forkhead box N1 (FOXN1) : Time behavioural study of 3rd order combinations in WNT3A stimulated HEK 293 cells

forkhead box N1 (FOXN1) : Time behavioural study of 3rd order combinations in WNT3A stimulated HEK 293 cells shriprakash sinha Independent Researcher; Orcid ID : 0000-0001-7027-5788 Address : 104-Madhurisha Heights Phase 1, Risali, Bhilai-490006, India Corresponding author email : [email protected] Abstract FOXN1 belongs to the family of FOX (forkhead box) proteins that act as transcription factors which play important roles in regulating the expression of genes involved in cell growth, proliferation, differentiation, and longevity. 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 FOXN1 related 3rd order combinations in a forest of 71C3combinations using four different sensitivity methods; •show the conserved rankings for FOXN1-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 FOXN1 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 in WNT3A stimulated cells 1Aspects of unpublished work were presented in a poster session at Cell Symposia: Technology.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 20, 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 FOXN1 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. forkhead box N1 (FOXN1) Franke et al. [4] calculated the nuclear pore flow rate (NPFR) for ribosomal and transfer RNA from the steady-state values (mean nuclear pore number and RNA synthesis rates) of the differentiated rat liver cells. They compared there hepatocyte values with the corresponding RNA transport performance of the nuclear pore complexes of other cell types. The region-specific homeotic gene fork head (fkh) is known to promote terminal as opposed to segmental development in the Drosophila embryo. Weigel et al. [5] cloned the fkh region by chromosomal walking. P element-mediated germ-line transformation and sequence comparison of wild-type and mutant alleles identified the fkh gene within the cloned region. They observed that fkh was expressed in the early embryo in the two terminal domains that were homeotically transformed in fkh mutant embryos. The nuclear localization of the fkh protein suggested that fkh regulated the transcription of other, subordinate, genes. The fkh gene product, however, did not contain a known protein motif, such as the homeodomain or the zinc fingers, nor was it similar in sequence to any other known protein. Forkhead genes are a subgroup of the helix-turn-helix class of proteins (Brennan and Matthews [6]). The hepatocyte nuclear factor 3 (HNF3) gene family is composed of three proteins (α,β, and γ) that are transcription factors involved in the coordinate expression of several liver genes. Pani et al. [7] focused on the HNF3βprotein, and reported the localization of two transcriptional activation domains with a cotransfection assay with HNF3βreporter and expression plasmids. More specifically, they developed a cotransfection assay in Hep-G2 cells to define amino acid residues responsible for HNF3β transcriptional activation. They defined a position-independent activation domain at the HNF3βcarboxyl terminus (361-458) which could potentiate the expression of a TATA box-CAT reporter construct containing multimeric DNA recognition sites for the HNF3 protein. They found that this HNF3βactivation domain required region II and III sequences which were conserved with the HNF3 family and the Drosophila fork head protein. Since their discovery, the conserved family of fork head/HNF3-related transcription factors gained increasing importance for the analysis of gene regulatory mechanisms during embryonic development and in differentiated cells. Different members of this family, which were defined by a conserved 110 amino acid residues encompassing DNA binding domain of winged helix structure (that has four helices and a two-strand beta-sheet), served as regulatory keys in embryogenesis, in tumorigenesis or in the 3 maintenance of differentiated cell states. The review by Kaufmann and Knochel [8] summarized the accumulating amount of data on structure, expression and function of fork head/HNF3-related transcription factors. FOXN1 belongs to the family of FOX (forkhead box) proteins. It is known that mutations at the nude locus of mice and rats disrupt normal hair growth and thymus development, thus causing nude mice and rats to be immune-deficient. Nehls et al. [9] showed that one of the genes from the mouse nude locus which had been localized on chromosome 11 (within a region of <1 megabase), designated winged helix nude (WHN / FOXN1), encoded a new member of the winged-helix domain family of transcription factors and that it was disrupted on mouse nu and rat rnuN alleles. Further, mutant transcripts did not encode the characteristic DNA-binding domain, thus pointing to the fact that WHN gene was the nude gene. The differentiation of primitive epithelial precursor cells in the thymic primordium into subcapsular, cortical, and medullary epithelial cells of the mature thymus required the activity of WHN. It was also required for proper keratinization of the hair shaft. Schorpp et al. [10] determined the nucleotide sequence of a 58 kilobase region on mouse chromosome 11 that encompassed the mouse WHN and part of the two neighboring genes. Using cross-hybridization, they isolated the human orthologue of the mouse WHN. They observed that the human WHN protein also consisted of 648 amino acids, 85% of which was identical to the mouse WHN protein. They found that like the mouse gene, the human gene consisted of eight coding exons and utilized two alternative first exons in a tissue-specific fashion. I present 3rd order combinations of FOXN1 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 4 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 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 SV MRank 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. 5 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 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 6.2. Enumeration and ranking of 2415 FOXN1-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 FOXN1-X-X combinations A total of 2415, 3rd order combinations involving FOXN1 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 KREMEN1-FOXN1-X combinations Fully mature and diverse epithelial microenvironment of the thymus is required for T cell development and selection. The acquisition of these characteristics is dependent on expression of FOXN1, as a lack of functional FOXN1 results in aberrant epithelial morphogenesis and an inability to attract lymphoid precursors to the thymus primordium. Balciunaite et al. [16] report that secreted WNT glycoproteins, expressed by thymic epithelial cells and thymocytes, regulate epithelial FOXN1 expression in both autocrine and paracrine fashions. Thus, WNT signaling has been reported to regulate thymocyte proliferation and selection at several stages during T cell ontogeny, as well as the expression of FOXN1 in thymic epithelial cells (TECs). KREMEN1 (KRM1) is a negative regulator of the canonical WNT signaling pathway, and functions together with the secreted WNT inhibitor Dickkopf (DKK) by competing for the lipoprotein receptor-related protein (LRP6) co-receptor for WNTs. Osada et al. [17] used KRM1 knockout mice to examine KRM1 expression in the thymus and its function in thymocyte and TEC development. They detected KRM1 expressionin both cortical and medullary TEC subsets, as well as in immature thymocyte subsets, beginning at the CD25+CD44+ (DN2) stage and continuing until the, CD4+CD8+(DP) stage. They observed that neonatal mice showed elevated expression of KRM1 in all TEC subsets, while KRM1−/−mice exhibited a severe defect in thymic cortical architecture, including large epithelial free regions. Further, a TOPFlash assay revealed a 2-fold increase in 7 RANKING @tiUSING HSIC - LINEAR 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 FOXN1-KREMEN1-WNT2B 170 28253 38493 15670 30910 FOXN1-KREMEN1-PPP2R1A 187 38390 45372 22596 7296 FOXN1-KREMEN1-WNT3 217 53012 56130 3088 18960 CSNK1G1-FOXN1-TLE2 226 3994 5316 18517 42187 FOXN1-KREMEN1-LRP5 260 52346 51858 34049 42979 FBXW11-FOXN1-KREMEN1 302 14589 4660 6330 19835 FZD5-FOXN1-LRP5 307 845 9386 28797 54200 DIXDC1-FOXN1-FBXW4 330 3748 17556 12016 30746 AES-FOXN1-FZD7 338 21947 6320 11489 39355 DKK1-FOXN1-SENP2 342 12351 29242 3585 34803 DKK1-FOXN1-FRZB 357 16995 47883 4088 8042 CSNK1G1-FOXN1-KREMEN1 390 10815 10745 12917 12924 FBXW11-FOXN1-SENP2 392 5743 6882 5378 5741 CSNK1G1-FOXN1-SLC9A3R1 395 1314 2366 9985 14166 AES-FOXN1-WNT2 402 20097 14407 14326 3071 AXIN1-FOXN1-WNT2 423 119 6856 11943 1929 FOSL1-FOXN1-FRZB 426 18495 3914 3925 50337 CSNK1D-FOXN1-LEF1 437 1005 10226 23334 20989 CTNNB1-FOXN1-FRZB 452 1997 7527 5341 50936 FZD5-FOXN1-SENP2 473 7569 6670 5785 7868 CTNNB1-FOXN1-RHOU 475 856 5984 14676 48295 DKK1-FOXN1-KREMEN1 480 13941 53110 3917 4043 CTNNB1-FOXN1-KREMEN1 484 1965 14838 7676 28339 AES-FOXN1-PPP2R1A 486 23646 9985 11397 37399 DAAM1-FOXN1-SFRP4 509 41264 31480 8686 35677 FOXN1-KREMEN1-SFRP4 510 51802 54641 8830 39747 FZD5-FOXN1-KREMEN1 511 1786 8577 7989 16170 AES-FOXN1-SENP2 518 22315 8527 7631 2724 FBXW11-FOXN1-SFRP4 561 2766 4849 11457 25387 FBXW11-FOXN1-FZD1 563 3393 3457 5989 33677 CTBP2-FOXN1-SENP2 566 19815 6401 2342 17983 DIXDC1-FOXN1-TLE2 581 740 1891 12323 28293 DIXDC1-FOXN1-FZD8 584 8409 1800 17070 5733 FZD5-FOXN1-SLC9A3R1 611 409 1127 6472 13971 DVL1-FOXN1-FRAT1 613 42124 2382 9631 35079 CSNK1D-FOXN1-SENP2 624 4830 5783 7469 36423 DVL1-FOXN1-FZD1 635 46799 7174 3229 38528 FOSL1-FOXN1-SFRP4 680 23049 4982 9425 19314 DAAM1-FOXN1-FZD6 681 45572 41066 6876 38373 CTNNB1-FOXN1-FZD6 691 3712 21540 10206 42092 FBXW11-FOXN1-FRZB 718 6154 5240 5636 29172 CSNK2A1-FOXN1-TCF7 736 11246 6655 11945 13187 FOSL1-FOXN1-KREMEN1 742 14009 6113 8523 21187 CSNK2A1-FOXN1-KREMEN1 775 7975 18477 6845 18012 AXIN1-FOXN1-FBXW4 790 11390 15320 10702 18907 DVL1-FOXN1-SFRP4 845 41274 6107 5709 48162 BTRC-FOXN1-FZD8 853 10860 9974 20227 10455 DKK1-FOXN1-WNT2B 864 31296 16351 4633 30734 DAAM1-FOXN1-FRZB 896 43246 33591 4294 17015 DVL1-FOXN1-SENP2 907 37314 6190 2465 34563 FBXW11-FOXN1-TCF7L1 916 13133 4109 10602 35818 CTNNB1-FOXN1-SLC9A3R1 917 2286 2153 7068 20569 FBXW11-FOXN1-LRP5 928 10686 10354 28842 25833 DIXDC1-FOXN1-KREMEN1 939 687 5376 7847 13303 CSNK1G1-FOXN1-PPP2R1A 960 4562 8931 10045 26824 FBXW11-FOXN1-FZD8 965 10160 4020 13865 51185 CTNNB1-FOXN1-PPP2R1A 966 1729 7371 11775 25639 DVL1-FOXN1-PPP2CA 977 25908 15392 4810 48741 AES-FOXN1-FZD8 981 7828 6668 16769 40810 FOSL1-FOXN1-GSK3B 983 14647 1776 17080 41173 FOXN1-KREMEN1-SFRP1 985 42401 54457 12806 414 CTNNB1-FOXN1-TLE2 988 5543 9432 15644 20859 FBXW11-FOXN1-WNT2B 989 38275 4532 5886 43707 AXIN1-FOXN1-FRAT1 990 1738 4384 15268 22220 FBXW11-FOXN1-TLE2 1004 10423 4208 12987 45489 DAAM1-FOXN1-LRP5 1009 45625 30080 34466 47125 DAAM1-FOXN1-PPP2CA 1014 50289 35190 8828 44977 FZD5-FOXN1-GSK3B 1022 3638 2875 20381 28710 CSNK2A1-FOXN1-WNT2B 1037 3175 7624 2538 6838 FOSL1-FOXN1-PPP2CA 1072 22257 16994 9632 25457 AES-FOXN1-GSK3A 1074 9734 18353 13099 38607 DKK1-FOXN1-TLE2 1080 18592 26323 11091 53163 CXXC4-FOXN1-SLC9A3R1 1089 2458 1605 6818 34700 CSNK1D-FOXN1-KREMEN1 1122 3656 11331 13933 38262 AXIN1-FOXN1-TCF7 1145 4915 2559 14562 18591 CSNK2A1-FOXN1-FZD8 1170 17750 11184 10347 9970 CCND3-FOXN1-FZD8 1183 34917 5116 11771 42093 CSNK1G1-FOXN1-FBXW4 1201 11491 20301 17590 26677 AES-FOXN1-SFRP4 1211 25834 8687 12229 21429 CSNK2A1-FOXN1-SENP2 1233 22059 16672 4525 3145 CTNNB1-FOXN1-TCF7L1 1237 6273 7647 12061 42159 AES-FOXN1-TLE1 1240 18162 8419 8417 9300 AES-FOXN1-FRZB 1247 23681 6537 2125 30062 CSNK1D-FOXN1-PPP2CA 1252 17427 16752 21509 34632 CXXC4-FOXN1-RHOU 1258 949 4951 19719 52102 DAAM1-FOXN1-FZD1 1262 38972 34040 4743 31791 FOSL1-FOXN1-SLC9A3R1 1275 28011 885 4881 51370 CSNK1G1-FOXN1-TCF7L1 1281 8663 5550 16139 11644 CSNK2A1-FOXN1-FZD6 1295 29245 25754 7254 28379 DAAM1-FOXN1-SLC9A3R1 1302 35893 19727 4671 23335 BTRC-FOXN1-WNT4 1304 7824 14183 18230 50189 FOSL1-FOXN1-FZD8 1314 19509 2343 16192 19984 DVL1-FOXN1-PPP2R1A 1316 40931 10069 3721 36477 FBXW11-FOXN1-FZD7 1326 9731 6528 14525 29594 CSNK1D-FOXN1-WNT2B 1328 17615 2986 7052 5219 EP300-FOXN1-TLE2 1334 6564 2241 419 8940 DKK1-FOXN1-WNT5A 1346 18310 46663 16625 23963 FBXW11-FOXN1-PPP2CA 1357 8568 16917 13742 17017 CSNK1D-FOXN1-FZD8 1359 10239 4066 27563 3347 DKK1-FOXN1-SFRP4 1382 19069 48285 11895 39000 DKK1-FOXN1-SLC9A3R1 1384 38790 31886 5342 8410 FOSL1-FOXN1-LRP5 1396 12463 8896 42948 43152 DVL1-FOXN1-SFRP1 1402 41345 3878 6051 10938 FBXW11-FOXN1-WNT4 1404 854 7156 11739 28714 CSNK2A1-FOXN1-PPP2CA 1415 18676 25465 10950 9597 APC-FOXN1-FRAT1 1418 596 2065 13524 33264 DVL1-FOXN1-RHOU 1438 38257 4549 11910 29038 AXIN1-FOXN1-TLE2 1439 321 2295 11236 23819 DIXDC1-FOXN1-FRZB 1442 1877 4968 4749 2791 AES-FOXN1-SLC9A3R1 1445 27866 3775 6327 34957 CTNNB1-FOXN1-FBXW4 1456 9113 25436 11398 551 AXIN1-FOXN1-FZD8 1457 7791 1656 16700 31078 CSNK2A1-FOXN1-SFRP4 1460 20356 19876 8892 40 FBXW11-FOXN1-PPP2R1A 1485 2012 6552 10843 25217 DIXDC1-FOXN1-SENP2 1491 2077 4322 6058 1110 CTNNB1-FOXN1-TCF7 1494 2724 3212 16430 18802 DVL1-FOXN1-FZD7 1495 41164 4874 7923 37918 CXXC4-FOXN1-FRZB 1502 99 9554 17642 46105 DAAM1-FOXN1-WNT2B 1503 50616 30398 2836 38247 FOSL1-FOXN1-TLE2 1506 19146 2341 7352 15489 CTNNB1-FOXN1-SFRP4 1509 2040 14820 13619 31097 AXIN1-FOXN1-RHOU 1511 738 4253 15602 21213 AXIN1-FOXN1-FZD7 1535 435 3620 13698 37972 AXIN1-FOXN1-SLC9A3R1 1537 5364 1010 5913 21906 FBXW11-FOXN1-FRAT1 1538 7360 6561 15328 24410 CSNK1G1-FOXN1-FRZB 1549 4678 4300 19697 7337 Table 1: Rankings of FOXN1-X-X. A list of approximately first 125 combinations with rankings below 10,000 out of 57,155. SA - HSIC; Kernel - linear canonical WNT, signaling in TEC lines derived from KRM1−/−mice, when compared with KRM+/+derived TEC lines. Fluorescence activated cell sorting (FACS) analysis of dissociated thymus revealed a reduced frequency of both cortical (BP1+EpCAM+) and medullary (UEA-1+EpCAMhi) epithelial subsets, within the KRM1−/−thymus. 8 RANKING @tiUSING HSIC - RBF 3rd order comb. t1t3t6t12 t24 3rd order comb. t1t3t6t12 t24 FOXN1-KREMEN1-WNT2B 20404 27973 36396 884 30778 FOXN1-KREMEN1-PPP2R1A 21547 15130 581 7154 55001 FOXN1-KREMEN1-WNT3 13156 54946 25548 2636 21279 CSNK1G1-FOXN1-TLE2 3512 4562 37294 11299 13219 FOXN1-KREMEN1-LRP5 11126 50667 3123 17750 44144 FBXW11-FOXN1-KREMEN1 332 11091 39492 5306 3719 FZD5-FOXN1-LRP5 6222 3631 43311 16286 12181 DIXDC1-FOXN1-FBXW4 3530 9085 27138 3878 80 AES-FOXN1-FZD7 1606 10949 51740 1419 32162 DKK1-FOXN1-SENP2 1097 45908 33686 6660 22662 DKK1-FOXN1-FRZB 2424 22948 17226 12105 45671 CSNK1G1-FOXN1-KREMEN1 7103 1710 43024 36832 31611 FBXW11-FOXN1-SENP2 128 6559 40566 11817 4361 CSNK1G1-FOXN1-SLC9A3R 4227 955 39936 15459 33468 AES-FOXN1-WNT2 1377 9293 42185 20047 814 AXIN1-FOXN1-WNT2 7365 9601 53234 18 663 FOSL1-FOXN1-FRZB 1736 15846 50466 9743 17620 CSNK1D-FOXN1-LEF1 6150 3755 45826 20298 28446 CTNNB1-FOXN1-FRZB 5276 5613 54024 3609 15356 FZD5-FOXN1-SENP2 755 12834 53767 11567 34539 CTNNB1-FOXN1-RHOU 3224 1823 49426 36371 35760 DKK1-FOXN1-KREMEN1 881 21837 24344 36049 9457 CTNNB1-FOXN1-KREMEN1 3133 5700 50426 39968 6394 AES-FOXN1-PPP2R1A 723 21691 22365 18788 29319 DAAM1-FOXN1-SFRP4 3365 36634 40858 22210 87 FOXN1-KREMEN1-SFRP4 21541 47835 13452 25040 38289 FZD5-FOXN1-KREMEN1 2473 6345 54537 47495 8093 AES-FOXN1-SENP2 1218 24082 52757 25404 14040 FBXW11-FOXN1-SFRP4 1186 2565 33626 28405 852 FBXW11-FOXN1-FZD1 808 5651 40991 17020 5225 CTBP2-FOXN1-SENP2 7635 19129 49786 125 42056 DIXDC1-FOXN1-TLE2 1845 16001 48828 19868 1211 DIXDC1-FOXN1-FZD8 2700 34651 54623 33036 17361 FZD5-FOXN1-SLC9A3R1 7530 2172 55354 30145 48570 DVL1-FOXN1-FRAT1 5430 52202 38249 34820 8736 CSNK1D-FOXN1-SENP2 1289 8520 39455 7070 25852 DVL1-FOXN1-FZD1 12403 42347 27218 6716 3146 FOSL1-FOXN1-SFRP4 3581 13413 50450 24051 14731 DAAM1-FOXN1-FZD6 1927 40856 30711 15954 7578 CTNNB1-FOXN1-FZD6 11515 4963 34761 21621 26869 FBXW11-FOXN1-FRZB 399 8848 44432 12716 405 CSNK2A1-FOXN1-TCF7 215 23416 42414 29565 28676 FOSL1-FOXN1-KREMEN1 2901 4767 41791 45598 13163 CSNK2A1-FOXN1-KREMEN1 393 11453 41930 49554 21564 AXIN1-FOXN1-FBXW4 10622 8925 10482 6312 6744 DVL1-FOXN1-SFRP4 7106 43765 40948 42922 1021 BTRC-FOXN1-FZD8 2072 28008 41249 39653 29423 DKK1-FOXN1-WNT2B 1531 23991 43537 22407 32006 DAAM1-FOXN1-FRZB 941 43695 29607 4270 188 DVL1-FOXN1-SENP2 3061 30960 37499 23295 15142 FBXW11-FOXN1-TCF7L1 214 35323 46275 16010 3890 CTNNB1-FOXN1-SLC9A3R1 5536 1236 55646 25818 55006 FBXW11-FOXN1-LRP5 3329 14741 47735 14948 367 DIXDC1-FOXN1-KREMEN1 3184 5794 52693 37317 689 CSNK1G1-FOXN1-PPP2R1A 3415 1125 22606 474 39586 FBXW11-FOXN1-FZD8 898 23493 49484 30381 27534 CTNNB1-FOXN1-PPP2R1A 6266 597 36708 568 27942 DVL1-FOXN1-PPP2CA 3346 22402 46063 15109 11834 AES-FOXN1-FZD8 952 20530 45343 38349 44784 FOSL1-FOXN1-GSK3B 1043 18255 47348 52130 53425 FOXN1-KREMEN1-SFRP1 27973 46263 7809 24618 51572 CTNNB1-FOXN1-TLE2 6280 17739 53356 13450 21250 FBXW11-FOXN1-WNT2B 3637 18134 11920 13018 6771 AXIN1-FOXN1-FRAT1 11810 7964 53217 15423 27549 FBXW11-FOXN1-TLE2 703 4758 34636 12462 1117 DAAM1-FOXN1-LRP5 3170 48457 41942 33504 19 DAAM1-FOXN1-PPP2CA 609 47634 41711 1552 16742 FZD5-FOXN1-GSK3B 2002 13824 55143 46713 44426 CSNK2A1-FOXN1-WNT2B 1203 7766 40805 5216 17738 FOSL1-FOXN1-PPP2CA 2050 20570 56654 7068 31115 AES-FOXN1-GSK3A 5614 14376 46340 29843 24805 DKK1-FOXN1-TLE2 2263 27657 20761 18942 14648 CXXC4-FOXN1-SLC9A3R1 5835 4242 55138 22461 50612 CSNK1D-FOXN1-KREMEN1 3060 1922 41865 30003 8137 AXIN1-FOXN1-TCF7 13244 8054 54509 20647 45016 CSNK2A1-FOXN1-FZD8 255 29912 45362 18779 48225 CCND3-FOXN1-FZD8 1397 31287 48789 38612 24901 CSNK1G1-FOXN1-FBXW4 6456 4971 6852 14306 22075 AES-FOXN1-SFRP4 2089 18334 55582 19404 4752 CSNK2A1-FOXN1-SENP2 142 13206 48212 17145 40808 CTNNB1-FOXN1-TCF7L1 2168 10793 56115 29127 14142 AES-FOXN1-TLE1 4311 39109 52117 30044 17018 AES-FOXN1-FRZB 1605 12489 50016 38983 5419 CSNK1D-FOXN1-PPP2CA 677 25144 54449 22625 44186 CXXC4-FOXN1-RHOU 4808 4398 51558 29746 40158 DAAM1-FOXN1-FZD1 988 42509 43886 1406 307 FOSL1-FOXN1-SLC9A3R1 3153 36795 52930 36241 48302 CSNK1G1-FOXN1-TCF7L1 2258 13561 45680 37954 10050 CSNK2A1-FOXN1-FZD6 138 14978 35128 24657 38128 DAAM1-FOXN1-SLC9A3R1 1344 32331 48311 28919 3119 BTRC-FOXN1-WNT4 5283 22892 41659 11506 604 FOSL1-FOXN1-FZD8 2757 28761 55000 20964 54956 DVL1-FOXN1-PPP2R1A 7337 28739 5315 11310 23091 FBXW11-FOXN1-FZD7 145 35268 39903 13575 28607 CSNK1D-FOXN1-WNT2B 2477 5135 39962 11954 23525 EP300-FOXN1-TLE2 1483 17209 53526 19752 11793 DKK1-FOXN1-WNT5A 6288 34859 41852 39340 54131 FBXW11-FOXN1-PPP2CA 551 13483 50027 13549 5121 CSNK1D-FOXN1-FZD8 484 13507 45944 36021 49258 DKK1-FOXN1-SFRP4 6549 20275 30679 15932 4932 DKK1-FOXN1-SLC9A3R1 4065 38156 35913 28567 35213 FOSL1-FOXN1-LRP5 6494 8694 40579 24357 5973 DVL1-FOXN1-SFRP1 4030 42573 44105 866 23391 FBXW11-FOXN1-WNT4 740 5464 41361 27962 1025 CSNK2A1-FOXN1-PPP2CA 278 35287 48412 13705 23060 APC-FOXN1-FRAT1 3161 1596 53917 10232 43563 DVL1-FOXN1-RHOU 5953 29822 43726 42040 18510 AXIN1-FOXN1-TLE2 5475 3810 51423 13930 10241 DIXDC1-FOXN1-FRZB 2525 19420 51785 5266 224 AES-FOXN1-SLC9A3R1 1732 22096 50779 28472 22844 CTNNB1-FOXN1-FBXW4 11108 9146 31589 18968 25721 AXIN1-FOXN1-FZD8 1586 21758 56745 32193 53352 CSNK2A1-FOXN1-SFRP4 363 18135 45540 43080 8388 FBXW11-FOXN1-PPP2R1A 848 15861 49514 22629 20720 DIXDC1-FOXN1-SENP2 1565 39282 50555 4197 2183 CTNNB1-FOXN1-TCF7 5722 8679 50274 17038 47411 DVL1-FOXN1-FZD7 3648 31690 36198 26788 18813 CXXC4-FOXN1-FRZB 2132 6567 49876 23824 32081 DAAM1-FOXN1-WNT2B 1209 51245 29982 6041 1467 FOSL1-FOXN1-TLE2 2685 18230 45559 25782 35344 CTNNB1-FOXN1-SFRP4 3275 1822 55437 22387 4399 AXIN1-FOXN1-RHOU 7032 3608 53224 33577 37035 AXIN1-FOXN1-FZD7 2665 2108 55426 10290 29770 AXIN1-FOXN1-SLC9A3R1 19889 17956 54483 21472 34039 FBXW11-FOXN1-FRAT1 1470 1272 34561 8385 2950 CSNK1G1-FOXN1-FRZB 4205 5593 40227 150 17600 Table 2: Rankings of FOXN1-X-X. A list of approximately first 125 combinations with rankings below 10,000 out of 57,155. SA - HSIC; Kernel - rbf However, their data suggested that a loss of KRM1 led to a severe defect in thymic architecture. Looking at the tables above, one finds the following combinations for KREMEN1 along with FOXN1, to be prominent at 3rd order level - FOXN1-KREMEN1-WNT2B, FOXN1-KREMEN1-WNT3, FOXN1-KREMEN1-LRP5, CTNNB1-FOXN1-KREMEN1, 9