CellGraph: bioimaging software to rapidly identify bacterial genes responsible for modifications to host cell organelle morphology

CellGraph：生物成像软件，可快速识别负责宿主细胞细胞器形态修饰的细菌基因

• 批准号: 10257615
• 负责人: Shannon Quinn
• 金额: $ 11.33万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-14 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10257615
• 关键词: A549; Address; Amphibia; Apoptotic; Appearance; Bacteria; Bacterial Genes; Behavior; Biological; Cause of Death; Cell Death; Cell Survival; Cells; Communities; Complex; Computer software; Data; Data Set; Development; Diffuse; Disease; Environment; Evolution; Extreme drug resistant tuberculosis; Feedback; Fiji; Fishes; Fluorescence Microscopy; Foundations; Future; Genes; Genome; Genomics; Genus Mycobacterium; Goals; Graph; Growth; Human; Image; Immune Evasion; Immune response; Infection; Infectious Agent; Informatics; Knowledge; Label; Lead; Libraries; Link; Location; Machine Learning; Manuals; Masks; Measures; Methods; Mitochondria; Mitochondrial Proteins; Modeling; Modification; Molecular; Monitor; Morphology; Multidrug-Resistant Tuberculosis; Mutate; Mutation; Mycobacterium marinum; Mycobacterium tuberculosis; Organelles; Outcome; Parents; Pathogenicity; Pathology; Pathway interactions; Pattern; Phagocytes; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Procedures; Process; Proteins; Psychological Techniques; Research Personnel; Resolution; Role; Running; Scheme; Series; Shapes; Social Network; Software Design; Spatial Distribution; Stimulus; Structure; Subcellular structure; Supervision; System; Targeted Toxins; Technical Expertise; Time; Tuberculosis; Validation; Virulence; Virulence Factors; Work; base; bioimaging; cell type; compliance behavior; computational pipelines; digital; flexibility; fluorescence imaging; gene complementation; gene product; human disease; image processing; imaging Segmentation; improved; insight; instrumentation; interest; microscopic imaging; mutant; mycobacterial; network models; novel; novel strategies; novel therapeutic intervention; novel therapeutics; open source; pathogen; predictive modeling; prevent; response; screening; side effect; spatiotemporal; therapeutic candidate; tool; trafficking

Abstract Tuberculosis (TB) has been a transmissible human disease for many thousands of years, and Mycobacterium tuberculosis (Mtb) is again the number one cause of death due to a single infectious agent. The intense 6- to 10-month process of multi-drug treatment, combined with the adverse side effects that can run the spectrum are major obstacles to patient compliance and therapy completion. The consequent increase in multidrug resistant TB (MDR-TB) and extensively drug resistant TB (XDR-TB) cases requires that we increase our arsenal of effective drugs and calls for the development of novel therapeutic approaches. Over the millennia, host and pathogen have evolved mechanisms and relationships that greatly influence the outcome of infection. Understanding these evolutionary interactions and their impact on pathogen clearance or host pathology will lead the way towards rational development of new therapeutics that favor a host protective response. These host-directed therapies have recently demonstrated promising results against Mtb, enhancing the cumulative effects of currently available anti-mycobacterial drugs or directly decreasing bacterial replication. However, our understanding of the host cell-pathogen interactions that lead to increased bacterial growth or host immune evasion is limited, and thus the ability to identify targets for novel host-directed drugs is hampered by a lack of mechanistic knowledge. Current methods for identifying Mtb virulence factors and imaging host cellular effects are slow and laborious with a general inability to simultaneously link multiple factors. Through the use of a high-throughput, large-scale computational pipeline, we can rapidly and effectively detect changes in the organellar morphology of host cells during infection with pathogens. Mycobacterium marinum, a biosafety level 2 bacterium, causes tuberculosis-like pathology in fish and amphibians and is used as a Mtb surrogate to study aspects of the infection process. The framework, CellGraph, will quantify changes in organellar shape, quantity, and spatial distribution over large sequences of Z-stack microscope images and digital videos, improving our understanding of cellular mechanisms as they respond to their environments. Any tagged subcellular component can be tracked within our system. This framework takes the novel approach of examining subcellular components as nodes in a social network. Characterizing ensembles of cellular machinery, such as tagged mitochondria in this study, as social networks allows our framework to study organellar evolution as a function of interconnectedness of cellular components. In addition to quantifying global information such as quantity and appearance, our framework's approach can also provide more detailed local feedback regarding how subsets of the organellar ensembles evolve. Mycobacterium marinum homologs of six of the Mtb genes predicted to impact host mitochondrial morphology, including rv3875 (encoding ESAT-6), will be deleted and assessed for mitochondrial morphology phenotypes. CellGraph will form the foundation for future high-throughput computational pipelines, enable rapid quantitative analysis of organellar temporal evolution for extremely large data, provide detailed results at high statistical resolutions, and be released as open source software that is available to the entire scientific community for additional applications and for validation.

摘要 结核病（TB）是数千年来可传播的人类疾病，并且结核分枝杆菌（Mtb）再次是由于单一传染剂导致的头号死亡原因。密集的6至10个月的多药物治疗过程，加上可能出现的不良副作用，是患者依从性和治疗完成的主要障碍。耐多药结核和广泛耐药结核病例随之增加，这要求我们增加有效药物的数量，并要求开发新的治疗方法。几千年来，宿主和病原体已经进化出了极大地影响感染结果的机制和关系。了解这些进化相互作用及其对病原体清除或宿主病理学的影响，将有助于合理开发有利于宿主保护性反应的新疗法。这些针对宿主的疗法最近已经证明了针对Mtb的有希望的结果，增强了目前可用的抗分枝杆菌药物的累积作用或直接减少了细菌复制。然而，我们对宿主细胞-病原体相互作用导致细菌生长增加或宿主免疫逃避的理解是有限的，因此缺乏机制知识阻碍了识别新型宿主导向药物靶点的能力。目前用于鉴定Mtb毒力因子和成像宿主细胞效应的方法是缓慢和费力的，通常不能同时连接多个因子。 通过使用高通量、大规模的计算管道，我们可以快速有效地检测病原体感染过程中宿主细胞细胞器形态的变化。海洋分枝杆菌是一种生物安全性2级的细菌，在鱼类和两栖动物中引起类似结核病的病理，并被用作Mtb替代品来研究感染过程的各个方面。该框架CellGraph将量化细胞器形状，数量和空间分布在Z-堆栈显微镜图像和数字视频的大序列上的变化，提高我们对细胞机制的理解，因为它们响应于它们的环境。任何标记的亚细胞成分都可以在我们的系统中被追踪。该框架采用了新的方法，将亚细胞组件作为社交网络中的节点进行检查。表征合奏的细胞机器，如标记的线粒体在这项研究中，作为社交网络允许我们的框架来研究细胞器的进化作为一个功能的相互连接的细胞成分。除了量化全球信息，如数量和外观，我们的框架的方法还可以提供更详细的本地反馈，关于细胞器合奏的子集如何演变。将删除预测影响宿主线粒体形态的六个Mtb基因（包括rv 3875（编码ESAT-6））的海分枝杆菌同源物，并评估线粒体形态表型。CellGraph将成为未来高通量计算管道的基础，能够快速定量分析超大数据的细胞器时间演变，以高统计分辨率提供详细的结果，并作为开源软件发布，可供整个科学界用于其他应用和验证。