Biochemical and biophysical research communications · 2024
Multi-omic analysis revealed the immunological patterns and diagnostic value of exhausted T cell-derived PTTG1 in patients with psoriasis.
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Zhou Xiangnan, Ning Jingyuan, Cai Rui, Liu Jiayi, Yang Haoyu, Liu Qingwu, Lv Jingjing, Bai Yanping
Аннотация
BACKGROUND: Psoriasis, characterized by chronic inflammation, is a persistent skin condition that is notoriously challenging to manage and prone to relapse. Despite significant advancements in its treatment, many adverse reactions still occur. Therefore, exploring the mechanisms behind the occurrence and development of psoriasis is extremely important.
METHODS: The weighted correlation network analysis (WGCNA) algorithm was used to identify phenotype-related genes in patients with psoriasis. We recruited clinical samples of patients with psoriasis, and used single-cell RNA sequencing (scRNA-seq) to visualize divergent genes and metabolisms of varied cells for the psoriasis. Various machine-learning methods were used to identify core genes, and molecular docking was used to analyze the stability of leptomycin B targeting pituitary tumor transforming 1 (PTTG1). Immunofluorescence (IHC) analysis, multiplex immunofluorescence (mIF) analysis, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to validate the results.
RESULTS: Our results identified 1391 genes associated with the phenotype in patients with psoriasis and highlighted the significant alterations in T-cell functionality observed in the disease by WGCNA. There were nine distinct cellular clusters in psoriasis analyzed with the aid of scRNA-seq data. Each subtype of cell exhibited distinct genetic profiles, functional roles, signaling mechanisms, and metabolic characteristics. Machine-learning methods further demonstrated the potential diagnostic value of T cell-derived PTTG1 and its relationship with T-cell exhaustion in psoriasis. Lastly, the leptomycin B was scrutinized and verified had high stability targeting PTTG1.
CONCLUSIONS: This study elucidates the biological basis of psoriasis. At the same time, it was discovered that PTTG1 derived from exhausted T cells serves as a diagnostic biomarker for psoriasis. Leptomycin B could be a potential drug for targeted treatment of psoriasis on PTTG1.
METHODS: The weighted correlation network analysis (WGCNA) algorithm was used to identify phenotype-related genes in patients with psoriasis. We recruited clinical samples of patients with psoriasis, and used single-cell RNA sequencing (scRNA-seq) to visualize divergent genes and metabolisms of varied cells for the psoriasis. Various machine-learning methods were used to identify core genes, and molecular docking was used to analyze the stability of leptomycin B targeting pituitary tumor transforming 1 (PTTG1). Immunofluorescence (IHC) analysis, multiplex immunofluorescence (mIF) analysis, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to validate the results.
RESULTS: Our results identified 1391 genes associated with the phenotype in patients with psoriasis and highlighted the significant alterations in T-cell functionality observed in the disease by WGCNA. There were nine distinct cellular clusters in psoriasis analyzed with the aid of scRNA-seq data. Each subtype of cell exhibited distinct genetic profiles, functional roles, signaling mechanisms, and metabolic characteristics. Machine-learning methods further demonstrated the potential diagnostic value of T cell-derived PTTG1 and its relationship with T-cell exhaustion in psoriasis. Lastly, the leptomycin B was scrutinized and verified had high stability targeting PTTG1.
CONCLUSIONS: This study elucidates the biological basis of psoriasis. At the same time, it was discovered that PTTG1 derived from exhausted T cells serves as a diagnostic biomarker for psoriasis. Leptomycin B could be a potential drug for targeted treatment of psoriasis on PTTG1.