FOS like 1, AP-1 transcription factor subunit (FOSL1) : Time behavioural study of 3rd order combinations in WNT3A stimulated HEK 293 cells

FOS like 1, AP-1 transcription factor subunit (FOSL1) : 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 104-Madhurisha Heights Phase 1, Risali, Bhilai-490006, India Abstract FOSL1 is one of the four members of the FOS gene family. These genes encode leucine zipper proteins that can dimerize with proteins of the JUN family, thereby forming the transcription factor complex AP-1. FOS proteins act as regulators of cell proliferation, differentiation, and transformation. 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 self-organizing 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 FOSL1 related 3rd order combinations in a forest of 71C3combinations using four different sensitivity methods; •show the conserved rankings for FOSL1-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 FOSL1 comb. in WNT3A stimulated cells Email address: [email protected] (shriprakash sinha) 1Aspects of unpublished work were presented in a poster session at Cell Symposia: Technology.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 February 25, 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 FOSL1 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. 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, 2 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. FOS like 1, AP-1 transcription factor subunit (FOSL1) FOSL1 (alias Fos-related antigen 1 or FRA-1), is one of the four members of the FOS gene family. Matsui et al. [4] isolated cDNA clones of human FRA-1/2 by screening human c-DNA libraries with human fos DNA as a probe. They obtained human FRA-1 cDNA clones that could code for a protein of 271 amino acid residues with a calculated molecular weight of 29,413 and showed 90% similarity with rat FRA-1 protein. FOSL1 (FRA-1) encodes leucine zipper proteins that can dimerize with proteins of the JUN family, thus forming the transcription factor complex AP-1. The basic leucine zipper domain (bZIP domain) is found in many DNA binding eukaryotic proteins, one part of which mediates sequence specific DNA binding properties and the leucine zipper that is required to hold together (dimerize) two DNA binding regions (Ellenberger [5] and Vinson et al. [6]). The human metallothionein IIa (hMTIIA) gene responds to induction by heavy metals and by steroid hormones through the action of metal regulatory elements (MRE) and glucocorticoid responsive elements (GRE). Lee et al. [7] report the identification of two cellular DNA-binding proteins that interact selectively with sequences governing the basal level expression of hMTIIA, one of which is a novel activator protein (AP-1) that interacts with sequences in the basal level enhancer (BLE) of hMTIIAand also binds to a site within the 72-base pair (bp) repeats of the simian virus 40 (SV40) enhancer region. The structure of AP-1 is a heterodimer composed of proteins belonging to the c-Fos, c-Jun, ATF and JDP families and the AP-1 family consists of several bZIP domain proteins, the Jun, the Fos, and the ATF subfamilies, which all have to dimerize before they can bind to their DNA target sites (Wagner [8]). The nuclear transcription factor AP-1, controls cellular events including cell transformation, proliferation, differentiation and apoptosis (Ameyar et al. [9]). Pognonec et al. [10] found that FRA-1 interacted with USF (a basic-helixloophelix-leucine zipper (bHLHZip) protein). Expression of exogenous USF led to a decrease in AP-1 dependent transcription in F9 cells and co-expression of exogenous FRA-1 restored the AP-1 activity in a dose-dependent manner. FRA-1 seems to play a role in the progression of many carcinomas and FRA-1 overexpression enhances the motility and invasion of breast and colorectal cancer cells, but inhibits the tumourigenicity of cervical carcinoma cell lines (Milde-Langosch [11]). In this research work, I present 3rd order combinations of FOSL1 with other genes, that the machine learning based search engine points to, as possible synergistic combinations that might be working in time. 3 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 [12]. 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 [12]. 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 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 4 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. 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 5 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 FOSL1-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 [13]) and Sobol indicies (with 2002 implementation in Saltelli [14] and martinez implementation in Martinez [15] and Baudin et al. [16]). 6.3. Conserved machine learning rankings for tested FOSL1-X-X combinations A total of 2415, 3rd order combinations involving FOSL1 were obtained from a full set of 71C3= 57155 combinations. Further, from this selected set, using the above criteria 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 JUN-FOSL1-X combinations Eferl and Wagner [17] review that AP-1 is a dimeric transcription factor that contains members from the JUN, FOS, ATF and MAF protein families. Further, two components of AP-1 i.e, c-JUN and c-FOS, were first identified as viral oncoproteins, so their role in tumorigenesis is well established. Placentation establishes the maternalfetal interface and is required for successful pregnancy. The epithelial component of 6 RANKING @tiUSING HSIC - LINEAR 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 FOSL1-FRAT1-FRZB 17 35368 17731 25382 51767 FOSL1-FRAT1-SENP2 24 25444 38488 23401 24549 CSNK1D-FGF4-FOSL1 30 6054 51426 42593 32863 CXXC4-FOSL1-SENP2 88 36639 18548 29008 12844 CCND1-FGF4-FOSL1 171 44847 19116 31051 26950 CTBP2-CTNNB1-FOSL1 193 49920 22884 20817 52920 CXXC4-FOSL1-PPP2R1A 255 24314 35136 31526 39110 FOSL1-PPP2R1A-SENP2 256 39545 1842 16944 18375 FOSL1-PORCN-SFRP4 292 44876 11834 15903 36394 DKK1-FOSL1-SENP2 326 21740 41468 36036 47108 FOSL1-PORCN-SENP2 349 31659 11861 10245 21849 FOSL1-NLK-SENP2 359 18070 3202 3757 21594 FOSL1-FRAT1-WNT4 372 34237 49971 11736 48629 FOSL1-NLK-WNT4 378 3397 2566 2260 40025 FOSL1-FOXN1-FRZB 426 18495 3914 3925 50337 DKK1-FGF4-FOSL1 432 8047 21605 31028 45972 FOSL1-PORCN-WNT4 455 37570 20729 10105 38487 FOSL1-JUN-SENP2 458 25557 50663 10786 1378 FBXW11-FGF4-FOSL1 485 34731 55734 37270 13666 APC-BCL9-FOSL1 503 44276 12767 10482 56943 CSNK1D-FOSL1-FZD1 568 999 47773 10250 51091 DVL2-FGF4-FOSL1 664 42437 30359 29718 55333 FOSL1-PORCN-WNT2B 670 19667 46545 40946 19709 FOSL1-FRAT1-TLE2 675 26389 36474 6816 48298 FOSL1-FOXN1-SFRP4 680 23049 4982 9425 19314 FOSL1-WIF1-WNT2B 727 2770 23114 37617 17980 CXXC4-FOSL1-FRAT1 731 20058 29538 14504 27891 FOSL1-FOXN1-KREMEN1 742 14009 6113 8523 21187 DAAM1-FGF4-FOSL1 749 45182 54951 56379 34801 DKK1-DVL2-FOSL1 754 5173 30314 36187 56024 FOSL1-SFRP1-WNT2B 802 20624 39428 24273 52148 FOSL1-FOXN1-SENP2 808 25273 3965 4409 8541 FOSL1-FRAT1-FBXW4 832 28714 21515 30364 38569 FOSL1-PPP2R1A-RHOU 846 41555 5035 55239 46031 FOSL1-NKD1-SFRP4 875 160 5276 11851 11033 FZD5-DVL2-FOSL1 982 7285 6435 17317 49867 FOSL1-FOXN1-GSK3B 983 14647 1776 17080 41173 FOSL1-JUN-TCF7 996 16387 31797 50574 23742 FOSL1-JUN-TCF7L1 1000 17132 38816 29827 1621 FOSL1-FZD2-SFRP4 1024 10811 26104 3869 54533 CSNK1G1-DVL2-FOSL1 1061 14048 12622 34641 48959 FOSL1-FOXN1-PPP2CA 1072 22257 16994 9632 25457 CSNK1G1-CXXC4-FOSL1 1094 46700 26853 25310 54546 FOSL1-FZD7-PPP2CA 1103 37962 25642 24479 50931 FOSL1-FRAT1-TCF7L1 1246 18255 19691 45038 20105 FOSL1-FOXN1-SLC9A3R1 1275 28011 885 4881 51370 FOSL1-FOXN1-FZD8 1314 19509 2343 16192 19984 FOSL1-GSK3A-SENP2 1322 23410 370 29385 29247 CSNK2A1-CTNNB1-FOSL1 1324 32995 31993 33693 996 FOSL1-PORCN-RHOU 1333 44749 10281 23020 23848 CXXC4-FGF4-FOSL1 1368 22243 14796 45725 54959 FOSL1-WNT1-WNT2B 1381 1526 2254 11678 47870 FOSL1-FOXN1-LRP5 1396 12463 8896 42948 43152 DVL1-FBXW11-FOSL1 1398 33771 7304 14350 55345 APC-FGF4-FOSL1 1403 38742 14424 52151 37505 FOSL1-NLK-WNT2B 1425 3331 440 49507 31594 FOSL1-GSK3A-SFRP4 1436 32274 8539 22527 42270 FOSL1-PORCN-TLE2 1446 44769 21226 13796 30066 CTBP1-FGF4-FOSL1 1455 29082 37073 39831 41035 FOSL1-JUN-PITX2 1479 1306 37107 54515 12375 FOSL1-FOXN1-TLE2 1506 19146 2341 7352 15489 FOSL1-NLK-WNT3A 1512 2335 4160 3374 12963 AES-EP300-FOSL1 1521 46072 48935 20712 49058 FOSL1-JUN-WNT3A 1542 4890 20754 29478 5342 CSNK2A1-CTBP1-FOSL1 1581 35982 53710 47786 1954 FOSL1-FRAT1-LRP5 1606 10313 45532 19498 50080 EP300-FGF4-FOSL1 1656 55025 38932 40354 51107 FOSL1-FOXN1-RHOU 1660 21880 3384 19296 29104 BCL9-FGF4-FOSL1 1679 19298 42532 40993 55112 FOSL1-GSK3A-GSK3B 1690 15629 2644 46105 37164 FOSL1-JUN-SLC9A3R1 1708 28629 15238 40456 52065 FZD5-FGF4-FOSL1 1718 8030 21299 33264 34465 FZD5-CCND3-FOSL1 1721 5130 11993 34398 15331 FOSL1-PORCN-SFRP1 1757 36317 8057 17969 48972 FOSL1-GSK3A-LRP5 1793 5589 2949 32274 52490 CXXC4-FOSL1-WNT5A 1800 15338 42242 52718 32578 FOSL1-JUN-FBXW4 1801 30399 19599 18597 6617 DIXDC1-FGF4-FOSL1 1834 22981 29680 27665 50757 FGF4-FOSL1-FRAT1 1842 39537 26432 55090 52175 FOSL1-NKD1-PPP2CA 1845 14335 40783 24581 23211 FOSL1-NLK-SFRP1 1846 2471 1853 521 54004 FOSL1-PORCN-FBXW4 1896 35711 18446 8569 19117 FOSL1-PORCN-PPP2CA 1917 33808 39279 26918 22451 CSNK1G1-FGF4-FOSL1 1936 28205 33826 36195 48093 FOSL1-PPP2R1A-SFRP4 1952 52448 1955 16120 30096 FOSL1-FOXN1-WNT5A 2034 6923 6744 9847 23516 FOSL1-FOXN1-TCF7L1 2041 16265 2181 10149 12942 CSNK1D-FOSL1-SENP2 2088 21782 40398 32697 32527 FOSL1-FOXN1-WNT2B 2092 14363 2122 5400 9642 FZD5-CCND2-FOSL1 2169 34455 22205 26665 51389 APC-CCND3-FOSL1 2190 27032 11642 22161 35558 CSNK1A1-FGF4-FOSL1 2196 29619 53247 49107 51945 DKK1-FOSL1-FRAT1 2248 20750 40597 26220 53540 FOSL1-FOXN1-FZD6 2256 14824 19723 8486 53548 FOSL1-PORCN-TCF7L1 2280 27789 13184 15773 14755 DVL1-FGF4-FOSL1 2292 40318 18510 24845 19218 FOSL1-PYGO1-WNT2 2296 35937 10116 9410 31529 FOSL1-MYC-SFRP4 2339 42470 6026 33033 29120 FOSL1-GSK3A-PPP2CA 2355 22861 911 45587 39936 FOSL1-FZD2-GSK3B 2382 12778 19494 45692 50453 FOSL1-FOXN1-FZD1 2391 9360 6982 9851 31253 FOSL1-FZD7-WNT2B 2454 17850 39272 36856 49039 FOSL1-NKD1-SENP2 2466 6869 5781 10026 30259 FOSL1-FOXN1-TCF7 2473 12271 1932 11488 24676 FOSL1-PORCN-SLC9A3R1 2483 39419 13608 18592 31025 FOSL1-WNT1-WNT3A 2512 4481 499 34674 10423 FOSL1-FBXW4-WNT3A 2570 6332 206 39656 6459 FOSL1-WNT1-WNT4 2598 5807 2371 11336 21308 FOSL1-PORCN-TCF7 2655 29633 37738 24610 27978 FOSL1-JUN-PPP2R1A 2696 46872 41797 35276 27908 FOSL1-FOXN1-LRP6 2703 18423 9454 15846 43299 FOSL1-SFRP1-TCF7 2828 29087 33743 34368 53991 CSNK1A1-CTBP2-FOSL1 2849 847 23417 33270 57047 FOSL1-FOXN1-WNT4 2926 19268 4984 7030 42733 DKK1-FOSL1-FRZB 2954 10112 50811 45748 42692 FGF4-FOSL1-SFRP4 2957 29593 15781 53739 51686 FOSL1-GSK3A-KREMEN1 3017 11044 1525 53908 3915 FOSL1-FOXN1-MYC 3046 33641 23663 11459 46671 FOSL1-WNT1-WNT2 3080 12766 6760 15272 13494 FOSL1-FRAT1-FZD7 3142 34552 21454 20799 55831 FOSL1-FOXN1-FBXW4 3153 30885 23159 9810 3473 FGF4-FOSL1-PPP2CA 3175 29507 37541 48577 51007 FOSL1-GSK3A-PITX2 3246 8961 918 7353 12922 FOSL1-FOXN1-WNT2 3267 27111 6567 10866 26206 FGF4-FOSL1-FZD1 3290 40378 18437 35554 52267 FOSL1-JUN-PPP2CA 3303 25232 24882 2685 4764 Table 1: Rankings of FOSL1-X-X. A list of approximately first 125 combinations with rankings below 10,000 out of 57,155. SA - HSIC; Kernel - linear the placenta consists of trophoblast cells, which possess the capacity for multilineage differentiation and are responsible for placenta-specific functions. FOSL1 contributes to the regulation of placental development. Kubota et al. [18] showed through 7 RANKING @tiUSING HSIC - RBF 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 FOSL1-FRAT1-FRZB 18486 33706 51536 16007 42608 FOSL1-FRAT1-SENP2 15910 20828 47008 39310 20122 CSNK1D-FGF4-FOSL1 3694 6645 6799 4138 45840 CXXC4-FOSL1-SENP2 27232 42351 44650 16863 48178 CCND1-FGF4-FOSL1 19400 39101 15057 5353 6829 CTBP2-CTNNB1-FOSL1 22087 47806 43102 26570 12950 CXXC4-FOSL1-PPP2R1A 52227 34156 6226 46113 43359 FOSL1-PPP2R1A-SENP2 36531 47500 56688 1297 8711 FOSL1-PORCN-SFRP4 1126 41952 37755 38422 51768 DKK1-FOSL1-SENP2 14095 26375 3333 23119 42212 FOSL1-PORCN-SENP2 314 29835 38646 3075 34319 FOSL1-NLK-SENP2 41514 24088 54636 21014 19349 FOSL1-FRAT1-WNT4 29015 37923 40684 6763 23547 FOSL1-NLK-WNT4 46914 7268 50547 1274 44200 FOSL1-FOXN1-FRZB 1736 15846 50466 9743 17620 DKK1-FGF4-FOSL1 4252 18471 3345 22302 52225 FOSL1-PORCN-WNT4 4252 18471 3345 22302 52225 FOSL1-JUN-SENP2 15271 29847 41615 22047 48378 FBXW11-FGF4-FOSL1 9717 17062 12881 5316 18991 APC-BCL9-FOSL1 26374 51523 20691 9548 17725 CSNK1D-FOSL1-FZD1 14359 1015 37515 5753 14234 DVL2-FGF4-FOSL1 3167 48007 8628 17742 42623 FOSL1-PORCN-WNT2B 4296 5489 1279 29512 55035 FOSL1-FRAT1-TLE2 33776 19243 42932 10498 52900 FOSL1-FOXN1-SFRP4 3581 13413 50450 24051 14731 FOSL1-WIF1-WNT2B 41773 13018 2276 54404 49689 CXXC4-FOSL1-FRAT1 46607 15174 26086 25252 40912 FOSL1-FOXN1-KREMEN1 2901 4767 41791 45598 13163 DAAM1-FGF4-FOSL1 2515 51205 7449 23613 7495 DKK1-DVL2-FOSL1 32822 15198 12108 3507 28223 FOSL1-SFRP1-WNT2B 21116 7705 2123 56839 56883 FOSL1-FOXN1-SENP2 1644 24800 52856 5786 32696 FOSL1-FRAT1-FBXW4 36692 36234 15133 298 43524 FOSL1-PPP2R1A-RHOU 19254 49825 54278 50968 41085 FOSL1-NKD1-SFRP4 38557 1693 50390 24818 21820 FZD5-DVL2-FOSL1 31108 15865 56297 1825 46027 FOSL1-FOXN1-GSK3B 31108 15865 56297 1825 46027 FOSL1-JUN-TCF7 31108 15865 56297 1825 46027 FOSL1-JUN-TCF7L1 5134 9823 24616 52464 27691 FOSL1-FZD2-SFRP4 4647 1210 36429 2252 17735 CSNK1G1-DVL2-FOSL1 24608 32567 25022 2699 31491 FOSL1-FOXN1-PPP2CA 2050 20570 56654 7068 31115 CSNK1G1-CXXC4-FOSL1 18872 46435 17556 11771 54033 FOSL1-FZD7-PPP2CA 54451 46826 3194 28518 28447 FOSL1-FRAT1-TCF7L1 6305 18815 46524 19747 38837 FOSL1-FOXN1-SLC9A3R1 3153 36795 52930 36241 48302 FOSL1-FOXN1-FZD8 2757 28761 55000 20964 54956 FOSL1-GSK3A-SENP2 3309 21823 56135 50608 24479 CSNK2A1-CTNNB1-FOSL1 26592 26504 31836 43474 14209 FOSL1-PORCN-RHOU 1550 39845 36998 42942 56120 CXXC4-FGF4-FOSL1 553 41867 29836 8057 53097 FOSL1-WNT1-WNT2B 8040 5830 31727 31 46871 FOSL1-FOXN1-LRP5 6494 8694 40579 24357 5973 DVL1-FBXW11-FOSL1 19045 18319 56904 35 28285 APC-FGF4-FOSL1 11913 34261 19328 3356 53756 FOSL1-NLK-WNT2B 20492 705 35136 30860 53018 FOSL1-GSK3A-SFRP4 6589 36249 56048 37361 34223 FOSL1-PORCN-TLE2 11375 42064 44489 3406 53873 CTBP1-FGF4-FOSL1 4851 14275 24640 778 55018 FOSL1-JUN-PITX2 42099 14135 52254 57017 24935 FOSL1-FOXN1-TLE2 2685 18230 45559 25782 35344 FOSL1-NLK-WNT3A 51755 5242 44005 49978 52983 AES-EP300-FOSL1 43507 49548 17732 5048 38399 FOSL1-JUN-WNT3A 37744 27522 48171 53533 24924 CSNK2A1-CTBP1-FOSL1 48595 20470 4367 24006 8607 FOSL1-FRAT1-LRP5 50831 2200 13168 50933 31960 EP300-FGF4-FOSL1 4014 52588 15864 2361 53651 FOSL1-FOXN1-RHOU 1989 10797 48585 37026 31459 BCL9-FGF4-FOSL1 17209 16838 16284 7375 47320 FOSL1-GSK3A-GSK3B 1419 30732 55587 54721 53577 FOSL1-JUN-SLC9A3R1 39137 42805 45435 35330 23074 FZD5-FGF4-FOSL1 3050 25364 36566 13304 54934 FZD5-CCND3-FOSL1 38034 34654 56227 16297 18221 FOSL1-PORCN-SFRP1 10522 38362 29766 6077 55724 FOSL1-GSK3A-LRP5 14584 286 49671 55368 36879 CXXC4-FOSL1-WNT5A 53822 4385 5771 55698 30961 FOSL1-JUN-FBXW4 28709 31696 9585 5972 36656 DIXDC1-FGF4-FOSL1 2494 21424 29421 5565 13873 FGF4-FOSL1-FRAT1 36045 19108 7369 17738 6729 FOSL1-NKD1-PPP2CA 21934 29546 42661 35675 17456 FOSL1-NLK-SFRP1 34233 941 55096 25592 35663 FOSL1-PORCN-FBXW4 996 45964 6696 11811 50330 FOSL1-PORCN-PPP2CA 857 51996 6161 7188 48966 CSNK1G1-FGF4-FOSL1 30497 29781 8021 11753 43558 FOSL1-PPP2R1A-SFRP4 50064 57024 56666 31648 53883 FOSL1-FOXN1-WNT5A 5307 14590 45152 44826 55367 FOSL1-FOXN1-TCF7L1 502 20490 56294 30140 13635 CSNK1D-FOSL1-SENP2 20750 28948 9391 39658 39465 FOSL1-FOXN1-WNT2B 3731 11944 46580 2296 53387 FZD5-CCND2-FOSL1 3513 41975 54460 12813 30675 APC-CCND3-FOSL1 43650 23498 55526 7318 50103 CSNK1A1-FGF4-FOSL1 16622 30625 6828 2304 55529 DKK1-FOSL1-FRAT1 33541 20184 16489 26495 35744 FOSL1-FOXN1-FZD6 4046 6143 29686 14520 37535 FOSL1-PORCN-TCF7L1 731 8052 33790 38315 51524 DVL1-FGF4-FOSL1 17015 27364 12609 5371 33295 FOSL1-PYGO1-WNT2 4646 17601 54904 1115 45855 FOSL1-MYC-SFRP4 2575 51076 50017 35470 8587 FOSL1-GSK3A-PPP2CA 12185 33599 52257 41997 37270 FOSL1-FZD2-GSK3B 5098 22623 40747 48566 47602 FOSL1-FOXN1-FZD1 1950 8922 42094 2898 13807 FOSL1-FZD7-WNT2B 50187 4425 4093 55495 52155 FOSL1-NKD1-SENP2 15372 3697 55084 25337 9352 FOSL1-FOXN1-TCF7 1675 7202 46849 34237 39333 FOSL1-PORCN-SLC9A3R1 7522 46541 51243 1546 46102 FOSL1-WNT1-WNT3A 9692 5532 35479 30879 13078 FOSL1-FBXW4-WNT3A 40808 8862 52958 56668 7351 FOSL1-WNT1-WNT4 4799 4228 46889 440 32783 FOSL1-PORCN-TCF7 851 19236 7477 4033 41750 FOSL1-JUN-PPP2R1A 20816 55928 16147 47523 47242 FOSL1-FOXN1-LRP6 8216 12122 52031 24335 23358 FOSL1-SFRP1-TCF7 6118 26200 9358 53523 34103 CSNK1A1-CTBP2-FOSL1 34000 288 23729 11736 43862 FOSL1-FOXN1-WNT4 2275 8743 50316 14275 47768 DKK1-FOSL1-FRZB 15443 4827 12607 32034 48635 FGF4-FOSL1-SFRP4 34496 9590 20805 42045 6769 FOSL1-GSK3A-KREMEN1 5655 3612 51883 54394 46466 FOSL1-FOXN1-MYC 7089 25106 47390 44956 48333 FOSL1-WNT1-WNT2 7001 10490 53012 815 33966 FOSL1-FRAT1-FZD7 7001 10490 53012 815 33966 FOSL1-FOXN1-FBXW4 3141 36195 30061 7895 5125 FGF4-FOSL1-PPP2CA 42277 28170 8091 25442 19223 FOSL1-GSK3A-PITX2 22118 26574 53921 57069 41599 FOSL1-FOXN1-WNT2 4844 21073 49643 390 10186 FGF4-FOSL1-FZD1 39663 26051 18681 32329 5201 FOSL1-JUN-PPP2CA 21063 49029 6511 48244 44672 Table 2: Rankings of FOSL1-X-X. A list of approximately first 125 combinations with rankings below 10,000 out of 57,155. SA - HSIC; Kernel - rbf co-immunoprecipitation, co-immunolocalization, and ChIP analyses, that FOSL1 interacted with JUNB and, to a lesser extent, JUN in differentiating trophoblast cells. Knockdown of FOSL1 and JUNB expression inhibited both endocrine and invasive properties of trophoblast cells. Looking at the tables above, one finds the following 8 RANKING @tiUSING SOBOL - 2002 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 FOSL1-FRAT1-FRZB 48750 46940 49058 47805 7521 FOSL1-FRAT1-SENP2 11214 7586 5074 7384 41915 CSNK1D-FGF4-FOSL1 31433 17351 34702 49796 3779 CXXC4-FOSL1-SENP2 9857 21302 3091 25681 49127 CCND1-FGF4-FOSL1 47946 1732 38933 38678 23755 CTBP2-CTNNB1-FOSL1 12477 34215 13015 7117 42120 CXXC4-FOSL1-PPP2R1A 33024 20784 45045 33319 8066 FOSL1-PPP2R1A-SENP2 17310 2495 82 9104 48865 FOSL1-PORCN-SFRP4 44543 52571 34231 46029 13871 DKK1-FOSL1-SENP2 3431 3346 23554 20246 9046 FOSL1-PORCN-SENP2 54760 25670 47342 32899 26575 FOSL1-NLK-SENP2 13461 35727 546 20026 44857 FOSL1-FRAT1-WNT4 11661 37114 3277 931 53168 FOSL1-NLK-WNT4 11701 13725 13502 3308 41595 FOSL1-FOXN1-FRZB 4845 4217 9346 8710 50240 DKK1-FGF4-FOSL1 56316 11460 37809 55590 6360 FOSL1-PORCN-WNT4 47380 31804 36756 47696 19151 FOSL1-JUN-SENP2 51877 55400 52885 34018 3734 FBXW11-FGF4-FOSL1 32715 28640 35906 29158 11589 APC-BCL9-FOSL1 15337 18912 25043 17653 30357 CSNK1D-FOSL1-FZD1 33939 40307 29863 45255 32407 DVL2-FGF4-FOSL1 28814 41386 42144 48902 4193 FOSL1-PORCN-WNT2B 9686 2290 2189 14059 39340 FOSL1-FRAT1-TLE2 50038 6818 42415 47626 4928 FOSL1-FOXN1-SFRP4 45107 48753 50599 52560 3992 FOSL1-WIF1-WNT2B 25173 2114 24567 1335 44580 CXXC4-FOSL1-FRAT1 13778 2749 16108 6403 43145 FOSL1-FOXN1-KREMEN1 5566 2122 4725 4323 49299 DAAM1-FGF4-FOSL1 39718 54241 55152 47140 6015 DKK1-DVL2-FOSL1 53868 6384 55605 30940 12601 FOSL1-SFRP1-WNT2B 36889 18888 44464 53547 13962 FOSL1-FOXN1-SENP2 50427 48059 30743 46224 6709 FOSL1-FRAT1-FBXW4 37417 42346 33119 29950 927 FOSL1-PPP2R1A-RHOU 40904 5712 55181 51304 16176 FOSL1-NKD1-SFRP4 26523 39016 6871 18225 44405 FZD5-DVL2-FOSL1 40182 25866 35056 29369 7043 FOSL1-FOXN1-GSK3B 284 8167 4904 8619 50083 FOSL1-JUN-TCF7 49067 35754 42264 40921 9123 FOSL1-JUN-TCF7L1 4213 41297 24152 15364 47738 FOSL1-FZD2-SFRP4 25818 32267 25224 27273 48695 CSNK1G1-DVL2-FOSL1 13304 16111 11364 20166 55668 FOSL1-FOXN1-PPP2CA 51329 15073 48971 51617 23899 CSNK1G1-CXXC4-FOSL1 25168 1437 11571 16056 25721 FOSL1-FZD7-PPP2CA 35591 32520 50960 44000 21144 FOSL1-FRAT1-TCF7L1 50590 19180 53417 52474 6976 FOSL1-FOXN1-SLC9A3R1 34742 13119 54271 41553 7238 FOSL1-FOXN1-FZD8 18145 45592 11421 12903 51267 FOSL1-GSK3A-SENP2 11356 7709 5408 9588 41158 CSNK2A1-CTNNB1-FOSL1 7348 26679 6679 8520 40678 FOSL1-PORCN-RHOU 2397 31652 9884 24315 30810 CXXC4-FGF4-FOSL1 52810 50470 34544 37927 3516 FOSL1-WNT1-WNT2B 27112 14023 16313 6628 52241 FOSL1-FOXN1-LRP5 19194 9612 13710 10552 49252 DVL1-FBXW11-FOSL1 1086 49040 10558 27196 54543 APC-FGF4-FOSL1 39629 40499 37919 40405 7494 FOSL1-NLK-WNT2B 42013 9877 52887 52662 11462 FOSL1-GSK3A-SFRP4 24080 48870 2981 6458 47742 FOSL1-PORCN-TLE2 11344 44887 618 8380 48021 CTBP1-FGF4-FOSL1 30113 26329 54976 56153 8704 FOSL1-JUN-PITX2 29544 47341 44123 52018 7644 FOSL1-FOXN1-TLE2 10111 34166 2997 1459 46410 FOSL1-NLK-WNT3A 43309 33771 50006 38500 4467 AES-EP300-FOSL1 27689 20431 21551 25851 56678 FOSL1-JUN-WNT3A 10579 5753 10670 19639 21636 CSNK2A1-CTBP1-FOSL1 51466 6276 44543 34556 2632 FOSL1-FRAT1-LRP5 31351 10581 42739 34529 664 EP300-FGF4-FOSL1 28028 36636 4319 2494 42274 FOSL1-FOXN1-RHOU 6665 9152 26476 10977 50432 BCL9-FGF4-FOSL1 101 1534 21498 16607 27262 FOSL1-GSK3A-GSK3B 53394 55855 50166 49917 857 FOSL1-JUN-SLC9A3R1 43183 49105 41385 39275 5077 FZD5-FGF4-FOSL1 46553 12322 55789 43921 10970 FZD5-CCND3-FOSL1 4245 7266 11704 15340 18858 FOSL1-PORCN-SFRP1 12651 4591 23008 11171 43358 FOSL1-GSK3A-LRP5 41882 45860 41982 56323 1496 CXXC4-FOSL1-WNT5A 45823 54747 52368 41434 48001 FOSL1-JUN-FBXW4 13997 8027 15717 17898 52096 DIXDC1-FGF4-FOSL1 3396 43706 23582 7101 52388 FGF4-FOSL1-FRAT1 10083 6588 8685 24287 30035 FOSL1-NKD1-PPP2CA 2142 38096 5085 20731 35691 FOSL1-NLK-SFRP1 43758 21393 56612 37171 12178 FOSL1-PORCN-FBXW4 19295 6262 4610 7514 13534 FOSL1-PORCN-PPP2CA 48261 18485 32801 52162 23392 CSNK1G1-FGF4-FOSL1 8963 28835 12476 27024 48361 FOSL1-PPP2R1A-SFRP4 11157 1063 12252 1915 27364 FOSL1-FOXN1-WNT5A 8341 10381 8155 9375 49603 FOSL1-FOXN1-TCF7L1 6256 50998 4857 10230 51120 CSNK1D-FOSL1-SENP2 27384 17302 26341 7301 43654 FOSL1-FOXN1-WNT2B 1186 35009 15480 12827 50006 FZD5-CCND2-FOSL1 13152 46996 20245 4220 49402 APC-CCND3-FOSL1 14004 19135 18446 27673 7344 CSNK1A1-FGF4-FOSL1 31936 30526 31908 35528 15251 DKK1-FOSL1-FRAT1 3553 54257 16310 25765 12367 FOSL1-FOXN1-FZD6 13640 15481 10464 19959 46450 FOSL1-PORCN-TCF7L1 14669 55 993 18720 27063 DVL1-FGF4-FOSL1 45386 4232 46196 56492 27411 FOSL1-PYGO1-WNT2 47297 50999 30260 50806 4015 FOSL1-MYC-SFRP4 48771 44097 44821 42504 15359 FOSL1-GSK3A-PPP2CA 1409 42580 3372 20682 38693 FOSL1-FZD2-GSK3B 30096 21061 34538 29703 9399 FOSL1-FOXN1-FZD1 25925 10874 2904 9032 52484 FOSL1-FZD7-WNT2B 21619 45 3513 13198 43493 FOSL1-NKD1-SENP2 16870 1004 11411 25457 43247 FOSL1-FOXN1-TCF7 49708 52975 52794 35953 2727 FOSL1-PORCN-SLC9A3R1 37873 50909 52614 49669 43825 FOSL1-WNT1-WNT3A 15630 39992 11099 5546 48850 FOSL1-FBXW4-WNT3A 14577 48454 4103 4489 48405 FOSL1-WNT1-WNT4 41560 17077 46101 51615 8307 FOSL1-PORCN-TCF7 30681 31215 55178 44882 16830 FOSL1-JUN-PPP2R1A 21985 646 17395 14514 28922 FOSL1-FOXN1-LRP6 37870 47647 43468 46668 7859 FOSL1-SFRP1-TCF7 2040 20827 11649 18240 45367 CSNK1A1-CTBP2-FOSL1 43122 17498 29136 41748 25756 FOSL1-FOXN1-WNT4 53448 12 54578 41681 9465 DKK1-FOSL1-FRZB 53609 2816 40791 31392 44680 FGF4-FOSL1-SFRP4 20560 6324 9945 23630 45285 FOSL1-GSK3A-KREMEN1 52307 56531 47402 55645 3684 FOSL1-FOXN1-MYC 10915 26427 7515 6321 53618 FOSL1-WNT1-WNT2 34611 47563 33805 56132 5656 FOSL1-FRAT1-FZD7 4534 8444 3333 12890 53657 FOSL1-FOXN1-FBXW4 22313 44309 2923 15637 49928 FGF4-FOSL1-PPP2CA 2668 4065 11722 25274 53689 FOSL1-GSK3A-PITX2 3336 36071 6692 8964 29002 FOSL1-FOXN1-WNT2 47191 48365 52960 44342 6468 FGF4-FOSL1-FZD1 32182 13714 52916 30292 9916 FOSL1-JUN-PPP2CA 47425 55951 39423 37994 17117 Table 3: Rankings of FOSL1-X-X. 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