Ramírez Medina Carlos R, Ali Ibrahim, Baricevic-Jones Ivona, Odudu Aghogho, Saleem Moin A, Whetton Anthony D, Kalra Philip A, Geifman Nophar
Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK.
Clin Proteomics. 2023 Apr 20;20(1):19. doi: 10.1186/s12014-023-09405-0.
Halting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking.
Plasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m/year or worse) and 244 stable patients (∂ eGFR of - 0.5 to + 1 ml/min/1.73 m/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses.
The resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin-proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched.
The in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.
阻止慢性肾脏病（CKD）进展至终末期肾病是全球健康研究的主要目标。CKD进展的机制涉及促炎、促纤维化和血管通路，但目前缺乏病理生理分化研究。
获取414例非透析CKD患者的血浆样本，其中170例快速进展者（估算肾小球滤过率[∂eGFR]下降≥3ml/min/1.73m²/年或更差）和244例稳定患者（∂eGFR为-0.5至+1ml/min/1.73m²/年），涵盖多种肾病病因，采用数据非依赖采集质谱法（SWATH-MS）检测蛋白质组信号。我们运用机器学习方法，使用Boruta算法对至少20%样本中可定量的蛋白质进行特征选择。通过ClueGo通路分析确定这些蛋白质富集的生物通路。
结合626种蛋白质的数字化蛋白质组图谱与现有临床数据进行研究，以识别进展的生物标志物。使用Boruta特征选择的机器学习模型确定25种生物标志物对进展类型分类很重要（曲线下面积=0.81，准确率=0.72）。我们的功能富集分析揭示了与补体级联反应通路的关联，由于肾脏特别容易受到补体过度激活的影响，这与CKD相关。这为将补体抑制作为调节糖尿病肾病进展的潜在方法提供了进一步证据。还发现参与泛素-蛋白酶体通路（一种关键的蛋白质降解系统）的蛋白质显著富集。
对这个大规模CKD队列进行深入的蛋白质组学表征是朝着生成基于机制的假设迈出的一步，这些假设可能适用于未来的药物靶点。候选生物标志物将在其他大型非透析CKD队列中选定患者的样本中，使用靶向质谱分析进行验证。
