46 research outputs found

    Directional memory arises from long-lived cytoskeletal asymmetries in polarized chemotactic cells

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    Chemotaxis, the directional migration of cells in a chemical gradient, is robust to fluctuations associated with low chemical concentrations and dynamically changing gradients as well as high saturating chemical concentrations. Although a number of reports have identified cellular behavior consistent with a directional memory that could account for behavior in these complex environments, the quantitative and molecular details of such a memory process remain unknown. Using microfluidics to confine cellular motion to a 1D channel and control chemoattractant exposure, we observed directional memory in chemotactic neutrophil-like cells. We modeled this directional memory as a long-lived intracellular asymmetry that decays slower than observed membrane phospholipid signaling. Measurements of intracellular dynamics revealed that moesin at the cell rear is a long-lived element that when inhibited, results in a reduction of memory. Inhibition of ROCK (Rho-associated protein kinase), downstream of RhoA (Ras homolog gene family, member A), stabilized moesin and directional memory while depolymerization of microtubules (MTs) disoriented moesin deposition and also reduced directional memory. Our study reveals that long-lived polarized cytoskeletal structures, specifically moesin, actomyosin, and MTs, provide a directional memory in neutrophil-like cells even as they respond on short time scales to external chemical cues

    Fast estimation of plant growth dynamics using deep neural networks

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    BackgroundIn recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining).ResultsOverall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves).ConclusionsIn all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.publishe

    Putative amino acid determinants of the emergence of the 2009 influenza A (H1N1) virus in the human population

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    The emergence of the unique H1N1 influenza A virus in 2009 resulted in a pandemic that has spread to over 200 countries. The constellation of molecular factors leading to the emergence of this strain is still unclear. Using a computational approach, we identified molecular determinants that may discriminate the hemagglutinin protein of the 2009 human pandemic H1N1 (pH1N1) strain from that of other H1N1 strains. As expected, positions discriminating the pH1N1 from seasonal human strains were located in or near known H1N1 antigenic sites, thus camouflaging the pH1N1 strain from immune recognition. For example, the alteration S145K (an antigenic position) was found as a characteristic of the pH1N1 strain. We also detected positions in the hemagglutinin protein differentiating classical swine viruses from pH1N1. These positions were mostly located in and around the receptor-binding pocket, possibly influencing binding affinity to the human cell. Such alterations may be liable in part for the virus’s efficient infection and adaptation to humans. For instance, 133A and 149 were identified as discriminative positions. Significantly, we showed that the substitutions R133AK and R149K, predicted to be pH1N1 characteristics, each altered virus binding to erythrocytes and conferred virulence to A/swine/NC/18161/02 in mice, reinforcing the computational findings. Our findings provide a structural explanation for the deficient immunity of humans to the pH1N1 strain. Moreover, our analysis points to unique molecular factors that may have facilitated the emergence of this swine variant in humans, in contrast to other swine variants that failed
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