Biology subjects

Cotter, C. R.

Publications and source records attributed to Cotter, C. R..

3 recordsLinked to original sources

bioRxiv2022

Genomic regions associate with major axes of variation in gas exchange and leaf construction traits in cultivated sunflower (Helianthus annuus L.)

Stomata and leaf veins play an essential role in transpiration and the movement of water throughout leaves. These traits are thus thought to play a key role in the adaptation of plants to drought and a better understanding of the genetic basis of their variation and coordination could inform efforts to improve drought tolerance. Here, we explore patterns of variation and covariation in leaf anatomical traits and analyze their genetic architecture via genome-wide association (GWA) analyses in cultivated sunflower (Helianthus annuus L.). Traits related to stomatal density and morphology as well as lower order veins were manually measured from digital images while the density of minor veins was estimated using a novel deep learning approach. Leaf, stomatal, and vein traits exhibited numerous significant correlations that generally followed expectations based on functional relationships. Correlated suites of traits could further be separated along three major principal component (PC) axes that were heavily influenced by variation in traits related to gas exchange, leaf hydraulics, and leaf construction. While there was limited evidence of colocalization when individual traits were subjected to GWA analyses, major multivariate PC axes that were most strongly influenced by several traits related to gas exchange or leaf construction did exhibit significant genomic associations. These results provide insight into the genetic basis of leaf trait covariation and showcase potential targets for future efforts aimed at modifying leaf anatomical traits in sunflower. Significance StatementUsing traditional and automated/high-throughput (using a novel deep learning approach) phenotyping methods we studied leaf anatomical variation in sunflower. Genome-wide association (GWA) analyses identified numerous genomic regions underlying individual trait variation and regions underlying major multivariate axes of phenotypic variation. These results illustrate the value of employing a multivariate approach to GWA analyses and shed light on the extent to which leaf trait (co-)variation can be genetically decoupled to explore novel phenotypic space.

plant biology↗

bioRxiv2021

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↗

bioRxiv2018

Agent-Based Modeling Reveals Possible Mechanisms for Observed Aggregation Cell Behaviors in Myxococcus xanthus

Myxococcus xanthus is a soil bacterium that serves as a model system for biological self-organization. Cells form distinct, dynamic patterns depending on environmental conditions. An agent-based model (ABM) was used to understand how M. xanthus cells aggregate into multicellular mounds in response to starvation. In this model, each cell is modeled as an agent, represented by a point-particle and characterized by its position and moving direction. At low agent density, the model recapitulates the dynamic patterns observed by experiments and a previous biophysical model. To study aggregation at high cell density, we extended the model based on the recent experimental observation that cells exhibit biased movement towards aggregates. We tested two possible mechanisms for this biased movement and demonstrate that a chemotaxis model with adaptation can reproduce the observed experimental results leading to the formation of stable aggregates. Furthermore, our model reproduces the experimentally observed patterns of cell alignment around aggregates.\n\nAuthor summaryCollective self-organization of cells into multicellular structures is important for lifestyle of many bacterial species. Myxococcus xanthus bacterium is a model system for studying this self-organization. In this work, we investigate how in response to starvation M. xanthus cells aggregate into multicellular mounds. A recent study identified the key cellular behaviors that are necessarily for the aggregation but the mechanisms of these behaviors remained unclear. To uncover these mechanisms, we developed a computational model that simulates interactions among a large number of cells. The results demonstrate that the observed bias in the cell reversal times as they move towards the aggregates can be explained by chemotaxis model. In this model cells secrete a chemical signal and respond to it via a partially-adapting biochemical network. The resulting aggregation dynamics are in good agreement with the experiments. Furthermore, chemotaxis signaling model reproduces the experimentally observed patterns of cell alignment around aggregates. On the other hand, an alternative model, based on contact-dependent signaling between cells, fails to aid in aggregation. Thus our models make important predictions about the cellular interactions that drives multicellular aggregation and can serve as a basis to investigate a wider range of developmental mutant strains.

microbiology↗