Su Kevin Y C, Reynolds John A, Reed Rachel, Da Silva Rachael, Kelsall Janet, Baricevic-Jones Ivona, Lee David, Whetton Anthony D, Geifman Nophar, McHugh Neil, Bruce Ian N
Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
Rheumatology Department, Sandwell and West Birmingham NHS Trust, Birmingham, UK.
Clin Proteomics. 2023 Jul 29;20(1):29. doi: 10.1186/s12014-023-09420-1.
Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients.
Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile.
In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617).
Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.
系统性红斑狼疮（SLE）是一种临床和生物学上具有异质性的自身免疫性疾病。我们旨在研究活动性SLE患者的血浆蛋白质组，以识别患者的新亚组或内型。
从参与不列颠群岛狼疮评估组生物制剂注册研究（BILAG-BR）的活动性SLE患者中采集血浆。使用数据非依赖采集方法，即全理论质谱的顺序窗口采集质谱法（SWATH-MS）分析血浆蛋白质组。采用无监督、数据驱动的聚类算法来划分具有共同蛋白质组特征的患者组。
在223例患者中，基于581种蛋白质的定量分析确定了六个聚类。各聚类之间在年龄（p = 0.012）和种族（p = 0.003）方面存在显著差异。聚类1（C1）中肌肉骨骼疾病活动增加，19/27（70.4%）（p = 0.002），聚类6（C6）中肾脏活动增加，15/24（62.5%）（p = 0.051）。抗SSa/Ro是各聚类之间唯一有显著差异的自身抗体（p = 0.017）。C1与p21激活激酶（PAK）和磷脂酶C（PLC）信号传导相关。在C1内有两个亚聚类（C1A和C1B），由与细胞骨架蛋白结合相关的49种蛋白质定义。C2和C6表现出相反的Rho家族GTP酶和Rho GDI信号传导。在C6中鉴定出的三种蛋白质（MZB1、SND1和AGL）增加了活动性肾脏疾病的分类，尽管未达到统计学显著性（p = 0.0617）。
无监督蛋白质组分析识别出了活动性SLE患者的聚类，这些聚类与临床和血清学特征相关，这可能有助于生物标志物的发现。观察到的蛋白质组异质性进一步支持了SLE治疗中采用个性化方法的必要性。
