Macrophage Polarization in Response to Infections and Inflammation

巨噬细胞极化对感染和炎症的反应

• 批准号: 10685988
• 负责人: Pradipta Ghosh
• 金额: $ 95.08万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-22 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685988
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Abstract Macrophages in Greek means “big eaters" are powerful cellular components of innate immunity. They play a pivotal role in immune defense by ‘eating’ pathogens, dead or cancerous cells. They also contribute to tissue homeostasis, development and repair. When doing their job, macrophages react to their surroundings and trigger acute inflammation to resolve the problems. They do so by assuming one of the two states that have been widely recognized, i.e., immunoreactive (proinflammatory) and immunotolerant (a.k.a, M1 and M2, respectively). While finite degrees of reactivity and tolerance are desirable in physiology, excess of either state is undesirable and invariably associated with disease pathogenesis (i.e., the Goldilocks conundrum). For example, hyperreactivity is recognized as the root cause of tissue injury in a wide array of diseases (colitis, sepsis, NASH) and hypertolerance is a common determinant that drives most, if not all chronic diseases that are incurable, e.g., cancers. Consensus on the definition of these physiologic and pathologic macrophage states has not been reached, perhaps because of 4 major challenges: heterogeneity, biological robustness, the temporal evolution of the network, and artifacts (tremendous plasticity of macrophages as they drift rapidly when isolated from tissues). We have used a novel computational methodology, Boolean Implication Network [Sahoo 2008], to analyze pooled human macrophage gene expression datasets. This method, which identifies asymmetric gene expression patterns, blurs noise (heterogeneity/artifacts) but reveals a temporal model of events that is invariably seen across all datasets. The analysis revealed hitherto unknown continuum transition states between reactive to tolerant states along five paths; machine-learning identified one of them as the major path which subsequently stood the rigorous test/validation on multiple publicly available transcriptomic datasets, across species (mouse and human), macrophage subtypes and disease states. Most importantly, unlike other commonly used gene cluster signatures, the Boolean path can prognosticate outcomes across diverse diseases. Preliminary validation studies on a genetic model confirm that the path could be exploited for modulating macrophage polarization by altering LPS/TLR4 responses. We will now interrogate the impact of these discoveries using an iterative approach, i.e., model-driven experimentation and experiment-driven model refinement, through three aims: Unravel the importance of novel molecular drivers in the newly identified gene signatures of macrophage polarization using semi-HTP chemical/genetic screens on murine and human monocyte-derived macrophages (Aim 1), in murine disease models of hyperreactivity and hypertolerance (Aim 2) and in “Humanoids”, i.e., human organoid-based microbe/immune cells co-culture models (“gut-in-a-dish”; Aim 3). Although our focus is gastrointestinal infection and inflammation, the findings will define macrophage transition states in multiple organs/disease contexts and therefore, impact many fields. We expect to identify high-value therapeutic targets that can restrict and/or reset macrophage responses to infections and inflammation within the “Goldilocks zone.”

摘要 巨噬细胞在希腊语中的意思是“大食者”，是先天免疫的强大细胞成分。起着 通过“吃掉”病原体、死亡或癌细胞，在免疫防御中发挥关键作用。它们也有助于组织 体内平衡，发育和修复。当它们工作时，巨噬细胞会对周围环境做出反应， 急性炎症来解决问题。他们这样做是通过假设两个国家之一， 认识到，即免疫反应性（促炎性）和免疫耐受性（分别称为M1和M2）。而 在生理学上，有限程度的反应性和耐受性是理想的，过度的任一状态都是不理想的， 总是与疾病发病机理相关（即，金发姑娘难题）。例如，高反应性 被认为是多种疾病（结肠炎、败血症、NASH）中组织损伤的根本原因， 超耐受性是驱动大多数（如果不是所有）不可治愈的慢性疾病的常见决定因素，例如， 癌的关于这些生理和病理巨噬细胞状态的定义尚未达成共识。 达到，也许是因为4个主要的挑战：异质性，生物鲁棒性，时间进化 和人工制品（巨噬细胞的巨大可塑性，因为它们在分离时迅速漂移， 组织）。我们使用了一种新的计算方法，布尔蕴涵网络[Sahoo 2008]， 分析合并的人巨噬细胞基因表达数据集。这种方法，它确定不对称基因， 表达模式，模糊噪声（异质性/伪影），但揭示了事件的时间模型， 在所有数据集上都可以看到。分析揭示了迄今未知的连续过渡态之间的反应 沿着沿着五条路径进入容忍状态;机器学习将其中一条确定为主要路径， 在多个公开可用的转录组数据集上经受住了严格的测试/验证，跨物种（小鼠 和人）、巨噬细胞亚型和疾病状态。最重要的是，与其他常用的基因不同， 聚类签名，布尔路径可以在不同的疾病中描述结果。初步验证 对遗传模型的研究证实，该途径可用于调节巨噬细胞极化， 改变LPS/TLR 4应答。现在，我们将使用迭代方法来询问这些发现的影响。 方法，即，模型驱动的实验和实验驱动的模型改进，通过三个目标： 揭示新型分子驱动因子在新发现的巨噬细胞基因特征中的重要性 在鼠和人单核细胞衍生的巨噬细胞上使用半HTP化学/遗传筛选的极化 (Aim 1），在高反应性和高耐受性的鼠疾病模型（Aim 2）和“类人”中，即，人类 基于类器官的微生物/免疫细胞共培养模型（“皿中肠”; Aim 3）。虽然我们的重点是 胃肠道感染和炎症，研究结果将定义巨噬细胞过渡状态在多个 器官/疾病背景，因此影响许多领域。我们希望能找到高价值的治疗靶点 可以限制和/或重置巨噬细胞对“金发区”内感染和炎症的反应。

项目成果

MDA5-autoimmunity and Interstitial Pneumonitis Contemporaneous with the COVID-19 Pandemic (MIP-C).

MDA5-自身免疫和与 COVID-19 大流行同时发生的间质性肺炎 (MIP-C)。

• DOI: 10.1101/2023.11.03.23297727
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Iqbal,Khizer;Sinha,Saptarshi;David,Paula;DeMarco,Gabriele;Taheri,Sahar;McLaren,Ella;Maisuria,Sheetal;Arumugakani,Gururaj;Ash,Zoe;Buckley,Catrin;Coles,Lauren;Hettiarachchi,Chamila;Smithson,Gayle;Slade,Maria;Shah,Rahul;Marzo-O
• 通讯作者: Marzo-O