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Comstock, J. A.

Publications and source records attributed to Comstock, J. A..

2 recordsLinked to original sources

Phenotypic similarity is a measure of functional redundancy within homologous gene families

Robustness to the impact of mutation can mitigate phenotypes that have the potential to inform gene function. This robustness is often encoded into the genome through gene duplication, among other mechanisms. Duplication is a source of structurally similar genes that can retain some functional overlap as they diverge, and as such contribute to functional redundancy in the face of mutation. While redundancies have been explored in groups of two or three paralogs by generating double and triple mutants, it is unclear to what extent larger homologous gene families contribute to robustness through functional redundancy. Here, we used phenotypic similarity as an indicator of functional redundancy to explore the extent to which homologous gene families contribute to redundancy in function. We hypothesize that, since functional redundancy is more likely to occur within gene families where genes are structurally similar, mutant strains within the same gene families would be more phenotypically similar. We generated 265 single-gene disruptions in four homologous gene families of Myxococcus xanthus, used time-lapse microscopy to generate time series of multicellular development, and developed an image analysis pipeline to compare phenotypic characteristics among different strains. We show that mutant strains cluster by gene family in the phenotypic feature space with principal component analysis, demonstrating that families of homologs can contain extensive functional redundancy networks.

systems biology↗

Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-learning Transformation of Phase-contrast into Fluorescence Microscopy Images

Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates. However, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to the cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into fluorescent images. By including an additional histogram-equalized output to the state-of-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies.

systems biology↗