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.

抽象的 数千年来，结核病（TB）一直是一种可传播的人类疾病，结核分枝杆菌（MTB）再次是由于单个感染剂而导致的死亡第一原因。多药治疗的6到10个月的激烈过程，结合可能运行频谱的不利副作用是患者合规性和治疗完成的主要障碍。随之而来的是抗药性结核病（MDR-TB）和广泛的耐药性结核病（XDR-TB）病例的增加，要求我们增加有效药物的武器库，并呼吁开发新的治疗方法。在千年中，宿主和病原体已经发展了机制和关系，从而极大地影响了感染的结果。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带来有利于宿主保护反应的新疗法的理性发展。这些宿主指导的疗法最近证明了针对MTB的有希望的结果，从而增强了当前可用的抗细菌药物的累积作用或直接降低细菌复制。然而，我们对导致细菌生长或宿主免疫逃避的宿主细胞病原体相互作用的理解是有限的，因此缺乏机械知识，可以妨碍鉴定新型宿主指导药物的靶标的能力。当前识别MTB毒力因子和成像宿主细胞效应的方法缓慢而费力，通常无法同时连接多个因素。 通过使用高通量，大规模计算管道，我们可以快速有效地检测病原体感染期间宿主细胞的细胞器形态变化。分枝杆菌是一种生物安全2级细菌，在鱼类和两栖动物中引起结核病样病理，并用作MTB替代物来研究感染过程的方面。该框架，CellGraph将量化大型Z-stack显微镜图像和数字视频的细胞器形状，数量和空间分布的变化，从而提高我们对细胞机制对环境的反应时的理解。任何标记的亚细胞组件都可以在我们的系统中跟踪。该框架采用了将亚细胞组件作为社交网络中的节点进行研究的新方法。表征细胞机制的集合，例如在这项研究中标记的线粒体，因为社交网络使我们的框架可以研究细胞器的进化，这是细胞成分相互联系的函数。除了量化数量和外观之类的全球信息外，我们的框架方法还可以提供有关细胞器集合子集如何发展的更详细的本地反馈。预测会影响宿主线粒体形态的六个MTB基因的分枝杆菌（包括RV3875）（RV3875）（编码ESAT-6）将被删除并评估用于线粒体形态学表型。 CellGraph将为未来的高通量计算管道构成基础，对非常大数据的细胞器时间演变进行快速定量分析，在高统计分辨率下提供详细的结果，并作为开源软件发布，该软件可用于整个科学界的开源软件，以进行其他应用程序和验证。