Macrophage Polarization in Response to Infections and Inflammation

巨噬细胞极化对感染和炎症的反应

基本信息

批准号：
10100201
负责人：
Soumita Das
金额：
$ 100.02万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
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/10100201
关键词：
Acute Acute Disease Antigens Arthritis Atherosclerosis Automobile Driving Big Data Biological Biological Models Biology Cancerous Cell physiology Cells Cellular biology Chemicals Chronic Disease Coculture Techniques Colitis Collection Colorectal Colorectal Adenoma Colorectal Cancer Communities Complex Computational Biology Computing Methodologies Consensus Data Data Set Development Diabetes Mellitus Discipline Disease Disease model Eating Enzyme-Linked Immunosorbent Assay Epithelial Cells Equilibrium Event Evolution Exhibits Exposure to Funding Mechanisms Gene Cluster Gene Deletion Gene Expression Gene Expression Profile Genes Genetic Models Genetic Screening Goals Greek Health Heterogeneity Homeostasis Human Immune Immune system Immunologics Immunology Infection Inflammation Inflammatory Bowel Diseases Innate Immune Response Knowledge Lead Liver Fibrosis Machine Learning Malignant Neoplasms Maps Measures Metabolic syndrome Methods Microbe Modeling Molecular Morphologic artifacts Mus National Institute of Allergy and Infectious Disease Natural Immunity Noise Nomenclature Occupations Organ Organoids Outcome Pathogenesis Pathologic Patients Phase Physicians Physiological Physiology Plant Roots Play Process Research Resources Role Scientist Sepsis Series Stimulus Structure T-Lymphocyte TLR4 gene Testing Therapeutic Tissues Validation adaptive immune response base beneficial microorganism body system bone cell type chemical genetics computerized tools cytokine design experimental study functional genomics gastrointestinal infection genetic signature human disease human model immunoreactivity innovation macrophage monocyte mouse model nonalcoholic steatohepatitis novel outcome prediction pathogen programs repaired response success targeted biomarker targeted treatment therapeutic target tissue injury tool transcriptome sequencing transcriptomics validation studies

项目摘要

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.”

抽象的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，它们发挥着重要作用。通过“吃掉”病原体、死亡细胞或癌细胞，在免疫防御中发挥关键作用。它们还对组织有贡献。巨噬细胞在发挥作用时会对周围环境做出反应并触发稳态、发育和修复。他们通过假设已广泛使用的两种状态之一来解决问题。识别，即免疫反应性（促炎性）和免疫耐受性（分别称为 M1 和 M2）。在生理学上，有限程度的反应性和耐受性是可取的，任何一种状态的过度都是不可取的，总是与疾病发病机制相关（即金发姑娘难题）。被认为是多种疾病（结肠炎、脓毒症、NASH）中组织损伤的根本原因，过度耐受是导致大多数（如果不是全部）慢性疾病无法治愈的常见决定因素，例如对于这些生理和病理巨噬细胞状态的定义尚未达成共识。达到这一目标，或许是因为 4 个主要挑战：异质性、生物鲁棒性、时间演化网络和伪影（巨噬细胞的巨大可塑性，因为它们在与我们使用了一种新颖的计算方法，即布尔蕴涵网络 [Sahoo 2008]。分析汇集的人类巨噬细胞基因表达数据集，该方法可识别不对称基因。表达模式，模糊噪声（异质性/伪影），但揭示事件的时间模型，该模型总是在所有数据集中都可以看到，分析揭示了反应之间迄今为止未知的连续过渡状态。机器学习将其中一条确定为主要路径，随后将其经受住了跨物种的多个公开转录组数据集的严格测试/验证（小鼠最重要的是，与其他常用基因不同。聚类特征，布尔路径可以预测不同疾病的结果。对遗传模型的研究证实，该路径可用于通过以下方式调节巨噬细胞极化：我们现在将使用迭代来探究这些发现的影响。方法，即模型驱动的实验和实验驱动的模型细化，通过三个目标：揭示新分子驱动因素在新发现的巨噬细胞基因特征中的重要性使用半 HTP 化学/遗传筛选对小鼠和人类单核细胞衍生的巨噬细胞进行极化（目标 1）、高反应性和高耐受性的小鼠疾病模型（目标 2）以及“类人动物”（即人类）基于类器官的微生物/免疫细胞共培养模型（“培养皿中的肠道”；目标 3）。胃肠道感染和炎症，这些发现将定义多种巨噬细胞的过渡状态器官/疾病背景，因此影响许多领域，我们期望确定高价值的治疗靶点。它可以限制和/或重置巨噬细胞对“金发姑娘区”内的感染和炎症的反应。