follicle stimulating hormone subunit beta (FSHB) : Time behavioural study of 3rd order combinations in WNT3A stimulated HEK 293 cells

follicle stimulating hormone subunit beta (FSHB) : 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 The pituitary glycoprotein hormone family includes follicle-stimulating hormone (FSH), luteinizing hormone (LH), chorionic gonadotropin (CG), and thyroid-stimulating hormone (TSH). They all consist of an identical alpha subunit and a hormone-specific beta subunit. FSHB encodes the beta subunit of FSH. Along with LH, FSH induces egg and sperm production. 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 FSHB related 3rd order combinations in a forest of 71C3combinations using four different sensitivity methods; •show the conserved rankings for FSHB-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 FSHB 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 in WNT3A stimulated cells 1Aspects of unpublished work were presented in a poster session at Cell Symposia: Technology.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 17, 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 FRAT1 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. follicle stimulating hormone subunit beta (FSHB) As recorded in Standring [4], the pituitary gland or hypophysis is an endocrine gland in vertebrates. It has two main lobes, namely, an anterior lobe, and a posterior lobe joined and separated by a small intermediate lobe. The anterior lobe (adenohypophysis) is the glandular part that produces and secretes several hormones. The posterior lobe (neurohypophysis) secretes neurohypophysial hormones produced in the hypothalamus. Both lobes have different origins and they are both controlled by the hypothalamus. Hormones secreted from the pituitary gland help to control growth, energy management, all functions of the sex organs, blood pressure, thyroid gland, metabolism, as well as some aspects of pregnancy, childbirth, breastfeeding, temperature regulation, water/salt concentration at the kidneys and pain relief. Glycoprotein hormones (GPHs) are the most complex molecules with hormonal activity. They exist only in vertebrates but the genes encoding their subunits’ ancestors are found in most vertebrate and invertebrate species although their roles are still unknown. Cahoreau et al. [5] review the available structural and functional data concerning GPHs and their subunits’ ancestors. Glycoprotein hormones (GPHs) are the most complex molecules with hormonal activity. The pituitary glycoprotein hormone family includes follicle-stimulating hormone (FSH), luteinizing hormone (LH), chorionic gonadotropin (CG), and thyroid-stimulating hormone (TSH). They all consist of an identical alpha subunit and a hormone-specific beta subunit. FSHB encodes the beta subunit of FSH and along with LH, FSH induces egg and sperm production. [6] analyzed a human genomic DNA fragment in phage λcontaining FSHB, and determined the nucleotide sequence of the region of the clone encoding FSHβ. Their analysis of a set of cell hybrids containing translocated derivatives of chromosome 11 further localized FSHB to the human chromosome region llpll.2→11pter. A Hind III restriction fragment length polymorphism (RFLP) detected by another subclone of the λphage containing FSHB provided a genetic marker for this region of the human genome. FSH, and the FSH receptor (FSHR), a G protein-coupled receptor, play central roles in human reproduction. Jiang et al. [7] reported the crystal structure of FSH in complex with the entire extracellular domain of FSHR (FSHRED), including the enig3 matic hinge region that is responsible for signal specificity. Surprisingly, they found that the hinge region did not form a separate structural unit, but was part of the integral structure of FSHRED. In addition to the known hormone-binding site, FSHRED provided interaction sites with the hormone: a sulfotyrosine (sTyr) site in the hinge region consistent with previous studies and a potential exosite resulting from putative receptor trimerization. Their structure, in comparison to others, suggested that FSHR interacted with its ligand in two steps: ligand recruitment followed by sTyr recognition. •FSH first binds to the high-affinity hormone-binding subdomain of FSHR and reshapes the ligand conformation to form a sTyr-binding pocket. •Next, FSHR inserts its sTyr (i.e., sulfated Tyr335) into the FSH nascent pocket, eventually leading to receptor activation. Summarizing Ulloa-Aguirre et al. [8], Tao and Segaloff [9] and Simoni et al. [10], FSH (a gonadotropin) stimulates steroidogenesis and gametogenesis in the gonads, while being secreted by the anterior pituitary gland. It regulates the menstrual cycle and ovarian follicular maturation in women and supports sperm production in men, by binding to the FSH receptor (FSHR) on the granulosa cell surface in ovaries and the Sertoli cell surface in testes. The stimulated receptor leads to the dissociation of αand βγsubunits of G protein heterotrimer inside the cell. The α-subunit activates adenylyl cyclase, resulting in an increase of cAMP levels, and ultimately leads to the increased steroid production that is necessary for follicular growth and ovulation in women, while the free βγ dimers recruit G protein-coupled receptor (GPCR) kinases to the receptor, which, in turn, lead to the recruitment of β-arrestin to the receptor. I present 3rd order combinations of FSHB 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]. 4 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 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 5 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 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 FSHB-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 6 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 FSHB-X-X combinations A total of 2415, 3rd order combinations involving FSHB 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. GnRH regulates FSH Gonadotropin-releasing hormone (GnRH) a decapeptide that is secreted in pulsatile fashion from the hypothalamus into the pituitary portal vasculature, regulates FSH synthesis and secretion (Bernard et al. [16]). Mason et al. [17] observe that the deletional mutation of at least 33.5 kilobases encompassing the distal half of the gene for the common biosynthetic precursor of GnRH and GnRH-associated peptide (GAP) causes hereditary hypogonadism in the hypogonadal (hpg) mouse. The partially deleted gene was found to be transcriptionally active as revealed by in situ hybridization histochemistry of hpg hypothalamic tissue sections, but immunocytochemical analysis failed to show the presence of antigen corresponding to any part of the precursor protein. 6.3.2. Examining the behaviour of JUN / TCF / LEF / CTNNB1 -FSHB-X combinations GnRH is known to regulate gonadotrope function through a complex transcriptional network that includes three members of the immediate early gene family: EGR1, JUN, and ATF3. These DNA-binding proteins act alone or in pairs to confer hormonal responsiveness to CGA, LHB, FSHB, and GnRHr. Salisbury et al. [18] suggested that the transcriptional response of JUN required a functional interaction between the T-cell factor (TCF)/lymphoid enhancer factor (LEF) family of DNA-binding proteins and βcatenin (CTNNB1), a coactivator of TCF/LEF. Their supporting data include demonstration that GnRH increases activity of TOPflash, a TCF/LEF-dependent luciferase reporter, in LβT2 cells (a gonadotrope-derived cell line). Their additional cotransfection experiments indicated that a dominant-negative form of TCF7L2 (TCFDN) that bound DNA, but not β-catenin, blocked GnRH induction of TOPflash. Overexpression of AXIN, an inhibitor of β-catenin, also reduced GnRH stimulation of TOPflash. Transduction of LβT2 cells with TCFDN adenoviruses diminished GnRH stimulation of JUN mRNA without altering expression of EGR1 and ATF3, two other immediate 7 RANKING @tiUSING HSIC - LINEAR 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 FSHB-T-WNT5A 122 29061 57139 24091 2613 FSHB-NKD1-PPP2R1A 134 11978 48147 4744 11658 FSHB-FZD2-SENP2 155 15792 56338 24860 16086 FSHB-FZD2-WNT4 180 18252 43713 35344 29882 FSHB-FZD2-PPP2R1A 216 20883 30530 35125 19903 FSHB-FZD2-FZD7 245 31877 48883 39603 50115 FSHB-T-WNT4 285 39661 52682 15922 9372 FSHB-FZD2-KREMEN1 286 34277 55693 24595 31187 FSHB-FZD2-TLE2 364 13058 45957 41222 23515 FSHB-FZD2-LRP5 366 26369 56367 45627 15311 FSHB-T-TLE2 387 33919 53269 21476 12396 FSHB-FZD2-SFRP4 413 14029 55048 31393 43835 FSHB-NKD1-TLE2 425 30274 24343 8644 12849 CSNK2A1-FSHB-SFRP4 494 33797 31035 11392 7911 CSNK1G1-FSHB-LEF1 506 19332 10676 684 44215 FSHB-LEF1-TCF7L1 545 41739 49954 24854 32409 CSNK1D-FSHB-LEF1 553 21733 36403 3295 16135 FSHB-NKD1-SENP2 574 7025 14205 7566 35992 CSNK1G1-FSHB-SFRP4 577 21679 6780 1575 25615 FSHB-T-TCF7 594 43385 15195 24841 22963 DKK1-FSHB-SENP2 641 24808 11118 11901 46995 FSHB-FZD2-FZD6 659 6508 55230 34443 24422 FSHB-NKD1-WNT2B 699 45757 26960 8191 17214 FSHB-LEF1-TLE2 716 51374 47443 17350 25939 DIXDC1-FSHB-LEF1 777 45272 10940 6689 22555 FSHB-GSK3A-KREMEN1 921 38254 26495 28443 3064 FSHB-FZD2-PPP2CA 922 15170 15248 37534 18116 FSHB-FZD2-TCF7L1 938 32749 50626 39797 16705 FSHB-NKD1-RHOU 974 40540 14067 9128 35039 FSHB-NKD1-SFRP1 1011 27987 21471 7065 1983 FRZB-FSHB-SENP2 1016 13136 7480 5202 23820 FSHB-FZD2-TCF7 1018 40516 45622 34770 44337 FSHB-T-TCF7L1 1029 40997 50899 17756 3833 CSNK1G1-FSHB-SENP2 1054 47479 13640 1228 8996 FSHB-GSK3A-SENP2 1073 16288 25162 18111 11005 FSHB-FZD2-LEF1 1118 14309 53366 39173 16692 FSHB-NKD1-WNT5A 1146 8921 29165 19619 2206 FSHB-FZD2-WNT5A 1190 10906 56570 47332 4072 FRZB-FSHB-SFRP4 1223 1819 11388 6342 54806 FSHB-LEF1-SENP2 1229 12578 50301 24457 7777 FSHB-FZD2-GSK3B 1266 6938 48999 24583 37158 FSHB-NKD1-TCF7L1 1387 37613 10650 8338 29279 FSHB-T-WNT2 1426 29977 55062 12965 5468 CSNK1G1-FSHB-PPP2CA 1518 8523 49087 1235 27660 DKK1-FSHB-LRP5 1519 2594 16842 22232 37962 FSHB-NKD1-TCF7 1528 46050 40792 9733 32939 CSNK2A1-FSHB-PPP2CA 1544 21794 56458 12205 17377 FSHB-FZD2-FBXW4 1577 20522 32605 35591 25593 CSNK2A1-FSHB-WNT2B 1655 8809 48217 20614 27385 FSHB-MYC-SENP2 1661 3841 29219 24971 7457 FSHB-FZD2-FZD8 1671 15948 53024 35438 29190 FSHB-FZD2-PITX2 1681 25060 56319 33266 14323 FSHB-FZD2-SLC9A3R1 1795 39694 55787 33593 41890 FSHB-FZD2-TLE1 2027 28569 54198 36983 37820 FSHB-LEF1-WNT4 2075 48049 40576 21210 20847 FSHB-FZD2-RHOU 2193 41442 55751 28895 48790 FSHB-T-WNT3A 2242 34175 56516 9226 7363 FSHB-GSK3A-PPP2R1A 2267 11196 31968 18565 24240 FSHB-FZD2-WNT3A 2298 5294 54044 44316 3638 CSNK1D-FGF4-FSHB 2441 17927 45150 34061 11599 FSHB-NKD1-WNT2 2558 23961 6398 3570 16109 FSHB-FZD2-SFRP1 2559 7953 55268 36582 36554 CSNK2A1-FSHB-FZD7 2601 20741 19320 11749 49197 FSHB-GSK3A-FBXW4 2618 22592 44503 22209 12645 FZD5-FSHB-SENP2 2620 37348 11372 8592 28360 FSHB-GSK3A-RHOU 2698 43462 34330 18933 10698 FSHB-FZD2-PORCN 2817 7691 52867 46696 28509 FSHB-MYC-TLE2 2923 22109 41704 26993 3530 AES-FOXN1-FSHB 2965 25071 19677 24885 18445 FSHB-MYC-FBXW4 2969 20328 49942 19643 3752 CSNK1G1-FSHB-MYC 2987 47541 16749 1201 23454 FSHB-FZD2-NLK 3007 15229 33503 42544 8088 DIXDC1-FSHB-FZD1 3136 45375 11211 11425 29794 FBXW11-FOXN1-FSHB 3234 11903 14136 23118 30110 DIXDC1-FSHB-T 3255 34040 47003 6560 49035 CSNK1G1-FSHB-WNT2B 3357 9060 28997 6593 37362 FSHB-PORCN-PPP2R1A 3420 9611 38143 42456 10626 CSNK2A1-FSHB-FZD1 3426 27759 33533 13224 35693 FSHB-GSK3A-WNT2B 3606 48867 25838 31124 32847 CSNK2A1-FSHB-SENP2 3609 22209 11628 8401 7649 CSNK1G1-FSHB-T 3636 19798 35345 312 15515 FOSL1-FOXN1-FSHB 3654 9548 17000 26396 39466 FSHB-NKD1-FBXW4 3657 26327 16817 8776 23172 CSNK1G1-FSHB-TLE2 3685 24521 6309 2390 53267 FRZB-FSHB-FZD1 3720 4368 14235 6831 35610 FSHB-FZD2-WNT2 3865 8188 47829 31525 37626 FSHB-GSK3A-TCF7 3904 42280 17499 22970 14161 FSHB-FZD2-MYC 3937 20248 51571 40207 23081 BCL9-FGF4-FSHB 3946 21353 21679 27526 39205 FSHB-LEF1-SFRP4 4021 45839 49234 24757 19506 FSHB-T-TLE1 4039 47213 53727 15401 29351 FRZB-FSHB-LEF1 4117 679 11864 4333 15549 CSNK1G1-FSHB-TCF7L1 4143 12682 12075 1247 30342 CSNK2A1-FSHB-TCF7L1 4195 17520 18854 11543 18761 CSNK1G1-FSHB-FZD1 4236 6426 15848 1850 51462 FSHB-JUN-SENP2 4244 14780 24139 39454 6681 CSNK2A1-FSHB-LEF1 4295 17954 29380 8714 37682 CTBP2-FOXN1-FSHB 4321 40069 14063 16057 55165 FSHB-LEF1-SLC9A3R1 4432 52834 53254 14595 12082 APC-FSHB-LEF1 4475 29086 10632 356 11132 DIXDC1-FSHB-TCF7L1 4549 31863 7056 10277 4669 FRZB-FSHB-LRP5 4578 8579 23418 10147 14329 FSHB-NKD1-WIF1 4621 8153 24750 5910 428 FSHB-GSK3A-WNT5A 4722 7262 52632 23091 38927 DIXDC1-FSHB-SFRP4 4856 39200 10508 10415 29896 CCND1-CTNNBIP1-FSHB 5004 56520 36248 44136 53863 FSHB-MYC-WNT4 5012 22216 28105 26520 25995 DVL1-FSHB-FZD1 5035 53508 16024 470 29060 FRZB-FSHB-PPP2CA 5204 19049 54010 6133 27813 EP300-FOXN1-FSHB 5265 2613 14431 12535 3811 FSHB-JUN-WNT4 5288 7708 26793 41418 20711 CSNK1G1-FSHB-LRP5 5322 17826 30783 6398 41762 CSNK1G1-FSHB-RHOU 5346 18138 12773 776 39643 FSHB-JUN-TCF7 5360 34303 50964 37006 36402 FSHB-FZD2-NKD1 5427 29500 40214 25746 8412 FSHB-PYGO1-WNT2 5434 16740 51092 5393 7521 CSNK1G1-FSHB-WNT2 5533 14900 6829 467 30316 FSHB-LEF1-MYC 5538 29950 53848 14480 13768 FSHB-MYC-RHOU 5540 30302 25407 22566 36470 FSHB-NKD1-NLK 5645 9268 38001 6547 21186 FSHB-PYGO1-WNT3A 5662 9149 57045 8437 25486 DAAM1-FOXN1-FSHB 5694 41648 33798 14726 32752 FBXW2-FSHB-FZD2 5712 39025 17476 10127 28195 CCND1-CTBP1-FSHB 5725 56748 29592 50558 40456 FSHB-JUN-LRP5 5767 3832 55208 46575 2660 CSNK1D-CTNNBIP1-FSHB 5796 33605 55368 17392 32696 Table 1: Rankings of FSHB-X-X. A list of approximately first 125 combinations with rankings below 10,000 out of 57,155. SA - HSIC; Kernel - linear early genes that confer GnRH responsiveness. Reduction of β-catenin in LβT2 cells, through stable expression of short hairpin RNA, also selectively compromised GnRH regulation of JUN expression and levels of JUN protein. Finally, overexpression of 8 RANKING @tiUSING HSIC - RBF 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 FSHB-T-WNT5A 38699 15943 1093 25527 28563 FSHB-NKD1-PPP2R1A 25236 3206 25555 29398 54336 FSHB-FZD2-SENP2 722 17184 1804 8318 40473 FSHB-FZD2-WNT4 6816 11813 3438 4079 43595 FSHB-FZD2-PPP2R1A 10535 30086 26859 11239 24594 FSHB-FZD2-FZD7 4675 7640 5535 26749 20732 FSHB-T-WNT4 34987 32997 6654 22504 22222 FSHB-FZD2-KREMEN1 5444 42081 8565 25988 38407 FSHB-FZD2-TLE2 7557 9982 1122 7042 3191 FSHB-FZD2-LRP5 21769 12398 268 13884 19062 FSHB-T-TLE2 47144 33788 2346 14889 40413 FSHB-FZD2-SFRP4 4323 24559 5053 16811 10612 FSHB-NKD1-TLE2 17403 24824 7167 5397 44661 CSNK2A1-FSHB-SFRP4 17159 7039 55890 17829 49642 CSNK1G1-FSHB-LEF1 46458 5148 44690 1362 34271 FSHB-LEF1-TCF7L1 9166 53185 6306 4873 46442 CSNK1D-FSHB-LEF1 55374 14244 35230 8216 18460 FSHB-NKD1-SENP2 14879 5325 16207 3306 6877 CSNK1G1-FSHB-SFRP4 49673 9604 39425 16774 51741 FSHB-T-TCF7 15963 48634 1342 15324 3240 DKK1-FSHB-SENP2 13659 23744 54431 4021 33075 FSHB-FZD2-FZD6 983 15277 13285 3027 53066 FSHB-NKD1-WNT2B 24792 50507 25909 42911 36692 FSHB-LEF1-TLE2 17915 43860 1667 9288 40897 DIXDC1-FSHB-LEF1 56469 45507 52727 16153 8570 FSHB-GSK3A-KREMEN1 9218 45608 28535 37479 51895 FSHB-FZD2-PPP2CA 3195 15838 9446 3827 55246 FSHB-FZD2-TCF7L1 6008 50769 1291 19412 38941 FSHB-NKD1-RHOU 13620 39126 15440 38560 46234 FSHB-NKD1-SFRP1 28217 31095 6105 12696 47885 FRZB-FSHB-SENP2 5870 14328 41622 369 53440 FSHB-FZD2-TCF7 1081 40554 555 14927 53924 FSHB-T-TCF7L1 18421 44307 4524 7756 18690 CSNK1G1-FSHB-SENP2 41253 35360 30966 2443 33408 FSHB-GSK3A-SENP2 6015 32214 13032 15982 30339 FSHB-FZD2-LEF1 31623 27017 5219 9724 12888 FSHB-NKD1-WNT5A 45339 3228 1592 43013 28219 FSHB-FZD2-WNT5A 4908 10915 532 36402 27866 FRZB-FSHB-SFRP4 29574 11603 47383 3209 39274 FSHB-LEF1-SENP2 16486 20092 5835 4015 10968 FSHB-FZD2-GSK3B 1138 11606 9514 35386 46428 FSHB-NKD1-TCF7L1 29187 51071 22006 21070 31900 FSHB-T-WNT2 50362 19754 5809 18153 37120 CSNK1G1-FSHB-PPP2CA 50070 6934 23453 5427 21042 DKK1-FSHB-LRP5 17832 17573 35336 9068 54014 FSHB-NKD1-TCF7 16167 43862 3127 18402 9845 CSNK2A1-FSHB-PPP2CA 14473 24470 49869 6189 28547 FSHB-FZD2-FBXW4 7085 29857 30431 35123 20442 CSNK2A1-FSHB-WNT2B 14373 4327 52378 10457 6308 FSHB-MYC-SENP2 11261 1740 20872 3892 32896 FSHB-FZD2-FZD8 2194 10044 3079 38866 42600 FSHB-FZD2-PITX2 11301 38653 1373 55358 36056 FSHB-FZD2-SLC9A3R1 10325 37190 5991 25853 20686 FSHB-FZD2-TLE1 15519 25551 3634 38322 51551 FSHB-LEF1-WNT4 41497 39387 3625 9699 38711 FSHB-FZD2-RHOU 1282 37769 4155 7339 22181 FSHB-T-WNT3A 37189 29725 873 25128 8181 FSHB-GSK3A-PPP2R1A 2994 22740 47062 16574 48029 FSHB-FZD2-WNT3A 10002 6702 2404 44003 56141 CSNK1D-FGF4-FSHB 8876 10878 93 40281 25736 FSHB-NKD1-WNT2 18619 15105 23935 22810 37921 FSHB-FZD2-SFRP1 4118 5933 771 28595 38823 CSNK2A1-FSHB-FZD7 32789 11231 55533 29970 24990 FSHB-GSK3A-FBXW4 6890 34487 49297 34455 41976 FZD5-FSHB-SENP2 9883 31485 55203 2031 44565 FSHB-GSK3A-RHOU 10474 45128 43164 20106 41469 FSHB-FZD2-PORCN 2992 1901 452 19552 44544 FSHB-MYC-TLE2 13836 3400 17675 6625 31499 AES-FOXN1-FSHB 1721 19780 28544 48157 1579 FSHB-MYC-FBXW4 20928 25297 33253 38049 15480 CSNK1G1-FSHB-MYC 43747 46283 35580 1289 17413 FSHB-FZD2-NLK 17474 18226 16777 16181 28351 DIXDC1-FSHB-FZD1 3527 44586 55840 4528 2895 FBXW11-FOXN1-FSHB 6061 13017 20945 50606 93 DIXDC1-FSHB-T 36455 36262 54691 1183 12572 CSNK1G1-FSHB-WNT2B 43414 19138 41449 6694 6668 FSHB-PORCN-PPP2R1A 16061 9508 29505 10633 56512 CSNK2A1-FSHB-FZD1 14124 9142 52833 6616 46703 FSHB-GSK3A-WNT2B 7286 43548 39375 36993 48203 CSNK2A1-FSHB-SENP2 11122 26033 53443 288 43776 CSNK1G1-FSHB-T 51659 22786 44198 2191 56498 FOSL1-FOXN1-FSHB 11030 8091 27557 40910 3512 FSHB-NKD1-FBXW4 37869 19703 30672 38958 40988 CSNK1G1-FSHB-TLE2 37782 18462 30690 1164 44080 FRZB-FSHB-FZD1 9269 6036 46659 4923 47946 FSHB-FZD2-WNT2 7061 8112 4177 36695 20465 FSHB-GSK3A-TCF7 3290 46377 13521 12930 56685 FSHB-FZD2-MYC 29737 23746 2961 11852 18465 BCL9-FGF4-FSHB 19360 11815 11389 48533 47985 FSHB-LEF1-SFRP4 30991 41207 7433 5395 45348 FSHB-T-TLE1 20294 41198 2613 16496 22703 FRZB-FSHB-LEF1 54972 6560 43425 13496 36529 CSNK1G1-FSHB-TCF7L1 33263 14566 33778 3739 35177 CSNK2A1-FSHB-TCF7L1 28512 36022 53375 46 53291 CSNK1G1-FSHB-FZD1 30998 2577 47731 11545 47835 FSHB-JUN-SENP2 8504 11359 1933 11062 13593 CSNK2A1-FSHB-LEF1 57055 1237 51589 12220 31790 CTBP2-FOXN1-FSHB 23856 40293 23082 53127 1837 FSHB-LEF1-SLC9A3R1 34110 47269 1903 15643 45779 APC-FSHB-LEF1 56896 21413 52867 5217 28793 DIXDC1-FSHB-TCF7L1 13822 52661 54003 3177 5869 FRZB-FSHB-LRP5 34073 16424 34151 9778 55580 FSHB-NKD1-WIF1 24095 15522 18330 7234 28767 FSHB-GSK3A-WNT5A 3423 15383 30952 30779 42858 DIXDC1-FSHB-SFRP4 13901 35834 56885 9145 10625 CCND1-CTNNBIP1-FSHB 10903 57067 84 14969 9086 FSHB-MYC-WNT4 29072 10195 10094 2456 2568 DVL1-FSHB-FZD1 3557 48584 50929 1986 26905 FRZB-FSHB-PPP2CA 18947 29614 39286 5288 45027 EP300-FOXN1-FSHB 2802 1330 33722 46172 2708 FSHB-JUN-WNT4 52891 1241 4783 6486 15806 CSNK1G1-FSHB-LRP5 47118 5185 21879 18285 54603 CSNK1G1-FSHB-RHOU 47722 23571 41553 10002 20180 FSHB-JUN-TCF7 7804 30904 565 20582 2501 FSHB-FZD2-NKD1 17626 47853 2527 46929 13014 FSHB-PYGO1-WNT2 5673 7061 3497 1837 50147 CSNK1G1-FSHB-WNT2 50502 3033 48214 9696 46010 FSHB-LEF1-MYC 10894 16685 5708 19074 53792 FSHB-MYC-RHOU 13055 25852 10079 20516 29919 FSHB-NKD1-NLK 28359 8433 16880 40917 24813 FSHB-PYGO1-WNT3A 9583 23325 1231 22957 44849 DAAM1-FOXN1-FSHB 2820 39084 4512 44503 45 FBXW2-FSHB-FZD2 9636 32142 51450 29379 5217 CCND1-CTBP1-FSHB 36917 56532 211 43826 268 FSHB-JUN-LRP5 56899 1565 391 32348 8019 CSNK1D-CTNNBIP1-FSHB 15509 42611 40 44944 2520 Table 2: Rankings of FSHB-X-X. A list of approximately first 125 combinations with rankings below 10,000 out of 57,155. SA - HSIC; Kernel - rbf TCFDN attenuated GnRH regulation of CGA promoter activity, a known downstream target of JUN. Together, their results indicated that GnRH regulation of JUN transcription required a functional interaction between TCF/LEF and β-catenin and that 9