An alveolus lung-on-a-chip model of Mycobacterium fortuitum lung infection.

IF 3.3 3区 医学 Q2 CELL BIOLOGY
Disease Models & Mechanisms Pub Date : 2025-09-01 Epub Date: 2025-08-01 DOI:10.1242/dmm.052085
Victoria Ektnitphong, Beatriz R S Dias, Priscila C Campos, Michael U Shiloh
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Abstract

Lung disease due to non-tuberculous mycobacteria (NTM) is rising in incidence. Although both two-dimensional cell culture and animal models exist for NTM infections, a major knowledge gap is the early responses of human alveolar and innate immune cells to NTM within the human alveolar microenvironment. Here, we describe the development of a humanized, three-dimensional, alveolus lung-on-a-chip (ALoC) model of Mycobacterium fortuitum lung infection that incorporates only primary human cells, such as pulmonary vascular endothelial cells, in a vascular channel, and type I and II alveolar cells and monocyte-derived macrophages in an alveolar channel along an air-liquid interface. M. fortuitum introduced into the alveolar channel primarily infected macrophages, with rare bacteria inside alveolar cells. Bulk RNA sequencing of infected chips revealed marked upregulation of transcripts for cytokines, chemokines and secreted protease inhibitors (SERPINs). Our results demonstrate how a humanized ALoC system can identify critical early immune and epithelial responses to M. fortuitum infection. We envision potential application of the ALoC to other NTM and in studies of new antibiotics.

福氏分枝杆菌肺部感染肺泡肺芯片模型的建立。
由非结核分枝杆菌(NTM)引起的肺部疾病的发病率正在上升。虽然NTM感染的二维细胞培养和动物模型都存在,但一个主要的知识缺口是人类肺泡微环境中人类肺泡和先天免疫细胞对NTM的早期反应。在这里,我们描述了一种人源化的三维肺泡肺芯片(ALoC) fortuitum分枝杆菌肺部感染模型的发展,该模型仅结合了原代人细胞,如血管通道中的肺血管内皮细胞,以及沿气液界面的肺泡通道中的I型和II型肺泡细胞和单核细胞来源的巨噬细胞。引入肺泡通道的福氏分枝杆菌主要感染巨噬细胞,肺泡细胞内有罕见细菌。受感染芯片的批量rna测序显示细胞因子、趋化因子和分泌蛋白酶抑制剂(SERPINs)的转录物显著上调。我们的研究结果证明了人源化的ALoC系统如何能够识别对幸运分枝杆菌感染的关键早期免疫和上皮反应。我们展望了ALoC在其他NTM和新抗生素研究中的潜在应用。
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
Disease Models & Mechanisms
Disease Models & Mechanisms 医学-病理学
CiteScore
6.60
自引率
7.00%
发文量
203
审稿时长
6-12 weeks
期刊介绍: Disease Models & Mechanisms (DMM) is an online Open Access journal focusing on the use of model systems to better understand, diagnose and treat human disease.
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