe pathological conditions94-96, and TFs function as downstream players of multiple

483 signalling cascades3, identifying TF PPIs make a crucial contribution to pharmacological targeting of

484 TF-related diseases.

485 486 METHODS

487

488 Used cell lines

489 Cell lines stably expressing selected TFs were generated from Flp-In™ T-REx™ 293 cells (Life

490 Sciences). siRNA silencing and lusiferase experiments were performed in HEK 293 cell line

491 (Atmerican Type Culture Collection, Manassas, VA). All the cells were cultured in low glucose

492 tetracycline-free DMEM (Sigma Aldrich) complemented with 10% FBS and 100 µg/ml

493 Penisillin/streptomysin (Life Technologies) at 37 °C with 5 % CO2.

494 Generation of TF expression constructs and stable, inducible Flp-In™ 293 T-REx cell lines

495 110 TFs from different TF families were selected for this study. Using Gateway® cloning, the TF

496 coding sequences without stop codons were obtained from ORF libraries and commercially cloned

497 into pDONR221 entry vectors (GenScript). To generate tetracycline-inducible stable cell lines,

498 constructs were cloned into N-terminal pTO_HA_StrepIII_BirA-N_GW_FRT, pTO_HA_StrepIII-

499 N_GW_FRT or MAC-tag vectors and introduced into Flp-In™ T-REx™ 293 cells (Life

500 Technologies, Carlsbad, CA) to generate stable, isogenic and inducible cell lines as described by Liu

501 et al15.

502 Affinity purification and mass spectrometry analysis

503 Approximately 1x108 Flp-In™ T-REx™ 293 cells stably expressing human TFs were induced with

504 2 μg/ml of tetracycline (AP-MS and BioID) and 50 μM biotin (BioID) for 24 hours. The cells were

505 pelleted using centrifugation, snap frozen in liquid nitrogen and stored at -80°C. The samples were

506 then suspended in 3 ml of lysis buffer (50 mM HEPES pH 8.0, 5 mM EDTA, 150 mM NaCl, 50 mM

507 NaF, 0.5% NP40, 1.5 mM Na3VO4, 1 mM PMSF, 1 x protease inhibitors cocktail, Sigma) on ice. 508 The BioID lysis buffer was completed with 0.1% SDS and 80 U/ml Benzonase Nuclease (Santa Cruz

509 Biotechnology, Dallas, TX), and the lysis was followed by incubation on ice for 15 min and three

510 cycles of sonication (3 min) and incubation (5 min) on ice.

511 All samples were then cleaned up by centrifugation and the supernatants were poured into microspin

512 columns (Bio-Rad, USA) that were pre-loaded with 200 µl of Strep-Tactin beads (IBA GmbH) and

513 allowed to drain under gravity. The beads were washed with 3 x 1 ml lysis buffer and then 4 x 1ml

514 lysis buffer without the detergents and inhibitors (wash buffer). The purified proteins were eluted

515 from the beads with 600 µl of wash buffer containing 0.5 mM biotin. To reduce and alkylate the

516 cysteine bonds, the proteins were treated to a final concentration of 5 mM TCEP (Tris(2-

517 carboxyethyl) phosphine) and 10 mM iodoacetamide, respectively. Finally, the proteins were

518 digested into tryptic peptides by incubating them with 1 µg sequencing grade trypsin (Promega)

519 overnight at 37°C. The digested peptides were purified using C-18 microspin columns (The Nest

520 Group Inc.) as instructed by the manufacturer. For the mass spectrometry analysis, the vacuum dried

521 samples were dissolved in buffer A (1% acetonitrile and 0.1% trifluoroacetic acid in MS grade water).

522 The peptides were analysed on EASY-nLC II system connected to an Orbitrap Elite ETD hybrid mass

523 spectrometer (Thermo Fisher Scientific, Waltham, MA). The digested peptides were first guided into

524 a precolumn (C18-packing; EASY-Column™ 2cm x 100 μm, 5 μm, 120 Å, Thermo Fisher Scientific)

525 and then into analytical column (C18-packing; EASY-Column™ 10 cm x 75 μm, 3 μm, 120 Å,

526 Thermo Fisher Scientific). The separation was completed with a 60-min linear gradient from 5 to

527 35% of buffer B (98% acetonitrile, 0.1% formic acid and 0.01% trifluoroacetic acid in MS grade

528 water) at a stable flow rate of 300 nl/min. Data-dependent acquisition analysis was performed as

529 follows: after one high-resolution (60,000) FTMS full scan (m/z 300-1700), top20 CID- MS2 scans

530 in ion trap were performed (energy 35). The highest fill time was 200 ms for FTMS (Full AGC target

531 1,000,000) and 200 ms for the ion trap (MSn AGC target of 50,000). Only precursor ions with higher 532 than 500 ion counts were chosen for MSn. The preview mode was applied for the FTMS scan to

533 achieve a high resolution.

534 Protein identification

535 The proteins were identified using SEQUEST search engine in Proteome Discoverer™ software

536 (version 1.4, Thermo Scientific). The raw data were analysed against the reviewed human proteins

537 from the UniProt-database (release 2018_01; 20,192 entries). FASTA library was complemented with

538 BSA, tag sequences, trypsin, biotin and GFP. Biotinylation (+226.078 Da) of lysine residues and

539 oxidation (+15.994491 Da) of methionine or N-terminus were used as dynamic modification. In

540 addition, cysteine residues’ carbamidomethylation (+57.021464 Da) was used as static modification.

541 A maximum of two missed cleavages and 15 ppm monoisotopic mass error were allowed. The peptide

542 false discovery rate (FDR) was set to <0.05.

543 The identified proteins were filtered using SAINT software tools97 with a SAINT score cut-off of

544 0.74. All the TFs were analysed in two or four replicates. TFs are known to have variable expression

545 levels and patterns, and some of them are present in cells at extremely low copy numbers98. To

546 efficiently filter the real interactions, we used 44 and 75 similarly tagged and analysed GFP control

547 runs for the BioID analysis and AP-MS analysis, respectively. We also included GFPs with a nuclear

548 localisation signal (NLS) to efficiently filter out unspecific nuclear interactions. The large nuclear

549 dataset further facilitated the frequency-based deletion of contaminating proteins. The Cytoscape

550 software platform was used to visualize the high-confidence TF PPIs99.

551

552 Data analysis

553 The subcellular localisations of interacting proteins were obtained from the Cell Atlas12. Enriched

554 biological process Gene Ontology terms for all PPIs were obtained from DAVID Bioinformatics

555 Resources100. We also used DAVID to study the enrichment of separate TF interactomes against all 556 the PPIs identified in our study. All the terms with the corresponding p-values and FDR are reported

557 in Table S4.

558 A hierarchical clustering of baits (studied TFs) by their preys (interacting proteins) was performed

559 using ProHits-viz with default settings28. Comparison of two cluster dendrograms were done using

560 dendexted R package (https://www.datanovia.com/en/lessons/comparing-cluster-dendrograms-in-r/).

561 The full amino acid sequences of the studied TFs were downloaded from UniProt101. The DNA-

562 binding motifs of the studied TFs were mainly extracted from the JASPAR database27. Motifs not

563 found in JASPAR were extracted from the HT-SELEX and ENCODE databases102,103. All extracted

564 DNA-binding motifs were aligned using the matrix-clustering tool RSAT24. Finally, the prey-prey

565 correlation analysis of the BioId data was performed using ProHits-viz’s correlation tool

566 (https://prohits-viz.lunenfeld.ca/Correlation/), where Pearson correlation and hierarchical clustering

567 with Euclidean distance metric was used28. Filtered SAINT-interactions were used as input. Apart

568 from default settings, score column was set to SaintScore and cut-off values for filtering were

569 removed as already filtered interaction data was used as input.

570 TF activity was accessed by luciferase assays in three replicates (Figure 4C and S4). Firefly luciferase

571 signals were normalized to renilla luciferase signals and the Student t-test was used to detect the

572 significance of the changes. Stars in figures (Figure 4C and S4) indicate following cut-offs: ***:

573 p<0.001, **: p<0.01, *:p<0.05.

574

575 NFIA silencing and reporter gene assays

576 HEK293 cells were cultured in 96-well plates (7000 cell/well) for 24 hours. This was followed by

577 NFIA siRNA (Dharmacon J-008661-06) transfection in final concentration of 100 nM using

578 Dharmafect transfection reagent (0.35 µl/well). After 24 hours siRNA silencing, the culturing media

579 was replaced with fresh one, and cells were transfected with 50 ng of selected TF or empty vector 580 (pTO-SH-GW-FRT) along with 47.5 ng reporter construct. The reporter constructs contained 6-8x

581 TF binding sites (TFBSs;27, minimal promoter and firefly-luciferase reporter. Only the constructs

582 displaying induction after introduction of the corresponding TF were chosen for further analysis.

583 These include reporters for KLF4 (both activating: [TFBS: 6x GGGTGTGG] and repressive: [TFBS:

584 8x TAAAGGAAGG]), SOX2 (TFBS: 6x CTTTGTT), PAX6 (TFBS: 6x TTCACGCTTGAGTT) and

585 EN1 (TFBS: 8x AAGTAGTGCCC).

586 In addition, cells were transfected with 2.5 ng of renilla-luciferase construct. After 24 hours cells

587 were collected and the firefly-luciferase and renilla-luciferase signals were detected using Dual-

588 GLOÒ luciferase Assay System (Promega). Firefly luciferase signals were normalized to renilla

589 luciferase signals and the analysis was performed in three replicates. NFIA silencing was confirmed

590 after 48 hours from siRNA transfection by western blotting using the specific antibody against NFIA

591 (Abcam, ab228897).

592 NFIA silencing and SOX2 Chromatin Immunoprecipitation Sequencing (ChIP-seq)

593 HEK293 cells were seeded in 10 cm dishes; 24 hours later, tetracycline with a final concentration of

594 2 μg/ml was added to induce the expression of SOX2 protein. siRNAs against NFIA or control were

595 transfected with a final concentration of 100 nM by the lipofectamine RNAiMAX reagent (Thermo

596 Fisher Scientific). HEK293 cells with depletion of NFIA were applied in ChIP-seq assays as

597 previously described104. In brief, HEK293 cells were cross-linked with 1% formaldehyde for 10 min

598 at room temperature after 48 hours of siRNA transfection. The reaction was quenched with 125 mM

599 glycine. Cells were collected after washing twice by pre-cold PBS and resuspended in hypotonic lysis

600 buffer (20 mM Tris-Cl, PH 8.0, with 10% glycerol, 10 mM KCl, 2 mM DTT, and complete protease

601 inhibitor cocktail (Roche)) to isolate nuclei. The nuclei pellets were washed with pre-cold PBS and

602 resuspended in 1:1 ratio of SDS lysis buffer (50 mM Tris-HCl, pH 8.1, with 1% SDS, 10 mM EDTA,

603 and complete Protease Inhibitor) and ChIP dilution buffer (16.7 mM Tris-HCl, pH 8.1, with 0.01% 604 SDS, 1.1% Triton X-100, 1.2 mM EDTA, 167 mM NaCl and Complete Protease Inhibitor). A Q800R

605 sonicator (QSonica) was used to generate an average size of 300 bp chromatin fragments at 4°C.

606 Dynabead protein G (Invitrogen) was washed with blocking buffer ((0.5% BSA in IP buffer (20 mM

607 Tris-HCl, pH8.0, with 2 mM EDTA, 150 mM NaCl, 1% Triton X-100, and Protease inhibitor

608 cocktail)) and incubated with antibody against HA (ab18181, Abcam). Chromatin lysate was

609 precipitated with Dynabead protein G for 12 hours, then wash the beads 4 times with washing buffer

610 (50 mM HEPES, PH 7.6, 1 mM EDTA, 0.7% sodium deoxycholate, 1% NP-40, 0.5 M LiCl) and 2

611 times 100 mM ammonium hydrogen carbonate (AMBIC) solution. The DNA-protein complexes

612 extracted from the beads by eluting in extraction buffer (10 mM Tris-HCl, pH8.0, with 1 mM EDTA,

613 and 1% SDS), Proteinase K and RNase A were added to reverse the cross-links. A Mini-Elute PCR

614 purification kit (Qiagen) was used to purify the DNA. The purified DNA was subjected to ChIP-seq

615 library preparation through using the TruSeq ChIP Sample Preparation kit (Illumina). Briefly, DNA

616 was blunted by using an End Repair Mix, then A-Tailing Mix was used to add a nucleotide to the 3’

617 Ends of the DNA fragments. RNA Adaptor Indices were ligated to the DNA fragments and fragment

618 size of 200-500 bp were selected on a 2% agarose gel. The DNA were enrichment by PCR

619 amplification and quantified by using KAPA Library Quantification Kit (Roche). A NextSeq550

620 sequencing system (Illumina) was used to sequence the DNA library.

621 Bioinformatics analysis of ChIP-Seq data

622 The ChIP-seq library was sequenced to generate 76 bp single-end reads. FastQC105 was applied to

623 assess the quality of raw data and followed by Trimmomatic106 for quality control. The cleaned reads

624 were aligned to the human genome hg38 assembly using Bowtie2107. The ChIP-seq peaks were

625 identified by applying findPeaks.pl from Hypergeometric Optimization of Motif Enrichment

626 (HOMER v4.10)108 with parameter “-mapq 20”, while all other parameters were kept as default. Motif

627 enrichment analysis was performed using HOMER findMotifsGenome.pl and peaks were annotated

628 by “annotatePeaks.pl”. Bioconductor package ChIPseeker (1.18.0)109 was applied to perform pathway 629 enrichment analysis. Bam files were first converted to bigWig files by using bamCoverage from

630 deepTools2. Heatmaps of aligned reads and average signal plots were generated by Samtools (v1.9)110

631 and deepTools2 (v3.3.2)111.

632

633 DATA AVAILABILITY

634 Lead contact

635 Further information and requests for resources and reagents should be directed to and will be fulfilled

636 by the Lead Contact, Markku Varjosalo ([email protected])

637 Material Availability

638 Plasmids generated in this study will be deposited in Addgene. No other unique reagents were

639 generated in this study.

640 MAC-tag-N destination vector (Addgene, plasmid no. 108078; RRID: Addgene_108078)

641 Data and Code Availability

642 The MS peptide raw data from the MS runs have been deposited to the Peptide Atlas

643 (http://www.peptideatlas.org) under accession number XXXXX and the identified high-confidence

644 protein-protein interactions are downloaded to IntAct -protein interaction database

645 (https://www.ebi.ac.uk/intact). Filtered protein-protein interactions are also available as table S1 and

646 ChIP-seq peak lists as table S6.

647

648

649 650 651 References

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868 869 Author Contributions

870 HG, MK and MV designed the study. HG and MK with help of LY generated the cell lines and HG

871 and MK performed the mass spectrometry analyses. HG did the data filtering, data-analyses, NFIA

872 silencing experiments, prepared the figures and wrote the manuscript. KS participated in data

873 analysis. GW, ZT and QZ designed and performed the ChIP-Seq analyses. All the authors reviewed

874 the manuscript carefully.

875

876 Acknowledgements

877 For the founding, we would like to thank Orion Research Foundation sr, Finnish Cultural Foundation

878 (HG), Academy of Finland (288475 and 294173), Sigrid Jusélius Foundation, Biocentrum Helsinki,

879 University of Helsinki Three-year Research Grant, Biocentrum Finland, HiLIFE, Instrumentarium

880 Research Foundation (MV), Jane and Aatos Erkko Foundation and Finnish Cancer Foundation (GW).

881 We would also like to thank Petri Auvinen and Juha Kere for the critical reading of the manuscript.

882

883 Competing interests

884 Authors report no competing interests.

885

886 887 FIGURES A TF

Tagged TF TFs Tag Streptavidin Bioinformatics Analysis Flp-In recombination, beads selection and expansion of isogenic cell clones Induction and Cell lysis and Streptavidin from 110 TFs Biotin supplementation affinity purification LC-MS/MS Analysis

KMT2AHCFC1TAF1H2AZH2B1JACL6AYEST65GTS4SUPT3H2B1KOGT1ATX7MYCCOF2ASH2LRAI14TMOD2MYO5CSMCE1SMRD3TIM50RAB8A SRCAPLIN9ATMO4L27L2ATTA7L3F10 MLJUND12ANPMJUNACTN4ARPC3 SGTADA1F29KANL1ASXL2 FOSEIRUVB2F3LSMC3 KAT8MED10LIN37MCRS1 PHF8TRI33RLKMT2B26L MRGBPMBIP1KANL2 AKYEP8LINO80TS2 MO4L1CXXC1MED17 ZHX1GMEB2BAZ1B TRRAPUBP22AT7L1 ZMYM2KANL3BICRA RL34ESR1TAF6L ING3DMAP1ARP8 STIP1NBN ADNPBRD8 RAD50HS71L ROAAMNT OZFSP2 PHF12EP400 GLI2PP6R3 LMBL3HIC2 SUMO1RGPD1 KAT5FUBP2 CEP85PCM1 KARREB1T2B ANR17SUFU BAP1GATD1 MYBTNR6A SP20HMGAP AF17BLM TAD2BYTHD3 KAISOBEND3 EMSYUBN2 ZN709PHF6 MCAF1NFRKB ZBZBT10TB9 MSD2CDCA2 IRF1DACH1 TAF3GPTC4 ERGPHF10 PRC2BBCORL AF10GATA4 EPC2VPS72 DPF3LMO4 ELF2TAF5 HIPK2ZN521 ZN638MEF2D ETS1K1551 ZMYM3TFDP1 NFKB1 CTBP1PRDM2 MYOD1DYL2 BPTF ITF2 GABPA TFE2 MBTD1 ZN121 MECOM HUWE1 FOXK2 ZN607 SRSF3 HTF4 NOP14 B UBE2O SUMO2 C SALL2 MAX CABIN PSA6 NKRF MAZ PR40A SEC13 PCF11 HSP72 CPSF4 ZN131 PTBP1 TAF6 HNRPM CTCF CSTF1 NR2C2 KHDR1 PRC2A RBM20 ZEB1 ZN318 SOX5 SUGP2 FUS RAVR1 HSP74 CPSF7 ELF1 FOG2 PSB5 DDX17 TYY1 FUBP1 BRD2 FUBP3 UBF1 DHX36 AT2A2 RBM12 HNRPL CPSF2 MYBB CREB1 ROA1 TAL1 HMGB1 TNPO2 RS17 PPM1G SP1 PTMS PTMA RL13A NOL4 RBM25 TAF5L SIR1 TADA3 SYLC DEAF1 RXRA ZN217 IKBE RAD21 NFKB2 ZBT14 RELB AKAP8 TNIP1 SET1A IKBB TAF4 TNIP2 PGRC1 EXOC4 UBP27 SLF1 KLF8 HNF1A TIM13 PHS ARIP4 ZNF24 HSP76 ARNT PRDM1 TSH3 RBM33 RING2 MYH14 GATA2 MYO1C ARI5B ZHX2 GATA3 MBNL1 SFR15 ZKSC4 TADBP NOC3L QKI CHD5 TYY2 TRM1L ZNHI1 NOG2 SMCA5 ROA0 TFPT EWS KI67 TFAP4 CHD3 FLI1 MDC1 IF6 NUMA1 RL29 LRIF1 P53 RIF1 EYA4 MSL1 E2F6 THOC7 SMHD1 IN80C SOX15 ASXL1 MRT4 BAP18 DDX18 ZN687 CCD86 KDM1B XRN2 IN80E GNL3 ZN592 CG050 INT12 DDX27 MBD6 DDX54 THA11 KRI1 Z512B DHX37 DIDO1 PUM3 IN80B KRR1 ZHX3 TF3C1 CHD8 PSA4 PKCB1 SKI ZFHX3UBAP2K1C9AT5F1CNOT3 UCHL5 PHB2CSR2BK2C5STAT4 DREBIRF5MED24EDC3MYO6 TOP1 TF3C3 TEX10PCGF6C3P1 IRF9CALL5PDIA4 SURF6 HCFC2 LS14AMCRI1 EIF3HLHX3 TLX2 LG3BPAT1A1 SPTN1LIN54 MORC2 RT16RL38 BRMS1CE152 NOP16 PAX6 GRNVSX1 GGYF2 RRP15 RFXAP CNOT9 ZN703CUX1 NOP53 RERE ADT2 SOX9 SIX5 MATR3RBM28 AP-MS ZN462 NP1L1 SALL1TPPC3 ZCCHV DCP1A REXO4 SP9 BRD7 RC3H1 ELK4 ATAD5 SDS3 RT25 ZN724 TBA1A TEAD1 ATX2 NAT10 TBR1 PIAS2 LARP4 DDX24 OTUD4 HXA10 SP7 NOG1 RT10 VGLL4 MYO1B PDIP3 UBC9 CBX2 TAF9B PYGO2 UBP7 UIMC1 ACACB EBP2 CALM2 PBX2 PTCD3 RBM14 PHB PPIA ZN512 HGS ZN260 NDUA8 TLX1 AUTS2 S10A8 ZN503 LMBL2 MYH10 EYA3 RT06 H2A1A PYGO1 PP6R1 CHD6 ATF1 ZMIZ2 PCCB TBP ZN107 SPB1 PAGR1 RBM11 SSBP4 PRSR1 MAGD1 HNF1B KAT2A THOC4 RT18B HIRA RA1L2 BCL9L SYK UBN1 SSF1 PB1 RS6 SOX13 NEMO PCGF5 GATA1 POLH RT28 RS27L IF2B2 ASF1A TAF9 TET2 PPIL2 ARID2 LAR4B P66A UBC GLYR1 TEAD3 MTA3 RT34 SMC1A STAU1 I2BPL MYL6B PWP1 ALBU MBD2 KIF7 FOXK1 SOX6 PITX1 ALBU_BOVIN SOBP VDAC1 WDR5 OFD1 NEMP1 RLA1 NKX25 RT27 TYDP2 ATPA ROA3 PP2AA MTA1 EMD DHX40 MYCBP RBM34 GRPE1 PPIL4 COF1 HNRH3 RT22 P66B RT18C HXC9 NACC1 ROA2 DHX30 PRD16 PRR12 CSK22 ECH1 ZBTB1 KCRB TFE3 4ET PPARG TNR6B DDX5 BRD9 HXB9 PHF2 STAT3 RT15 PO2F1 ARP5 NUP62 HAUS4 XRCC5 STAT1 XRCC6 ANXA2 COPA TNG6 NSA2 SPZ1 ARFG1 SC16A HXC11 TF65 ETV5 TLX3 ETV3 CO039 RT23 PDIA3 XPOT COP1 DDX50 2AAA RFC1 ZFR DDX10 IKBA RT24 SQSTM DDX31 TAF1L RENT1 ACTB DDX52 RT31 LAS1L GGYF1 ZN516 TPM3 ETV6 SMD2 RRP12 RL36A RHG32 ZN326 NU214 ZSC21 RRP5 CSK2B NUP98 RFX5 RRP1B NUP54 CASP CLH1 TAB3 ZN770 ILF3 MYO1D NUP88 ATPB DHX9 WDCP RT09 KBTB6 TNR6C CNOT8 RS30 PRC2C CYLD RS24 LIMD1 ST134 RL26 ARP3 SPAT2 SRP72 RT29 HNF4A NOP2 RL28 RFXK H3C RBMX 38961.0 PML RT02 FBRL ATRX H14 SPTB2 DYL1 EAF6 HNRPC TBB5 SUH RL1D1 SETB1 ANR28 RT14 K1C10 SLX4 SMAP2 HNRPR XPF NOP56 FIL1L RMXL1 ACTBL KLF4 RN214 ZKSC3 ING1 ZN471 MCCB SIX4 RT17 MED4 CE192 TCF20 ZN629 CHD7 RBMS1 ZBT21 ZN746 TF7L2 MYO5A HDAC5 SHCAF ZN451 RS14 FBSL PF21A ELK3 SMG7 ING2 VEZF1 SP130 RS25 ETV4 RT05 BCL7B RT26 BioID ZMIZ1 MINT SOX2 XRN1 EHMT2 FOXL1 MEF2A MED8 RL7L IKZF3 SMRC1 1433F MIER1 ACTC DOT1L NIP7 MORC4 TIM9 BMS1 PDIA6 SMRC2 GLO2 NEPRO RYBP MK67I TF3C2 YAF2 SC23B SDA1 TRIP6 HOMEZ LHX2 CCAR1 ODB2 HMGB3 LRC59 ZFHX4 PFD3 SMCA1 POGZ HELLS AGO1 NIPBL PAX7 DX39A ZMYM4 UBP11 MY18A BRM1L SERPH JHD2C DCP1B BCL7A PAX9 ARI4B IF2B3 HXD11 EFTU QSER1 JIP4 FIGN NCOA2 BC11A ARPC4 SSXT RM11 SOX10 RBBP5 KLF3 DI3L1 NCOA6 MITF RL13 PATL1 GPS2 ELF3 EP300 MYH9 E2F1 ZFP30 RING1 E2F3 TLE4 COPB2 RB KIFC1 SATB1 HS74L CCNA2 CBX1 TP53B UBP54 SSBP2 RL36L ZN420 SKP1 IRF8 ALMS1 DCAF7 STA5B NPM3 TBL1Y ATN1 UBXN7 SMCA4 K1671 NP1L4 ZNF66 MNX1 RCC2 LLPH YAP1 RAD18 WDR1 PYR1 PIMT CFDP1 WIZ PAX2 MOES CREST TPM1 HME1 KDM1A PAX8 LDHB ZEB2 RT35 SOX4 EPC1 HDAC3 CASPE TIF1A ISL2 MMTA2 ANKH1 ZN536 CTBP2 MED15 BRAC FOXI1 MOV10 NCOA3 ARF3 YYAP1 NCOR2 NFAC3 BASI ZN281 METH TPPC9 ZN148 WAC2A CPVL SATB2 RFLB PACN2 SNTB2 ZN608 KLF9 RCOR1 SVIL SIN3B ECHA TRPS1 RL37A ARI4A RCOR3 NPAT LHX1 HM20B SRP68 GON4L HM20A BBX 2ABA FBRS TBB4B SIN3A EHMT1 PHF23 PRDX4 FOXP1 ASURF NUSAP PIAS1 NKX24 S18L2 TDIF1 ZMYM1 NOP58 CH60 DPY30 G3P KLF5 TBL1R RBM6 TAD2A PCGF1 LIMA1 RBP56 ARI3A LENG8 ZN644 CIC RL23 NFYC TIM10 SMRD1 NFYA SMRD2 TRIM9 NFYB TRAF3 CBP ZN143 REQU HXA5 FKBP4 TFAM DPF1 SSBP3 TFCP2 MAML1 RFX7 IF2B1 BCL7C RS18 0 2000 4000 6000 8000 KDM2B CYBP RT12 ARI1B RAI2 SC24A RASF8 KMT2C SC24B ARI3B TMOD3 NFAC4PDS5A MCRI2 FOXP4 H31 ZBT40IRF4 SNTB1 LDB1I2BP1 COPB TEAD2 SSBP I2BP2 PFD4 KDM5AMEF2C YTHD1 BCORPAXI1 COPE BANPZN507 HEN1 NFIXEMSA1 TPGS1 ZFP91 AP2M1 GSE1 YTHD2 ISXLHX8 UNK FOXC1RLF RL39 TCRG1N4BP1 AAAT SNF5NFIA RT07 CD2B2 STAT2 AES REL RU17SNRPA GCM1TLE1TLE3 TAF2 RSMB FODDX56XA1 KLF10 ZNF3RFX1 SMCA2ATX1 PABP2 SLX4I PBX1 ZEP1 K22E BRCA1 YLPM1DLX6 ATNRX1L2C1 ADT3 ENPL RBM10ENL NCOR1NFIC DCD SH3K1 CPSF1RBM39 KDM6ATBL1X MYL6COX41 LASP1 NCOA1PCGF3 KMT2DZN609 CALX HMGX4ZIC3 RPRD2ZN691 BCL9ARI1A RPN1CP131 PAN3 RBBP6SUGP1 ETV7NFIB LRC49 RADXM1IP1 CDPRCCC26 KLF12B3GL2 SDCG3TPM4 PUM2GLI3 CLP1ESS2 ELF5LHX6 ARP6PHS2 RL35AKLF15 RSRC1TREF1 NOL4LGMEB1 ABCD3BAP31ZZZ3 SMG5TAF12STA5A KCD15WDR83 IKZF5ERF MARF1K1C14MPRIPRT11PCCA BASP1CDC37RM47SENP5TIM8B RBM4SRRTWDR33 FOXQ1SOX17TCF7 CNOT1MCCA PPIL3PININRBM27 AGO2CNO11R3HD2 TFP11GPSCAF8TC8 AGO3CNOT2CNOT7 CDWBP1111ALHX4 ASLS14BPP1CNO10 Number of PPIs HMGB2MGN C2D1AEDC4 FEVFOZBXP2T7ATAF8 PUM1R3HD1ELAV1TTC31 PHC3GAMPP5TA6ELF4KLF6 PFD1HELZIRAK1RACK1UBP2L SMAD5CC138DTNAKLF16RECQ5SLU7MFAP1KISNW1F23RSBN1CC174H1XLTV1SCAFBUT14ARGAP1TPX2NOSIPPPIL1CPEB1CCGT85CK0355PB1 nuclear localization novel interaction non-nuclear localization known interaction bait proteins D

350 BioID: Average 65 PPIs / Bait BioID 300 AP-MS: Average 21 PPIs / Bait AP-MS 250 200 150 100 Number of PPIs 50 0 T ISX SP1 SP7 YY1 FEV EN1 FOS MYB ZIC3 ERG IRF1 IRF3 IRF4 IRF5 IRF8 IRF9 GLI2 GLI3 MYC NFIA NFIB NFIX NFIC KLF3 KLF4 KLF5 KLF6 KLF8 KLF9 E2F1 ELF1 ELF2 ELF3 ELF4 ELF5 ELK3 ELK4 TP53 TAL1 TLX1 TLX2 TLX3 LHX1 LHX2 LHX3 LHX4 LHX6 LHX8 ETS1 ETV3 ETV4 ETV5 ETV6 ETV7 SPZ1 PAX2 PAX6 PAX7 PAX8 PAX9 VSX1 ESR1 SOX2 SOX4 SOX5 SOX6 SOX9 NFYC RXRA FOXI1 GCM1 KLF10 KLF12 KLF15 KLF16 STAT1 STAT3 STAT4 NFKB1 FOXL1 TEAD1 TEAD2 TEAD3 SOX10 SOX15 SOX17 HNF1A HNF1B HNF4A MEF2A NHLH1 GATA1 GATA2 GATA3 CPEB1 CREB1 GATA4 FOXQ1 MYOD1 PPARG PRDM1 NKX2-5 STAT5B STAT5A NFATC3 NFATC4 Baits

888 889 Figure 1. TF protein interactome identified using the BioID and AP-MS methods

890 A. Schematic illustration of used methods. TFs were tagged N-terminally with a MAC, StrepIII-HA

891 or BirA -tags (Table S1A) and co-transfected with Flp-In recombinase to generate stable isogenic and

892 inducible cell lines. Cells were induced by tetracycline addition and, for the BioID analysis,

893 supplemented with biotin for 24 hours. This was followed by harvesting, lysis and affinity purification

894 with Streptavidin beads. Purified proteins were further digested into peptides and analysed by LC-

895 MS/MS. Proteins were later identified and analysed using different bioinformatic methods.

896 B. Localisation of interacting prey-proteins according the annotated localisations of Cell Atlas 12.

897 Yellow nodes indicate nuclear localisation and red non-nuclear. From mapped proteins, more than

898 80% had nuclear localisation.

899 C. Protein-protein interactions identified using the AP-MS (2176) and BioID (7232) methods.

900 Interactions were compared to interactions from the PINA2, Intact, Biogrid and String experimental

901 protein interaction databases and to interactions from a study by Li et al., resulting in 345 and 1525

902 previously reported interactions in the AP-MS and BioID data, respectively. The proportions of

903 known interactions are shown in red.

904 D. Number of high-confidence protein-protein interactions of different TF baits detected by AP-MS

905 (blue) or BioID (red) affinity purification combined to mass spectrometry.

906 A ELF1 ELF2 ELF3 ELF4 ELF5 ELK3 ELK4 GLI2 GLI3 NFATC3 NFATC4

ELFs ELKs GLIs NFATCs ETV3 ETV4 ETV5 ETV6 ETV7 FOXQ1 FOXI1 FOXL1 HNF1A HNF1B HNF4A

ETVs FOXs HNFs GATA1 GATA2 GATA3 GATA4 NFIA NFIB NFIC NFIX TLX1 TLX2 TLX3

GATAs NFIs TLXs IRF1 IRF3 IRF4 IRF5 IRF8 IRF9 PAX2 PAX6 PAX7 PAX8 PAX9 TEAD1 TEAD2TEAD3

IRFs PAXs TEADs LHX1 LHX2 LHX3 LHX4 LHX6 LHX8 STAT1 STAT3 STAT4 STAT5A STAT5B

LHXs STATs KLF3 KLF4 KLF5 KLF6 KLF8 KLF9 KLF10 KLF12 KLF15 KLF16

KLFs SOX2 SOX4 SOX5 SOX6SOX9 SOX10 SOX15 SOX17 SP1 SP7

SOXs SPs CPEB1 CREB1 E2F1 HEN1/ ERG1 NFYC HME1/EN1 NKX25 PPARG ESR1 ETS1 FEV TBR1 P53 ZIC3 NHLH1

RXRA SMAD5 SPZ1 FOS PRDM1RREB1 GCM1 ISX MYB MYC BRAC/T TAL1 TYY1 VSX1

Others

BioID interaction AP-MS interaction

B 200

150

100

50

0 KLFs SPs TLXs HNFs SOXs PAXs NFIs GATAs LHXs ELFs FOXs ELKs TEADs ETVs GLIs STATs IRFs NFATCs Average number of PPIs Average 907 >150 PPIs/Bait 150-100 PPIs/Bait 100-50 PPIs/Bait <50 PPIs/Bait 908 Figure 2. Comprehensive protein interactomes of the studied TF families

909 A. TFs belonging to a given family are indicated in orange nodes, other interacting TFs in green and

910 the rest of the interacting proteins in white. Blue edges indicate interactions from the BioID analysis,

911 red from the AP-MS analysis and black from both.

912 B. The average number of PPIs of different TF families are shown under the interaction maps. p-value A transcription, DNA-templated 6.1 x 10-104 positive regulation of transcription from RNA polymerase IIpromotor 2.3 x 10-72 negative regulation of transcription from RNA polymerase II promoter 1.3 x 10-62 positive regulation of transcription, DNA-templated 4.1 x 10-52 transcription from RNA polymerase II promoter 5.6 x 10-50 regulation of transcription, DNA-templated 2.2 x 10-45 regulation of transcription from RNA polymerase II promoter 1.1 x 10-41 negative regulation of transcription, DNA-templated 2.3 x 10-37 chromatin remodeling 3.0 x 10-32 covalent chromatin modification 1.1 x 10-23 B 0 50 100 150 200 250 300 350 400

20

15

10

5

Lambert 46% 12% 2% 2% 1% 1% <1% 7% 1% 3% 3% <1% 1% <1% 4% 3% <1% 1% 1% <1% <1% <1% 1% <1% et al.

Ets IRF Rel E2F p53 TEA bZIP GCM SMAD STAT bHLH GATA T-box C2H2 ZF Forkhead Unknown HMG/Sox Paired box MADS boxMyb/SANT Ets; AT hook Homeodomain Nuclear receptor

Homeodomain; Paired box C BAITS PREYS IRF8 ETV7 HNF1a FOXL1 HME1 MYB VSX1 ISX FOXQ1 PRDM1 SOX5 SOX6 PAX9 TBR1 PAX8 PAX2 GCM1 FOXI1 NFIB NFIA NFIC G G SP7 ERG KLF5 HNF1B PAX7 BRAC G TLX1 TEAD1 HNF4a NFIX PAX6 FEV LHX3 LHX4 LHX1 LHX2 SOX2 TLX3 TLX2 SOX15 SOX17 IRF4 SOX10 KLF4 ETV4 PPARG ELF5 IRF1 ETS1 SOX9 MYOD1 FOS NFYC ELF2 ELF1 ELF4 TYY1 KLF12 KLF3 ELK3 KLF16 KLF8 KLF15 HEN1 KLF9 ETV3 KLF6 MYC E2F1 ESR1 S NFAC4 SOX4 ELF3 ETV5 ELK4 SMAD5 LHX8 LHX6 KLF10 SP1 CREB1 NFAC3 ETV6 GLI2 GLI3 CPEB1 SPZ1 NFKB1 TAL1 S S MEF2A IRF5 IRF3 RREB1 TEAD3 TEAD2 STA5B IRF9 TA TA TA AT AT AT T4 T1 T3 A1 A3 A2 Ets; AT hook Ets Ets SMAD Homeodomain Homeodomain C2H2 ZF C2H2 ZF bZIP Rel Ets C2H2 ZF C2H2 ZF Unknown Unknown Rel bHLH S S MADS box IRF IRF C2H2 ZF TEA TEA S IRF G Homeodomain TEA Nuclear receptor SMAD Homeodomain; Paired box Ets Homeodomain Homeodomain Homeodomain Homeodomain HMG/Sox Homeodomain Homeodomain HMG/Sox HMG/Sox IRF HMG/Sox C2H2 ZF Ets Nuclear receptor Ets IRF Ets HMG/Sox bHLH bZIP Unknown Ets Ets Ets C2H2 ZF C2H2 ZF C2H2 ZF Ets C2H2 ZF C2H2 ZF C2H2 ZF bHLH C2H2 ZF Ets C2H2 ZF bHLH E2F Nuclear receptor S Rel HMG/Sox G C2H2 ZF Ets C2H2 ZF Homeodomain Homeodomain; Paired box T-box G IRF Ets Homeodomain Forkhead Homeodomain Myb/SANT Homeodomain Homeodomain Forkhead C2H2 ZF HMG/Sox HMG/Sox Paired box T-box Paired box Homeodomain; Paired box GCM Forkhead SMAD SMAD SMAD TA TA TA TA AT AT AT T T T T A A A

913 914 Figure 3. Hierarchical clustering of baits by preys and its correlations to DNA-binding domains

915 and protein sequences.

916 A. Enriched GO-BP terms of interacting proteins from the BioID analysis.

917 B. The distribution of the DNA-binding domains of the studied TFs. The corresponding proportion

918 of each DNA-binding domain from 1,639 TFs in the study of Lambert et al. is shown as a percentage

919 value below the graph.

920 C. TFs (baits, named below the heatmap) and their interacting proteins (preys) were hierarchically

921 clustered (Biohit-viz). Corresponding colour coded DNA -binding domains are shown below the

922 baits. LHX4LHX3LHX2 STAT4 STAT3 RXRA NFKB1 LHX8 GLI3 MEF2A LHX1KLF9 MYB KLF8 A MYOD1 KLF5 NFIA KLF4 NFIB KLF3 NFIC KLF16 NFIX KLF12 NKX25 IRF8 STAT1 PPARG ETS1 LHX6 GLI2 P53 IRF4

PAX2 HNF4A STA5B ETV4 STA5A TAL1 ZIC3 PAX6 HNF1B

PAX7 HNF1A

PAX8 HME1 SPZ1 SMAD5 KLF6 TEAD3 GATA1

PAX9 HEN1

PRDM1 GCM1 IRF1 ETV5 ISX TEAD2 NFAC4 RREB1 GATA4

SOX10 GATA3

SOX15 GATA2 KLF15 IRF3 FOXQ1 NFYC ELK4 SOX17 FOXL1

SOX2 FOXI1

SOX5 FOS

SOX6 FEV IRF5 KLF10 NFAC3 ESR1 SOX4

SOX9 ETV7 SP1 ETV6 SP7 ETV3 TBR1 ERG CPEB1 E2F1 IRF9 ELF3 MYC TEAD1 ELK3 B TLX1 ELF5 TLX2 ELF4 TLX3 ELF2 TYY1 ELF1 VSX1BRACCREB1 LHX4 LHX3 LHX2 MEF2A LHX1KLF9 NFIA MYB KLF8 MYOD1 KLF5 NFIB KLF4 NFIC KLF3 KLF16 PAX6 LHX4 NFIX KLF12 SOX15 KLF12 NKX25 IRF8 KLF3 LHX2 HNF4A P53 IRF4 SOX17 HNF1B

PAX2 HNF4A NFIs PAX2 HME1 TBR1 FOXL1 PAX6 HNF1B PAX9 SOX10 ETV3 SOX9 PAX7 HNF1A Interactions TLX2 ELK3 ETV7 LHX1 ELF2 ELF1 PAX8 HME1 VSX1 ERG to NFIs BRAC PAX9 HEN1 TYY1 HEN1 FOXI1 IRF8 PRDM1 GCM1 Interactions GATA3 KLF5 MYOD1 NFIB RREB1 GATA4 from NFIs RREB1 IRF4 MYB SOX10 GATA3 NFIA NFIC SOX15 GATA2 Interactions KLF16 FOS SOX17 FOXL1 to and from NFIs GCM1 KLF8 PAX8 SOX2 NFIX SOX2 FOXI1 SOX6 TLX1 LHX3 PRDM1 SOX5 FOS No NFI KLF4 SOX5 TEAD1 SOX6 FEV interactions

SOX9 ETV7 SP7 GATA2 SP1 ETV6 SP7 ETV3 FEV PAX7 TBR1 ERG TLX3 TEAD1 ELK3 TLX1 ELF5 ELF4 TLX2 ELF2 TLX3 ELF1 TYY1 VSX1 BRACCREB1 PAX6 C 7 *** ** D 6 SOX2 ChIP-seq Average signal of 5 SOX2 binding 4 Control siRNA NFIA siRNA anti-NFIA 3 + siRNA Control NFIA siRNA - + 2 40 NFIA 1 Relative luciferase activity 0 HME1 -- + + 20 NFIA siRNA --+ +

0 KLF4 repressive KLF4 activating -3 0 3 kb 20 *** 18 0,18 *** ** 16 0,16 0,14

14 Lost

12 0,12 (6,921) 400 10 0,1 8 0,08 6 *** 0,06 200 4 0,04 2 0,02 Relative luciferase activity 0 Relative luciferase activity 0 KLF4 -- + + KLF4 -- + + 0 NFIA siRNA --+ + NFIA siRNA --+ + -3 0 3 kb

EN1 SOX2 0,08 ** 0,25 0,07 *** 0,2 *** 40 0,06 *** (362) Shared 0,05 0,15 0,04 20 0,03 0,1 0,02 (1,341) 0,05 Gained 0 0,01 -3 3 -3 3 kb -3 0 3 kb Relative luciferase activity Relative luciferase activity 0 0 HME1 -- + + SOX2 -- + + 0 30 0 30 923 NFIA siRNA --+ + NFIA siRNA --+ + 924 Figure 4. TF-TF (bait-bait) interactions of 110 TFs studied

925 A. Of 110 studied TFs, 80 had 203 interactions with other studied TFs. Blue edges indicate

926 interactions from the BioID analysis, red from the AP-MS analysis and black from both.

927 B. Most of these TF-TF (175) interactions were TF interactions with NFIs (left panel). The right panel

928 shows the separate groups shared by one or multiple NFIs. Colour code: Green nodes = NFIs, yellow

929 nodes = interactions to NFIs, orange nodes = interactions from NFIs, red nodes = interactions to and

930 from NFIs and grey nodes = no interactions to or from NFIs. Colour coding of the nodes is shown in

931 the right side of the figure.

932 C. NFIA was silenced using siRNA transfection and NFIA levels were detected 48 hours after

933 transfection by western blotting using specific antibody against NFIA. TFs’ activity was investigated

934 after NFIA silencing using both repressive and activating reporter gene analysis. Both repressing and

935 activating functions of KLF4 were reduced upon the NFIA silencing. In addition, SOX2 and PAX6

936 activity was reduced, while EN1 activity was increased upon NFIA silencing. N=3, ***: p<0.001,

937 **: p<0.01, *:p<0.05

938 D. Heatmap representation of SOX2 binding intensity based on ChIP-seq signals in 293T cells while

939 treated with siControl and siNFIA, respectively. Signals within 3 kb around the center of binding

940 peaks are displayed in a descending order for each SOX2 binding event (lost, shared, and gained upon

941 siNFIA). (Right) Plots of average signal of SOX2 binding at each corresponding region. A Selected clusters

RNA processing linked CPEB1 cluster 3 1 Actin and myosin linked cluster 2 NFKB1 interactome cluster 4

5

preys 7 9 6 8 Preys linked to RNA splicing 10 and processing 12 11

13 14 15 Chromatin modulating preys Histone acetyltransferase complexes baits

preys B Cluster 2: STAT1 and FOS interactomes

TIF1A TLE1 DPF1 ARI3A ATP proteins ENPL KCRB ATPB KDM2B 2ABA ARI3B CIC 2AAA PHB ATPA other FOS ZEP1 REQU CBP WIZ CALX interacting PHB2 AT1A1 JUN SSXT AT2A2 M1IP1 BCL9 BCL7C proteins TIM50 ADT2 JUND BRD7 SSBP PB1 EP400 CREST STAT3 ACACB STAT1 FOS PGRC1 BCL7A AES MED8 TRI33 STAT2 NFIA COF1 CALM2 ARPC4 COF2 ARPC3 ARP3 RBBP5 ANXA2 DREB PAXI1 KDM6A ACTN4 SMRD3 LASP1 MYO1B KMT2D MYO1C MYL6B SMRD1 KMT2C ACTC SMRD2 SMRC2 LIMA1 PAGR1 ASH2L MYO1D MYL6 ACTBL SNF5 SMRC1 ML12A MLL3/4 complex ACTB SMCE1 MOES MYO5A MYH9

SEPT6 ARI1A SMCA4 MPRIP MYO5C MYH14 ARI1B SMCA2 Prey in cluster 2 WDR1 ARID2 RAB8A MYO6 MYH10 Prey outside MY18A TPM4 BAF complex/ RAI14 of cluster 2 TPM3 SPTB2 BRG1-SIN3A SPTN1 TPM1 Bait SVIL TMOD2TMOD3 Actin and myosin linked proteins 942 943 Figure 5. Biological clusters from prey-prey correlation analysis

944 A. Prey-prey correlation analysis (ProHits-viz) on preys identified in the BioID experiments. The

945 results are shown in the heatmap with preys in both the x and y axis. A corresponding bait-prey heat

946 map is shown below to assist in determining which and how many baits are driving the prey clusters.

947 Preys in clusters (1-15) and baits driving the clusters are shown in Table S7. Color scale indicates

948 Pearson correlation of prey PSMs that was done by Prohits-viz prior to hierarchical clustering.

949 B. FOS and STAT1 interactomes. Many proteins interacting with FOS and STAT1 were clustered in

950 Cluster 2 of the prey-prey correlation analysis (highlighted in red). Actin and myosin, ATP signalling,

951 BAF complex and MLL3/4 complex linked proteins are highlighted in different groups. RBM33 RPRD2 PBX2 RBM12 MATR3 A Clusters 10 and 11 SP9 H31 ZFR FUBP1 p300-CBP EWS TBA1A ENL EP300 -p270-SWI/SNF YLPM1 EFTU SMRCA4 CSK2B CBP complex SMRC1 BEND3 ARI1A SMRC2 SNF5 Top 3 BAITS GATA1 ZN318 GATA3 SP7 PSA4

CCAR1 RBM20 PTBP1 ZN638 SUGP1 WDR33 FUS FUBP2 CDK11A RBM14 NCOA6

RSRC1 SNRPA CD2B2 RSMB NKRF AKAP8 CPSF2 CPSF7 RBM10 CPSF1 CLP1 RU17 Splicing related proteins RAVR1 PCF11 HNRPR SFR15 DDX5 DX39A TCRG1 N4BP1 SRRT ROA2 ROA3 ESS2

HNRPM CPSF4 SUGP2 CSTF1 SCAF8 PININ WBP11 WDR83 PPIL3 IF2B2 RBM39 RBM11

DDX17 KHDR1 PR40A TFP11 ROA1 PRCC RBM25 RBM28 QKI MBNL1

Other mRNA processing DHX36 XRN2 RBBP6 RBM27 RBM4 related proteins Cluster 10 Cluster 11 Selected SP7 interactions outside of clusters 10 and 11 B Cluster 14 MLL1-WDR5 complex ZN687 ZN592 KMT2B Zinc finger ZNHI1 ZMYM3 SET1A HCFC2 Outside of roteins KMT2A ZHX3 BAP18 cluster 14 HCFC1 KANL3 NUMA1 TAF2 TAF1 NSL complex RIF1 NOP56 KANL1 WDR5 TAF3 CHD8 KANL2 RT17 MDC1 KAT8 OGT1 THA11 MBD6 IN80C MCRS1 NFRKB INO80 THOC7 INT12 HNF4a ELF1 ZMYM1 HMGX4 TYY1 IN80E DIDO1 TYY2 KLF8 TFPT ELF4 UCHL5

ARP5 IN80B KI67 ARP8 MYC ELF2 LRIF1 KDM1B ARP6 MSL1 CXXC1 INO80 complex NFIX

BAITS PKCB1 CDCA2 BAITS 2-4 Other histone/ SMCA5 CBX1 5-8 chromating SRCAP BPTF ASXL1 modifying proteins C Cluster 15 Transcription MED17 regulation MED10 TADA1 SAGA complex, TADA3 TAD2B MBTD1 H2B1J TAF10 SUPT3 GCN5-linked TAF12 ST65G MYC H2AZ ELF4 TAF5L SP20H MAX RPN1 LRC59

KLF6 RS17 LIN37 TAF6L SGF29 TYY1

TAF9 KAT2A VDAC1 G3P KAT2B HNF4a ELF1 ECHA TAF9B ATX7 AT7L3 ACL6AYETS4 TRRAP KLF8 BRD8 UBP22 AT7L2 KLF3 AT7L1 DMAP1 RUVB2

BAITS EAF6 MRGBP BAITS

1-4 EP400 MO4L1 5-8 MO4L2 NuA4/Tip60 EPC1 KAT5 Other EPC2 ING3 -HAT complex A VPS72 histone-related proteins 952 953 Figure 6. The interactions in Clusters 10, 11, 14 and 15 of the prey-prey correlation analysis

954 A. The three baits (GATA1, GATA3, SP7) with the most interactions in Clusters 10 and 11 are shown

955 in the centre of the figure with white nodes. Most of the preys in Clusters 10 (blue nodes) and 11 (red

956 nodes) were linked to mRNA splicing (highlighted in the grey box). Moreover, SP7 interactions

957 linked to mRNA splicing, mRNA processing and the p300-CBP-p270-SWI/SNF complex found

958 outside of Clusters 10 and 11 are presented on the right side of the figure (grey nodes).

959 B. Protein-protein interactions in Cluster 14. The eight baits with the most interactions are highlighted

960 in white and the interacting protein complexes are colour coded. WDR5’s (outside of Cluster 14)

961 interactions are shown with dashed lines in the upper right corner of the figure.

962 C. Protein-protein interactions in Cluster 15. The eight baits with the most interactions are highlighted

963 in white and the interacting protein complexes are colour coded. Figures

Figure 1

TF protein interactome identied using th 889 e BioID and AP-MS methods A. Schematic illustration of used methods. TFs were tagged N-terminally with a MAC, StrepIII-HA or BirA -tags (Table S1A) and co- transfected with Flp-In recombinase to generate stable isogenic and inducible cell lines. Cells were induced by tetracycline addition and, for the BioID analysis, supplemented with biotin for 24 hours. This was followed by harvesting, lysis and anity purication with Streptavidin beads. Puried proteins were further digested into peptides and analysed by LC MS/MS. Proteins were later identied and analysed using different bioinformatic methods. B. Localisation of interacting prey-proteins according the annotated localisations of Cell Atlas 12. Yellow nodes indicate nuclear localisation and red non-nuclear. From mapped proteins, more than 80% had nuclear localisation. C. Protein-protein interactions identied using the AP-MS (2176) and BioID (7232) methods. Interactions were compared to interactions from the PINA2, Intact, Biogrid and String experimentaln protein interaction databases and to interactions from a study by Li et al., resulting in 345 and 1525 previously reported interactions in the AP-MS and BioID data, respectively. The proportions of known interactions are shown in red. D. Number of high-condence protein-protein interactions of different TF baits detected by AP-MS (blue) or BioID (red) anity purication combined to mass spectrometry. Figure 2

Comprehensive protein interactomes 908 of the studied TF families A. TFs belonging to a given family are indicated in orange nodes, other interacting TFs in green and the rest of the interacting proteins in white. Blue edges indicate interactions from the BioID analysis, red from the AP-MS analysis and black from both. B. The average number of PPIs of different TF families are shown under the interaction maps. Figure 3

Hierarchical clustering of baits by preys and its correlations 914 to DNA-binding domains and protein sequences. A. Enriched GO-BP terms of interacting proteins from the BioID analysis. B. The distribution of the DNA-binding domains of the studied TFs. The corresponding proportion of each DNA-binding domain from 1,639 TFs in the study of Lambert et al. is shown as a percentage value below the graph. C. TFs (baits, named below the heatmap) and their interacting proteins (preys) were hierarchically clustered (Biohit-viz). Corresponding colour coded DNA -binding domains are shown below the baits.

Figure 4

TF-TF (bait-924 bait) interactions of 110 TFs studied A. Of 110 studied TFs, 80 had 203 interactions with other studied TFs. Blue edges indicate interactions from the BioID analysis, red from the AP-MS analysis and black from both. B. Most of these TF-TF (175) interactions were TF interactions with NFIs (left panel). The right panel shows the separate groups shared by one or multiple NFIs. Colour code: Green nodes = NFIs, yellow nodes = interactions to NFIs, orange nodes = interactions from NFIs, red nodes = interactions to and from NFIs and grey nodes = no interactions to or from NFIs. Colour coding of the nodes is shown in the right side of the gure. C. NFIA was silenced using siRNA transfection and NFIA levels were detected 48 hours after transfection by western blotting using specic antibody against NFIA. TFs’ activity was investigated after NFIA silencing using both repressive and activating reporter gene analysis. Both repressing and activating functions of KLF4 were reduced upon the NFIA silencing. In addition, SOX2 and PAX6 activity was reduced, while EN1 activity was increased upon NFIA silencing. N=3, ***: p<0.001, **: p<0.01, *:p<0.05 D. Heatmap representation of SOX2 binding intensity based on ChIP-seq signals in 293T cells whil treated with siControl and siNFIA, respectively. Signals within 3 kb around the center of binding peaks are displayed in a descending order for each SOX2 binding event (lost, shared, and gained upon siNFIA). (Right) Plots of average signal of SOX2 binding at each corresponding region. Figure 5

Biological clusters from prey-943 prey correlation analysisA. Prey-prey correlation analysis (ProHits-viz) on preys identied in the BioID experiments. The results are shown in the heatmap with preys in both the x and y axis. A corresponding bait-prey heat map is shown below to assist in determining which and how many baits are driving the prey clusters. Preys in clusters (1-15) and baits driving the clusters are shown in Table S7. Color scale indicates Pearson correlation of prey PSMs that was done by Prohits-viz prior to hierarchical clustering. B. FOS and STAT1 interactomes. Many proteins interacting with FOS and STAT1 were clustered in Cluster 2 of the prey-prey correlation analysis (highlighted in red). Actin and myosin, ATP signalling, BAF complex and MLL3/4 complex linked proteins are highlighted in different groups.

Figure 6

The interactions in Clusters 10, 11, 14 and 15 of the prey-953 prey correlation analysis A. The three baits (GATA1, GATA3, SP7) with the most interactions in Clusters 10 and 11 are shown in the centre of the gure with white nodes. Most of the preys in Clusters 10 (blue nodes) and 11 (red nodes) were linked to mRNA splicing (highlighted in the grey box). Moreover, SP7 interactions linked to mRNA splicing, mRNA processing and the p300-CBP-p270-SWI/SNF complex found outside of Clusters 10 and 11 are presented on the right side of the gure (grey nodes). B. Protein-protein interactions in Cluster 14. The eight baits with the most interactions are highlighted in white and the interacting protein complexes are colour coded. WDR5’s (outside of Cluster 14) interactions are shown with dashed lines in the upper right corner of the gure. C. Protein-protein interactions in Cluster 15. The eight baits with the most interactions are highlighted in white and the interacting protein complexes are colour coded.

Supplementary Files

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TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx SupplementaryFigures.pdf