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.

The central hypothesis of the genotype–phenotype relationship is that the phenotype of a developing organism (i.e., its set of observable attributes) depends on its genome and the environment. However, as we learn more about the genetics and biochemistry of living systems, our understanding does not fully extend to the complex multiscale nature of how cells move, interact, and organize; this gap in understanding is referred to as the genotype-to-phenotype problem. The physics of soft matter sets the background on which living organisms evolved, and the cell environment is a strong determinant of cell phenotype. This inevitably leads to challenges as the full function of many genes, and the diversity of cellular behaviors cannot be assessed without wide screens of environmental conditions. Cellular mechanobiology is an emerging field that provides methodologies to understand how cells integrate chemical and physical environmental stress and signals, and how they are transduced to control cell function. Biofilm forming bacteria represent an attractive model because they are fast growing, genetically malleable and can display sophisticated self-organizing developmental behaviors similar to those found in higher organisms. Here, we propose mechanobiology as a new area of study in prokaryotic systems and describe its potential for unveiling new links between an organism's genome and phenome.

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 cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into synthesized fluorescent images. By including an additional histogram-equalized output to the state-of-the-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.

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.