Crosstalk Mediators Implicated in the Stevens-Johnson Syndrome through Gene Regulatory Network Analysis

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Our previous research identified that four key genes, Ikzf1 , Ptger3 , Mavs , and Tlr3 are involved in SJS susceptibility and the conjunctival epithelial innate immune response, demonstrating their role in regulating interferon-stimulated genes. However, the interplay among these regulatory factors remains unclear. This study aimed to elucidate the crosstalk mechanisms between the pathways regulated by these four genes in conjunctival epithelial cells. We constructed a comprehensive gene regulatory network using transcriptomic data from murine conjunctival epithelial cells under 16 distinct conditions, including polyI:C stimulation across wild-type, knockout, and transgenic backgrounds for the key genes. A targeted network analysis systematically identified numerous candidate genes mediating the crosstalk between the regulatory pathways initiated by Ikzf1 , Ptger3 , Mavs , and Tlr3 . The identified candidates suggest the involvement of diverse signaling pathways previously unlinked to SJS pathology. Our findings suggest that the pathogenesis of SJS may arise not from the dysfunction of isolated genes but from the disruption of a balance maintained by intricate pathway crosstalk. Stevens-Johnson syndrome Gene regulatory network Crosstalk Innate immunity Transcriptome analysis Figures Figure 1 INTRODUCTION Stevens-Johnson syndrome (SJS) is a rare, severe mucocutaneous disease, often triggered by medications or infections, characterized by erythematous skin lesions and ulcerative erosions of mucous membranes, including the conjunctiva [ 1 – 3 ]. Although common drugs, such as some cold medicines, can precipitate this condition, leading to a significant detrimental impact on patients' quality of life, the precise molecular mechanisms underlying its pathogenesis remain largely elusive. Our previous research has focused on four key genes implicated in SJS susceptibility and/or conjunctival epithelial innate immunity: Ikzf1, Ptger3, Mavs , and Tlr3 . Ikzf1 encodes the IKAROS family zinc finger DNA-binding protein and was identified as an SJS susceptibility gene through genome-wide association studies (GWAS) across diverse populations, including Japanese, Korean, Indian, and Brazilian individuals [ 4 ]. Ptger3 , encoding prostaglandin E receptor 3 (EP3), was also identified as an SJS-associated gene in GWAS [ 5 , 6 ]. Mavs (Mitochondrial Antiviral Signaling protein; also known as IPS-1, CARDIF, or VISA) is a critical adaptor protein located on the mitochondrial outer membrane, essential for promoting antiviral interferon production in response to cytoplasmic viral RNA [ 7 – 9 ]. Tlr3 (Toll-like receptor 3) is a receptor important for innate immunity, recognizing virus-derived double-stranded RNA (dsRNA) primarily within endosomes to initiate downstream signaling cascades [ 10 – 12 ]. To investigate the interplay among these factors, we established an in vitro experimental system using conjunctival epithelial cells [ 13 , 14 ]. Utilizing this system, we demonstrated that polyinosine-polycytidylic acid (polyI:C), a synthetic analog of dsRNA, potently induces the expression of various inflammatory cytokines. The production of these cytokines and related molecules can be quantified by monitoring the expression of a panel of 23 genes: Eif2ak2, Timp3, Aim2, Adm, Gbp2, Apol9b, Sp100, Usp18, Cxcl10, Rsad2, Rtp4, Oasl2, Mx2, Ifitm1, Oasl1, Ifit3, Ifi44, Ifih1, Ifit1, Ddx60, Irf7, Ddx58 , and Dhx58 , which are predominantly interferon-stimulated genes (ISGs) [ 15 , 16 ]. While the signaling pathways leading to the production of these ISGs are becoming clearer, the specific factors that mediate crosstalk between the four key genes ( Ikzf1, Ptger3, Mavs , and Tlr3 ) remain largely unknown. Elucidating the complex gene regulatory networks (GRNs) is essential for a comprehensive understanding of these signaling interactions. GENIE3 (Gene Network Inference with Ensemble of Trees) is an algorithm that infers GRNs from gene expression data by employing tree-based regression methods, such as random forests, to estimate the strength of directed interactions between genes based on genome-wide expression profiles measured under multiple conditions [ 17 ]. In this study, we generated genome-wide gene expression profiles from murine conjunctival epithelial cells under 16 distinct experimental conditions. These conditions involved polyI:C stimulation of cells derived from wild-type mice and mice harboring knockout (KO) or transgenic modifications in each of the four key genes ( Ikzf1 , Ptger3 , Mavs , Tlr3 ). By applying GENIE3 to this extensive dataset, we aimed to identify mediators linking these four principal factors to the 23 downstream ISGs. Furthermore, we sought to pinpoint molecules responsible for crosstalk by identifying overlapping regulatory interactions within the reconstructed GRN. METHODS Mice BALB/c mice, purchased from CLEA (Tokyo, Japan), were used as controls. Ikzf1 transgenic mice are those described in ref [ 18 ]. Ptger3 −/− [ 19 ], Mavs −/− [ 20 ], and Tlr3 −/− [ 21 ] mice were backcrossed for more than 10 generations onto a BALB/c background. All mice were used at 8–12 weeks of age and were maintained on a 12-h light/dark cycle under specific pathogen-free conditions. Mice were humanely euthanized by an overdose of pentobarbital anesthesia. Gene Expression Microarray Analysis Microarray analysis was performed as previously described [ 18 ]. Briefly, samples were hybridized to Affymetrix GeneChip® Mouse Gene 1.0 ST arrays (Affymetrix, Santa Clara, CA, USA). These microarrays contain 35,557 probe sets targeting over 28,853 genes. All procedures were conducted according to the manufacturer’s protocols. The microarrays were scanned using a GeneChip Scanner 3000 7G (Affymetrix) and the resulting images were visually inspected to check for hybridization artifacts. Gene Regulatory Network Analysis Gene regulatory network (GRN) inference was conducted using the GENIE3 algorithm (version 1.24.0) on R (version 4.3.3). The analysis was performed by invoking the GENIE3() function with its default parameters. To ensure reproducibility of the results, the random seed was explicitly set to 123 using the set.seed(123) function prior to analysis. Use of AI in Manuscript Preparation During the preparation of this work, the authors used Google Gemini (Google LLC) and ChatGPT (OpenAI) for the purposes of English language proofreading, editing, and improving clarity. The authors reviewed and edited the AI-generated suggestions and take full responsibility for the content of this publication. RESULTS Generation of a Gene Regulatory Network from Murine Conjunctival Epithelial Cells To elucidate the molecular interactions relevant to SJS pathogenesis, we first analyzed a comprehensive gene expression dataset. This dataset comprised genome-wide expression profiles for 25,235 genes derived from murine conjunctival epithelial cells under 16 distinct experimental conditions. These conditions were systematically designed by considering the presence or absence of polyI:C stimulation across four genetic backgrounds: wild-type (WT), Ptger3 knockout (KO), Mavs KO, and Tlr3 KO, each further stratified by the presence or absence of an Ikzf1 transgene. This design resulted in a total of 16 conditions (2 states of polyI:C stimulation × 4 genetic backgrounds × 2 states of Ikzf1 transgene). Using the GENIE3 algorithm, we inferred a GRN by estimating the weights of directed regulatory interactions between all possible regulator-target gene pairs. This analysis yielded a total of 636,304,067 interactions. The interaction weights ranged from a maximum of 0.0141 (observed for the Gm11361 → Gm9005 interaction) to a minimum of 0. The distribution of these weights indicated that the 10th, 100th, 1000th, and 10000th ranked interactions exhibited weights of 0.0133, 0.0104, 0.00806, and 0.00574, respectively. A significant number of pairs, specifically 466,114,692, constituting 73.25% of the total, had an interaction weight of 0, suggesting no direct regulatory relationship under the conditions tested. Identification of Mediators Linking Key SJS-Associated Genes to Interferon-Stimulated Genes (ISGs) Next, we focused on identifying mediators that link the four key SJS-associated genes ( Ikzf1, Ptger3, Mavs, Tlr3 ) as regulators to the panel of 23 downstream ISGs as targets (as defined in the Introduction). To achieve this, we employed a strategy based on overlapping high-confidence interactions (Fig. 1 ). Specifically, for each of the four key genes, we extracted the top 1,000 target genes with the highest regulatory interaction weights. Concurrently, for each of the 23 ISGs, we extracted the top 1,000 regulator genes with the highest interaction weights. Putative mediators were then identified as genes present in both lists – i.e., genes that are strongly regulated by one of the four key factors and, in turn, strongly regulate one or more ISGs. The process began with the inference of a gene regulatory network (GRN) from 16 distinct expression microarray datasets using GENIE3, yielding approximately 636 million potential regulator-target gene pairs. Subsequently, to identify genes mediating the response to the four key SJS-associated genes ('4 regulators') and influencing the 23 interferon-stimulated genes (ISGs; '23 target genes'), the following steps were performed: (1) For each of the '4 regulators,' the top 1,000 putative target genes were selected. (2) For each of the '23 target genes' (ISGs), the top 1,000 putative upstream regulator genes were selected. (3) 'Candidate mediators' were then identified for each of the '4 regulators' by finding overlapping genes between its top 1,000 targets (from step 1) and the collective list of top 1,000 regulators of any of the '23 target genes' (from step 2). This effectively identifies genes that are downstream of a key regulator and upstream of one or more ISGs. (4) Finally, 'candidate crosstalk mediators' were identified by finding those 'candidate mediators' (from step 3) that were common to the mediator lists derived for at least two of the '4 regulators,' thus suggesting a role in mediating signal convergence from multiple key SJS-associated pathways onto the ISG response. This approach successfully identified several candidate mediators. For instance, Parp12 , a gene known for its involvement in the NF-κB signaling pathway pertinent to innate immunity [ 22 , 23 ], was repeatedly identified as a mediator connecting Tlr3 to a broad set of 18 ISGs ( Aim2, Apol9b, Ddx58, Ddx60, Dhx58, Eif2ak2, Gbp2, Ifih1, Ifit1, Ifit3, Irf7, Mx2, Oasl1, Oasl2, Rsad2, Rtp4, Sp100 , and Usp18 ). This implies a (not necessarily direct) regulatory cascade of Tlr3 → Parp12 → multiple ISGs. Furthermore, other factors implicated in innate immune signaling, such as Nmi (linked to 11 ISGs) and Irf9 (linked to 8 ISGs), both components of the Jak/STAT pathway [ 24 – 26 ], were also identified as mediators. These results suggest that our extraction methodology can effectively uncover both direct and indirect regulatory links. Pinpointing Crosstalk Molecules Between Ikzf1 and Ptger3 Signaling To further delineate the signaling architecture and identify molecules mediating crosstalk between the pathways initiated by the four key SJS-associated genes, we analyzed the mediators identified above for overlaps (as depicted schematically in Fig. 1 , bottom panel). We specifically investigated mediators common to the Ikzf1 -driven and Ptger3 -driven responses influencing ISG expression. This focused analysis yielded a list of 12 candidate crosstalk genes (Table 1 ). Notably, this list of 12 genes included Nfatc4 (Nuclear Factor of Activated T-cells 4), a transcription factor with known roles in immune regulation [ 27 ], whose significance in this context will be further explored in the Discussion. To validate the expression changes of these 12 candidate crosstalk genes observed in the microarray data, we performed quantitative PCR (qPCR), most of which were consistent with the microarray profiles. Table 1 Ikzf1 and Ptger3 Gene Count Description Psmb8 8 proteasome (prosome, macropain) subunit, beta type 8 (large multifunctional peptidase 7) Fam24a 7 family with sequence similarity 24, member A Prpf40b 5 pre-mRNA processing factor 40B Nfatc4 4 nuclear factor of activated T cells, cytoplasmic, calcineurin dependent 4 Nnmt 4 nicotinamide N-methyltransferase n-R5s220 4 nuclear encoded rRNA 5S 220 Casc1 3 dynein axonemal intermediate chain 7 Ccdc50-ps 1 Ccdc50 retrotransposed pseudogene Cdc34-ps 1 cell division cycle 34B Gm26300; Gm11285 1 predicted gene, 26300 predicted gene, 11285 Olfr270 1 olfactory receptor family 13 subfamily D member 1 Plin2 1 perilipin 2 Given the four key SJS-associated genes ( Ikzf1, Ptger3, Mavs , and Tlr3 ), a total of six distinct pairwise crosstalk interactions can be systematically investigated. Following the initial analysis of Ikzf1 and Ptger3 crosstalk (Table 1 ), we extended the same methodology—identifying overlapping mediators between a key gene's targets and an ISG's regulators and then identifying common mediators between pairs of key genes—to the remaining five pairwise combinations. Specifically, we sought to identify candidate genes mediating crosstalk between Ikzf1 and Mavs (Table 2 ), Ikzf1 and Tlr3 (Table 3 ), Ptger3 and Mavs (Table 4 ), Ptger3 and Tlr3 (Table 5 ), and finally, Mavs and Tlr3 (Table 6 ). Table 2 Ikzf1 and Mavs Gene Count Desciption 4930523C07Rik 7 RIKEN cDNA 4930523C07 gene H2-D1; H2-L 7 histocompatibility 2, D region locus 1; histocompatibility 2, D region locus L Mfap1a 7 microfibrillar-associated protein 1A Slirp 6 SRA stem-loop interacting RNA binding protein Ero1l 5 endoplasmic reticulum oxidoreductase 1 alpha Mfap1b 5 microfibrillar-associated protein 1B Acp2 3 acid phosphatase 2, lysosomal Aplp2 2 amyloid beta precursor-like protein 2 Coq2 2 coenzyme Q2 4-hydroxybenzoate polyprenyltransferase Invs 2 inversin Skiv2l 2 SKI2 subunit of superkiller complex Tmem263 2 transmembrane protein 263 Dnah7b 1 dynein, axonemal, heavy chain 7B Hist2h3c2; Hist2h3c1 1 H3 clustered histone 15; H3 clustered histone 14 LOC105246409; 3222401L13Rik 1 RIKEN cDNA 3222401L13 gene Serpinb9 1 serine (or cysteine) peptidase inhibitor, clade B, member 9 Usp3 1 ubiquitin specific peptidase 3 Table 3 Ikzf1 and Tlr3 Gene Count Description Nrk 5 Nik related kinase Wdfy3 5 WD repeat and FYVE domain containing 3 Ost4 3 oligosaccharyltransferase complex subunit 4 (non-catalytic) Cep250 2 centrosomal protein 250 Fbxw7 2 F-box and WD-40 domain protein 7 Gm25595; Gm16106 2 predicted gene, 25595; predicted gene 16106 Gm28795; Gm28275; Gm28203 2 predicted gene 28795; predicted gene 28275; predicted gene 28203 Gm5464 2 predicted gene 5464 Grn 2 granulin Ppp1ca 2 protein phosphatase 1 catalytic subunit alpha Rapgef3 2 Rap guanine nucleotide exchange factor (GEF) 3 Siglece 2 sialic acid binding Ig-like lectin E Suox 2 sulfite oxidase Ube2s 2 ubiquitin-conjugating enzyme E2S Wrb 2 guided entry of tail-anchored proteins factor 1 Abcc4 1 ATP-binding cassette, sub-family C member 4 Cpne2 1 copine II Cyp2d11 1 cytochrome P450, family 2, subfamily d, polypeptide 11 Gm8388 1 predicted gene 8388 Mdm2 1 transformed mouse 3T3 cell double minute 2 Npy5r 1 neuropeptide Y receptor Y5 Olfr195 1 olfactory receptor family 5 subfamily K member 3 Rnf181 1 ring finger protein 181 Sulf1 1 sulfatase 1 Thyn1 1 thymocyte nuclear protein 1 Ubxn1 1 UBX domain protein 1 Vcam1 1 vascular cell adhesion molecule 1 Table 4 Ptger3 and Mavs Gene Count Description Gm23193 9 predicted gene, 23193 Rexo2 9 RNA exonuclease 2 Emp2 5 epithelial membrane protein 2 Efcab2 4 EF-hand calcium binding domain 2 F5 2 coagulation factor V Pou6f1 2 POU domain, class 6, transcription factor 1 4933409K07Rik; Gm21093; Gm3883 1 RIKEN cDNA 4933409K07 gene; predicted gene, 21093; predicted gene 3883 Mir466 1 microRNA 466 Olfr1205 1 olfactory receptor family 4 subfamily C member 11C Rce1 1 Ras converting CAAX endopeptidase 1 Scin 1 scinderin Suv39h2 1 suppressor of variegation 3–9 2 Tox 1 thymocyte selection-associated high mobility group box Vti1b 1 vesicle transport through interaction with t-SNAREs 1B Zic3 1 zinc finger protein of the cerebellum 3 n-R5s40 1 nuclear encoded rRNA 5S 40 Table 5 Ptger3 and Tlr3 Gene Count Description Gm21242 5 predicted gene, 21242 Csde1 3 cold shock domain containing E1, RNA binding Gm21163; Gm20818 3 predicted gene, 21163 predicted gene, 20818 Mapk14 3 mitogen-activated protein kinase 14 Arhgdig 2 Rho GDP dissociation inhibitor gamma Gm20907; Ssty2 2 predicted gene, 20907; spermiogenesis specific transcript on the Y 2 Cat 1 catalase Chit1 1 chitinase 1 Emx2 1 empty spiracles homeobox 2 Lgi2 1 leucine-rich repeat LGI family, member 2 Nr1d1 1 nuclear receptor subfamily 1, group D, member 1 Pfas 1 phosphoribosylformylglycinamidine synthase (FGAR amidotransferase) R3hdm1 1 R3H domain containing 1 Smarca2 1 SWI/SNF related BAF chromatin remodeling complex subunit ATPase 2 Tchh 1 trichohyalin Tirap 1 toll-interleukin 1 receptor (TIR) domain-containing adaptor protein Tpcn1 1 two pore channel 1 Table 6 Mavs and Tlr3 Gene Count Description B930041F14Rik 3 fibronectin type III domain containing 10 Ccnk 3 cyclin K Gm21943; Gm20852; Gm20868 2 predicted gene, 21943; predicted gene, 20852; predicted gene, 20868 Moxd1 2 monooxygenase, DBH-like 1 Tmem94 2 transmembrane protein 94 4933405O20Rik 1 RIKEN cDNA 4933405O20 gene 4933415F23Rik 1 protein phosphatase 1, regulatory inhibitor subunit 14B like Fndc9 1 fibronectin type III domain containing 9 Gm5449 1 predicted pseudogene 5449 Gmfb 1 glia maturation factor, beta Lrig1 1 leucine-rich repeats and immunoglobulin-like domains 1 Pdcd6ip 1 programmed cell death 6 interacting protein Plk2 1 polo like kinase 2 Rap2c 1 RAP2C, member of RAS oncogene family Sf3a2 1 splicing factor 3a, subunit 2 Snap91 1 synaptosomal-associated protein 91 Sos1 1 SOS Ras/Rac guanine nucleotide exchange factor 1 This comprehensive analysis revealed several specific candidate crosstalk mediators for distinct pairs. Notably, for the interaction between Ptger3 and Tlr3 signaling pathways, Tirap (TIR domain containing adaptor protein) [ 28 ], Arhgdig (Rho GDP dissociation inhibitor gamma) [ 29 ], and Mapk14 (Mitogen-activated protein kinase 14, also known as p38α) [ 30 ] were identified as potential mediators (Table 5 ). Furthermore, Pdcd6ip (Programmed cell death 6 interacting protein, also known as Alix) [ 31 ] and Plk2 (Polo like kinase 2) [ 32 ] emerged as candidate mediators of crosstalk specifically between the Mavs and Tlr3 pathways (Table 6 ). The identification of these functionally diverse molecules, implicated in various signaling cascades (e.g., TLR signaling via TIRAP, MAPK signaling, and cell fate regulation), highlights the complex and multifaceted nature of the signal transduction pathways mediating crosstalk among these core SJS-associated genes. DISCUSSION In this study, we aimed to unravel the complex interplay between four key genes associated with SJS— Ikzf1, Ptger3, Mavs , and Tlr3 —by identifying candidate genes that mediate their crosstalk in the regulation of ISG expression. To achieve this, we first constructed an extensive GRN, comprising over 600 million gene-gene interactions, from transcriptomic data derived from 16 distinct cellular conditions in murine conjunctival epithelial cells. We then developed and applied a targeted analytical approach to pinpoint factors commonly situated on the regulatory pathways connecting these key SJS-associated genes to a downstream panel of 23 ISGs. This approach successfully highlighted several promising candidates, including Nfatc4 and other genes identified in our screen (summarized in Table 1 for the Ikzf1/Ptger3 interaction, and Tables 2 – 6 for other pairs), as potential crosstalk mediators between these critical pathways. Our findings suggest that the four key genes contribute to SJS pathogenesis not in isolation, but rather as part of a complex network of crosstalk with various signaling pathways. This regulatory imbalance likely represents a core aspect of the pathogenesis of SJS. [ 33 ]. The emergence of Nfatc4 as a candidate mediator, particularly for crosstalk between Ikzf1 and Ptger3 signaling, warrants special attention. NFAT transcription factors are well-established, pivotal regulators involved in the expression of numerous inflammatory cytokines [ 27 ]. Their transcriptional activity is tightly controlled by their phosphorylation status, with nuclear translocation and subsequent DNA binding critically dependent on dephosphorylation by calcineurin, a calcium-dependent phosphatase. Considering that EP3 (the protein product of Ptger3 ) activation is known to trigger downstream calcium signaling cascades [ 34 ], a functional link from PTGER3 signaling to NFAT activation is biologically plausible and provides a coherent mechanistic hypothesis. The possible link between NFAT and Ikzf1 is further emphasized by recent literature reporting that NFAT family members and IKAROS (the protein product of Ikzf1 ) can interact on gene expression [ 35 ], a dynamic that may also be operational within the conjunctival epithelial system investigated here. A significant innovation of the present study lies in our methodological framework. Rather than solely focusing on direct regulatory links predicted by GENIE3 from the four key SJS-associated genes to the ISG panel, our approach prioritized the identification of mediator genes that bridge these upstream and downstream effector genes. Furthermore, by systematically searching for mediators common to pathways initiated by different key SJS genes, we could specifically unmask candidate molecules responsible for pathway crosstalk. This hierarchical, network-based interrogation suggests an effective strategy for applying GRN data to dissect complex biological problems, particularly those involving interconnected signaling pathways. Our results, such as the identification of Tirap , Arhgdig , and Mapk14 as candidate mediators between Ptger3 and Tlr3 , or Pdcd6ip and Plk2 between Mavs and Tlr3 , further exemplify the utility of this approach in generating testable hypotheses about specific molecular interactions. We must note a general limitation inherent in GRN inference: networks derived from gene expression data, including those generated by GENIE3, primarily reflect statistical associations and do not, in themselves, definitively prove direct, causal regulatory relationships. The underlying predictions are based on regression analyses of co-expression patterns across various conditions. However, the overall validity of our analytical pipeline is supported by its ability to correctly identify factors already known to participate in relevant signaling pathways. For instance, our analysis pinpointed Parp12 , implicated in NF-κB signaling, and Nmi and Irf9 , components of the Jak/STAT pathway, as mediators in ISG regulation (as detailed in the Results). This concordance with established knowledge suggests that our method can identify factors involved in the web of gene expression regulation, thereby providing a valuable foundation and experimentally testable hypotheses for future investigations into SJS pathogenesis. CONCLUSIONS This study successfully constructed a comprehensive gene regulatory network from a Stevens-Johnson syndrome (SJS) model system, providing a powerful resource for dissecting its complex pathology. Through a targeted, stepwise analytical approach, we identified specific candidate genes mediating crosstalk between the pathways of four key SJS-associated regulators. The identification of these mediators suggests the involvement of diverse and previously unlinked signaling cascades, reinforcing the conclusion that SJS pathogenesis is driven not by single gene defects, but by a critical imbalance within this intricate regulatory network. The crosstalk molecules uncovered here offer novel insights into the molecular basis of SJS and represent a rich set of potential targets for future therapeutic development. Abbreviations • ARVO Association for Research in Vision and Ophthalmology • dsRNA Double-stranded RNA • EP3 Prostaglandin E receptor 3 • GENIE3 Gene Network Inference with Ensemble of Trees • GRN Gene Regulatory Network • GWAS Genome-Wide Association Study • ISG Interferon-Stimulated Gene • PolyI C:Polyinosine-polycytidylic acid • qPCR Quantitative Polymerase Chain Reaction • SJS Stevens-Johnson Syndrome Declarations Ethics approval and consent to participate Ethics approval was provided by the institutional review board at Kyoto Prefectural University of Medicine, Kyoto, Japan. All experimental procedures were approved by the Committee on Animal Research of Kyoto Prefectural University of Medicine, Kyoto, Japan and all studies were in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available in the GEO repository, GSE307435, and in the Zenodo repository, https://doi.org/ 10.5281/zenodo.15655596. Competing interests The authors declare that they have no competing interests. Funding This work was supported in part by JSPS KAKENHI Grant Number JP25K10208 to JT and by JP24K12748 to MU, and by JST Moonshot R&D Program Grant Number JPMJMS2023 to JT. Authors' contributions MW and JT performed computational analysis. HN, KM, and YN performed biological experiments. JT, MU, SK, and CS supervised the project. MW and JT wrote the manuscript with the assistance from other authors. Acknowledgements The Ptger3 −/− mice were a gift from Professor Narumiya, and the Mavs −/− and Tlr3 −/− mice were a gift from Professor Akira. We also thank Professor Tamiya for his insightful discussion. References Hung SI, Mockenhaupt M, Blumenthal KG, Abe R, Ueta M, Ingen-Housz-Oro S, et al. 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NFAT, immunity and cancer: a transcription factor comes of age. Nat Rev Immunol. 2010;10(9):645–56. Akira S, Takeda K. Toll-like receptor signalling. Nat Rev Immunol. 2004;4(7):499–511. Etienne-Manneville S, Hall A. Rho GTPases in cell biology. Nature. 2002;420(6916):629–35. Zarubin T, Han J. Activation and signaling of the p38 MAP kinase pathway. Cell Res. 2005;15(1):11–8. Hurley JH, Hanson PI. Membrane budding and scission by the ESCRT machinery: it's all in the neck. Nat Rev Mol Cell Biol. 2010;11(8):556–66. Archambault V, Glover D. Polo-like kinases: conservation and divergence in their functions and regulation. Nat Rev Mol Cell Biol. 2009;10:265–75. Ueta M. Pathogenesis of Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis with severe ocular complications. Front Med. 2021;8:651247. Hatae N, Sugimoto Y, Ichikawa A. Prostaglandin receptors: advances in the study of EP3 receptor signaling. J Biochem. 2002;131(6):781–4. Ichiyama K, Long J, Kobayashi Y, Horita Y, Kinoshita T, Nakamura Y, et al. Transcription factor Ikzf1 associates with Foxp3 to repress gene expression in Treg cells and limit autoimmunity and anti-tumor immunity. Immunity. 2024;57(9):2043–e206010. Additional Declarations No competing interests reported. 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14:05:10","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130921,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7540138/v1/182e113079616fc66e8b1a4b.html"},{"id":93146703,"identity":"100c709b-8149-4bf1-9f6c-86ceca5fdd13","added_by":"auto","created_at":"2025-10-09 13:57:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273048,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the analytical pipeline for identifying candidate crosstalk mediators.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7540138/v1/6096643a3b2f5f6c3ec29cec.jpeg"},{"id":93147976,"identity":"8747065c-c21b-4a87-95c7-0303ece645ca","added_by":"auto","created_at":"2025-10-09 14:13:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1202266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7540138/v1/16d6d425-4889-4aee-a03c-a4c321014d15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Crosstalk Mediators Implicated in the Stevens-Johnson Syndrome through Gene Regulatory Network Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eStevens-Johnson syndrome (SJS) is a rare, severe mucocutaneous disease, often triggered by medications or infections, characterized by erythematous skin lesions and ulcerative erosions of mucous membranes, including the conjunctiva [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although common drugs, such as some cold medicines, can precipitate this condition, leading to a significant detrimental impact on patients' quality of life, the precise molecular mechanisms underlying its pathogenesis remain largely elusive.\u003c/p\u003e\u003cp\u003eOur previous research has focused on four key genes implicated in SJS susceptibility and/or conjunctival epithelial innate immunity: \u003cem\u003eIkzf1, Ptger3, Mavs\u003c/em\u003e, and \u003cem\u003eTlr3\u003c/em\u003e. \u003cem\u003eIkzf1\u003c/em\u003e encodes the IKAROS family zinc finger DNA-binding protein and was identified as an SJS susceptibility gene through genome-wide association studies (GWAS) across diverse populations, including Japanese, Korean, Indian, and Brazilian individuals [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cem\u003ePtger3\u003c/em\u003e, encoding prostaglandin E receptor 3 (EP3), was also identified as an SJS-associated gene in GWAS [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eMavs\u003c/em\u003e (Mitochondrial Antiviral Signaling protein; also known as IPS-1, CARDIF, or VISA) is a critical adaptor protein located on the mitochondrial outer membrane, essential for promoting antiviral interferon production in response to cytoplasmic viral RNA [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003cem\u003eTlr3\u003c/em\u003e (Toll-like receptor 3) is a receptor important for innate immunity, recognizing virus-derived double-stranded RNA (dsRNA) primarily within endosomes to initiate downstream signaling cascades [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo investigate the interplay among these factors, we established an in vitro experimental system using conjunctival epithelial cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Utilizing this system, we demonstrated that polyinosine-polycytidylic acid (polyI:C), a synthetic analog of dsRNA, potently induces the expression of various inflammatory cytokines. The production of these cytokines and related molecules can be quantified by monitoring the expression of a panel of 23 genes: \u003cem\u003eEif2ak2, Timp3, Aim2, Adm, Gbp2, Apol9b, Sp100, Usp18, Cxcl10, Rsad2, Rtp4, Oasl2, Mx2, Ifitm1, Oasl1, Ifit3, Ifi44, Ifih1, Ifit1, Ddx60, Irf7, Ddx58\u003c/em\u003e, and \u003cem\u003eDhx58\u003c/em\u003e, which are predominantly interferon-stimulated genes (ISGs) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile the signaling pathways leading to the production of these ISGs are becoming clearer, the specific factors that mediate crosstalk between the four key genes (\u003cem\u003eIkzf1, Ptger3, Mavs\u003c/em\u003e, and \u003cem\u003eTlr3\u003c/em\u003e) remain largely unknown. Elucidating the complex gene regulatory networks (GRNs) is essential for a comprehensive understanding of these signaling interactions. GENIE3 (Gene Network Inference with Ensemble of Trees) is an algorithm that infers GRNs from gene expression data by employing tree-based regression methods, such as random forests, to estimate the strength of directed interactions between genes based on genome-wide expression profiles measured under multiple conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we generated genome-wide gene expression profiles from murine conjunctival epithelial cells under 16 distinct experimental conditions. These conditions involved polyI:C stimulation of cells derived from wild-type mice and mice harboring knockout (KO) or transgenic modifications in each of the four key genes (\u003cem\u003eIkzf1\u003c/em\u003e, \u003cem\u003ePtger3\u003c/em\u003e, \u003cem\u003eMavs\u003c/em\u003e, \u003cem\u003eTlr3\u003c/em\u003e). By applying GENIE3 to this extensive dataset, we aimed to identify mediators linking these four principal factors to the 23 downstream ISGs. Furthermore, we sought to pinpoint molecules responsible for crosstalk by identifying overlapping regulatory interactions within the reconstructed GRN.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMice\u003c/h2\u003e\u003cp\u003eBALB/c mice, purchased from CLEA (Tokyo, Japan), were used as controls. \u003cem\u003eIkzf1\u003c/em\u003e transgenic mice are those described in ref [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. \u003cem\u003ePtger3\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], \u003cem\u003eMavs\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and \u003cem\u003eTlr3\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] mice were backcrossed for more than 10 generations onto a BALB/c background. All mice were used at 8\u0026ndash;12 weeks of age and were maintained on a 12-h light/dark cycle under specific pathogen-free conditions. Mice were humanely euthanized by an overdose of pentobarbital anesthesia.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGene Expression Microarray Analysis\u003c/h3\u003e\n\u003cp\u003eMicroarray analysis was performed as previously described [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Briefly, samples were hybridized to Affymetrix GeneChip\u0026reg; Mouse Gene 1.0 ST arrays (Affymetrix, Santa Clara, CA, USA). These microarrays contain 35,557 probe sets targeting over 28,853 genes. All procedures were conducted according to the manufacturer\u0026rsquo;s protocols. The microarrays were scanned using a GeneChip Scanner 3000 7G (Affymetrix) and the resulting images were visually inspected to check for hybridization artifacts.\u003c/p\u003e\n\u003ch3\u003eGene Regulatory Network Analysis\u003c/h3\u003e\n\u003cp\u003eGene regulatory network (GRN) inference was conducted using the GENIE3 algorithm (version 1.24.0) on R (version 4.3.3). The analysis was performed by invoking the GENIE3() function with its default parameters. To ensure reproducibility of the results, the random seed was explicitly set to 123 using the set.seed(123) function prior to analysis.\u003c/p\u003e\n\u003ch3\u003eUse of AI in Manuscript Preparation\u003c/h3\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used Google Gemini (Google LLC) and ChatGPT (OpenAI) for the purposes of English language proofreading, editing, and improving clarity. The authors reviewed and edited the AI-generated suggestions and take full responsibility for the content of this publication.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGeneration of a Gene Regulatory Network from Murine Conjunctival Epithelial Cells\u003c/h2\u003e\u003cp\u003eTo elucidate the molecular interactions relevant to SJS pathogenesis, we first analyzed a comprehensive gene expression dataset. This dataset comprised genome-wide expression profiles for 25,235 genes derived from murine conjunctival epithelial cells under 16 distinct experimental conditions. These conditions were systematically designed by considering the presence or absence of polyI:C stimulation across four genetic backgrounds: wild-type (WT), \u003cem\u003ePtger3\u003c/em\u003e knockout (KO), \u003cem\u003eMavs\u003c/em\u003e KO, and \u003cem\u003eTlr3\u003c/em\u003e KO, each further stratified by the presence or absence of an \u003cem\u003eIkzf1\u003c/em\u003e transgene. This design resulted in a total of 16 conditions (2 states of polyI:C stimulation \u0026times; 4 genetic backgrounds \u0026times; 2 states of \u003cem\u003eIkzf1\u003c/em\u003e transgene).\u003c/p\u003e\u003cp\u003eUsing the GENIE3 algorithm, we inferred a GRN by estimating the weights of directed regulatory interactions between all possible regulator-target gene pairs. This analysis yielded a total of 636,304,067 interactions. The interaction weights ranged from a maximum of 0.0141 (observed for the Gm11361 \u0026rarr; Gm9005 interaction) to a minimum of 0. The distribution of these weights indicated that the 10th, 100th, 1000th, and 10000th ranked interactions exhibited weights of 0.0133, 0.0104, 0.00806, and 0.00574, respectively. A significant number of pairs, specifically 466,114,692, constituting 73.25% of the total, had an interaction weight of 0, suggesting no direct regulatory relationship under the conditions tested.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of Mediators Linking Key SJS-Associated Genes to Interferon-Stimulated Genes (ISGs)\u003c/h3\u003e\n\u003cp\u003eNext, we focused on identifying mediators that link the four key SJS-associated genes (\u003cem\u003eIkzf1, Ptger3, Mavs, Tlr3\u003c/em\u003e) as regulators to the panel of 23 downstream ISGs as targets (as defined in the Introduction). To achieve this, we employed a strategy based on overlapping high-confidence interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, for each of the four key genes, we extracted the top 1,000 target genes with the highest regulatory interaction weights. Concurrently, for each of the 23 ISGs, we extracted the top 1,000 regulator genes with the highest interaction weights. Putative mediators were then identified as genes present in both lists \u0026ndash; i.e., genes that are strongly regulated by one of the four key factors and, in turn, strongly regulate one or more ISGs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe process began with the inference of a gene regulatory network (GRN) from 16 distinct expression microarray datasets using GENIE3, yielding approximately 636\u0026nbsp;million potential regulator-target gene pairs. Subsequently, to identify genes mediating the response to the four key SJS-associated genes ('4 regulators') and influencing the 23 interferon-stimulated genes (ISGs; '23 target genes'), the following steps were performed: (1) For each of the '4 regulators,' the top 1,000 putative target genes were selected. (2) For each of the '23 target genes' (ISGs), the top 1,000 putative upstream regulator genes were selected. (3) 'Candidate mediators' were then identified for each of the '4 regulators' by finding overlapping genes between its top 1,000 targets (from step 1) and the collective list of top 1,000 regulators of any of the '23 target genes' (from step 2). This effectively identifies genes that are downstream of a key regulator and upstream of one or more ISGs. (4) Finally, 'candidate crosstalk mediators' were identified by finding those 'candidate mediators' (from step 3) that were common to the mediator lists derived for at least two of the '4 regulators,' thus suggesting a role in mediating signal convergence from multiple key SJS-associated pathways onto the ISG response.\u003c/p\u003e\u003cp\u003eThis approach successfully identified several candidate mediators. For instance, \u003cem\u003eParp12\u003c/em\u003e, a gene known for its involvement in the NF-κB signaling pathway pertinent to innate immunity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], was repeatedly identified as a mediator connecting \u003cem\u003eTlr3\u003c/em\u003e to a broad set of 18 ISGs (\u003cem\u003eAim2, Apol9b, Ddx58, Ddx60, Dhx58, Eif2ak2, Gbp2, Ifih1, Ifit1, Ifit3, Irf7, Mx2, Oasl1, Oasl2, Rsad2, Rtp4, Sp100\u003c/em\u003e, and \u003cem\u003eUsp18\u003c/em\u003e). This implies a (not necessarily direct) regulatory cascade of \u003cem\u003eTlr3\u003c/em\u003e \u0026rarr; \u003cem\u003eParp12\u003c/em\u003e \u0026rarr; multiple ISGs. Furthermore, other factors implicated in innate immune signaling, such as \u003cem\u003eNmi\u003c/em\u003e (linked to 11 ISGs) and \u003cem\u003eIrf9\u003c/em\u003e (linked to 8 ISGs), both components of the Jak/STAT pathway [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], were also identified as mediators. These results suggest that our extraction methodology can effectively uncover both direct and indirect regulatory links.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePinpointing Crosstalk Molecules Between\u003c/b\u003e \u003cb\u003eIkzf1\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003ePtger3\u003c/b\u003e \u003cb\u003eSignaling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further delineate the signaling architecture and identify molecules mediating crosstalk between the pathways initiated by the four key SJS-associated genes, we analyzed the mediators identified above for overlaps (as depicted schematically in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, bottom panel). We specifically investigated mediators common to the \u003cem\u003eIkzf1\u003c/em\u003e-driven and \u003cem\u003ePtger3\u003c/em\u003e-driven responses influencing ISG expression. This focused analysis yielded a list of 12 candidate crosstalk genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, this list of 12 genes included \u003cem\u003eNfatc4\u003c/em\u003e (Nuclear Factor of Activated T-cells 4), a transcription factor with known roles in immune regulation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], whose significance in this context will be further explored in the Discussion. To validate the expression changes of these 12 candidate crosstalk genes observed in the microarray data, we performed quantitative PCR (qPCR), most of which were consistent with the microarray profiles.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eIkzf1\u003c/em\u003e and \u003cem\u003ePtger3\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePsmb8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eproteasome (prosome, macropain) subunit, beta type 8 (large multifunctional peptidase 7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFam24a\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003efamily with sequence similarity 24, member A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePrpf40b\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epre-mRNA processing factor 40B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNfatc4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003enuclear factor of activated T cells, cytoplasmic, calcineurin dependent 4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNnmt\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003enicotinamide N-methyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003en-R5s220\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003enuclear encoded rRNA 5S 220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCasc1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003edynein axonemal intermediate chain 7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCcdc50-ps\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCcdc50 retrotransposed pseudogene\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCdc34-ps\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecell division cycle 34B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm26300;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm11285\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene, 26300\u003c/p\u003e\u003cp\u003epredicted gene, 11285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOlfr270\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eolfactory receptor family 13 subfamily D member 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePlin2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eperilipin 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGiven the four key SJS-associated genes (\u003cem\u003eIkzf1, Ptger3, Mavs\u003c/em\u003e, and \u003cem\u003eTlr3\u003c/em\u003e), a total of six distinct pairwise crosstalk interactions can be systematically investigated. Following the initial analysis of \u003cem\u003eIkzf1\u003c/em\u003e and \u003cem\u003ePtger3\u003c/em\u003e crosstalk (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we extended the same methodology\u0026mdash;identifying overlapping mediators between a key gene's targets and an ISG's regulators and then identifying common mediators between pairs of key genes\u0026mdash;to the remaining five pairwise combinations. Specifically, we sought to identify candidate genes mediating crosstalk between \u003cem\u003eIkzf1\u003c/em\u003e and \u003cem\u003eMavs\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), \u003cem\u003eIkzf1\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), \u003cem\u003ePtger3\u003c/em\u003e and \u003cem\u003eMavs\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), \u003cem\u003ePtger3\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and finally, \u003cem\u003eMavs\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eIkzf1\u003c/em\u003e and \u003cem\u003eMavs\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDesciption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e4930523C07Rik\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRIKEN cDNA 4930523C07 gene\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eH2-D1;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eH2-L\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehistocompatibility 2, D region locus 1;\u003c/p\u003e\u003cp\u003ehistocompatibility 2, D region locus L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMfap1a\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emicrofibrillar-associated protein 1A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSlirp\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRA stem-loop interacting RNA binding protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEro1l\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eendoplasmic reticulum oxidoreductase 1 alpha\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMfap1b\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emicrofibrillar-associated protein 1B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAcp2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eacid phosphatase 2, lysosomal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAplp2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eamyloid beta precursor-like protein 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCoq2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecoenzyme Q2 4-hydroxybenzoate polyprenyltransferase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInvs\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003einversin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSkiv2l\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSKI2 subunit of superkiller complex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTmem263\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etransmembrane protein 263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDnah7b\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003edynein, axonemal, heavy chain 7B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHist2h3c2;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eHist2h3c1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eH3 clustered histone 15;\u003c/p\u003e\u003cp\u003eH3 clustered histone 14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLOC105246409;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e3222401L13Rik\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRIKEN cDNA 3222401L13 gene\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSerpinb9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eserine (or cysteine) peptidase inhibitor, clade B, member 9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUsp3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eubiquitin specific peptidase 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eIkzf1\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNrk\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNik related kinase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWdfy3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWD repeat and FYVE domain containing 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOst4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eoligosaccharyltransferase complex subunit 4 (non-catalytic)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCep250\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecentrosomal protein 250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFbxw7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-box and WD-40 domain protein 7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm25595;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm16106\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene, 25595;\u003c/p\u003e\u003cp\u003epredicted gene 16106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm28795;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm28275;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm28203\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene 28795;\u003c/p\u003e\u003cp\u003epredicted gene 28275;\u003c/p\u003e\u003cp\u003epredicted gene 28203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm5464\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene 5464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrn\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003egranulin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePpp1ca\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eprotein phosphatase 1 catalytic subunit alpha\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRapgef3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRap guanine nucleotide exchange factor (GEF) 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSiglece\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esialic acid binding Ig-like lectin E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSuox\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esulfite oxidase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUbe2s\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eubiquitin-conjugating enzyme E2S\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWrb\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eguided entry of tail-anchored proteins factor 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAbcc4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATP-binding cassette, sub-family C member 4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCpne2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecopine II\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCyp2d11\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecytochrome P450, family 2, subfamily d, polypeptide 11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm8388\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene 8388\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMdm2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etransformed mouse 3T3 cell double minute 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNpy5r\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eneuropeptide Y receptor Y5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOlfr195\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eolfactory receptor family 5 subfamily K member 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRnf181\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ering finger protein 181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSulf1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esulfatase 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eThyn1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ethymocyte nuclear protein 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUbxn1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUBX domain protein 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVcam1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003evascular cell adhesion molecule 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003ePtger3\u003c/em\u003e and \u003cem\u003eMavs\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm23193\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene, 23193\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRexo2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRNA exonuclease 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEmp2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eepithelial membrane protein 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEfcab2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEF-hand calcium binding domain 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecoagulation factor V\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePou6f1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePOU domain, class 6, transcription factor 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e4933409K07Rik;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm21093;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm3883\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRIKEN cDNA 4933409K07 gene;\u003c/p\u003e\u003cp\u003epredicted gene, 21093;\u003c/p\u003e\u003cp\u003epredicted gene 3883\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMir466\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emicroRNA 466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOlfr1205\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eolfactory receptor family 4 subfamily C member 11C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRce1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRas converting CAAX endopeptidase 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eScin\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003escinderin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSuv39h2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esuppressor of variegation 3\u0026ndash;9 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTox\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ethymocyte selection-associated high mobility group box\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVti1b\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003evesicle transport through interaction with t-SNAREs 1B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eZic3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ezinc finger protein of the cerebellum 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003en-R5s40\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003enuclear encoded rRNA 5S 40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003ePtger3\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm21242\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene, 21242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCsde1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecold shock domain containing E1, RNA binding\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm21163;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm20818\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene, 21163\u003c/p\u003e\u003cp\u003epredicted gene, 20818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMapk14\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emitogen-activated protein kinase 14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eArhgdig\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRho GDP dissociation inhibitor gamma\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm20907;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSsty2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene, 20907;\u003c/p\u003e\u003cp\u003espermiogenesis specific transcript on the Y 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecatalase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChit1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echitinase 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEmx2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eempty spiracles homeobox 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLgi2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eleucine-rich repeat LGI family, member 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNr1d1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003enuclear receptor subfamily 1, group D, member 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePfas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ephosphoribosylformylglycinamidine synthase (FGAR amidotransferase)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR3hdm1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR3H domain containing 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSmarca2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSWI/SNF related BAF chromatin remodeling complex subunit ATPase 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTchh\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etrichohyalin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTirap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etoll-interleukin 1 receptor (TIR) domain-containing adaptor protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTpcn1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etwo pore channel 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eMavs\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eB930041F14Rik\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003efibronectin type III domain containing 10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCcnk\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecyclin K\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm21943;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm20852;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGm20868\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted gene, 21943; predicted gene, 20852; predicted gene, 20868\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMoxd1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emonooxygenase, DBH-like 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTmem94\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etransmembrane protein 94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e4933405O20Rik\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRIKEN cDNA 4933405O20 gene\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e4933415F23Rik\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eprotein phosphatase 1, regulatory inhibitor subunit 14B like\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFndc9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003efibronectin type III domain containing 9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGm5449\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epredicted pseudogene 5449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGmfb\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eglia maturation factor, beta\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLrig1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eleucine-rich repeats and immunoglobulin-like domains 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePdcd6ip\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eprogrammed cell death 6 interacting protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePlk2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epolo like kinase 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRap2c\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRAP2C, member of RAS oncogene family\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSf3a2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esplicing factor 3a, subunit 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSnap91\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esynaptosomal-associated protein 91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSos1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSOS Ras/Rac guanine nucleotide exchange factor 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis comprehensive analysis revealed several specific candidate crosstalk mediators for distinct pairs. Notably, for the interaction between \u003cem\u003ePtger3\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e signaling pathways, \u003cem\u003eTirap\u003c/em\u003e (TIR domain containing adaptor protein) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], \u003cem\u003eArhgdig\u003c/em\u003e (Rho GDP dissociation inhibitor gamma) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and \u003cem\u003eMapk14\u003c/em\u003e (Mitogen-activated protein kinase 14, also known as p38α) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] were identified as potential mediators (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, \u003cem\u003ePdcd6ip\u003c/em\u003e (Programmed cell death 6 interacting protein, also known as Alix) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and \u003cem\u003ePlk2\u003c/em\u003e (Polo like kinase 2) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] emerged as candidate mediators of crosstalk specifically between the \u003cem\u003eMavs\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e pathways (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The identification of these functionally diverse molecules, implicated in various signaling cascades (e.g., TLR signaling via TIRAP, MAPK signaling, and cell fate regulation), highlights the complex and multifaceted nature of the signal transduction pathways mediating crosstalk among these core SJS-associated genes.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we aimed to unravel the complex interplay between four key genes associated with SJS\u0026mdash;\u003cem\u003eIkzf1, Ptger3, Mavs\u003c/em\u003e, and \u003cem\u003eTlr3\u003c/em\u003e\u0026mdash;by identifying candidate genes that mediate their crosstalk in the regulation of ISG expression. To achieve this, we first constructed an extensive GRN, comprising over 600\u0026nbsp;million gene-gene interactions, from transcriptomic data derived from 16 distinct cellular conditions in murine conjunctival epithelial cells. We then developed and applied a targeted analytical approach to pinpoint factors commonly situated on the regulatory pathways connecting these key SJS-associated genes to a downstream panel of 23 ISGs. This approach successfully highlighted several promising candidates, including \u003cem\u003eNfatc4\u003c/em\u003e and other genes identified in our screen (summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the \u003cem\u003eIkzf1/Ptger3\u003c/em\u003e interaction, and Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e for other pairs), as potential crosstalk mediators between these critical pathways. Our findings suggest that the four key genes contribute to SJS pathogenesis not in isolation, but rather as part of a complex network of crosstalk with various signaling pathways. This regulatory imbalance likely represents a core aspect of the pathogenesis of SJS. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe emergence of \u003cem\u003eNfatc4\u003c/em\u003e as a candidate mediator, particularly for crosstalk between \u003cem\u003eIkzf1\u003c/em\u003e and \u003cem\u003ePtger3\u003c/em\u003e signaling, warrants special attention. NFAT transcription factors are well-established, pivotal regulators involved in the expression of numerous inflammatory cytokines [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Their transcriptional activity is tightly controlled by their phosphorylation status, with nuclear translocation and subsequent DNA binding critically dependent on dephosphorylation by calcineurin, a calcium-dependent phosphatase. Considering that EP3 (the protein product of \u003cem\u003ePtger3\u003c/em\u003e) activation is known to trigger downstream calcium signaling cascades [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], a functional link from PTGER3 signaling to NFAT activation is biologically plausible and provides a coherent mechanistic hypothesis. The possible link between NFAT and \u003cem\u003eIkzf1\u003c/em\u003e is further emphasized by recent literature reporting that NFAT family members and IKAROS (the protein product of \u003cem\u003eIkzf1\u003c/em\u003e) can interact on gene expression [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], a dynamic that may also be operational within the conjunctival epithelial system investigated here.\u003c/p\u003e\u003cp\u003eA significant innovation of the present study lies in our methodological framework. Rather than solely focusing on direct regulatory links predicted by GENIE3 from the four key SJS-associated genes to the ISG panel, our approach prioritized the identification of \u003cem\u003emediator\u003c/em\u003e genes that bridge these upstream and downstream effector genes. Furthermore, by systematically searching for mediators common to pathways initiated by different key SJS genes, we could specifically unmask candidate molecules responsible for pathway crosstalk. This hierarchical, network-based interrogation suggests an effective strategy for applying GRN data to dissect complex biological problems, particularly those involving interconnected signaling pathways. Our results, such as the identification of \u003cem\u003eTirap\u003c/em\u003e, \u003cem\u003eArhgdig\u003c/em\u003e, and \u003cem\u003eMapk14\u003c/em\u003e as candidate mediators between \u003cem\u003ePtger3\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e, or \u003cem\u003ePdcd6ip\u003c/em\u003e and \u003cem\u003ePlk2\u003c/em\u003e between \u003cem\u003eMavs\u003c/em\u003e and \u003cem\u003eTlr3\u003c/em\u003e, further exemplify the utility of this approach in generating testable hypotheses about specific molecular interactions.\u003c/p\u003e\u003cp\u003eWe must note a general limitation inherent in GRN inference: networks derived from gene expression data, including those generated by GENIE3, primarily reflect statistical associations and do not, in themselves, definitively prove direct, causal regulatory relationships. The underlying predictions are based on regression analyses of co-expression patterns across various conditions. However, the overall validity of our analytical pipeline is supported by its ability to correctly identify factors already known to participate in relevant signaling pathways. For instance, our analysis pinpointed \u003cem\u003eParp12\u003c/em\u003e, implicated in NF-κB signaling, and \u003cem\u003eNmi\u003c/em\u003e and \u003cem\u003eIrf9\u003c/em\u003e, components of the Jak/STAT pathway, as mediators in ISG regulation (as detailed in the Results). This concordance with established knowledge suggests that our method can identify factors involved in the web of gene expression regulation, thereby providing a valuable foundation and experimentally testable hypotheses for future investigations into SJS pathogenesis.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study successfully constructed a comprehensive gene regulatory network from a Stevens-Johnson syndrome (SJS) model system, providing a powerful resource for dissecting its complex pathology. Through a targeted, stepwise analytical approach, we identified specific candidate genes mediating crosstalk between the pathways of four key SJS-associated regulators. The identification of these mediators suggests the involvement of diverse and previously unlinked signaling cascades, reinforcing the conclusion that SJS pathogenesis is driven not by single gene defects, but by a critical imbalance within this intricate regulatory network. The crosstalk molecules uncovered here offer novel insights into the molecular basis of SJS and represent a rich set of potential targets for future therapeutic development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; ARVO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAssociation for Research in Vision and Ophthalmology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; dsRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDouble-stranded RNA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; EP3\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProstaglandin E receptor 3\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; GENIE3\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Network Inference with Ensemble of Trees\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; GRN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Regulatory Network\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; GWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-Wide Association Study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; ISG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterferon-Stimulated Gene\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; PolyI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC:Polyinosine-polycytidylic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; qPCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantitative Polymerase Chain Reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; SJS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStevens-Johnson Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was provided by the institutional review board at Kyoto Prefectural University of Medicine, Kyoto, Japan. All experimental procedures were approved by the Committee on Animal Research of Kyoto Prefectural University of Medicine, Kyoto, Japan and all studies were in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the GEO repository, GSE307435, and in the Zenodo repository, https://doi.org/ 10.5281/zenodo.15655596.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by JSPS KAKENHI Grant Number JP25K10208 to JT and by JP24K12748 to MU, and by JST Moonshot R\u0026amp;D Program Grant Number JPMJMS2023 to JT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMW and JT performed computational analysis. HN, KM, and YN performed biological experiments. JT, MU, SK, and CS supervised the project. MW and JT wrote the manuscript with the assistance from other authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003ePtger3\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice were a gift from Professor Narumiya, and the \u003cem\u003eMavs\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e\u003c/em\u003e and \u003cem\u003eTlr3\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e\u003c/em\u003e mice were a gift from Professor Akira. We also thank Professor Tamiya for his insightful discussion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHung SI, Mockenhaupt M, Blumenthal KG, Abe R, Ueta M, Ingen-Housz-Oro S, et al. Severe cutaneous adverse reactions. 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Allergy. 2018;73(2):395\u0026ndash;404.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUshikubi F, Segi E, Sugimoto Y, Murata T, Matsuoka T, Kobayashi T, et al. Impaired febrile response in mice lacking the prostaglandin E receptor subtype EP3. Nature. 1998;395(6699):281\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawai T, Takahashi K, Sato S, Coban C, Kumar H, Kato H, et al. IPS-1, an adaptor triggering RIG-I- and Mda5-mediated type I interferon induction. Nat Immunol. 2005;6(10):981\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUeta M, Uematsu S, Akira S, Kinoshita S. Toll-like receptor 3 enhances late-phase reaction of experimental allergic conjunctivitis. J Allergy Clin Immunol. 2009;123(5):1187\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWelsby I, Hutin D, Gueydan C, Kruys V, Rongvaux A, Leo O. PARP12, an interferon-stimulated gene involved in the control of protein translation and inflammation. J Biol Chem. 2014;289(38):26642\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFehr AR, Singh SA, Kerr CM, Mukai S, Higashi H, Aikawa M. The impact of PARPs and ADP-ribosylation on inflammation and host-pathogen interactions. Genes Dev. 2020;34(5\u0026ndash;6):341\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePruitt HC, Devine DJ, Samant RS. Roles of N-Myc and STAT interactor in cancer: From initiation to dissemination. Int J Cancer. 2016;139(3):491\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVillarino AV, Kanno Y, Ferdinand JR, O'Shea JJ. Mechanisms of Jak/STAT signaling in immunity and disease. J Immunol. 2015;194(1):21\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaul A, Ismail MN, Tang TH, Ng SK. Phosphorylation of interferon regulatory factor 9 (IRF9). 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Nat Rev Mol Cell Biol. 2010;11(8):556\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArchambault V, Glover D. Polo-like kinases: conservation and divergence in their functions and regulation. Nat Rev Mol Cell Biol. 2009;10:265\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUeta M. Pathogenesis of Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis with severe ocular complications. Front Med. 2021;8:651247.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHatae N, Sugimoto Y, Ichikawa A. Prostaglandin receptors: advances in the study of EP3 receptor signaling. J Biochem. 2002;131(6):781\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIchiyama K, Long J, Kobayashi Y, Horita Y, Kinoshita T, Nakamura Y, et al. Transcription factor \u003cem\u003eIkzf1\u003c/em\u003e associates with \u003cem\u003eFoxp3\u003c/em\u003e to repress gene expression in Treg cells and limit autoimmunity and anti-tumor immunity. Immunity. 2024;57(9):2043\u0026ndash;e206010.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stevens-Johnson syndrome, Gene regulatory network, Crosstalk, Innate immunity, Transcriptome analysis","lastPublishedDoi":"10.21203/rs.3.rs-7540138/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7540138/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStevens-Johnson syndrome (SJS) is a rare and severe mucocutaneous disorder often triggered by medications or infections. Our previous research identified that four key genes, \u003cem\u003eIkzf1\u003c/em\u003e, \u003cem\u003ePtger3\u003c/em\u003e, \u003cem\u003eMavs\u003c/em\u003e, and \u003cem\u003eTlr3\u003c/em\u003e are involved in SJS susceptibility and the conjunctival epithelial innate immune response, demonstrating their role in regulating interferon-stimulated genes. However, the interplay among these regulatory factors remains unclear. This study aimed to elucidate the crosstalk mechanisms between the pathways regulated by these four genes in conjunctival epithelial cells. We constructed a comprehensive gene regulatory network using transcriptomic data from murine conjunctival epithelial cells under 16 distinct conditions, including polyI:C stimulation across wild-type, knockout, and transgenic backgrounds for the key genes. A targeted network analysis systematically identified numerous candidate genes mediating the crosstalk between the regulatory pathways initiated by \u003cem\u003eIkzf1\u003c/em\u003e, \u003cem\u003ePtger3\u003c/em\u003e, \u003cem\u003eMavs\u003c/em\u003e, and \u003cem\u003eTlr3\u003c/em\u003e. The identified candidates suggest the involvement of diverse signaling pathways previously unlinked to SJS pathology. Our findings suggest that the pathogenesis of SJS may arise not from the dysfunction of isolated genes but from the disruption of a balance maintained by intricate pathway crosstalk.\u003c/p\u003e","manuscriptTitle":"Crosstalk Mediators Implicated in the Stevens-Johnson Syndrome through Gene Regulatory Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 13:57:05","doi":"10.21203/rs.3.rs-7540138/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T11:20:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T15:36:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58809341100540168780649930891542928137","date":"2025-10-23T12:00:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T10:31:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275319851473917132827376325388919300853","date":"2025-09-30T07:09:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132572337548285924521816600945851942190","date":"2025-09-28T06:30:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-28T06:27:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T14:02:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T12:37:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-09T11:58:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Genomics","date":"2025-09-09T11:55:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df2a443c-4527-4ca2-ac3c-9ca7b46a5dfa","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T10:23:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-09 13:57:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7540138","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7540138","identity":"rs-7540138","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}