Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis

IF 4.6 2区 医学 Q2 IMMUNOLOGY Clinical & Translational Immunology Pub Date : 2024-01-24 DOI:10.1002/cti2.1485
Céline Pattaroni, Christina Begka, Bailey Cardwell, Jade Jaffar, Matthew Macowan, Nicola L Harris, Glen P Westall, Benjamin J Marsland
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Abstract

Objectives

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.

Methods

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).

Results

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.

Conclusion

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.

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多组学整合揭示了肺纤维化进展前的非线性特征。
目的:特发性肺纤维化(IPF)是一种破坏性进行性间质性肺病,治疗效果不佳。虽然数十年的研究已经揭示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。通过这项研究,我们旨在利用数据驱动方法建立纤维化前沿模型:方法:采用无偏方法将代表不同纤维化阶段的健康肺和 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)结合起来。对数据集进行的多组学因子分析揭示了与既定纤维化疾病(因子1)和疾病进展(因子2)相关的潜在因子:结果:因子1的特征包括纤维化疾病的公认标志,如表面活性物质缺陷、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确定了一个特征,揭示了疾病进展的非线性轨迹。表征因子2的分子特征包括与细胞分化转录调控相关的基因、纤毛生成和内大麻素类脂质子集。根据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,能准确预测疾病的状态和进展:这种多组学整合方法揭示了一个独特的特征,它可能代表了疾病进展的拐点,为确定治疗目标提供了一个很有希望的途径,以解决疾病的进展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinical & Translational Immunology
Clinical & Translational Immunology Medicine-Immunology and Allergy
CiteScore
12.00
自引率
1.70%
发文量
77
审稿时长
13 weeks
期刊介绍: Clinical & Translational Immunology is an open access, fully peer-reviewed journal devoted to publishing cutting-edge advances in biomedical research for scientists and physicians. The Journal covers fields including cancer biology, cardiovascular research, gene therapy, immunology, vaccine development and disease pathogenesis and therapy at the earliest phases of investigation.
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