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

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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.