E1A binding protein p300 / histone acetyltransferase p300 (EP300) : Time behavioural study of 3rd order combinations in WNT3A stimulated HEK 293 cells

E1A binding protein p300 / histone acetyltransferase p300 (EP300) : Time behavioural study of 3rd order combinations in WNT3A stimulated HEK 293 cells shriprakash sinha Independent Researcher; Orcid ID : orcid.org/0000-0001-7027-5788 Address : 104-Madhurisha Heights Phase 1, Risali, Bhilai-490006, India Corresponding author email : [email protected] Abstract Histone acetyltransferase p300 (p300 HAT) or adenovirus early region 1A(E1A)-associated protein p300 (EP300) gene encodes the adenovirus E1A-associated cellular p300 transcriptional co-activator protein. It functions as histone acetyltransferase that regulates transcription of genes via chromatin remodeling by allowing histone proteins to wrap DNA less tightly. Gujral and MacBeath [1] provides a quantitative, and dynamic study of WNT3A-mediated stimulation of HEK 293 cells, where they record time based expression profiles of several response genes which correlated significantly with proliferation and migration. By monitoring the dynamics of gene expression using selforganizing maps, they identified clusters of genes that exhibit similar expression dynamics and uncovered previously unrecognized positive and negative feedback loops. However, their study depicts/uses singular measurements of individual gene expression at different time snapshots/points to infer the system wide analysis of the pathway. At any particular time point, it is often the case that genes are working synergistically in combinations, even though their expression measurements are singular in nature. Here, I•enumerate and rank all 2415 EP300 related 3rd order combinations in a forest of 71C3combinations using four different sensitivity methods; •show the conserved rankings for EP300-X-X combinations, which point to existence of biological synergy of some of these combinations across the different sensitivity methods; and •study the behaviour of some of these combinations related to WNT3A response genes that are ranked by the machine learning search engine (Sinha [2]) in time. Patterns of combinations emerge, some of which have been tested in wet lab, while others require further wet lab analysis. Keywords: Sensitivity analysis, Support vector ranking, Hilbert Schmidt Independence Criterion indices (HSIC) and Sobol indicies, WNT3A ITime behavioural study of 3-odr EP300 comb. in WNT3A stimulated cells 1Aspects of unpublished work were presented in a poster session at Cell Symposia: Technology. Biology. Data Science, 9-11 October 2016, Berkeley, California, USA. Preprint submitted to Preprint March 4, 2025 1. Significance Sinha [2] recently demonstrated the use of machine learning based search engine to rank/reveal gene combinations at 2nd order for the time series data by Gujral and MacBeath [1] and showed how it is possible to locate combinations of priority that might be working synergistically, using sensitivity methods and powerful support vector ranking algorithm. However, the problem explodes combinatorially with even a small set of 71 recorded genes in the study by Gujral and MacBeath [1], when one steps to explore 3rd order combinations. With the total number of 71C3(= 57155) combinations, it becomes nearly impossible for any biologist to study the system wide dynamics of any pathway. Also, the amount of time usually needed to search for and test a combination is far more than the search down by the machine learning based search engine. Here, I extend the research work by Sinha [2] to conduct a behavioral study of 3rd order EP300 related combinations using individual gene expressions measured in time, in WNT3A stimulated HEK 293 cells. 2. Introduction The details of the machine learning based search engine has been recently published in Sinha [2] and deployed to explore the 2nd order combinations of genes in the data set provided by Gujral and MacBeath [1]. Nevertheless, here, I point to the fundamentals of the published work for completeness. 2.1. A combinatorial problem Sensitivity analysis plays a major role in computing the strength of the influence of involved factors in any phenomena under investigation. When applied to expression profiles of various intra/extracellular factors that form an integral part of a signaling pathway, the variance and density based analysis yields a range of sensitivity indices for individual as well as various combinations of factors. These combinations denote the higher order interactions among the involved factors. Computation of higher order interactions is often time consuming but it gives a chance to explore the various combinations that might be of interest in the working mechanism of the pathway. For example, in a range of fourth order combinations among the various factors of the Wnt pathway, it would be easy to assess the influence of the destruction complex formed by APC, AXIN, CSKI and GSK3 interaction. But the effect of these combinations vary over time as measurements of fold changes and deviations in fold changes vary. So it is imperative to know how an interaction or a combination of the involved factors behave in time and Sinha [2] develops a procedure to track the behaviour by exploiting the influences of these involved factors. 2 2.2. A possible solution In this work, after estimating the individual effects of factors for a higher order combination, the individual indices are considered as discriminative features. A combination, then, is a feature set in higher order (≥2 ,i.e multivariate). With an excessively large number of factors involved in the pathway, it is difficult to search for important combinations in a wide search space over different orders. Exploiting the analogy with the issues of prioritizing webpages using ranking algorithms, for a particular order, a full set of combinations of interactions can then be prioritized based on these features using a powerful ranking algorithm via support vectors Joachims [3]. Recording the changing rankings of the combinations over time reveals how higher order interactions behave within the pathway and when an intervention might be necessary to influence the interaction within the pathway. 2.3. E1A binding protein p300 / histone acetyltransferase p300 (EP300) The growth-controlling functions of the adenovirus E1A oncoprotein depend on its ability ot interact with a set of cellular proteins. Among these are the retinoblastoma protein, p107, p130, and p300. Eckner et al. [4] isolated a cDNA encoding full-length human p300 and mapped the chromosomal location of the gene on the long (q) arm of the human chromosome 22 at position 13.2. They show that p300 contains three cysteineand histidine-rich regions of which the most carboxy-terminal region interacts specifically with E1A. In its center, it contains a bromodomain which is a hallmark of certain transcriptional coactivators. Bromodomain was identified as a novel structural motif when Tamkun et al. [5] showed that the brahma (BRM) gene is required for the activation of multiple homeotic genes in Drosophila. BRM encodes a 1638 residue protein that is similar to SNF2SWI2, a protein involved in transcriptional activation in yeast, suggesting possible models for the role of BRM in the transcriptional activation of homeotic genes. In addition, both BRM and SNF2 contain a 77 amino acid motif that is found in other Drosophila, yeast, and human regulatory proteins and may be characteristic of a new family of regulatory proteins. Ogryzko et al. [6] demonstrate that p300/CBP is not only a transcriptional adaptor but also a histone acetyltransferase. Is a transcriptional adaptor that integrates signals from many sequence-specific activators via direct interactions. The cellular p300/CBP associated factor (PCAF) possesses intrinsic histone acetyltransferase activity, that targets p300/CBP to modulate transcription and/or cell cycle progression. p300/CNP acetylates all four core histones in nucleosomes and Various cellular and viral factors. Histone acetylation helps in chromatin remodelling and gene activation. Nearly all known histone-acetyltransferase (HAT)-associated transcriptional co-activators contain bromodomains (approximately 110-amino-acid modules found in many chromatinassociated proteins). Dhalluin et al. [7] reported the solution structure of the bromodomain of the HAT co-activator P/CAF (p300/CBP-associated factor) which revealed an unusual left-handed up-and-down four-helix bundle. The nature of the recognition of acetyl-lysine by the P/CAF bromodomain is similar to that of acetyl-CoA by histone acetyltransferase. Thus, the bromodomain is functionally linked to the HAT activity of 3 co-activators in the regulation of gene transcription. Owen et al. [8] report the crystal structure at 1.9 ˚ A resolution of the Saccharomyces cerevisiae GCN5P bromodomain complexed with a peptide corresponding to residues 1529 of histone H4 acetylated at the ζ-N of lysine 16. They show that this bromodomain preferentially binds to peptides containing an N-acetyl lysine residue. Only residues 1619 of the acetylated peptide interact with the bromodomain. Zeng and Zhou [9] summarize the bromodomains an an acetyl-lysine binding domain. The 61 bromodomains (BRDs) in the human genome cluster into eight families based on structure/sequence similarity. Filippakopoulos et al. [10] present 29 high-resolution crystal structures, covering all BRD families. I present 3rd order combinations of EP300 with other genes, that the machine learning based search engine points to, as possible synergistic combinations that might be working in time. 3. Methods Please refer to sections of Sinha [2] for methods, design of study and analysis of data for 2nd order combinations. The same method and design of study is used to generate results for 3rd order combinations presented in this study. 4. Time series data Gujral and MacBeath [1] present a set of 71 WNT-related gene expression values for 6 different times points over a range of 24-hour period using qPCR. The changes represent the fold-change in the expression levels of genes in 200 ng/mL WNT3A-stimulated HEK 293 cells in time relative to their levels in unstimulated, serum-starved cells at 0hour. Gujral and MacBeath [1] state that qPCR data are the means of three biological replicates. Only genes whose mean transcript levels changed by more than two-fold at one or more time points during the 24-hour time course were considered significant. Positive (negative) numbers represent up (down) -regulation. We have already covered the issues related to these data sets in detail in Sinha [11]. Readers are requested to go through them in the pointed reference. The tools of study which are used here have been published in another foundational work in Sinha [11]. 5. Design of experiment 5.1. Pipeline for time series data For the case of time series data, interactions among the contributing factors are studied by comparing triplets of fold-changes at single time points. The prodecure begins with the generation of distribution around measurements at single time points with added noise is done to estimate the indices. A distribution is generated for the fold changes at single time points. Then for every gene, there is a vector of values representing fold changes as well as deviations in fold changes for different time points and durations 4 between time points, respectively. Next a listing of all Cn kcombinations for knumber of genes from a total of ngenes is generated. kis ≥2 and ≤(n−1). Each of the combination of order krepresents a unique set of interaction between the involved genetic factors. After this, the datasets are combined in a specifed format which go as input as per the requirement of a particular sensitivity analysis method. Thus for each pth combination in Cn kcombinations, the dataset is prepared in the required format from the distributions for two separate cases which have been discussed above. (See .R code in mainScript-1-1.R). After the data has been transformed, vectorized programming is employed for density based sensitivity analysis and looping is employed for variance based sensitivity analysis to compute the required sensitivity indices for each of the pcombinations. This procedure is done for different kinds of sensitivity analysis methods. After the above sensitivity indices have been stored for each of the pth combination, the next step in the design of experiment is conducted. Since there is only one recording of sensitivity index per combination, each combination forms a training example which is alloted a training index and the sensitivity indices of the individual genetic factors form the training example. Thus there are Cn ktraining examples for kth order interaction. Using this training set SVMRank learn Joachims [3] is used to generate a model on default value Cvalue of 20. In the current experiment on toy model Cvalue has not been tunned. The training set helps in the generation of the model as the different gene combinations are numbered in order which are used as rank indices. The model is then used to generate score on the observations in the testing set using the SV MRank classi f y Joachims [3]. Note that due to availability of only one example per combination, after the model has been built, the same training data is used as test data to generates the scores. This procedure is executed for each and every sensitivity analysis method. This is followed by sorting of these scores along with the rank indices (i.e the training indices) already assigned to the gene combinations. The end result is a sorted order of the gene combinations based on the ranking score learned by the SV MRank algorithm. Finally, this entire procedure is computed for sensitivity indices generated for each and every fold change at time point and deviations in fold change at different durations. Observing the changing rank of a particular combination at different times and different time periods will reveal how a combination is behaving. Note that the following is the order in which the files should be executed in R, in order, for obtaining the desired results (Note that the code will not be explained here) - • use source(”mainScript-1-1.R”) with arguments for Dynamic data •source(”SVMRankResults-D.R”), to rank the interactions (again this needs to be done separately for different kinds of SA methods), •use source(”Combine-Time-files.R”), if computing indices separately via previous file, •source(”Sort-n-Plot-D.R”) to sort the interactions. Note that the sorting is chages the interaction ranking in time. Thus •use source(”Interaction-Priority-Intime.R”) to find the prioritized ranking of each and every interaction over the different time points and finally •use source(”Print-RankingAND-Interaction-Rank.R”) to print individual ranking of the required input factor with other interaction factors. 5 6. Results & Discussion 6.1. Time series data by Gujral and MacBeath [1] NOTE - Ranking was assigned on scores that were sorted in DECREASING values. So, 1 was assigned to highest score and vice versa. Results for the 3rd order interactions are presented here. The results first discuss the behaviour of interactions across the snapshots of time using the computed sensitivities on fold change measurements per time snapshot. The analysis was done using 4 different sensitivity indices. Out of the 71C3combinations, I consider/present only those combinations that show a ranking within first 10,000 out of 57,155. This choice is liberal and biologists/oncologists can have a more stricter choice as per need. Two observations are made, •the ranking of a particular combination is conserved (i.e within the 10,000 range) in a particular time point or in the early phase or late phase of WNT3A stimulation, across the majority of the four sensitivity methods, which is a strict criteria of assessment or •the ranking of a particular combination is conserved across time points/phase (i.e they are within the 10,000 range) and the majority of the four sensitivity methods, which is relaxed criteria of assessment. Applying this filter helps reveal important combinations of interest that might be working synergistically at a higher order level in the cell. Regarding technical points of implementation, the rankings were generated without scaling/normalizing the time series data provided by Gujral and MacBeath [1]. For estimating the sensitivity indices, a small gaussian distribution using the function rnorm that generates a vector of normally distributed random variables given a vector length n (here 9, the 10th one is the mean/recorded gene regulation itself), a population mean µand population standard deviation σ. The syntax for using rnorm is as follows: rnorm(n, mean, sd). Further, I use the jitter funtion to add a little bit of noise to the data. This helps to see if the generated rankings are robust or not. 6.2. Enumeration and ranking of 2415 EP300-X-X combinations from Gujral and MacBeath [1] In the supplementary section, I present four files, each containing the rankings of 3rd order combinations, that wary in time (shown for 5 time points). Each file represents the rankings computed using a particular sensitivity method. The changing rankings in time for a particular combination represents the importance of contribution/role that combination plays in the cell stimulated with WNT3A. The sensitivity methods used are Hilbert Schmidt Independence Criterion indices (HSIC) indices (with rbf and linear kernel in Da Veiga [12]) and Sobol indicies (with 2002 implementation in Saltelli [13] and martinez implementation in Martinez [14] and Baudin et al. [15]). 6.3. Conserved machine learning rankings for tested EP300-X-X combinations A total of 2415, 3rd order combinations involving EP300 were obtained from a full set of 71C3= 57155 combinations. Further, from this selected set, using the above criteria 6 for conserved rankings, I report/tabulate the meaningful combinations that might be working synergistically. Tables 2, 3 and 4 show the rankings for the same combinations as in table 1, but using rbf kernel for HSIC, 2002 implementation for SOBOL and martinez implementation for SOBOL, respectively. As one tallies the rankings of across these tables for a particular combination, one finds that the role of the combination of interest is conserved. This conservation points to the existence of the biological synergy, whether the combination has been tested or unexplored/untested. 6.3.1. Examining the behaviour of TCF-EP300-X combinations Sun et al. [16] report that the transcriptional coactivator p300 interacts with β-catenin in vitro and in vivo and is critical for β-catenin-mediated neoplastic transformation. It was found to activate β-catenin/TCF transcription, and their biochemical association requires the CH1 domain of p300 and a region of β-catenin that includes its NH2terminal transactivation domain and the first two armadillo repeats. Looking at the tables above, one finds the following combinations for members of TCF family along with EP300, to be prominent at 3rd order level - AES-EP300-TCF7, AXIN1-EP300TCF7, DVL1-EP300-TCF7, AES-EP300-TCF7L1, DVL1-EP300-TCF7L1, EP300-GSK3BTCF7, EP300-LRP6-TCF7, EP300-FOXN1-TCF7L1, EP300-FZD2-TCF7L1 and EP300PORCN-TCF7L1. All these combinations indicate the existence of a possible synergy when they take a higher rank in the list of combinations. 6.3.2. Examining the behaviour of LEF1-EP300-X combinations To test whether downstream components of the WNT/β-catenin signal transduction pathway contribute to the differential regulation of Wnt target genes, Hecht and Stemmler [17] asked whether LEF1 and TCF4E were equally capable of supporting activation of WNT-regulated promoters by β-catenin and p300. At the Siamois promoter and in conjunction with LEF1, recruitment and activation of p300 by β-catenin appears to be sufficient. Also, their preliminary results indicated that TCF1E, but not TCF1B, which resembles LEF1, could cooperate with β-catenin and p300 to activate the CDX1 promoter. Looking at the tables above, one finds the following combinations for LEF1 along with EP300, to be prominent at 3rd order level - EP300-FZD2-LEF1. All these combinations indicate the existence of a possible synergy when they take a higher rank in the list of combinations. 6.3.3. Examining the behaviour of CCND-EP300-X combinations Hecht et al. [18] show that β-catenin and p300 synergize to stimulate a synthetic reporter gene construct, whereas activation of the CCND1 promoter by β-catenin is refractory to p300 stimulation. Looking at the tables above, one finds the following combinations for members of CCND family along with EP300, to be prominent at 3rd order level - FZD5-CCND2-EP300, FZD5-CCND1-EP300 and CCND1-CTBP1EP300. All these combinations indicate the existence of a possible synergy when they take a higher rank in the list of combinations. 7 RANKING @tiUSING HSIC - LINEAR 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 DVL1-EP300-LRP5 45 28131 44550 28209 50928 AES-EP300-FRZB 107 47237 52432 18109 22006 DVL1-EP300-FRZB 116 18151 11685 9254 40545 AXIN1-EP300-GSK3B 163 24228 14803 32053 17651 DVL1-EP300-GSK3B 207 32638 7688 33872 35334 DVL1-EP300-WNT2B 247 6893 42213 52392 47037 AES-EP300-TCF7 257 52340 43783 40882 1516 AES-EP300-FZD1 258 49278 55037 15347 47022 AES-EP300-SENP2 266 39979 55044 31248 12673 DVL1-EP300-WNT4 272 18110 37869 41167 44769 DVL1-EP300-FBXW11 404 20946 9783 53163 54751 DVL1-EP300-FZD1 420 47837 24856 16322 41749 AXIN1-EP300-TCF7 421 7016 43416 54620 14023 EP300-JUN-KREMEN1 445 18545 22047 46724 18178 EP300-FBXW11-LRP6 502 27632 22035 22910 27044 EP300-LRP6-WNT2 638 44313 22577 3663 7256 EP300-GSK3B-RHOU 762 29861 9716 10624 2207 DVL1-EP300-SFRP4 803 36937 10257 53811 47697 AES-EP300-FBXW2 878 39324 31038 27397 3825 EP300-WNT1-WNT2 884 39083 11026 13187 5410 EP300-WNT1-WNT2B 940 30973 2609 12191 47783 EP300-GSK3B-TCF7 954 29218 27018 16078 23635 AXIN1-DVL1-EP300 1031 6065 16319 47978 31309 AXIN1-EP300-LRP5 1069 2036 46947 24321 4379 DKK1-EP300-FRZB 1088 10676 53202 44534 37008 EP300-GSK3B-SENP2 1093 42030 6696 7981 765 FZD5-CCND2-EP300 1109 33708 3119 30209 55972 EP300-RHOU-WNT2 1132 42400 48712 18390 28303 DVL1-EP300-TCF7 1158 46231 45665 56151 39460 AES-EP300-WNT5A 1232 50236 54302 23216 39305 EP300-FZD2-FZD7 1306 49961 52239 51266 19772 AXIN1-EP300-RHOU 1312 1646 32903 47413 5408 EP300-FOXN1-TLE2 1334 6564 2241 419 8940 EP300-LRP6-TCF7 1342 30447 49509 44129 4032 EP300-WNT1-WNT3A 1360 30527 695 53063 7375 EP300-FZD2-SFRP4 1428 43571 54834 3371 29414 EP300-LRP6-RHOU 1441 36401 11469 47312 1505 DVL1-EP300-SLC9A3R1 1490 18769 9009 57150 34851 AES-EP300-FOSL1 1521 46072 48935 20712 49058 EP300-FGF4-FRAT1 1588 28152 39282 54116 41791 EP300-FOXN1-SFRP4 1612 6840 5991 389 280 EP300-FOXN1-KREMEN1 1620 7884 6802 739 13419 AES-EP300-TCF7L1 1631 44460 34641 28386 39724 EP300-FZD2-SENP2 1639 43293 36216 3329 8258 EP300-FGF4-FOSL1 1656 55025 38932 40354 51107 EP300-WNT1-WNT5A 1667 20162 16144 44114 26527 AES-EP300-TLE2 1736 45932 19846 34789 55409 EP300-GSK3B-SFRP4 1785 42024 20409 8178 12158 EP300-FOXN1-FRAT1 1814 1637 5045 2015 815 AXIN1-EP300-SLC9A3R1 2064 7574 22759 53828 11686 EP300-GSK3B-WNT2B 2076 31297 1204 24013 16338 EP300-FGF4-WNT2 2093 38634 33059 42983 46200 AES-EP300-PITX2 2125 50901 43504 52709 17083 AXIN1-EP300-FBXW4 2130 13751 7349 18366 36793 EP300-LRP6-SLC9A3R1 2241 54665 6734 14185 31200 EP300-GSK3B-WNT2 2254 34471 22817 3013 1572 EP300-GSK3A-SENP2 2275 42378 2094 22319 42143 DVL1-EP300-FBXW4 2314 4738 8056 35532 41452 EP300-FZD2-WNT2B 2320 24458 22441 44903 18140 EP300-FBXW11-WNT2 2352 32380 5400 26107 31089 EP300-GSK3A-WNT2 2458 45111 24489 42270 47520 AES-EP300-NLK 2549 52374 48380 27425 55437 AXIN1-EP300-FRZB 2553 866 20410 2676 6170 EP300-PORCN-SENP2 2667 50915 5651 10035 52083 EP300-JUN-TLE2 2679 44828 31 25970 46852 EP300-FZD2-PPP2CA 2702 12448 13262 50046 20911 AES-EP300-FZD8 2713 38543 49818 22196 49165 EP300-FOXN1-FZD7 2714 25117 4781 3184 35097 EP300-GSK3B-SLC9A3R1 2733 48653 10966 10820 22885 EP300-PYGO1-WNT2 2948 43158 15314 8724 20816 DVL1-EP300-FZD8 2951 29540 12120 18903 44386 DVL1-EP300-WNT5A 2981 45215 32109 50904 36947 EP300-FZD2-LRP5 3002 41575 22148 54563 13500 EP300-PORCN-WNT4 3047 52212 12666 11882 56673 EP300-FOXN1-PPP2R1A 3054 31254 9485 1045 4738 AXIN1-EP300-SENP2 3106 10788 31481 23191 4091 EP300-FOXN1-FZD1 3113 1863 8770 1719 923 EP300-FOXN1-TCF7L1 3126 3782 3353 1217 4819 EP300-FZD2-WNT2 3139 39887 31858 26340 25585 EP300-JUN-WIF1 3213 26743 1259 1082 53260 AES-EP300-PPP2CA 3218 22740 43678 26060 41565 EP300-PORCN-SFRP1 3229 50142 13492 14922 54985 EP300-LRP6-FBXW4 3274 44837 28655 8993 14599 DKK1-EP300-LRP5 3312 11909 56702 45179 37104 EP300-SLC9A3R1-WNT2 3350 46806 34723 47893 8400 CCND1-CTBP1-EP300 3358 42042 42344 35857 48984 CTBP2-CTNNB1-EP300 3365 40136 27775 17397 7468 DVL1-EP300-KREMEN1 3366 29392 28100 54892 20990 EP300-PORCN-SFRP4 3399 53557 14432 13953 55277 DIXDC1-EP300-FBXW11 3401 6056 10476 24069 55750 EP300-FOXN1-FZD6 3411 4860 17749 501 25541 EP300-FRZB-FZD1 3480 44015 31205 1022 5684 EP300-FOXN1-LRP5 3486 2188 7346 15917 9396 AXIN1-EP300-FZD1 3503 369 27768 3828 12795 EP300-PORCN-PPP2R1A 3588 55435 38638 36180 44771 EP300-FZD2-WNT4 3653 44086 18558 21712 24613 AXIN1-EP300-WNT3A 3721 4489 13963 10072 11451 EP300-FZD2-TCF7L1 3731 10207 47941 32679 8909 AES-EP300-WNT2 3733 42752 47227 32967 26785 EP300-FZD2-PITX2 3771 12315 30396 16799 22718 DVL1-EP300-TCF7L1 3789 46595 17797 39924 51184 AES-EP300-KREMEN1 3796 45394 51834 52960 6499 EP300-FZD2-LEF1 3817 40487 19516 56270 7480 EP300-PORCN-WNT2B 3826 37202 48507 42391 54306 AXIN1-EP300-LRP6 3878 5642 33651 11423 13761 DVL1-EP300-PITX2 3886 37126 11319 53194 36735 DKK1-EP300-SENP2 3891 20732 50914 37657 44749 EP300-GSK3A-NLK 3902 24187 13268 22163 37611 EP300-FBXW11-SENP2 3933 42083 2536 20991 19946 EP300-FOXN1-WNT2B 3966 8828 3051 1383 6560 EP300-FBXW11-FBXW4 4014 31495 811 16846 39181 EP300-FGF4-PPP2R1A 4059 54172 38740 54434 30530 FZD5-CCND1-EP300 4060 18083 4714 3951 48122 EP300-JUN-PPP2R1A 4089 45635 48485 28347 10942 EP300-FOXN1-RHOU 4098 4360 4723 4476 13723 EP300-FOXN1-WNT4 4156 11103 5561 886 8761 EP300-GSK3B-LRP5 4172 45460 31410 26873 1387 AXIN1-EP300-WNT5A 4180 9970 29920 45061 25015 EP300-NKD1-WNT2 4238 5464 3982 38439 1323 AXIN1-EP300-WNT4 4260 2683 36665 20580 7537 EP300-GSK3B-FBXW4 4291 44257 21194 7041 8080 AXIN1-EP300-PITX2 4304 11293 12887 53659 13073 EP300-FOXN1-GSK3B 4325 2497 2297 15564 19880 EP300-PORCN-TCF7L1 4329 18128 11086 13559 52464 EP300-LRP6-SFRP4 4353 54017 12211 6672 17900 EP300-GSK3A-WNT2B 4370 31237 5657 53835 53021 Table 1: Rankings of EP300-X-X. A list of approximately first 125 combinations with rankings below 10,000 out of 57,155. SA - HSIC; Kernel - linear 8 RANKING @tiUSING HSIC - RBF 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 DVL1-EP300-LRP5 52736 14122 3935 57133 11155 AES-EP300-FRZB 40882 51536 7119 33764 20536 DVL1-EP300-FRZB 50198 6937 30267 52396 2625 AXIN1-EP300-GSK3B 35345 3731 2610 56872 11778 DVL1-EP300-GSK3B 53115 9324 29348 56804 16941 DVL1-EP300-WNT2B 53233 2104 15799 56735 2280 AES-EP300-TCF7 39855 49176 5264 28073 35221 AES-EP300-FZD1 38410 50414 31342 5312 36922 AES-EP300-SENP2 36742 43851 9360 56088 25857 DVL1-EP300-WNT4 44214 8987 34040 43683 919 DVL1-EP300-FBXW11 52680 9754 26824 56227 7252 DVL1-EP300-FZD1 40171 42833 3293 53576 7127 AXIN1-EP300-TCF7 23920 5040 28002 41498 26154 EP300-JUN-KREMEN1 4639 142 27356 54948 19911 EP300-FBXW11-LRP6 51387 7749 39604 26908 55449 EP300-LRP6-WNT2 4956 43850 46302 4230 21219 EP300-GSK3B-RHOU 18018 15875 23808 17448 24731 DVL1-EP300-SFRP4 20719 16744 18619 55573 10227 AES-EP300-FBXW2 37663 38475 8684 26951 43448 EP300-WNT1-WNT2 1580 44154 51942 5979 10465 EP300-WNT1-WNT2B 2458 13992 29930 2629 26795 EP300-GSK3B-TCF7 10805 6455 14967 4039 31117 AXIN1-DVL1-EP300 3584 5186 25284 4738 39794 AXIN1-EP300-LRP5 46163 2225 27682 55146 32996 DKK1-EP300-FRZB 6801 5011 12885 30599 43081 EP300-GSK3B-SENP2 8327 37174 36829 10925 19701 FZD5-CCND2-EP300 2650 37698 56502 40799 52677 EP300-RHOU-WNT2 9914 43007 45594 678 52515 DVL1-EP300-TCF7 50918 34541 3163 55278 13295 AES-EP300-WNT5A 40140 51612 23938 4009 35408 EP300-FZD2-FZD7 16954 49764 37714 257 32349 AXIN1-EP300-RHOU 31074 6355 22262 56648 46901 EP300-FOXN1-TLE2 1483 17209 53526 19752 11793 EP300-LRP6-TCF7 2128 9836 35075 32339 23660 EP300-WNT1-WNT3A 1327 24404 26980 31626 25194 EP300-FZD2-SFRP4 2005 26142 28568 1038 19151 EP300-LRP6-RHOU 2252 29244 36048 53718 12738 DVL1-EP300-SLC9A3R1 53857 20248 11438 50841 13844 AES-EP300-FOSL1 43507 49548 17732 5048 38399 EP300-FGF4-FRAT1 2201 12179 25784 4990 46565 EP300-FOXN1-SFRP4 1251 4474 55453 13622 8030 EP300-FOXN1-KREMEN1 2486 110 54841 38583 4454 AES-EP300-TCF7L1 40724 47358 6323 29692 43075 EP300-FZD2-SENP2 4197 39519 18150 40601 47963 EP300-FGF4-FOSL1 4014 52588 15864 2361 53651 EP300-WNT1-WNT5A 2374 24154 16830 20295 34795 AES-EP300-TLE2 39879 47530 4903 40946 36137 EP300-GSK3B-SFRP4 8611 32039 42196 33914 2265 EP300-FOXN1-FRAT1 1892 1651 56769 4475 20697 AXIN1-EP300-SLC9A3R1 55614 15848 7372 37686 44246 EP300-GSK3B-WNT2B 17031 3072 9839 9763 7695 EP300-FGF4-WNT2 4612 34033 39415 40763 15139 AES-EP300-PITX2 46927 53837 1549 50756 32506 AXIN1-EP300-FBXW4 39191 32647 632 24287 49848 EP300-LRP6-SLC9A3R1 14878 54303 41153 14723 43225 EP300-GSK3B-WNT2 39218 19231 50369 11449 1474 EP300-GSK3A-SENP2 699 37681 53286 49758 22127 DVL1-EP300-FBXW4 42764 5391 2864 37529 23352 EP300-FZD2-WNT2B 10327 30918 21265 5368 12988 EP300-FBXW11-WNT2 28485 14388 54560 3248 12860 EP300-GSK3A-WNT2 11607 41760 54123 6698 20631 AES-EP300-NLK 38886 52106 1233 23736 42435 AXIN1-EP300-FRZB 24331 5929 26268 54326 22003 EP300-PORCN-SENP2 6992 46691 29845 18550 22225 EP300-JUN-TLE2 7910 43504 27573 32054 25082 EP300-FZD2-PPP2CA 8059 21295 864 5359 44323 AES-EP300-FZD8 42215 43804 6126 33921 25164 EP300-FOXN1-FZD7 3215 8590 56511 7008 39469 EP300-GSK3B-SLC9A3R1 36320 50164 49824 9817 11726 EP300-PYGO1-WNT2 5709 43420 44821 441 34191 DVL1-EP300-FZD8 47942 8331 10133 56556 21119 DVL1-EP300-WNT5A 45556 27855 659 56344 29253 EP300-FZD2-LRP5 36812 15690 6117 6565 23126 EP300-PORCN-WNT4 5823 49192 43675 49247 45902 EP300-FOXN1-PPP2R1A 1354 35385 45759 2306 27930 AXIN1-EP300-SENP2 19823 15581 10252 51019 28168 EP300-FOXN1-FZD1 800 1001 54131 249 2703 EP300-FOXN1-TCF7L1 1435 5591 57140 32094 6830 EP300-FZD2-WNT2 16652 30806 29163 34567 17667 EP300-JUN-WIF1 11523 21819 31185 9903 31253 AES-EP300-PPP2CA 40675 14926 3496 32198 22117 EP300-PORCN-SFRP1 25523 50771 36657 24398 41003 EP300-LRP6-FBXW4 2183 36856 32532 30812 36337 DKK1-EP300-LRP5 52435 1604 7145 16640 29132 EP300-SLC9A3R1-WNT2 12103 46092 24912 6681 4849 CCND1-CTBP1-EP300 31521 40575 10441 16973 2244 CTBP2-CTNNB1-EP300 30000 37842 42325 7348 17273 DVL1-EP300-KREMEN1 54034 12200 13668 57031 15029 EP300-PORCN-SFRP4 1223 52664 18944 37247 43717 DIXDC1-EP300-FBXW11 24668 2475 1975 54678 8438 EP300-FOXN1-FZD6 2964 2284 38649 17089 43579 EP300-FRZB-FZD1 35048 46610 23256 469 13611 EP300-FOXN1-LRP5 4494 2900 43340 32353 1904 AXIN1-EP300-FZD1 31915 846 24550 32377 43633 EP300-PORCN-PPP2R1A 9705 56176 25373 738 50930 EP300-FZD2-WNT4 7958 30171 46102 282 28923 AXIN1-EP300-WNT3A 29773 11569 2079 55580 29836 EP300-FZD2-TCF7L1 16132 9911 19549 19635 28746 AES-EP300-WNT2 41014 49014 5733 12100 31675 EP300-FZD2-PITX2 21066 1261 6278 52068 50788 DVL1-EP300-TCF7L1 52409 38334 12684 54201 14361 AES-EP300-KREMEN1 46085 42522 27876 8199 46619 EP300-FZD2-LEF1 22014 24731 6164 1694 20705 EP300-PORCN-WNT2B 15830 18382 7434 20043 39500 AXIN1-EP300-LRP6 48971 2895 29183 50900 39151 DVL1-EP300-PITX2 56689 28520 27891 57038 12113 DKK1-EP300-SENP2 8223 22887 7362 18319 12719 EP300-GSK3A-NLK 1488 36811 54049 43805 46079 EP300-FBXW11-SENP2 28012 28278 53048 10057 32539 EP300-FOXN1-WNT2B 3438 4494 43475 1580 25168 EP300-FBXW11-FBXW4 40244 31834 28900 400 16176 EP300-FGF4-PPP2R1A 434 56284 13608 15792 27194 FZD5-CCND1-EP300 14768 7896 41900 37382 55614 EP300-JUN-PPP2R1A 5997 52619 15125 40032 31600 EP300-FOXN1-RHOU 1535 1411 47672 37234 19244 EP300-FOXN1-WNT4 1123 1458 56189 3851 27498 EP300-GSK3B-LRP5 26984 34947 6303 3091 5561 AXIN1-EP300-WNT5A 45393 1954 35430 55047 50435 EP300-NKD1-WNT2 39516 745 54249 16318 30610 AXIN1-EP300-WNT4 36955 1563 18777 20017 24055 EP300-GSK3B-FBXW4 22624 38473 17932 6602 3482 AXIN1-EP300-PITX2 56749 23129 25963 57120 24869 EP300-FOXN1-GSK3B 2642 3165 56576 55942 32657 EP300-PORCN-TCF7L1 21837 509 26965 44207 37905 EP300-LRP6-SFRP4 2105 53738 39562 38275 32599 EP300-GSK3A-WNT2B 1984 14463 31882 45984 24146 Table 2: Rankings of EP300-X-X. A list of approximately first 125 combinations with rankings below 10,000 out of 57,155. SA - HSIC; Kernel - rbf 6.3.4. Examining the behaviour of WNT-EP300-X combinations Sun et al. [16] report that the transcriptional coactivator p300 interacts with β-catenin in vitro and in vivo and is critical for β-catenin-mediated neoplastic transformation. 9