Rewired JAK-STAT Pathway in Circulating CD4+CLA+ and CD4+ Naïve T Cells from Atopic Dermatitis and Psoriasis Patients

Rewired JAK-STAT Pathway in Circulating CD4+CLA+ and CD4+ Naïve T Cells from Atopic Dermatitis and Psoriasis Patients | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 October 2025 V1 Latest version Share on Rewired JAK-STAT Pathway in Circulating CD4+CLA+ and CD4+ Naïve T Cells from Atopic Dermatitis and Psoriasis Patients Authors : Martin Pook 0000-0002-8386-0203 [email protected] , Regina Maruste , Peep Kolberg 0000-0001-8898-8153 , Kaur Alasoo , Tonis Org , Liisi Raam , Anu Remm , Dario Greco , Antonio Federico , Külli Kingo , Kai Kisand , and Ana Rebane 0000-0001-6051-1361 Authors Info & Affiliations https://doi.org/10.22541/au.176129863.38437296/v1 212 views 198 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Skin-homing Cutaneous Lymphocyte-Associated Antigen (CLA) expressing T cells play a key role in the pathogenesis of atopic dermatitis (AD) and psoriasis (Ps). We aimed to characterize transcriptomic and epigenetic profiles of circulating CD4 + CLA + and CD4 + naïve T cells from patients with AD and Ps to find shared and unique molecular signatures associated with the diseases. Methods: Circulating CD4 + CLA + and CD4 + naïve T cells were sorted from peripheral blood mononuclear cells of patients with AD (n=11), Ps (n=10), and healthy individuals (n=11), followed by ATAC- and mRNA sequencing and data analyses. Results: Transcriptomic and epigenetic landscapes differed markedly between CD4 + CLA + and CD4 + naïve T cells. In both AD and Ps, transcriptomic alterations within these cell populations were substantial, whereas changes in chromatin accessibility were relatively modest. In CLA + T cells, AD and Ps patients exhibited altered expression of genes involved in T cell activation, proliferation, and JAK-STAT signaling. These effects were more pronounced in AD, while a stronger association with innate immune activation was seen in Ps. Notably, also CD4 + naïve T cells displayed disease-associated transcriptomic changes in both AD and Ps, including the JAK-STAT pathway and heightened sensitivity to IL-2-mediated activation. Epigenetic profiling further revealed disease-associated chromatin regions linked to transcription factors involved in immune regulation. Conclusion: Both, the CD4 + CLA + and CD4 + naïve T cells exhibit transcriptomic and epigenetic alterations in AD and Ps, suggesting the influence of the chronic inflammatory milieu with partially overlapping as well as disease-specific pathways being affected. Rewired JAK-STAT Pathway in Circulating CD4⁺CLA⁺ and CD4⁺ Naïve T Cells from Atopic Dermatitis and Psoriasis Patients Short title: Rewired JAK–STAT in T cells of AD and psoriasis Martin Pook PhD a , Regina Maruste MSc a , Peep Kolberg MSc b , Kaur Alasoo PhD b , Tõnis Org PhD c,d , Liisi Raam MD, PhD e,f , Anu Remm MSc a , Dario Greco PhD g,h,i , Antonio Federico PhD g,h,i , Külli Kingo MD, PhD e,f , Kai Kisand MD, PhD a , Ana Rebane PhD a a Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia b Institute of Computer Science, Faculty of Science and Technology, University of Tartu, Tartu, Estonia c Institute of Molecular and Cell Biology, Faculty of Science and Technology, University of Tartu, Tartu, Estonia d Institute of Genomics, Faculty of Science and Technology, University of Tartu, Tartu, Estonia e Institute of Clinical Medicine, Faculty of Medicine, University of Tartu, Tartu, Estonia f Clinic of Dermatology, Tartu University Hospital, Tartu, Estonia g Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland h Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland i Institute of Biotechnology, University of Helsinki, Helsinki, Finland Acknowledgments This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 821511 (BIOMAP). The JU receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. This publication/dissemination reflects only the author’s view, and the JU is not responsible for any use that may be made of the information it contains. The research was additionally funded by personal research grants to Ana Rebane (PRG1259), Kai Kisand (PRG1117) and Külli Kingo (PRG1189) from Estonian Research Council. Disclosure of potential conflict of interest: The authors declare that they have no relevant conflicts of interest. Abstract Background: Skin-homing Cutaneous Lymphocyte-Associated Antigen (CLA) expressing T cells play a key role in the pathogenesis of atopic dermatitis (AD) and psoriasis (Ps). We aimed to characterize transcriptomic and epigenetic profiles of circulating CD4⁺CLA⁺ and CD4⁺ naïve T cells from patients with AD and Ps to find shared and unique molecular signatures associated with the diseases. Methods: Circulating CD4⁺CLA⁺ and CD4⁺ naïve T cells were sorted from peripheral blood mononuclear cells of patients with AD (n=11), Ps (n=10), and healthy individuals (n=11), followed by ATAC- and mRNA sequencing and data analyses. Results: Transcriptomic and epigenetic landscapes differed markedly between CD4⁺CLA⁺ and CD4⁺ naïve T cells. In both AD and Ps, transcriptomic alterations within these cell populations were substantial, whereas changes in chromatin accessibility were relatively modest. In CLA⁺ T cells, AD and Ps patients exhibited altered expression of genes involved in T cell activation, proliferation, and JAK-STAT signaling. These effects were more pronounced in AD, while a stronger association with innate immune activation was seen in Ps. Notably, also CD4⁺ naïve T cells displayed disease-associated transcriptomic changes in both AD and Ps, including the JAK-STAT pathway and heightened sensitivity to IL-2-mediated activation. Epigenetic profiling further revealed disease-associated chromatin regions linked to transcription factors involved in immune regulation. Conclusion: Both, the CD4⁺CLA⁺ and CD4⁺ naïve T cells exhibit transcriptomic and epigenetic alterations in AD and Ps, suggesting the influence of the chronic inflammatory milieu with partially overlapping as well as disease-specific pathways being affected. Key words ATAC sequencing; Atopic dermatitis; CD4⁺ T cells; Cutaneous Lymphocyte-Associated Antigen; psoriasis Introduction Atopic dermatitis (AD) and psoriasis (Ps) are chronic inflammatory skin diseases. While AD is primarily associated with increased T helper (Th)2-driven inflammation and Ps with Th17-mediated immunity, Th1, Th22, Th17 and innate immune pathways may additionally influence both conditions 1–4 . In lesional skin, both diseases share striking similarities in gene expression signatures, yet each also exhibits distinct disease-specific alterations, with AD displaying greater molecular heterogeneity than Ps 5,6 . Altered T cell responses in AD and Ps are strongly, though not exclusively, associated with circulating CD4⁺ memory T cells expressing cutaneous lymphocyte-associated antigen (CLA), which enables their homing to the skin 7,8 . Therefore, at least a subset of circulating CD4⁺CLA⁺ T cells is influenced by inflammatory events in the skin and may serve as valuable marker cells for AD and Ps. CD4⁺CLA⁺ T cells consist almost exclusively of memory T cells capable of infiltrating cutaneous tissue, with very limited location to other tissues or lymphoid organs 9,10 . Traditionally, memory T cells are classified into two main subsets based on CCR7 expression: central memory T cells (CCR7⁺, T CM ) and effector memory T cells (CCR7\RL־, T EM ) 11 . However, CCR7 expression appears to have little impact on the egress of CD4⁺ T cells from inflamed skin 12 . Both the T CM and T EM are part of the circulating CD4⁺CLA⁺ T cell population along with the T regulatory (Treg) cells 13 . Notably, tissue resident memory T cells (T RM ) can also contribute to the circulating population 14 . To date, CD4⁺CLA⁺ T cells have been predominantly characterized in terms of phenotype and function, including transcriptomic profiling. However, previous epigenetic studies in AD have focused on DNA methylation 15 , while alterations at chromatin level have not yet been addressed in either AD or Ps. Given the translational significance 16 , a deeper understanding is needed of how CD4⁺CLA⁺ T cells can be used as marker cells in AD and Ps. In this study, we used assay for transposase-accessible chromatin sequencing (ATAC-seq) and RNA sequencing (RNA-seq) for analysis of circulating CD4⁺CLA⁺ T cells and CD4⁺ naïve T cells from peripheral blood of patients with AD, Ps and healthy controls (HC) with the aim to identify disease-specific and shared epigenetic and transcriptomic signatures and signaling pathways in both diseases. Methods Whole blood samples from 11 patients with AD, 10 patients with Ps (plaque psoriasis), and 11 HC were collected at Tartu University Hospital (Table E1, Table E2), followed by same-day isolation of peripheral blood mononuclear cells (PBMCs). All patients were with moderate to severe disease severity status and treatment-free for at least two weeks prior the recruitment. None of the patients had prior exposure to conventional systemic therapies, biologics or small molecule drugs. The study was approved by the Human Research Ethics Committee of the University of Tartu (approval no. 340/M-29) and performed according to the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants. PBMC were cryopreserved until immunostaining with fluorochrome-conjugated antibodies (Table E3) and sorted into CD4⁺CLA⁺ and CD4⁺ naïve T cell populations using LE-MA900FP (Sony) sorter with settings provided in Tables E4 and E5 followed by ATAC-seq and RNA-seq protocols and data analysis as overviewed in Fig 1A and B. More detailed methods are provided in the Online Repository available at www.jacionline.org. Data availability Patient sensitive raw sequencing data are deposited into BIOMAP Data Platform and can be made available upon request. Flow cytometric analysis of CD4⁺CLA⁺ T cells reveals differences between AD and Ps in the proportions of T CM cells and Treg subpopulations To perform transcriptome and epigenetic analysis of CD4⁺CLA⁺ T cells populations in AD and Ps, a broad population of CD4⁺ T cells expressing CLA (CD4⁺CLA⁺) was gated (Fig 1, B and Fig E14). CD4⁺ naïve T cells were selected (Fig 1, B and Fig E14) for comparison as control cells circulating in the blood and supposedly not influenced by tissue-specific cues. Flow cytometric analysis and subsequent sorting of both populations (Fig 1A and B) was performed. No significant differences in proportions of viable cells between samples from AD, Ps or HC was seen, while in general less CD4⁺CLA⁺ (average 2 %) compared to CD4⁺ naïve T cells (average 19 %) was among live PBMC-s (Fig E1, A and B). When comparing the sub-populations of CD4⁺ T cells between AD and Ps groups (Fig 1, C and D), there was increase in CD4⁺ naïve T cells in AD (average 58%) compared to Ps (average 42%) (Fig 1, D). Flow cytometric analysis of CCR7 expression of CD4⁺CLA⁺ T cells suggested that there were more T CM cells (CCR7⁺) in Ps (Fig 1, E). In addition, there were more Treg cells in case of AD among the CD4⁺CLA⁺ T cells compared to Ps or HC sample groups (Fig 1, F), while no differences between naïve Treg cell proportions among CD4⁺ naïve T cells (Fig 1, G). Differences in transcriptome of CD4⁺CLA⁺ and naïve T cells are more pronounced than those in chromatin accessibility in AD and Ps Next, sorted CD4⁺CLA⁺ and CD4⁺ naïve T cell populations were subjected to ATAC-seq and RNA-seq analyses, followed by quantitative identification of chromatin accessibility regions (ChARs) and differentially expressed genes (DEGs). First, principal component analysis (PCA) was performed on ATAC-seq (Fig 2, A) and RNA-seq (Fig 3, A) normalized read counts, which revealed clear distinctions between CD4⁺CLA⁺ and CD4⁺ naïve T cells. The overall differences in transcriptomic profiles within different cell types showed the strongest segregation for CD4⁺CLA⁺ T cells, with AD segregating more than Ps from HC group (Fig 3, A). ATAC-seq did not reveal major changes between disease and HC group (Fig 2, A), while still a few statistically different ChARs (Fig 2, B) were detected. Overall, there were more downregulated than upregulated ChARs and DEGs in both cell types and diseases compared to HC group, except for DEGs in the CD4⁺CLA⁺ T cells cells from Ps patients (Fig 3, B). The most significant changes were also detected among downregulated ChARs (Fig 2, B) and DEGs (Fig 3, B). When comparing disease groups (Fig 2, B and Fig 3, B), AD had more DEGs among both CD4⁺CLA⁺ and CD4⁺ naïve T cells (AD-CLA: 2511; AD-naïve: 1345) and differences in the accessible chromatin regions (AD-CLA: 114; AD-naïve: 67) than Ps (DEGs, Ps-CLA: 928, Ps-naïve: 647; ChAR, Ps-CLA: 19, Ps-naïve: 33). Next, the ATAC-seq and RNA-seq results were analyzed to identify disease-specific ChARs and DEGs and those shared between AD and Ps. Shared ChARs (Fig 2, C) were identified exclusively among the downregulated regions (CLA 8; naïve: 12), which was expected due to the limited number of upregulated regions in the Ps, while shared DEGs (Fig 3, C) were detected among up- and downregulated genes (CLA up: 332, CLA down: 329, naïve up: 181, naïve down: 252). In AD, more disease-specific ChARs (Fig 2, C; AD-CLA up: 38, AD-naïve up: 3; AD-CLA down: 68; AD-naïve down: 52) and DEGs (Fig 3, C; AD-CLA up: 877, AD-naïve up: 448; AD-CLA down: 973; AD-naïve down: 464) were detected. Contrary to AD, Ps had more disease-specific upregulated genes (Fig 3, C; Ps-CLA up: 157, naïve up: 116; Ps-CLA down: 110, Ps-naïve down: 98). However, more disease-specific downregulated ChAR-s (Fig 2, C) were found in CD4⁺ naïve (18) compared to CD4⁺CLA⁺ T cells (9), while only a few disease-specific upregulated ChAR-s (Fig 2, C) were identified in Ps (Ps-CLA: 2; Ps-naïve: 3). CD4⁺CLA⁺ T cells from AD and Ps are mitotically active and show altered cytokine signaling affecting the JAK-STAT pathway Next, DEGs of CD4⁺CLA⁺ T cells from AD and Ps were subjected to functional enrichment analysis with STRING 17 , focusing on the Reactome 18 database (STR-RCT). First, the top 250 of the most upregulated or downregulated genes from AD and Ps compared to HC were used in analysis, revealing that CD4⁺CLA⁺ T cells from AD show upregulation of numerous genes associated with cell cycle regulation (Fig E2, A). In contrast, upregulated genes from Ps showed strongest association with immune system related pathways, however, moderate enrichment of genes from mitotic cell cycle pathway was also found (Fig E2, C). Surprisingly, in CD4⁺CLA⁺ T cells from AD, genes regulating cytokine signaling, including IL-4 and IL-13 signaling pathway, were found to be downregulated (Fig E2, B), while downregulated genes from Ps showed functional association with the SMAD2/SMAD3:SMAD4 heterotrimer pathways suggesting alterations in TGF-β related signaling (Fig E2, D). In addition, CD4⁺CLA⁺ T cells form both diseases showed downregulation of genes involved in general transcription regulation (Fig E2, B, D). To further look similarities and differences between AD and Ps, the shared and disease-specific DEGs were subjected to functional enrichment analyses with STR-RCT and Ingenuity Pathway Analysis (IPA). As expected, STR-RCT analysis of shared DEGs of CD4⁺CLA⁺ T cells suggested the activation of mitotic cell cycle (Fig 4, A; Fig E3, A). In line with this, IPA (Fig E6, C and D) highlighted CDKN1A as one of the central regulators supporting active cell cycle. Among other shared influenced pathways, STR-RCT analysis revealed RNA polymerase II transcription regulation and signaling by interleukins to be associated with the downregulated genes, while cellular response to stress was associated with both, up- and downregulated genes (Fig 4, A, Fig E3). Although innate immune system related pathways were associated with shared upregulated genes by STR-RCT analysis, (Fig 4, A, Fig E3), IPA suggested inhibition of upstream regulators, such as IL1B and TNF (Fig E6, C and D). STR-RCT analysis of disease specific DEGs showed many upregulated cell cycle related genes for AD, including those involved in regulation of cell cycle checkpoint and active cell cycle (Fig 4, A; Fig E4, A; Fig E6, A), while IPA additionally revealed increased cell death (Fig E6, A). In addition, STR-RCT analysis of AD specific DEGs suggested that immune system associated pathways are upregulated (Fig 4, A; Fig E4, A), while pathways related to translation elongation, infectious diseases and transcriptional regulation by RUNX1 were downregulated (Fig 4 A; Fig E4, B). Among Ps specific DEGs, STR-RCT analysis revealed the enrichment of upregulated genes from innate and adaptive immune system and TLR4 signaling (Fig 4, A; Fig E5, A), while there was no significant enrichment within downregulated genes. In line with this, IPA suggested activation and crosstalk in immune responses regulated by upstream regulators, such as IL1B, TNF, IL6, IL2, IFNG in Ps (Fig E6, B). Interestingly, among shared DEGs of CD4⁺CLA⁺ T cells from both diseases, several interleukins, chemokines and related receptors were found, of which IL23A , IL4R and CXCR4 were downregulated and IL10RB upregulated (Fig 4, B). Of these genes, IL23A , IL4R and IL10RB were classified as components of JAK-STAT signaling pathway by the enrichment in local STRING network cluster database (Fig E10, A and B). In addition, AD specific genes showed association with interleukin-1 signaling, chemokine signaling and adaptive immunity (Fig 4, B; Fig E10, A and B). Gene expression signatures of CD4⁺ naïve T cells from AD and Ps indicate increased sensitivity to IL-2-dependent activation The same strategy as for CD4⁺CLA⁺ T cells was further applied to identify disease related pathways altered in CD4⁺ naïve T cells from AD and Ps patients. Interestingly, the top 250 most upregulated genes in AD naïve cells were enriched for immune system related genes, with a stronger representation of those involved in the regulation to adaptive immunity (Fig E7, A). Minor association of upregulated genes with interferon gamma signaling and MHC II antigen presentation were also identified (Fig E7, A). The top 250 most downregulated genes in AD suggested an association with transcription regulation with the most significant enrichment for NGF-stimulated transcription (Fig E7, B). In addition, senescence and estrogen signaling were detected as the less significant associations (Fig E7, B). The upregulated genes in Ps showed remarkable enrichment for immune system related genes, especially those regulating innate immunity, of which toll like receptor signaling can be highlighted (Fig E7, C). Among downregulated genes in Ps, genes involved in transcriptional regulation were significantly enriched (Fig E7, D). STR-RCT analysis of shared upregulated genes for AD and Ps revealed enrichment of immune system related genes, with a stronger association with innate immune system (Fig 5, A; Fig E8, A). IPA confirmed the activation of immune system related pathways and highlighted IL2 as one of the possible upstream regulators of the shared gene expression signature (Fig E9, C and D). STR-RCT analysis of shared downregulated genes predicted influence on general and RUNX1-related transcriptional regulation (Fig 5, A; Fig E8, B). Among others, IPA suggested ETS1 and KITLG as key shared inhibited upstream regulators, which in parallel were suggested to trigger activation through IL-2 and increased cell death via FAS-dependent signaling (Fig E9, C and D). STR-RCT analysis of AD specific upregulated genes showed enrichment in immune system related pathways with leading role of adaptive immunity (Fig 5, A; Fig E8, C). The AD specific downregulated genes were associated with transcription regulation and cellular stress (Fig 5, A; Fig E8, D), with specific but weaker association with transcriptional regulation by TP53 (Fig 5, A; Fig E8, D). Of note, IPA suggested IL6, TNF, and IL1B among others as potential upstream regulators being inhibited in AD (Fig E9, A). STR-RCT analysis of Ps specific upregulated genes indicated changes in immune system, while there was no enrichment found for the downregulated genes (Fig 5, A; Fig E8, E). Interestingly, IPA suggested activation of IL33, TNFSF12, IL1A and IL17A as the top upstream regulators among Ps specific genes (Fig E9, B). Analysis of shared DEGs expressing interleukins, chemokines and related receptors showed IL23A being downregulated, while IL2RB and CX3CR1 were upregulated (Fig 5, B). AD specific DEGs for interleukins, chemokines and related receptors were associated with JAK-STAT signaling pathway and interleukin-1 family as detected by the enrichment in local STRING network cluster database (Fig 5, B; Fig E10, C). Similar analysis in Ps showed association with JAK-STAT, signaling from interleukin-2 family and involvement of interleukins 4 and 13 (Fig 5, B; Fig E10, D). Shared gene expression changes in CD4⁺CLA⁺ and CD4⁺ naïve T cells from AD and Ps patients indicate systemic inflammation Since CD4⁺CLA⁺ and CD4⁺ naïve T cells from AD and Ps showed gene expression alterations related to JAK-STAT signaling pathway, we next focused on DEGs shared in both cell types. We found 300 shared upregulated and 404 shared downregulated genes in AD while the corresponding numbers in Ps were 89 and 121, respectively. Further, STR-RCT enrichment analysis showed that shared upregulated genes in AD were enriched in signaling pathways related to immune system regulation (Fig E11, A), while downregulated genes were associated with general transcription regulation (Fig E11, B). In Ps, general immune system related pathways, more specifically, Toll-like receptor signaling were identified (Fig E11, C). Shared downregulated genes in Ps were associated with transcriptional activity of SMAD2/SMAD3:SMAD4 heterotrimer (Fig E11, D). Of note, CISH, known as cytokine-inducible SH2-containing protein, was among the top upregulated genes in both diseases and cell types (Fig 4, A; Fig 5, A). ATAC-seq chromatin accessibility analysis predicts transcription factors that may orchestrate CD4⁺CLA⁺ and CD4⁺ naïve T cell responses in AD and Ps Given the limited number of differential ChARs, integrated analysis of RNA-seq and ATAC-seq showed overlap between DEGs and ChARs in relatively few annotated genes (Table E6). Therefore, we next focused to transcription factor (TF) binding sites and analyzed differential ChARs identified in AD and Ps using WhichTF 19 tool, which applies an ontology-guided functional approach to find context-specific TFs. We focused on the top 20 TFs with the highest ranks and considered those with confirmed expression based on RNA-seq and ranked among the top 10 as the most biologically relevant (Fig 6). However, as epigenetic landscape may be associated with the past or future events in gene expression regulation, all top 20 ranked TFs were included in the functional enrichment analysis using the local STRING network cluster database. Interestingly, the binding sites for top 20 TFs suggested active regulation in association with early growth response for AD CLA⁺ cells (Fig E12, A), whereas negative regulation of calcium ion import across plasma membrane was linked with the more closed chromatin areas (Fig E12, B). Consistent with this, analysis of TFs with confirmed expression from both, more open and closed regions highlighted the same pathways, among others (Fig E13, A). Similar analysis for Ps revealed TFs only for the more downregulated regions (Fig 6, B), while functional association analysis suggested a connection between these TFs and granulocyte differentiation (Fig E12, C; Fig E13, B). In addition, altered ChAR in naïve cells from AD and Ps suggested overlap with mesenchyme development in regards the TF involved (Fig E12, D and E and F), while similar analysis for TFs with confirmed expression proposed influence on general transcription regulation in both diseases (Fig E13, C and D), and the association with mesenchyme development for AD (Fig E13, C). Discussion Although the pivotal role of CD4⁺CLA⁺ T cells in AD and Ps is well documented 7,8 , the studies that compare changes in CD4⁺CLA⁺ T cells between the two diseases or with disease-associated alterations in other T cell subsets are very limited. Here, we show that both the CD4⁺CLA⁺ and CD4⁺ naïve T cells display transcriptomic and epigenetic alterations in AD and Ps with partially overlapping as well as disease-specific pathways being affected. When comparing the two diseases, changes were first determined in cell type numbers. We observed higher proportion of CD4⁺ naïve T cells among all CD4⁺ T cells and confirm the increased frequency of Treg cells 13 among the CD4⁺CLA⁺ T cell subset in AD. Although we did not detect increased proportion of CD4⁺CLA⁺ T cells for the studied diseases 20 , we observed higher proportion of CCR7 expressing cells among CD4⁺CLA⁺ T cells from Ps patients, consistent with previous findings 21 . As expected, we observed that transcriptomic and epigenetic landscapes differ markedly between CD4⁺CLA⁺ and CD4⁺ naïve T cells in all study groups. However, disease-associated transcriptomic changes were more pronounced within these T cell populations compared with epigenetic alterations in both AD and Ps. When we subjected DEGs from CD4⁺CLA⁺ and CD4⁺ naïve T cells to pathway analyses, we observed involvement of JAK-STAT signaling in both diseases and cell types. Interestingly, although activation of the JAK-STAT pathway is a well-known feature in AD and Ps 22,23 , our results do not directly point to the activation of JAK-STAT but rather suggest a rewiring of cytokine signaling in studied T cell subsets. Among other highlights, CD4⁺CLA⁺ cells were observed to be mitotically active, especially in AD and a stronger link with innate immune activation was found in Ps than AD, while CD4⁺ naïve T cells showed lowered threshold for IL-2 dependent activation in both diseases. When we focused on CD4⁺CLA⁺ T cells, we observed that CXCR4 , a marker associated with migration to the lymph nodes 24 was downregulated in both diseases, while chemokine receptors responsible for T cell migration to inflammatory sites ( CCR5 25 associated primarily with Th1 phenotype, CCR3 25,26 with Th2 phenotype) and to skin ( CCR10 27,28 ) were increased in AD and CX3CR1 29,30 in Ps. This, together with functional enrichment for mitotic activity, indicates that CD4⁺CLA⁺ T cells from AD and Ps may be shifted from T CM to T EM . In line with this, upregulation of IL6R 31 and IL2RA 32 in AD, known factors contributing to survival and expansion of T cells, also points to disease-related activation of CD4⁺CLA⁺ T cells. Interestingly, within the shared DEGs, IL4R was downregulated, while IL10RB was upregulated, additionally suggesting that CD4⁺CLA⁺ T cells from both diseases attempt to cope with increased systemic inflammation by rewiring JAK-STAT pathway. Although our main focus was CD4⁺CLA⁺ T cells, it is noteworthy that we observed significant gene expression changes also in CD4⁺ naïve T cells from AD and Ps. To our best knowledge, this finding is novel for AD and Ps, however, it is consistent with recent studies showing that, in particular cases, such as autoimmunity, CD4⁺ naïve T cells may harbor poised and skewed subpopulations 33–35 . We observed increased expression of the IL-2 receptor components in naïve T cells of both diseases, suggesting heightened IL-2 sensitivity and chronically “primed” state for IL-2/STAT5 signaling. This priming likely lowers the threshold for these cells to differentiate into effector or regulatory lineage 36–38 , while simultaneous upregulation of CX3CR1 in diseased conditions in CD4⁺ naïve cells suggests shift towards effector phenotype 29 . As IL-2 is known to signal through the JAK1/JAK3-STAT5 axis, involvement of JAK-STAT pathway may be linked to IL-2 signaling, which is pivotal in regulating T cell proliferation, differentiation, and survival 32,39 . Interestingly, we detected decreased expression of IL23A in both diseases and analyzed cell types, which could be a result of influence of inflammatory environment in AD and Ps, as T cells are not primary source for IL-23 40 . Another intriguing finding was the disease-related increase in CISH expression, which ranked among the top upregulated genes in both CD4⁺CLA⁺ and CD4⁺ naïve T cells in AD and Ps. Although CISH is best known as a member of the suppressor of cytokine signaling family proteins, it has also been linked to accelerated immune aging and suggested as a potential target to reduce inflammaging 41 . This coincides well with the increased low-level chronic inflammation characteristic of AD and Ps, potentially contributing to immune system dysregulation. In line with transcriptome analyses, the ATAC-seq TF binding site analysis from both analyzed cell types and conditions indicated dysfunctional regulation under chronic inflammatory conditions, while there was no obvious association with T cell proliferation or differentiation, suggesting that those pathways may be regulated at the transcriptional or post-transcriptional level. Among the relatively few overlaps we detected between the transcriptome and ATAC-seq results, we found that the transcription factor IKZF1 was associated with the more closed regions in CD4⁺ naïve T cells in AD, while its expression was decreased in CD4⁺ naïve T cells in both diseases. IKZF1 is needed for normal differentiation and activation of T cells, and lack of it could promote inflammatory phenotypes in CD4⁺ naïve T cells in diseased conditions 42–44 . In CD4⁺CLA⁺ T cells in AD, increased HINFP expression was detected coherently with its association with more open chromatin areas. HINFP is linked to increased mitotic activity 45,46 , which aligns well with the transcriptional profile enriched for cell cycle associated DEGs in CD4⁺CLA⁺ cells from AD in the current study. Interestingly, STAT1 binding sites showed associations with more closed chromatin regions in CD4⁺CLA⁺ T cells in AD, which is in line with Th2 skewing and previous knowledge on role of JAK-STAT signaling in AD 47 . This finding also may indicate changes in CD4⁺ T cell effector or memory functions 48,49 . Simultaneous increase in STAT1 mRNA expression in CD4⁺CLA⁺ T cells from AD may be associated with compensatory mechanisms. In CD4⁺CLA⁺ T cells from Ps, ETS1, which is involved in Treg functions 50 , was associated with the more closed regions. In CD4⁺ naïve T cells in Ps, RUNX2, a protein not directly linked with naïve T cell status 51 , while important in T cell development in thymus 52 , was found to be associated with open regions. Our study has also several limitations. First, the heterogeneity of CD4⁺CLA⁺ T cell population containing T CM , T EM and Tregs may have influence on our results. It should be also noted that used patient cohort included only male patients and that age of recruited individuals was variable. In addition, the potential significance of highlighted TFs should be further validated for their functional effects, while functional studies are also needed to determine whether the shared upregulated genes in CD4⁺ T cell subsets, including CISH, may have potential as drug targets. In summary, our findings reveal distinct transcriptomic and epigenetic landscapes of CD4⁺CLA⁺ and naïve T cells in AD and Ps, including JAK-STAT pathway dysregulation in both cell types, mitotic activation in CD4⁺CLA⁺ T cells and increased IL-2 sensitivity in CD4⁺ naïve T cells, all indicative of ongoing chronic inflammation. References 1. Li M, Wang J, Liu Q, et al. Beyond the dichotomy: understanding the overlap between atopic dermatitis and psoriasis. 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B , Overview of gating strategy for cell sorting and subpopulation analyses. Sorted cell populations are in gray boxes. C-F , Proportions (%) of CD4+ CLA+ T-cells ( C ) and CD4+ naïve T-cells ( D ) among CD4+ T-cells, CCR7+ T-cells among CD4+ CLA+ T-cells ( E) , Treg cells among CD4+ CLA+ T-cells ( F), and naïve Treg cells (G) among CD4 + naïve T-cells . Statistical differences in proportions (%) of cell populations are compared with Kruskal-Wallis and Dunn’s multiple comparison test. Fig 2. ATAC-seq analysis of CD4+CLA+ (CLA) and CD4+ naïve (naïve) T-cells shows relatively few disease-linked differences in accessibility of the chromatin. A , Principle component analysis of VST-normalized counts from ATAC-seq. B , Volcano blots of more open (up, red color) and closed (down, blue color) chromatin areas areas (p-adj<0.05) are presented compared with HC. The peaks among the top 10 most up and the top 10 most down based on FC are presented with annotations (GREAT, single nearest gene, 1000 kb) while annotations in bold are among the top 10 most significant differences based on adjusted P value. C , Venn diagrams of shared and disease specific up or downregulated chromatin regions (p-adj<0.05). Fig 3. RNA-seq analysis of CD4+CLA+ (CLA) and CD4 + naïve (naïve) T-cells highlights differences between AD and Ps. A , Principle component analysis of VST-normalized counts from RNA-seq. B , Volcano blots of DEGs significantly (p-adj<0.05) up- (up, red color) or downregulated (down, blue color) compared with HC. C , Venn diagrams of shared and disease specific DEGs being up- or downregulated (p-adj<0.05). Fig 4. Functional enrichment analysis of DEGs from CD4+CLA+ T-cells (CLA) from AD and Ps patients. The overlapping and specific DEGs were analyzed for functional enrichment in Reactome database using STRING web tool. A , Heatmap (DESeq2 VST-normalized reads, scaled on rows) of the top 10 DEGs from each signature. B , Enrichment analysis of differentially expressed interleukins, chemokines and related receptors using local network cluster (STRING) database. Shared (yellow circle) and specific upregulated (red circle) or downregulated (blue circle) genes are presented as full STRING network (line thickness indicates the strength of data support; minimum required interaction score: medium confidence 0.4). Color coded nodes indicate corresponding functional groups. Fig 5. Functional enrichment analysis of transcriptome changes in CD4+ naïve T-cells (naïve) from AD and Ps patients. A , The overlapping and specific DEGs were analyzed for functional enrichment in Reactome database using STRING web tool. Heatmap (DESeq2 VST-normalized reads, scaled on rows) of the top 10 DEGs from each signature. B , Enrichment analysis of differentially expressed interleukins, chemokines and related receptors using local network cluster (STRING) database. Shared (yellow circle) and specific genes upregulated (red circle) or downregulated (blue circle) in this category are presented as full STRING network (line thickness indicates the strength of data support; minimum required interaction score: medium confidence 0.4). Color coded nodes indicate corresponding functional groups. Fig 6. Changes in the accessibility of the chromatin binding sites suggests functionally important transcription factors in CD4 + CLA + (CLA) and CD4 + naïve (naïve) T-cells from AD and Ps patients. ( A-C ) WhichTF tool was used to identify TFs using more open (up) or more closed (down) chromatin regions from ATAC-seq in AD and Ps. No association was found with Ps CLA up regions. Top 20 TF with highest rank are presented as wordclouds, where top 10 of up (red) or down (blue) regions are highlighted. TFs with confirmed expression by transcriptomic analysis are marked with dark edges. Information & Authors Information Version history V1 Version 1 24 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords atopic dermatitis inflammation t cells Authors Affiliations Martin Pook 0000-0002-8386-0203 [email protected] Tartu Ulikool Bio- ja siirdemeditsiini instituut View all articles by this author Regina Maruste Tartu Ulikool Bio- ja siirdemeditsiini instituut View all articles by this author Peep Kolberg 0000-0001-8898-8153 Tartu Ulikooli Arvutiteaduse instituut View all articles by this author Kaur Alasoo Tartu Ulikooli Arvutiteaduse instituut View all articles by this author Tonis Org Tartu Ulikool Molekulaar- ja Rakubioloogia Instituut View all articles by this author Liisi Raam Tartu Ulikool Kliinilise meditsiini instituut View all articles by this author Anu Remm Tartu Ulikool Bio- ja siirdemeditsiini instituut View all articles by this author Dario Greco Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE View all articles by this author Antonio Federico Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE View all articles by this author Külli Kingo Tartu Ulikool Kliinilise meditsiini instituut View all articles by this author Kai Kisand Tartu Ulikool Bio- ja siirdemeditsiini instituut View all articles by this author Ana Rebane 0000-0001-6051-1361 Tartu Ulikool Bio- ja siirdemeditsiini instituut View all articles by this author Metrics & Citations Metrics Article Usage 212 views 198 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Martin Pook, Regina Maruste, Peep Kolberg, et al. Rewired JAK-STAT Pathway in Circulating CD4+CLA+ and CD4+ Naïve T Cells from Atopic Dermatitis and Psoriasis Patients. Authorea . 24 October 2025. DOI: https://doi.org/10.22541/au.176129863.38437296/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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