Laboratory of Autoimmunity and Inflammation, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
Department of Nephrology, Limassol General Hospital, Limassol, Cyprus.
Ann Rheum Dis. 2022 Oct;81(10):1409-1419. doi: 10.1136/annrheumdis-2021-222069. Epub 2022 Jul 29.
Patients with lupus nephritis (LN) are in urgent need for early diagnosis and therapeutic interventions targeting aberrant molecular pathways enriched in affected kidneys.
We used mRNA-sequencing in effector (spleen) and target (kidneys, brain) tissues from lupus and control mice at sequential time points, and in the blood from 367 individuals (261 systemic lupus erythematosus (SLE) patients and 106 healthy individuals). Comparative cross-tissue and cross-species analyses were performed. The human dataset was split into training and validation sets and machine learning was applied to build LN predictive models.
In murine SLE, we defined a kidney-specific molecular signature, as well as a molecular signature that underlies transition from preclinical to overt disease and encompasses pathways linked to metabolism, innate immune system and neutrophil degranulation. The murine kidney transcriptome partially mirrors the blood transcriptome of patients with LN with 11 key transcription factors regulating the cross-species active LN molecular signature. Integrated protein-to-protein interaction and drug prediction analyses identified the kinases TRRAP, AKT2, CDK16 and SCYL1 as putative targets of these factors and capable of reversing the LN signature. Using murine kidney-specific genes as disease predictors and machine-learning training of the human RNA-sequencing dataset, we developed and validated a peripheral blood-based algorithm that discriminates LN patients from normal individuals (based on 18 genes) and non-LN SLE patients (based on 20 genes) with excellent sensitivity and specificity (area under the curve range from 0.80 to 0.99).
Machine-learning analysis of a large whole blood RNA-sequencing dataset of SLE patients using human orthologs of mouse kidney-specific genes can be used for early, non-invasive diagnosis and therapeutic targeting of LN. The kidney-specific gene predictors may facilitate prevention and early intervention trials.
狼疮肾炎 (LN) 患者急需针对受影响肾脏中富集的异常分子途径进行早期诊断和治疗干预。
我们使用效应（脾脏）和靶（肾脏、大脑）组织中的 mRNA 测序在狼疮和对照小鼠的连续时间点进行，以及在 367 个人（261 例系统性红斑狼疮 (SLE) 患者和 106 名健康个体）的血液中进行。进行了比较跨组织和跨物种分析。将人类数据集分为训练集和验证集，并应用机器学习来构建 LN 预测模型。
在小鼠 SLE 中，我们定义了一个肾脏特异性分子特征，以及一个从临床前到显性疾病的转变的分子特征，其中包括与代谢、先天免疫系统和中性粒细胞脱颗粒相关的途径。鼠肾转录组部分反映了 LN 患者的血液转录组，其中 11 个关键转录因子调节跨物种活跃的 LN 分子特征。整合蛋白质-蛋白质相互作用和药物预测分析确定了激酶 TRRAP、AKT2、CDK16 和 SCYL1 作为这些因子的潜在靶点，并能够逆转 LN 特征。使用鼠肾特异性基因作为疾病预测因子，并对人类 RNA-seq 数据集进行机器学习训练，我们开发并验证了一种基于外周血的算法，该算法能够区分 LN 患者与正常个体（基于 18 个基因）和非 LN SLE 患者（基于 20 个基因），具有出色的灵敏度和特异性（曲线下面积范围为 0.80 至 0.99）。
使用小鼠肾脏特异性基因的人类同源物对 SLE 患者的大型全血 RNA-seq 数据集进行机器学习分析可用于 LN 的早期、非侵入性诊断和治疗靶向。肾脏特异性基因预测因子可能有助于预防和早期干预试验。
