Pattaroni Céline, Begka Christina, Cardwell Bailey, Jaffar Jade, Macowan Matthew, Harris Nicola L, Westall Glen P, Marsland Benjamin J
Department of Immunology, School of Translational Medicine Monash University Melbourne VIC Australia.
Department of Respiratory Medicine Alfred Hospital Melbourne VIC Australia.
Clin Transl Immunology. 2024 Jan 24;13(1):e1485. doi: 10.1002/cti2.1485. eCollection 2024.
Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is limited. With this study, we aimed to model the leading edge of fibrosis using a data-driven approach.
Multiple omics modalities (transcriptomics, metabolomics and lipidomics) of healthy and IPF lung explants representing different stages of fibrosis were combined using an unbiased approach. Multi-Omics Factor Analysis of datasets revealed latent factors specifically linked with established fibrotic disease (Factor1) and disease progression (Factor2).
Features characterising Factor1 comprised well-established hallmarks of fibrotic disease such as defects in surfactant, epithelial-mesenchymal transition, extracellular matrix deposition, mitochondrial dysfunction and purine metabolism. Comparatively, Factor2 identified a signature revealing a nonlinear trajectory towards disease progression. Molecular features characterising Factor2 included genes related to transcriptional regulation of cell differentiation, ciliogenesis and a subset of lipids from the endocannabinoid class. Machine learning models, trained upon the top transcriptomics features of each factor, accurately predicted disease status and progression when tested on two independent datasets.
This multi-omics integrative approach has revealed a unique signature which may represent the inflection point in disease progression, representing a promising avenue for the identification of therapeutic targets aimed at addressing the progressive nature of the disease.
特发性肺纤维化（IPF）是一种具有毁灭性的进行性间质性肺病，预后较差。尽管数十年的研究已阐明了与该疾病相关的病理生理机制，但我们对驱动IPF及其进展的早期分子事件的了解仍然有限。通过本研究，我们旨在使用数据驱动的方法对纤维化的前沿进行建模。
采用无偏倚方法，将代表纤维化不同阶段的健康和IPF肺外植体的多种组学模式（转录组学、代谢组学和脂质组学）进行整合。对数据集进行多组学因子分析，揭示了与已确立的纤维化疾病（因子1）和疾病进展（因子2）特异性相关的潜在因子。
表征因子1的特征包括纤维化疾病的公认特征，如表面活性剂缺陷、上皮-间质转化、细胞外基质沉积、线粒体功能障碍和嘌呤代谢。相比之下，因子2确定了一个揭示疾病进展非线性轨迹的特征。表征因子2的分子特征包括与细胞分化转录调控、纤毛发生相关的基因以及内源性大麻素类的一部分脂质。基于每个因子的顶级转录组学特征训练的机器学习模型，在两个独立数据集上进行测试时，能够准确预测疾病状态和进展。
这种多组学整合方法揭示了一个独特的特征，它可能代表疾病进展的转折点，为识别旨在解决该疾病进行性本质的治疗靶点提供了一条有前景的途径。
