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Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity

Genes & Immunity (2024)Cite this article

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Abstract

Despite the abundance of epidemiological evidence for the high comorbid rate between psoriasis and obesity, systematic approaches to common inflammatory mechanisms have not been adequately explored. We performed a meta-analysis of publicly available RNA-sequencing datasets to unveil putative mechanisms that are postulated to exacerbate both diseases, utilizing both late-stage, disease-specific meta-analyses and consensus gene co-expression network (cWGCNA). Single-gene meta-analyses reported several common inflammatory mechanisms fostered by the perturbed expression profile of inflammatory cells. Assessment of gene overlaps between both diseases revealed significant overlaps between up- (n = 170, P value = 6.07 × 10−65) and down-regulated (n = 49, P value = 7.1 × 10−7) genes, associated with increased T cell response and activated transcription factors. Our cWGCNA approach disentangled 48 consensus modules, associated with either the differentiation of leukocytes or metabolic pathways with similar correlation signals in both diseases. Notably, all our analyses confirmed the association of the perturbed T helper (Th)17 differentiation pathway in both diseases. Our novel findings through whole transcriptomic analyses characterize the inflammatory commonalities between psoriasis and obesity implying the assessment of several expression profiles that could serve as putative comorbid disease progression biomarkers and therapeutic interventions.

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Fig. 1: Single-gene meta-analysis characterizes the inflammatory milieu in both comorbid diseases.
Fig. 2: Significant overlap between psoriasis and obesity DEGs illustrates the shared molecular profile.
Fig. 3: Consensus WGCNA confirms the shared inflammatory profile between psoriasis and obesity.

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Data availability

All datasets included in our study are available from the Gene Expression Omnibus database repository (https://www.ncbi.nlm.nih.gov/geo/, accessed on 3 September 2022).

Code availability

Softwares used in this study are available at: SRA tookit, https://github.com/ncbi/sra-tools; FastQC, https://github.com/s-andrews/FastQC; TrimGalore, https://github.com/FelixKrueger/TrimGalore; STAR, https://github.com/alexdobin/STAR; RSeQC, https://github.com/MonashBioinformaticsPlatform/RSeQC; Subread, https://github.com/ShiLab-Bioinformatics/subread; DESeq2, https://github.com/thelovelab/DESeq2; MetaVolcanoR, https://github.com/csbl-usp/MetaVolcanoR; clusterProfiler, https://github.com/YuLab-SMU/clusterProfiler; WGCNA, https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/; Cytoscape, https://cytoscape.org/. Code used in the manuscript is available at: https://github.com/antonatosc/psobesity.

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Acknowledgements

CA was financially supported by the «Andreas Mentzelopoulos Foundation».

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Author notes

  1. Deceased: Evangelos Evangelou.

Authors and Affiliations

  1. Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, 26504, Patras, Greece

    Charalabos Antonatos, Georgios K. Georgakilas & Yiannis Vasilopoulos

  2. Information Management Systems Institute (IMSI), ATHENA Research Center, 15125, Athens, Greece

    Georgios K. Georgakilas

  3. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, 45110, Greece

    Evangelos Evangelou

  4. Biomedical Research Institute, Foundation for Research and Technology-Hellas, 45110, Ioannina, Greece

    Evangelos Evangelou

  5. Department of Epidemiology & Biostatistics, MRC Centre for Environment and Health, Imperial College London, London, W2 1PG, UK

    Evangelos Evangelou

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Conceptualization, CA and YV; methodology, CA, GKG, EE and YV; software, formal analysis, CA; data curation, CA; writing—original draft preparation, CA; writing—review and editing, CA, GKG, EE and YV; visualization, CA; supervision, YV. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yiannis Vasilopoulos.

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Antonatos, C., Georgakilas, G.K., Evangelou, E. et al. Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity. Genes Immun (2024). https://doi.org/10.1038/s41435-024-00271-w

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