Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

Data availability

Sharing of (personally identifiable) raw sequencing data and detailed clinical data is limited by institutional and UK ethical, consent, privacy and governance frameworks. The raw and adjusted gene count data from our RNA-seq analysis are available at Array Express under accession number E-MTAB-14509 with associated clinical data, specifically PASI and BMI. This approach allows us to share valuable processed data for replication, validation and further analysis while respecting participant privacy and adhering to governance and ethical guidelines. The supplementary data files,in conjunction with the data on Array Express and the code on GitHub, should facilitate full reproduction of figures. The supplementary data is provided in a number of Excel documents: “Supplementary Data 1. WGCNA module data.xlsx” and “Supplementary Data 2. ICA factor data.xlsx”, which are required for Figs. 2–7; “Supplementary Data 3. Module and factor-trait correlations.xlsx”, which is required for Fig. 2; “Supplementary Data 4. Metascape results for module and factor genes.xlsx” and “Supplementary Data 5. Module and factor metadata.xlsx”, which were used to derive the module and factor descriptors displayed in Fig. 2a; “Supplementary Data 6. PASI differential expression results.xlsx”, which is required for Figs. 3, 6 and 7; “Supplementary Data 7. Expression data for example PASI DEGs.xlsx”, which is required for Figs. 3 and 6; “Supplementary Data 8. Metascape results for PASI DEGs.xlsx”, which is required for Fig. 7d; “Supplementary Data 9. IPA results for PASI DEGs.xlsx”, which is required for Figs. 6e and 7e; “Supplementary Data 10. BMI differential expression results.xlsx”, which is required for Fig. 3; “Supplementary Data 11. HLA endotype data.xlsx”, which is required for Fig. 5; “Supplementary Data 12. GPR – PASI prediction of Gaussian process model using skin modules.xlsx” and “Supplementary Data 13. GPR—PASI prediction of Gaussian process model using skin factors.xlsx”, which are required for Fig. 4a; “Supplementary Data 14. GPR—Feature importance for skin modules.xlsx” and “Supplementary Data 15. GPR—Feature importance for skin factors.xlsx”, which are required for Fig. 4b; “Supplementary Data 16. GPR—PASI prediction importance for turquoise and blue genes.xlsx”, which is required for Fig. 4c; and “Supplementary Data 17. GPR – Gene signature from turquoise and blue.xlsx”, which is required for Fig. 4d. The predicted cell type fractions from single cell deconvolution analysis are also available on GitHub (https://github.com/C4TB/PSORT/tree/master/Cell_Type_Correlations/paper_data). Additionally, the PSORT skin and blood RNA-seq dataset may be visualised and further explored through an R Shiny web interface⁴² (https://shiny-whri-c4tb.hpc.qmul.ac.uk/psort).

Code availability

Data analysis scripts can be found on our GitHub repository (https://github.com/C4TB/PSORT), along with extended supplemental markdown documents. These are also deposited⁸⁷ on Zenodo (https://doi.org/10.5281/zenodo.15847636).

References

Griffiths, C. E. M., Armstrong, A. W., Gudjonsson, J. E. & Barker, J. N. W. N. Psoriasis. Lancet Lond. Engl. 397, 1301–1315 (2021).

Google Scholar
Dand, N. et al. GWAS meta-analysis of psoriasis identifies new susceptibility alleles impacting disease mechanisms and therapeutic targets. MedRxivhttps://doi.org/10.1101/2023.10.04.23296543 (2023).
Griffiths, C. E. M. et al. Establishing an Academic-Industrial Stratified Medicine Consortium: Psoriasis Stratification to Optimize Relevant Therapy. J. Invest. Dermatol. 135, 2903–2907 (2015).

Google Scholar
Saelens, W., Cannoodt, R. & Saeys, Y. A comprehensive evaluation of module detection methods for gene expression data. Nat. Commun. 9, 1090 (2018).

Google Scholar
Foulkes, A. C. et al. A framework for multi-omic prediction of treatment response to biologic therapy for psoriasis. J. Invest Dermatol. 139, 100–107 (2019).

Google Scholar
Guo, Y., Logan, H. L., Glueck, D. H. & Muller, K. E. Selecting a sample size for studies with repeated measures. BMC Med. Res. Methodol. 13, 100 (2013).

Google Scholar
Dand, N. et al. HLA-C*06:02 genotype is a predictive biomarker of biologic treatment response in psoriasis. J. Allergy Clin. Immunol. 143, 2120–2130 (2019).

Google Scholar
Jia, X. et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLoS ONE 8, e64683 (2013).

Google Scholar
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

Google Scholar
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

Google Scholar
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

Google Scholar
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

Google Scholar
Maleki, F., Ovens, K., Hogan, D. J. & Kusalik, A. J. Gene set analysis: challenges, opportunities, and future research. Front. Genet. 11, 654 (2020).

Google Scholar
Helwig, N. E. ICA: Independent Component Analysis (2022). Available from https://cran.r-project.org/web/packages/ica/index.html.
Kairov, U. et al. Determining the optimal number of independent components for reproducible transcriptomic data analysis. BMC Genomics. 18, 712 (2017).

Google Scholar
Gisby, J. & Barnes, M. Reduced Experiment: Containers and tools for dimensionally-reduced -omics data (2025). Available from http://www.bioconductor.org/packages/ReducedExperiment.
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

Google Scholar
Hughes, T. K. et al. Second-strand synthesis-based massively parallel scRNA-Seq reveals cellular states and molecular features of human inflammatory skin pathologies. Immunity 53, 878–894.e7 (2020).

Google Scholar
Krämer, A., Green, J., Pollard, J. & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523–530 (2014).

Google Scholar
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

Google Scholar
Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018).

Google Scholar
Rasmussen C. E., Williams C. K. I. Gaussian Processes for Machine Learning (MIT Press, 2006). 248 (Adaptive computation and machine learning).
Iqbal, W. A., Lisitsa, A. & Kapralov, M. V. Predicting plant Rubisco kinetics from RbcL sequence data using machine learning. J. Exp. Bot. 74, 638–650 (2023).

Google Scholar
Cheng, L. F. et al. Sparse multi-output Gaussian processes for online medical time series prediction. BMC Med. Inf. Decis. Mak. 20, 152 (2020).

Google Scholar
Yang, K. K., Wu, Z., Bedbrook, C. N. & Arnold, F. H. Learned protein embeddings for machine learning. Bioinforma. Oxf. Engl. 34, 2642–2648 (2018).

Google Scholar
Deringer, V. L. et al. Gaussian process regression for materials and molecules. Chem. Rev. 121, 10073–10141 (2021).

Google Scholar
Duvenaud, D. K., Nickisch, H. & Rasmussen, C. in Advances in Neural Information Processing Systems (eds Shawe-Taylor J. et al.) (Curran Associates, Inc., 2011). Available from https://proceedings.neurips.cc/paper_files/paper/2011/file/4c5bde74a8f110656874902f07378009-Paper.pdf.
Matthews, A. G. et al. GPflow: A Gaussian Process Library using TensorFlow. J. Mach. Learn Res. 18, 1–6 (2017).

Google Scholar
Lundberg, S. M. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. In Curran Associates, Inc.; 2017. 4765-4774.
Andres-Ejarque, R. et al. Enhanced NF-κB signaling in type-2 dendritic cells at baseline predicts non-response to adalimumab in psoriasis. Nat. Commun. 12, 4741 (2021).

Google Scholar
Geifman, N. et al. Defining trajectories of response in patients with psoriasis treated with biologic therapies. Br. J. Dermatol. 185, 825–835 (2021).

Google Scholar
Solvin, ÅØ et al. Meta-analysis of RNA sequencing data from 534 skin samples shows substantial IL-17 effects in non-lesional psoriatic skin (2023). Available from http://medrxiv.org/lookup/doi/10.1101/2023.11.03.23298021.
Kim, J. et al. The spectrum of mild to severe psoriasis vulgaris is defined by a common activation of il-17 pathway genes, but with key differences in immune regulatory genes. J. Invest Dermatol. 136, 2173–2182 (2016).

Google Scholar
Fredriksson, T. & Pettersson, U. Severe psoriasis-oral therapy with a new retinoid. Dermatologica 157, 238–244 (1978).

Google Scholar
Ma, F. et al. Single cell and spatial sequencing define processes by which keratinocytes and fibroblasts amplify inflammatory responses in psoriasis. Nat. Commun. 14, 3455 (2023).

Google Scholar
Castillo, R. L. et al. Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems. Sci. Immunol. 8, eabq7991 (2023).

Google Scholar
Reynolds, G. et al. Developmental cell programs are co-opted in inflammatory skin disease. Science 371, eaba6500 (2021).

Google Scholar
Steele, L. et al. A single cell and spatial genomics atlas of human skin fibroblasts in health and disease. Cell Biol.https://doi.org/10.1101/2024.12.23.629194 (2024).
Gudjonsson, J. E. et al. Global gene expression analysis reveals evidence for decreased lipid biosynthesis and increased innate immunity in uninvolved psoriatic skin. J. Invest. Dermatol. 129, 2795–2804 (2009).

Google Scholar
Rawat, A. et al. A neutrophil-driven inflammatory signature characterizes the blood transcriptome fingerprint of psoriasis. Front. Immunol. 11, 587946 (2024).
Prinz, J. C. Human leukocyte antigen-class I alleles and the autoreactive T cell response in psoriasis pathogenesis. Front. Immunol. 9, 954 (2018).

Google Scholar
Henkin, R., Goldmann, K., Lewis, M. & Barnes, M. R. shinyExprPortal: a configurable ‘shiny’ portal for sharing analysis of molecular expression data. Bioinformatics 40, btae172 (2024).

Google Scholar
Yiu, Z. Z. N. et al. Drug survival associated with effectiveness and safety of treatment with Guselkumab, Ixekizumab, Secukinumab, Ustekinumab, and Adalimumab in patients with psoriasis. JAMA Dermatol. 158, 1131 (2022).

Google Scholar
Jin, Y. et al. Risk of hospitalization for serious infection after initiation of Ustekinumab or other biologics in patients with psoriasis or psoriatic arthritis. Arthritis Care Res. 74, 1792–1805 (2022).

Google Scholar
Walker, J. M. et al. Obesity and ethnicity alter gene expression in skin. Sci. Rep. 10, 14079 (2020).

Google Scholar
Gisondi, P., Fostini, A. C., Fossà, I., Girolomoni, G. & Targher, G. Psoriasis and the metabolic syndrome. Clin. Dermatol. 36, 21–28 (2018).

Google Scholar
Barros, G., Duran, P., Vera, I. & Bermúdez, V. Exploring the links between obesity and psoriasis: a comprehensive review. Int J. Mol. Sci. 23, 7499 (2022).

Google Scholar
Park, J. R. et al. DNER modulates adipogenesis of human adipose tissue-derived mesenchymal stem cells via regulation of cell proliferation. Cell Prolif. 43, 19–28 (2010).

Google Scholar
Hanson, R. L. et al. A genome-wide association study in american indians implicates DNER as a susceptibility locus for type 2 diabetes. Diabetes 63, 369–376 (2014).

Google Scholar
Onderdijk, A. J. et al. Regulated genes in psoriatic skin during treatment with fumaric acid esters. Br. J. Dermatol. 171, 732–741 (2014).

Google Scholar
Quaranta, M. et al. Intraindividual genome expression analysis reveals a specific molecular signature of psoriasis and eczema. Sci. Transl. Med. 6, 244ra90 (2014).

Google Scholar
Errafii, K., Boujraf, S. & Chikri, M. Transcriptomic analysis from normal glucose tolerance to T2D of obese individuals using bioinformatic tools. Int J. Mol. Sci. 24, 6337 (2023).

Google Scholar
Seiringer, P. et al. Spatial transcriptomics reveals altered lipid metabolism and inflammation-related gene expression of sebaceous glands in psoriasis and atopic dermatitis. Front. Immunol. 15, 1334844 (2024).

Google Scholar
Mishra, S. et al. Inhibition of phosphodiesterase type 9 reduces obesity and cardiometabolic syndrome in mice.(American Society for Clinical Investigation, 2021). Available from https://www.jci.org/articles/view/148798/pdf.
Sivasami, P. et al. Obesity-induced dysregulation of skin-resident PPARγ+ Treg cells promotes IL-17A-mediated psoriatic inflammation. Immunity 56, 1844–1861.e6 (2023).

Google Scholar
Chang, G., Wang, J., Song, J., Zhang, Z. & Zhang, L. Efficacy and safety of pioglitazone for treatment of plaque psoriasis: a systematic review and meta-analysis of randomized controlled trials. J. Dermatol. Treat. 31, 680–686 (2020).

Google Scholar
Liu, L. et al. Anti-TNF- αtreatment-related pathways and biomarkers revealed by transcriptome analysis in Chinese psoriasis patients. BMC Syst. Biol. 13, 29 (2019).

Google Scholar
Grivas, A. et al. Combined – whole blood and skin fibroblasts- transcriptomic analysis in Psoriatic Arthritis reveals molecular signatures of activity, resistance and early response to treatment. Front. Immunol. 13, 964274 (2022).

Google Scholar
Chen, L. & Tsai, T. F. HLA-Cw6 and psoriasis. Br. J. Dermatol. 178, 854–862 (2018).

Google Scholar
Zhang, M. et al. Multi-ancestry genome-wide meta-analysis with 472,819 individuals identifies 32 novel risk loci for psoriasis. J. Transl. Med. 23, 133 (2025).

Google Scholar
Chandra, A. et al. Functional mapping of genetic interactions between HLA-Cw6 and LCE3A in psoriasis. J. Invest. Dermatol. 141, 2630–2638.e7 (2021).

Google Scholar
Hsiao, C. C. et al. The adhesion G protein-coupled receptor GPR97/ADGRG3 is expressed in human granulocytes and triggers antimicrobial effector functions. Front. Immunol. 9, 2830 (2018).

Google Scholar
Stuart, P. E. et al. Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine mapping in the MHC and genome wide. Hum. Genet. Genomics Adv. 3, 100069 (2022).

Google Scholar
Lei, H. et al. Exploration of the causality of frailty index on psoriasis: a Mendelian randomization study. Skin Res. Technol. 30, e13641 (2024).

Google Scholar
Zhang, X. et al. HLA-DQA1 and DQB1 alleles are associated with genetic susceptibility to psoriasis vulgaris in Chinese Han. Int J. Dermatol. 43, 181–187 (2004).

Google Scholar
Pugliese, A. et al. HLA-DRB1*15:01-DQA1*01:02-DQB1*06:02 haplotype protects autoantibody-positive relatives from type 1 diabetes throughout the stages of disease progression. Diabetes 65, 1109–1119 (2016).

Google Scholar
Casciano, F., Pigatto, P. D., Secchiero, P., Gambari, R. & Reali, E. T. Cell hierarchy in the pathogenesis of psoriasis and associated cardiovascular comorbidities. Front. Immunol. 9, 1390 (2018).

Google Scholar
Romero, P. A., Krause, A. & Arnold, F. H. Navigating the protein fitness landscape with Gaussian processes. Proc. Natl. Acad. Sci. 110, E193–201 (2013).
Yang, K. K., Wu, Z. & Arnold, F. H Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).

Google Scholar
Brodmerkel, C. et al. Modulation of inflammatory gene transcripts in psoriasis vulgaris: differences between ustekinumab and etanercept. J. Allergy Clin. Immunol. 143, 1965–1969 (2019).
Francis, L. et al. Single-cell analysis of psoriasis resolution demonstrates an inflammatory fibroblast state targeted by IL-23 blockade. Nat. Commun. 15, 913 (2024).

Google Scholar
Xiao, Y. & Guengerich, F. P. Metabolomic analysis and identification of a role for the orphan human cytochrome P450 2W1 in selective oxidation of lysophospholipids. J. Lipid Res. 53, 1610–1617 (2012).

Google Scholar
Zhao, Y., Wan, D., Yang, J., Hammock, B. D. & Ortiz De Montellano, P. R. Catalytic activities of tumor-specific human cytochrome P450 CYP2W1 toward endogenous substrates. Drug Metab. Dispos. 44, 771–780 (2016).

Google Scholar
Rioux, G. et al. Transcriptome profiling analyses in psoriasis: a dynamic contribution of keratinocytes to the pathogenesis. Genes 11, 1155 (2020).

Google Scholar
Li, C. et al. Psoriasis-associated impairment of CCL27/CCR10-derived regulation leads to IL-17A/IL-22–producing skin T-cell overactivation. J. Allergy Clin. Immunol. 147, 759–763.e9 (2021).

Google Scholar
Silverstein, R. L. & Febbraio, M. CD36, a scavenger receptor involved in immunity, metabolism, angiogenesis, and behavior. Sci. Signal. 2, re3 (2009).

Google Scholar
Das, S., Majumder, P. P., Chatterjee, R., Chatterjee, A. & Mukhopadhyay, I. A powerful method to integrate genotype and gene expression data for dissecting the genetic architecture of a disease. Genomics 111, 1387–1394 (2019).

Google Scholar
Komatsu, N. et al. Aberrant human tissue kallikrein levels in the stratum corneum and serum of patients with psoriasis: dependence on phenotype, severity and therapy. Br. J. Dermatol. 156, 875–883 (2007).

Google Scholar
Xing, L. et al. Exploration of biomarkers of psoriasis through combined multiomics analysis. Mediators Inflamm. 2022, 7731082 (2022).

Google Scholar
Deng, J. et al. Multi-omics integration reveals a core network involved in host defence and hyperkeratinization in psoriasis. Clin. Transl. Med. 12, e976 (2022).

Google Scholar
Mancino, G., Miro, C., Di Cicco, E. & Dentice, M. Thyroid hormone action in epidermal development and homeostasis and its implications in the pathophysiology of the skin. J. Endocrinol. Invest. 44, 1571–1579 (2021).

Google Scholar
Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E. & Hogenesch, J. B. A circadian gene expression atlas in mammals: Implications for biology and medicine. Proc. Natl. Acad. Sci. 111, 16219–16224 (2014).

Google Scholar
Greenberg, E. N. et al. Circadian control of interferon-sensitive gene expression in murine skin. Proc. Natl. Acad. Sci. USA 117, 5761–5771 (2020).

Google Scholar
Chow, M. et al. Effect of adalimumab on gene expression profiles of psoriatic skin and blood. J. Drugs Dermatol. 15, 988–994 (2016).

Google Scholar
Suárez-Fariñas, M. et al. Expanding the psoriasis disease profile: interrogation of the skin and serum of patients with moderate-to-severe psoriasis. J. Invest. Dermatol. 132, 2552–2564 (2012).

Google Scholar
Ramessur, R. et al. Biomarkers of disease progression in people with psoriasis: a scoping review. Br. J. Dermatol. 187, 481–493 (2022).

Google Scholar
Rider, A. et al. C4TB/PSORT: First release. Zenodo Available from https://zenodo.org/doi/10.5281/zenodo.15847636 (2025).

Download references

Acknowledgements

Non-author contributions. We are grateful to all subjects for their participation. We thank the Independent Advisory Board who provided valuable advice and an independent international stakeholder perspective to the PSORT Consortium (Iain McInnes [Chair], John Armstrong, Anne Bowcock, James Krueger, Christy Langan and Peter van de Kerkhof). We are grateful to the Psoriasis Association for their Patient and Public Involvement and Engagement. We acknowledge the enthusiastic collaboration and support of dermatologists, specialist and research nurses in the UK who recruited to this study including Alberto Barea (Kingston Hospital NHS Foundation Trust), Dr Anna Chapman (Lewisham & Greenwich Trust), Dr Rob Ellis (South Tees Hospitals NHS Foundation Trust), Dr Abigail Fogo (Kingston Hospital NHS Foundation Trust), Dr Bronwyn Hughes (Portsmouth Hospitals University NHS Trust), Dr Evmorfia Ladoyanni (Dudley Group NHS Foundation Trust), Dr Philip Laws (Leeds Teaching Hospitals NHS Trust), Dr Richard Parslew (Liverpool University Hospitals NHS Foundation Trust), Dr Gayathri Perera (Chelsea and Westminister Hospital NHS Foundation Trust), Dr Beth Poyner (Newcastle upon Tyne Hospitals NHS Foundation Trust), Sara Wilkinson (Newcastle upon Tyne Hospitals NHS Foundation Trust). We thank Hira Ali, Rosa Andres-Ejarque, Zaynep Catak, Tejus Dasandi, Nadya Dinev, Michael Duckworth, Katarzyna Grys, Freya Meynell, Alice Russel and Isabella Tosi (London), and Dhanisha Lukka and Panagiotis Maniatis (Newcastle) for sample and data management. We thank Federica Villanova (London) for her contribution to obtain ethical approval. Frank Nestle contributed to the initial stages of this work and the authors wish to acknowledge his contribution. This study was funded by The Medical Research Council (MRC)(MICA MRC Precision Medicine Consortium; MR/L011808/1); The Psoriasis Association; The British Association of Dermatologists (BAD); The Rosetrees Trust; UCB; The NIHR Newcastle Biomedical Research Centre (BRC); and a MRC/BAD/British Skin Foundation clinical research training fellowship to HJG. Partners of the PSORT consortium are AbbVie, the British Association of Dermatologists, Becton Dickinson and Company, Celgene Limited, GlaxoSmithKline, Guy’s and St Thomas’ NHS Foundation Trust, Eli Lilly, Janssen Research & Development, King’s College London, LEO Pharma, MedImmune, Novartis Pharmaceuticals UK, Pfizer Italy, the Psoriasis Association, Qiagen Manchester, Queen Mary University of London, the Royal College of Physicians, Sanquin Blood Supply Foundation, the University of Liverpool, the University of Manchester, and Newcastle University. We particularly acknowledge generous in-kind support from the PSORT industrial partners GSK and Abbvie who supported the RNA sequencing. All decisions concerning analysis, interpretation, and publication are made independently of any of the 12 industrial contributions. NJR’s research/laboratory is funded in part by the NIHR Newcastle HealthTech Research Centre in Diagnostic and Technology Evaluation and the NIHR Newcastle Patient Safety Research Collaboration. N.J.R. is a NIHR Senior Investigator. P.Z. received funding from Sapienza University (#RP123188F6A67836) CEMG and RBW are funded in part by the Manchester NIHR BRC (NIHR203308). M.R.B. and D.W. were funded by the NIHR as part of the portfolio of translational research of the NIHR BRC at Barts and The London School of Medicine and Dentistry. C.S. receives research funding from European consortia with multiple industry partners (see BIOMAP-imi.eu and HIPPOCRATES-imi.eu, MRC-industry PhD studentships from AstraZeneca, Boehringer Ingelheim). This project was enabled through access to the MRC eMedLab Medical Bioinformatics infrastructure supported by the Medical Research Council [grant number MR/L016311/1].

Author information

Author notes

These authors contributed equally: Ashley Rider, Henry J. Grantham, Graham R. Smith, David S. Watson.
These authors jointly supervised this work: Michael R. Barnes, Nick J. Reynolds.
A full list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

Institute of Translational and Clinical Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

Ashley Rider, Henry J. Grantham, Tom Ewen & Nick J. Reynolds
Bioinformatics Support Unit, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

Graham R. Smith, John Casement, Simon J. Cockell & Wasim A. Iqbal
Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, UK

David S. Watson, Jack Gisby, Rafael Henkin, Sandra Ng & Michael R. Barnes
Department of Informatics, King’s College London, London, UK

David S. Watson
Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

Simon J. Cockell & Shoba Amarnath
The Manchester Centre for Dermatology Research, The University of Manchester, Manchester, UK

Amy C. Foulkes, Christopher E. M. Griffiths & Richard B. Warren
School of Computing, Newcastle University, Newcastle upon Tyne, UK

Paolo Zuliani
Dipartimento di Informatica, Sapienza University, Rome, Italy

Paolo Zuliani
Department of Medical and Molecular Genetics, Kings College London, London, UK

Nick Dand
Leeds Institute of Clinical Trials Research, The University of Leeds, Leeds, UK

Deborah Stocken
Computational Biology, GlaxoSmithKline, Collegeville, PA, USA

Christopher Traini, Elizabeth Thomas, Shanker Kalyana-Sundaram & Deepak K. Rajpal
Takeda Development Center Americas, Inc. (TDCA), Lexington, MA, USA

Deepak K. Rajpal
AbbVie Genome Research Center, Immunology Bioinformatics, 200 Sidney Street, Cambridge, MA, 02139, USA

Kathleen M. Smith
St. Johns Institute of Dermatology, Kings College London, London, UK

Jonathan N. Barker, Paola Di Meglio, Catherine H. Smith & Paola Di Meglio
NIHR Newcastle Biomedical Research Centre & Department of Dermatology, Royal Victoria Infirmary, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK

Nick J. Reynolds

Authors

Ashley Rider
Henry J. Grantham
Graham R. Smith
David S. Watson
John Casement
Simon J. Cockell
Jack Gisby
Amy C. Foulkes
Rafael Henkin
Wasim A. Iqbal
Tom Ewen
Shoba Amarnath
Sandra Ng
Paolo Zuliani
Nick Dand
Deborah Stocken
Christopher Traini
Elizabeth Thomas
Shanker Kalyana-Sundaram
Deepak K. Rajpal
Kathleen M. Smith
Jonathan N. Barker
Christopher E. M. Griffiths
Paola Di Meglio
Catherine H. Smith
Richard B. Warren
Michael R. Barnes
Nick J. Reynolds

Consortia

On behalf of the PSORT consortium

Jonathan N. Barker
, Christopher E. M. Griffiths
, Paola Di Meglio
, Catherine H. Smith
, Richard B. Warren
, Michael R. Barnes
& Nick J. Reynolds

Contributions

A.R., H.J.G., G.R.S. and D.S.W. contributed equally as joint first authors. J.C., S.J.C., J.G., A.C.F., R.H. and W.A.I. contributed equally as joint second authors. J.N.B., C.E.M.G., P.D.M., C.H.S. and R.B.W. contributed equally as joint penultimate authors. M.R.B. and N.J.R. jointly supervised the study. Conceptualisation: M.R.B., J.N.B., C.E.M.G., N.J.R., C.H.S., D.S., R.W. Data Curation: A.R., D.S.W., N.D. Formal Analysis: H.J.G., A.R., D.S.W., G.R.S., J.C., J.G., S.J.C., S.N., W.A.I. Funding acquisition: M.R.B., J.N.B., C.E.M.G., N.J.R., C.H.S., D.S., R.W., P.Z. Investigation: A.C.F., S.K.S., T.E., C.T., E.T., D.K.R., K.M.S., J.N.B., C.E.M., C.H.S., R.W., N.J.R. Methodology: D.S., S.K.S., C.T., E.T., D.K.R., K.M.S., W.A.I., N.D., S.A. Project administration: M.R.B., J.N.B., P.D.M., C.E.M.G., N.J.R., C.H.S., R.W. Resources: M.R.B., J.N.B., C.E.M.G., N.J.R., C.H.S., R.W. Software: R.H. Supervision: M.R.B., N.J.R Validation: H.J.G., A.R., G.R.S., J.C., J.G., W.A.I. Visualisation: H.J.G., A.R., G.R.S., J.C., J.G., W.A.I. Writing—original draft: H.J.G., A.R., J.G., W.A.I., M.R.B., N.J.R. Writing—review & editing: all authors.

Corresponding authors

Correspondence to Michael R. Barnes or Nick J. Reynolds.

Ethics declarations

Competing interests

N.J.R. has received, through Newcastle University, grants from Novartis and UCB outside the submitted work; travel support and/or consultancy income from AbbVie, Boehringer Ingelheim, Galderma, and UCB. M.R.B. has received research funding from Janssen and Benevolent AI, and consultancy with United Health group, Eli Lilly and Sanofi. All other authors declare no competing interests.

Peer review

Peer review information

Communications Medicine thanks Yeon-Hee Lee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Rider, A., Grantham, H.J., Smith, G.R. et al. Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity. Commun Med (2026). https://doi.org/10.1038/s43856-025-01325-4

Download citation

Received: 14 January 2025
Accepted: 09 December 2025
Published: 21 January 2026
DOI: https://doi.org/10.1038/s43856-025-01325-4